Embark on a comprehensive AI journey as we delve into Meta AI’s groundbreaking ‘Belebele’ dataset, designed to gauge the prowess of text models across diverse languages. Witness Stability AI’s remarkable innovation: a Japanese vision-language model tailored to aid the visually impaired. Gain clarity on the intriguing relationship between transformers and Support Vector Machines and address the pressing concern of hallucination within AI language models. Experience the seamless integration of Canva in ChatGPT Plus for effortless graphic creation. Keep up with the latest AI announcements and advancements. Conclude with our top book recommendation, “AI Unraveled“, for a profound understanding of the AI universe.
X (Twitter) trains our data to AI; How to analyze your business performance with ChatGPT?; Introducing Refact Code LLM, for real-time code completion and chat; Virtual (AI) influencer to make a music video
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover the following topics: Virtual influencer Noonoouri signing a record deal with Warner Music, Twitter’s plans to use data for AI models while assuring DM privacy, the use of AI like ChatGPT for real-time analytics, Amazon One’s AI-powered palm recognition device, Intel’s expansion into AI opportunities beyond data centers, the introduction of Refact Code LLM for developers, various updates in the AI landscape including OpenAI’s Canva Plugin for ChatGPT and Epic Games Store accepting generative AI games, AI predicting smells and generating COVID drugs, and a book recommendation on artificial intelligence as well as a podcast tool called Wondercraft AI.
Have you heard the news? Noonoouri, the virtual influencer who’s made a name for herself in the fashion world, has just signed a record deal with Warner Music. But here’s the twist: she’s not your typical artist. In fact, she doesn’t even exist in the real world!
With a staggering 400k followers on Instagram and impressive collaborations with major fashion brands like Dior and Valentino, Noonoouri is the brainchild of artist Joerg Zuber. And while her fashion gigs have gained her plenty of attention, it’s her AI-crafted voice that’s taking her to the next level.
Although her voice is entirely artificial, the song itself is a product of human creativity, thanks to the collaboration between Warner and German producer DJ Alle Farben. So, while Noonoouri may be a virtual creation, the heart and soul of her music still comes from real people.
But what does this mean for the future of human artists? It’s a question that’s been on the minds of many in the music industry. As avatars like Noonoouri continue to gain popularity, will human artists be overshadowed or replaced? Only time will tell. In the meantime, Noonoouri is using her virtual platform not just for music, but also to advocate for important issues like veganism and anti-racism.
So, keep an eye out for Noonoouri’s music video and see how this AI influencer is making waves in the music scene. It’s an exciting time, full of possibilities and questions about the future of artistry.
So, there’s some interesting news about X, which used to be known as Twitter. They have some big plans in store! X is now going to use the data they collect from us, the users, to train their AI models. Yep, you heard that right!
Their updated privacy policy is going to allow X to tap into all sorts of information like our biometric data, job details, and even our education background. Pretty cool, right?
You know who might be particularly excited about this? Elon Musk! That’s because he’s got this new AI project called xAI, and all that data collected by X might just be a goldmine for him. But hey, don’t get too worried. Musk assures us that it’s only the public information that they’ll be using. So, your DM secrets are safe and sound.
Now, here’s why this matters. With X using all this public data to train their AI, we’re looking at a future where our little online chirps actually help shape how AI understands things. It’s a bit mind-boggling, isn’t it? So, maybe it’s a good idea to be careful about what you say online, because who knows, your words might just end up training some future AI models!
So, you’re eager to analyze your business performance, but you’re wondering how ChatGPT can help you out? Well, let me tell you, AI, especially ChatGPT, can be a powerful tool in unraveling the intricacies of your business’s performance.
Picture this: a real-time analytics dashboard that goes beyond mere financial indicators. This dashboard monitors crucial aspects like customer satisfaction scores, employee engagement levels, and market share growth. And let’s not forget about predictive analytics models, which add an extra layer of insight.
But what exactly does this dashboard do for you? Well, it’s not just about crunching numbers. It’s about grasping the underlying trends and patterns that drive your business forward. With the integration of AI, you’re not simply reacting to past data; you’re also equipped to make informed predictions about the future.
Imagine having a clear understanding of how your side-hustle is performing at any given moment. You can easily identify areas that need improvement or capitalize on opportunities for growth. ChatGPT becomes your trusty companion, helping you analyze your business’s performance with ease.
So, why wait? Embrace the power of AI and let ChatGPT guide you on your journey to business success.
Have you heard about Amazon’s latest breakthrough in AI-powered technology? It’s called Amazon One, and it’s revolutionizing the way we interact with everyday activities. Forget about fumbling for your phone or wallet – all you need is the palm of your hand!
Amazon One is a fast, convenient, and contactless device that utilizes the power of generative AI, machine learning, cutting-edge biometrics, and optical engineering. This futuristic technology allows customers to perform various tasks like making payments, presenting loyalty cards, verifying age, and even gaining entry into venues, all with a simple scan of their palm.
What makes Amazon One even more impressive is its ability to detect and reject fake hands. This ensures that the system maintains a high level of security and accuracy. In fact, it has already been used over 3 million times with an astonishing 99.9999% accuracy rate.
But why does this breakthrough matter? Well, generative AI has been making waves in the tech world for its ability to summarize text, write, and compose code. Now, with Amazon One, we can see how this technology can solve complex real-world problems and completely reimagine convenience in various aspects of our lives, such as shopping, entertainment, and access.
Imagine never having to worry about carrying multiple cards or forgetting your wallet again. Amazon One is paving the way for a future where our palms become the key to a more convenient and efficient world.
Intel is making bold moves in the AI space and expanding beyond data center-based AI accelerators. CEO Pat Gelsinger recognizes that AI will become more accessible to end-users due to economic, physical, and privacy factors. To capitalize on this, Intel is integrating AI into a range of products, such as server CPUs like Sapphire Rapids, which boast built-in AI accelerators for inference tasks.
But that’s not all. Intel also has plans to launch Meteor Lake PC CPUs equipped with dedicated AI hardware, allowing for the direct acceleration of AI workloads on user devices. This approach leverages Intel’s dominant position in the CPU market, making it attractive for software providers to support their AI hardware.
This multi-pronged strategy places Intel in a competitive position within the AI landscape, alongside other major players like Nvidia. With the growing demand for AI chips, Intel’s initiatives could provide a potential solution to the industry-wide challenge and play a significant role in shaping the future of AI.
In conclusion, Intel’s diversified approach to AI highlights its commitment to innovation and staying ahead of the game. By expanding into new areas and integrating AI capabilities into their products, Intel is positioning itself as a key player in the evolving AI landscape.
Introducing Refact Code LLM, the ultimate tool for real-time code completion and chat! This amazing 1.6B model is designed to fulfill all your coding needs in multiple programming languages. You won’t believe the performance it delivers!
Refact LLM 1.6B achieves state-of-the-art results in code completion, coming really close to HumanEval as Starcoder. And the best part? It’s 10 times smaller than other code LLMs with similar capabilities. Impressive, right? But that’s not all!
Let me break it down for you with a quick summary. This powerhouse features 1.6 billion parameters, supports a whopping 20 programming languages, and can handle 4096 tokens of context. Plus, it excels not just in code completion, but also in chat functionalities.
And here’s the cherry on top: Refact LLM is pre-trained on permissive licensed code and is available for commercial use. This matters because while other models are getting bigger, our focus is on making this tool accessible to all developers, regardless of their hardware setups.
With its smaller size, Refact LLM runs faster and more efficiently, giving you an affordable solution for your coding projects. Say goodbye to slow and clunky code completion and embrace the future with Refact Code LLM!
Today, we have some exciting updates from the world of AI.
First up, we have Amazon’s latest innovation called Amazon One. This breakthrough AI-powered palm recognition device allows customers to use the palm of their hand for various activities like paying at a store or entering a venue. No need for a phone or a wallet. Amazon One combines generative AI, machine learning, cutting-edge biometrics, and optical engineering to bring us this fast, convenient, and contactless device.
Next, Intel is showing great enthusiasm for the AI space. They are not only expanding their data center-based AI accelerators but also incorporating AI into various products. For example, their upcoming Sapphire Rapids server CPUs will come with built-in AI accelerators for inference tasks. They are also set to launch Meteor Lake PC CPUs with dedicated AI hardware, enabling AI workloads directly on user devices.
OpenAI has introduced a Canva Plugin for ChatGPT. This means that ChatGPT Plus users can now easily interact with Canva, making their workflow even smoother. It’s all about enhancing user experiences!
In the gaming world, Epic Games Store has made an interesting move. They will now accept games created with generative AI. This sets them apart from their biggest competitor, Valve, who currently rejects games with AI content on Steam.
In other news, an AI model has achieved human-level proficiency in predicting smells based on a molecule’s structure. Trained using an industry dataset of 5,000 known odorants, this AI model also showcased capabilities like accurately predicting the strength of odors, opening up possibilities for broader olfactory tasks.
There’s also good news on the medical front. A new AI-generated COVID drug has entered Phase I clinical trials and is effective against all variants. If approved, it could become the first-ever alternative to Paxlovid. This is a significant development in the fight against the pandemic.
Lastly, a startup called AI Scout is using automation to find football’s next star. Football players can showcase their skills to top clubs by recording themselves and using the AI scout app. The app analyzes the intricate movements of the player and the ball, helping identify promising talent.
That’s it for today’s AI update. Exciting times lie ahead, and we’ll continue to keep you informed on the latest developments.
Welcome to the podcast, folks! Today, we’re diving headfirst into the fascinating world of artificial intelligence. If you’re keen on unraveling the mysteries surrounding AI, you’re in luck! We’ve got just the thing for you: “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” This essential book is an absolute gem for all the curious minds out there.
But wait, it gets even better! You don’t have to go on a wild goose chase to find this marvelous piece of literature. Nope, no need for that! You can grab a copy right now from Apple, Google, or Amazon. With just the click of a button, you’ll be one step closer to expanding your understanding of AI in an easily digestible manner.
Now, here’s a little tidbit for our fellow podcast enthusiasts out there. This podcast you’re listening to right now? It’s actually brought to you by the incredible Wondercraft AI platform. Yup, you heard that right! Wondercraft AI is a nifty tool that makes diving into the world of podcasting a piece of cake. It’s super user-friendly and perfect for anyone looking to start their own podcast. So, if you’re itching to share your thoughts and ideas with the world, give Wondercraft AI a go!
That’s it for today’s episode, folks! Remember, grab yourself a copy of “AI Unraveled” and unleash your curiosity about artificial intelligence. And hey, if you’re feeling inspired, why not start your own podcast with Wondercraft AI? Until next time, keep exploring and keep questioning!
In this episode, we discussed the rise of virtual influencers, Twitter’s plans to use data for AI training, the impact of AI on business analytics, the introduction of Amazon One and Intel’s expansion into AI, the launch of Refact Code LLM for developers, and various exciting advancements in the AI landscape, including AI-generated COVID drugs and AI scouting for football stars—plus, don’t forget to check out “AI Unraveled” for a comprehensive guide and start your podcast with Wondercraft AI. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover Meta AI’s Belebele dataset evaluating text models in multiple languages, Stability AI’s Japanese vision-language model for visually impaired individuals, the connection between transformers and Support Vector Machines, the issue of hallucination in AI language models and its mitigation, the Canva integration in ChatGPT Plus for graphic creation, various AI-related announcements and developments, and lastly, a recommendation to listen to the AI Unraveled Podcast and get the book “AI Unraveled.”
Meta AI recently made an exciting announcement about their new dataset called Belebele.
This dataset is designed to understand 122 different languages, making it a significant advancement in the field of natural language understanding.
Belebele is a multilingual reading comprehension dataset that allows for the evaluation of text models in high, medium, and low-resource languages. By expanding the language coverage of natural language understanding benchmarks, it enables direct comparison of model performance across all languages.
The dataset consists of questions based on short passages from the Flores-200 dataset, featuring four multiple-choice answers. These questions were carefully designed to test various levels of general language comprehension. By evaluating multilingual masked language models and large language models using the Belebele dataset, researchers found that smaller multilingual models actually perform better in understanding multiple languages. This finding challenges the notion that larger models always outperform smaller ones.
So why does this matter? Well, the Belebele dataset opens up new opportunities for evaluating and analyzing the multilingual capabilities of NLP systems. It also benefits end users by providing better AI understanding in a wider range of languages. Additionally, this dataset sets a benchmark for AI models, potentially reshaping the competition as smaller models show superior performance compared to larger ones.
Overall, Meta AI’s Belebele dataset is a game-changer in the field of multilingual understanding, offering exciting possibilities for advancing language comprehension in AI systems.
Stability AI just dropped some exciting news! They have now released their very first Japanese vision-language model called Japanese InstructBLIP Alpha.
This model is a game-changer as it generates textual descriptions for input images and can even answer questions about them. Talk about innovation!
What makes this model so special is that it’s built upon the Japanese StableLM Instruct Alpha 7B and uses the powerful InstructBLIP architecture. This means it can accurately recognize specific objects that are unique to Japan and process text input like a champ. It’s like having your own personal tour guide right at your fingertips.
If you’re interested, you can find this amazing model on the Hugging Face Hub. It’s open for inference and additional training, but keep in mind it’s exclusively for research purposes. Nonetheless, this model has incredible applications. For instance, it could improve search engine functionality, provide detailed scene descriptions, and offer textual descriptions for individuals who are visually impaired. That’s some serious accessibility right there!
But why does this matter on a larger scale? Well, it’s a groundbreaking development that not only ensures better image understanding for the visually impaired in the Japanese-speaking community, but it also sets a precedent for future innovations in other languages. This could mean expanding the reach of text-to-image AI models worldwide. It’s not just beneficial for end users, but it also sets a new benchmark for AI model performance and availability. That’s something that can potentially shake up the competitive landscape in different language markets. Exciting stuff all around!
Did you know that transformers, the popular model used in natural language processing, have a deep connection with Support Vector Machines (SVM)?
A recent paper has established a fascinating equivalence between the optimization geometry of self-attention in transformers and a hard-margin SVM problem.
In simple terms, the study reveals that when we optimize the attention layer of transformers, it converges towards an SVM solution that minimizes the nuclear norm of the combined parameter. This implies that transformers can be seen as a hierarchy of SVMs, allowing them to separate and select the most optimal tokens.
But why is this discovery important? Well, it sheds light on how transformers optimize attention layers, giving us a deeper understanding of their inner workings. This newfound understanding can lead to significant improvements in AI models.
Imagine AI models that can better understand and select tokens, resulting in more accurate and efficient language processing. This has the potential to benefit end users in various ways, from improved language translation to enhanced search algorithms and even more advanced chatbots.
So, this connection between transformers and SVMs has paved the way for exciting possibilities in the world of artificial intelligence. It’s all about pushing the boundaries of how we process and understand language, and this research takes us one step closer to achieving that goal.
AI models, like ChatGPT, often find themselves in a state of hallucination.
They have a tendency to conjure up false facts, which is undoubtedly problematic. However, there are ways to address this issue, even though it may not be completely solvable.
The main culprit behind this hallucination is how these models predict words based solely on statistical patterns and their training data. This can lead to the generation of false claims that appear plausible at first glance. The models lack a true understanding of the concept of truth, relying merely on word associations. Thus, they end up propagating the misinformation present in their training data.
To mitigate this problem, it is crucial to curate the training data with care. Additionally, fine-tuning the models using human feedback through reinforcement learning can be helpful. Engineering specific use cases that prioritize utility rather than aiming for perfection is another viable strategy.
It is important to understand that some degree of hallucination will always be present in these models. The goal is to strike a balance between utility and the potential harm caused by false claims, rather than striving for perfection. In fact, this inherent flaw could even become a source of creativity, sparking unexpected associations.
While it is true that all major AI language models suffer from hallucination, steps such as improving training data can significantly reduce the occurrence of false claims. Although the flaw may not be completely eliminated, it is manageable.
Hey there! Have you heard the big news? OpenAI has added a new feature to ChatGPT called the Canva plugin.
This integration with Canva simplifies the process of creating visuals, such as logos and banners, using just conversational prompts. How cool is that?
So, let me break it down for you. With the Canva plugin, you can now do graphic design by simply describing the visual you want and picking your favorite option from a list. It’s all about making design simpler and more accessible, right from within ChatGPT.
OpenAI aims to revolutionize the way users create graphics with this new integration. However, it’s important to note that currently, it’s only available for ChatGPT Plus subscribers. They definitely want to give their paying users an edge!
This Canva plugin also helps ChatGPT keep up with its competitors like Claude and Google’s Bard. Additionally, it nicely complements ChatGPT’s existing web browsing capabilities through its integration with Bing.
This is a pretty exciting development. OpenAI is really working hard to make ChatGPT a versatile tool for all its users. And with this Canva integration, generating graphics through AI has become easier than ever before. It’s all about expanding the capabilities and staying ahead in this heated competition.
So, get ready to dive into the world of design with ChatGPT and the Canva plugin. Happy creating!
Today we have some exciting updates from the world of AI. Let’s dive right in.
Meta AI has recently announced a new multilingual reading comprehension dataset called Belebele. This dataset consists of multiple-choice questions and answers in 122 different language variants, allowing for the evaluation of text models across a wide range of languages. It’s a great way to expand the language coverage of natural language understanding benchmarks.
Stability AI, on the other hand, has released its first Japanese vision-language model called Japanese InstructBLIP Alpha. This model generates textual descriptions for input images and can answer questions about them. It’s specifically trained to recognize Japan-specific objects and has various applications, including search engine functionality and providing textual descriptions for blind individuals.
In other news, the small Caribbean island of Anguilla is making waves in the AI world by leasing out domain names with the “.ai” extension. This unexpected boom has brought in significant revenue for the country, with registration fees estimated to bring in $30 million this year.
Moving on, there’s been an update regarding Twitter, now known as X. Their revised policy reveals that they will be using public data, including biometric data, job history, and education history, to train their AI models. Some speculate that X’s owner, Elon Musk, may be utilizing this data for his other AI company, xAI.
Pika Labs has introduced a new feature that allows users to customize the frame rate of their videos. This parameter, called -fps N, ranges from 8 to 24 frames per second and aims to provide more flexibility and control to users when creating videos using Pika Labs’ product.
The founder of Google DeepMind sees great potential for AI in mental health. He believes AI can offer support, encouragement, coaching, and advice to individuals, particularly those who may not have had positive family experiences. However, he emphasizes that AI is not a replacement for human interaction, but rather a tool to fill in gaps.
Last but not least, Microsoft has filed a patent for AI-assisted wearables, including a backpack that can provide assistance to users. Equipped with sensors to gather information from the user’s surroundings, this backpack relays the data to an AI engine for analysis and support.
That’s all for today’s AI update. Exciting developments are happening in the field, and we can’t wait to see what the future holds.
Hey there, AI Unraveled Podcast listeners! Have you been itching to dive deeper into the world of artificial intelligence?
Transformers as Support Vector Machines: AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence
But wait, there’s more! You’re currently listening to a podcast that’s been brought to life with the help of the Wondercraft AI platform. This platform is a game-changer, folks. It makes creating your own podcast a breeze. And the best part? You can even use hyper-realistic AI voices as your host, just like mine! How cool is that?
So, whether you’re a seasoned AI enthusiast or just beginning to explore this fascinating field, “AI Unraveled” is the ultimate resource to expand your knowledge. And don’t forget to explore the limitless possibilities of the Wondercraft AI platform for all your podcasting dreams.
Now, get ready to unravel the mysteries of artificial intelligence like never before. Happy listening!
In this episode, we explored how smaller models excel in understanding multiple languages, the positive impact of a Japanese vision-language model for the visually impaired, the fascinating connection between transformers and Support Vector Machines, the challenges of AI language models hallucinating false facts, the Canva integration to enhance ChatGPT Plus, and a roundup of recent AI news. Don’t forget to check out the AI Unraveled Podcast and grab the book “AI Unraveled” to delve deeper into the world of AI. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
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Emerging AI Innovations: Top Trends Shaping the Landscape in September 2023.
In the dynamic landscape of Artificial Intelligence, September 2023 stands as a testament to the relentless pace of innovation. Titans of the industry such as Google, OpenAI, and Meta are at the forefront, unveiling cutting-edge developments with each passing day. The remarkable feats achieved by models like GPT are now being complemented by advancements in Lifelong Learning Machine Systems (LLMS). As we delve deeper into the realms of Generative AI and Discriminative AI, we are ushered into an era where machines don’t just compute—they conceptualize. Moreover, the rise of Explainable AI emphasizes the industry’s dedication to clarity and understanding, ensuring that as we integrate these AI systems more deeply into our daily lives, we remain fully informed about their decision-making processes. Join us as we navigate through the pivotal AI trends shaping September 2023 and discover how these technological wonders are transforming the world we live in.
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover ChatGPT’s voice capabilities and image inclusion, Amazon’s $4 billion investment in Anthropic, Meta’s plan for various chatbot personas, the efficiency of LongLoRA for extending context sizes of pre-trained LLMs, the differences between RAG and Finetuning LLMs, Coinbase CEO’s opposition to AI regulation, various AI-related news including Meta’s chatbots and Google Pixel 8’s AI camera, and the recommendation to expand AI knowledge with the book ‘AI Unraveled’. Guess what! ChatGPT just got an awesome upgrade! Now, it’s not just about typing and reading messages. It can actually see, hear, and even talk! How cool is that? Hold on, there’s more! If you’re a Plus user, you’re going to love this update. Over the next two weeks, you’ll be able to have voice conversations with ChatGPT, whether you’re using iOS or Android. Yep, you heard that right. You can actually have conversations with ChatGPT using your own voice! It’s like having a real back-and-forth chat with a super smart AI buddy. But wait, there’s one more exciting thing coming your way. Are you tired of explaining things only with words? Well, now you don’t have to! With this update, all platforms will allow you to include images in your conversations. That means you can now send pictures to ChatGPT to help illustrate what you’re talking about. It’s another way to make your conversations more engaging and dynamic. So, strap in and get ready for an even more immersive experience with ChatGPT. It’s no longer just a text-based AI companion—it’s now a full-on interactive conversational partner that can see, hear, and speak. Enjoy the future of chat! So, get this: Amazon is planning to invest a whopping $4 billion in Anthropic. But wait, what’s Anthropic, you ask? Well, it’s this company that’s all about developing the most reliable and high-performing foundation models in the industry. Here’s the game-changing part: Anthropic’s safety research and products, along with the expertise of Amazon Web Services (AWS) in running secure and reliable infrastructure, will make Anthropic’s safe and controllable artificial intelligence (AI) accessible to AWS customers. In other words, they’re joining forces to bring us safe and steerable AI in a big way. Now, here’s where things get even more interesting. AWS is set to become Anthropic’s primary cloud provider for those mission-critical workloads. And that’s not all—they’re also expanding Anthropic’s support of Amazon Bedrock, whatever that may be. But why is this such a big deal, you ask? Well, this collaboration will open up new possibilities for enterprises. It means they can build with Anthropic models on Amazon Bedrock and responsibly scale the adoption of Claude (no, not the guy down the street, but another AI model). This could revolutionize the delivery of safe AI cloud technologies to organizations worldwide. So, brace yourselves, folks. We’re about to witness some serious advancements in the world of AI, courtesy of Amazon and Anthropic! So, here’s a juicy update! Meta, the tech giant, has got some interesting plans in the pipeline. Apparently, they’re working on creating a bunch of sassy chatbot ‘personas’ specifically designed to captivate and interact with younger users. I mean, who wouldn’t want a chatbot with a colorful personality, right? But wait, there’s more! Meta isn’t stopping there. They’re also cooking up some chatbot personas for celebrities to connect with their devoted fans. Imagine having a virtual conversation with your favorite superstar! And if that wasn’t enough, they’ve got some chatbots that are all about productivity too. These ones are geared towards helping with coding and other nifty tasks. Now, why is all of this such a big deal? Well, rumor has it that Meta’s got something else up their sleeve. They’re apparently working on a super-powered LLM (that’s a language model, by the way) to rival the likes of OpenAI. This could be a major leap forward in AI capabilities, my friends. And hey, it might also give a boost to engagement on Meta’s social media platforms. Exciting times ahead, folks! Meta’s bringing some sass and brains to the chatbot game. Keep your eyes peeled for these new chatty personalities hitting your screens soon! Today, we’re talking about a new research development called LongLoRA. This method aims to make fine-tuning of long-context Language Models (LLMs) more efficient, without requiring a lot of computational power. The ability to extend the context size of pre-trained LLMs is crucial in many natural language processing tasks. Traditionally, training LLMs with longer context sizes comes with a hefty computational cost and demands powerful GPU resources. For example, increasing the context length from 2048 to 8192 can lead to a 16-fold increase in computational costs, especially in self-attention layers. However, LongLoRA tackles this challenge by employing two main strategies. First, it utilizes sparse local attention instead of dense global attention, which can be optional during inference. This helps in reducing the computational burden. Second, LongLoRA incorporates LoRA (Low-Rank Adaptation) for context extension. The beauty of LongLoRA lies in its simplicity. It has shown promising results across various tasks using LLaMA-2 models, ranging from 7B/13B to 70B. Notably, it successfully extended the context size of LLaMA-2 7B from 4k to 100k and LLaMA-2 70B to 32k using just a single 8x A100 machine. What’s impressive is that these extensions were achieved while keeping the original model architectures intact. So, why does this matter? Well, LongLoRA is a significant step forward in creating more computationally efficient model expansion. If you’re interested in developing open-source LLMs with longer context lengths, LongLoRA might just be the solution that lowers the barrier to entry. In the world of language models, there are two popular methods that many AI developers use with “custom” data: RAG (Retrieval Augmented Generation) and finetuning. But here’s the thing – it can be confusing to determine which method to use, when to use it, and why. Luckily, John Hwang, in his insightful article, dives deep into this topic to clarify everything for us. First and foremost, Hwang points out that RAG and finetuning are fundamentally different tools meant for different problems. To help us understand better, he even includes a table comparing the two methods. Not stopping there, Hwang also lists out the right use cases for RAG and finetuning. He explains that these methods have their own strengths and weaknesses, and it’s important to consider the specific problem you’re trying to solve. But wait, there’s more! Hwang also provides us with a list of other factors we should consider when contemplating RAG and finetuning. These factors could range from the availability of training data to the constraints of real-time applications. To wrap it all up, Hwang presents a set of heuristics – essentially guidelines – for choosing the appropriate method depending on the situation. This is immensely helpful for AI developers who often find themselves stuck in analysis paralysis or caught up in premature optimization. Ultimately, this article matters because it not only helps AI developers navigate the complexities of RAG and finetuning, but it also supports enterprises in making well-informed investment decisions. By clarifying when and how to apply these methods effectively, Hwang ensures that we don’t waste our time, effort, and resources on the wrong approach. Hey everyone! I’ve got some interesting news for you today. Coinbase CEO, Brian Armstrong, recently expressed his opposition to regulating artificial intelligence (AI). He believes that imposing regulations on AI could actually hinder innovation. Armstrong is all about a “decentralize” and “open source” approach when it comes to AI. If you’re familiar with his stance on the crypto industry, this might sound familiar to you. He sees similar potential in AI and believes that a more flexible approach is needed. According to Armstrong, rapid progress in AI is of utmost importance. He even cites national security as one of the reasons why we need to keep pushing forward. It’s true that AI has its dark side, with nefarious uses like promoting crypto scams, but Armstrong emphasizes the benefits outweigh the risks. In a world that’s constantly evolving, we need innovation to thrive. And that means embracing new technologies like AI. So, according to Armstrong, let’s keep the regulations at bay and allow AI to continue its rapid development. It’s an interesting perspective, and it’ll be fascinating to see how this debate unfolds. Hey there! I’ve got some interesting AI updates for you today. Let’s jump right in! First up, Amazon is investing a whopping $4 billion in Anthropic. This collaboration aims to develop the most reliable and high-performing foundation models. Anthropic is known for its frontier safety research and products, while AWS brings its expertise in running secure and reliable infrastructure. Together, they will make Anthropic’s safe and steerable AI widely accessible to all AWS customers. Exciting stuff! Moving on, Meta has some cool plans for their AI chatbot. They want to develop a range of chatbot personas that cater to different users. There will be personas for engaging younger users with more colorful behavior, as well as ones for celebrities to interact with their fans. They also have chatbots geared towards productivity, like helping with coding and other tasks. Meta is really bringing chatbots to life! Now, let’s talk about some new research called LongLoRA. This method allows for efficient fine-tuning of long-context Language Models (LLMs). The goal here is to extend the context sizes of pre-trained LLMs without a huge computation cost. LongLoRA has shown impressive performance on various tasks using LLaMA-2 models, ranging from 7B/13B to 70B. It can extend the context size of LLaMA-2 7B from 4k to 100k and LLaMA-2 70B to 32k on a single 8x A100 machine. And the best part? It keeps the original model architectures intact. Quite impressive! Next up, Microsoft’s mobile keyboard app SwiftKey is getting some AI-powered features. Get ready for AI camera lenses, AI stickers, an AI-powered editor, and the ability to create AI images directly from the app. Now you can take your mobile photography game to the next level with these cool additions. SwiftKey is really stepping up its game! Speaking of AI camera updates, the latest leak about Google Pixel 8 has got us excited. The AI photo editing with Magic Editor is going to blow your mind. You’ll be able to remake any picture you take using this feature. And that’s not all! The Pixel 8 will also have DSLR-style manual camera controls, allowing you to tweak the shutter speed and ISO of an image. Plus, there will be a focus slider for that perfect shot. Get ready to capture some stunning photos with the Google Pixel 8! Now here’s something unique. A drinks company in Poland, Dictador, has appointed an AI robot as its “experimental” CEO. This robot, named Mika, will oversee the company’s growth into one-off collectables, communication, and even strategy planning. It’s definitely an interesting move to have an AI robot at the helm of a company. We’ll have to wait and see how this experiment unfolds! If you’re a fan of classic stories, you’re in for a treat. ElevenLabs has launched free book classics narrated by high-quality AI voices. Now you can enjoy stories like “Winnie the Pooh” and “The Picture of Dorian Gray” narrated by compelling AI voices in multiple languages. And guess what? The entire recording process took only one day. Talk about efficiency! Last but not least, Salesforce is making moves in the AI space. They’re set to acquire Airkit.ai, a low-code platform for building AI customer service agents. This platform, based on GPT-4, allows e-commerce companies to build specialized customer service chatbots. These chatbots can handle queries related to order status, refunds, product information, and more. It’s all about enhancing the customer service experience with the power of AI. And that’s a wrap for today’s AI update news! From investments in safer AI to AI-powered features in various apps, the world of AI continues to evolve and amaze us. Stay tuned for more exciting updates in the future! Hey there! If you’re excited about diving deeper into the world of artificial intelligence, I’ve got just the thing for you! There’s this amazing book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, it’s a game-changer! Now, let me tell you why you should totally get your hands on this gem. “AI Unraveled” is packed with all the answers to those burning questions you may have about AI. Think of it as your ultimate AI guidebook. It’s like having a knowledgeable expert right by your side, unravelling the mysteries of artificial intelligence in a way that’s easy to comprehend. The best part? You can grab a copy of this must-read book at three different platforms: Apple, Google, or Amazon. So, no matter whether you’re an Apple aficionado, a Google guru, or an Amazon enthusiast, there’s a way for you to access this invaluable resource. So, why wait any longer? Dive into “AI Unraveled” today and expand your understanding of artificial intelligence like never before. This book is a game-changer, and it’s ready to be enjoyed by curious minds like yours. Happy reading! On today’s episode, we covered the addition of voice capabilities and image inclusion in ChatGPT, Amazon’s $4 billion investment in Anthropic for reliable AI models, Meta’s plan to create various chatbot personas, the efficient method of LongLoRA for extending context sizes of LLMs, insights on RAG vs. Finetuning LLMs, Coinbase CEO’s opposition to AI regulation, and other AI updates including Google Pixel 8’s AI camera and Salesforce’s acquisition of Airkit.ai for AI customer service agents. Don’t forget to expand your AI knowledge with the essential book ‘AI Unraveled’ available at Apple, Google, or Amazon! Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Apple, Google, or Amazon today!
Rolling out over next two weeks, Plus users will be able to have voice conversations with ChatGPT (iOS & Android) and to include images in conversations (all platforms).
Amazon to Invest $4B in Anthropic
Amazon will invest up to $4 billion in Anthropic. The agreement is part of a broader collaboration to develop the industry’s most reliable and high-performing foundation models.
Anthropic’s frontier safety research and products, together with Amazon Web Services’ (AWS) expertise in running secure, reliable infrastructure, will make Anthropic’s safe and steerable AI widely accessible to AWS customers. AWS will become Anthropic’s primary cloud provider for mission-critical workloads, and this will also expand Anthropic’s support of Amazon Bedrock.
It will enable enterprises to build with Anthropic models on Amazon Bedrock, responsibly scaling the adoption of Claude and delivering safe AI cloud technologies to organizations worldwide.
Meta to develop a ‘sassy chatbot’ for younger users
Meta has plans to develop dozens of chatbot ‘personas’ geared toward engaging young users with more colorful behavior. It also includes ones for celebrities to interact with their fans and some more geared towards productivity, such as to help with coding and other tasks.
Why does this matter?
Reportedly, Meta is also working on developing a more powerful LLM to rival OpenAI. Perhaps this could serve as a stepping stone towards more advanced AI capabilities and also boost engagement on Meta’s social media platforms.
LongLoRA: Efficient fine-tuning of long-context LLMs
New research has introduced LongLoRA, an ultra-efficient fine-tuning method designed to extend the context sizes of pre-trained LLMs without a huge computation cost.
Typically, training LLMs with longer context sizes consumes a lot of time and requires strong GPU resources. For example, extending the context length from 2048 to 8192 increases computational costs 16 times, particularly in self-attention layers. LongLoRA makes it way cheaper by:
1. Using sparse local attention instead of dense global attention (optional at inference time).
2. Using LoRA (Low-Rank Adaptation) for context extension
LongLoRA: Efficient fine-tuning of long-context LLMs
This approach seems both easy to use and super practical. LongLoRA performed strongly on various tasks using LLaMA-2 models ranging from 7B/13B to 70B. Notably, it extended LLaMA-2 7B from 4k context to 100k and LLaMA-2 70B to 32k on a single 8x A100 machine, all while keeping the original model architectures intact.
Why does this matter?
LongLoRA is an important step toward making model expansion more computationally efficient. For those interested in creating open-source LLMs with longer context lengths, LongLoRA may be the lowest barrier to entry.
RAG vs. Finetuning LLMs – What to use, when, and why
RAG (Retrieval Augmented Generation) and finetuning are two popular methods for using LLMs with “custom” data. However, it can be confusing to know which method to use, when, and why.
Clarifies that RAG and finetuning are fundamentally different tools for different problems. (includes a table comparing the two)
Lists out the right use cases of RAG and finetuning.
Lists out other factors to consider when considering RAG and finetuning.
Presents a set of heuristics for choosing what method to use and when.
Why does this matter?
The article helps AI developers navigate between the two methods and avoid analysis paralysis and premature optimization. Moreover, it assists enterprises in making informed investment decisions by clarifying when and how to apply these methods effectively.
Coinbase CEO calls for AI deregulation
Coinbase CEO Brian Armstrong opposes regulation on artificial intelligence, arguing it would slow innovation.
Armstrong supports a “decentralize” and “open source” approach in AI, similar to his stance on the crypto industry.
Fast progress on AI, he argues, is critical for various reasons including national security, and despite nefarious uses like promoting crypto scams.
Amazon to invest up to $4 billion in Anthropic, expanding access to safer AI – It is part of a broader collaboration to develop the most reliable and high-performing foundation models. Anthropic’s frontier safety research and products, together with AWS’s expertise in running secure, reliable infrastructure, will make Anthropic’s safe and steerable AI widely accessible to AWS customers.
Meta’s AI chatbot plan includes a ‘sassy robot’ for younger users – Meta has plans to develop dozens of chatbot personas geared towards engaging young users with more colorful behavior. It also includes ones for celebrities to interact with their fans and some more geared towards productivity, such as to help with coding and other tasks.
LongLoRA: Efficient fine-tuning of long-context LLMs – New research has introduced LongLoRA, an efficient fine-tuning method designed to extend the context sizes of pre-trained LLMs without a huge computation cost. In practical terms, LongLoRA performed strongly on various tasks using LLaMA-2 models ranging from 7B/13B to 70B. Notably, it extended LLaMA-2 7B from 4k context to 100k and LLaMA-2 70B to 32k on a single 8x A100 machine, all while keeping the original model architectures intact.
Microsoft’s mobile keyboard app SwiftKey gains new AI-powered features – It will now include AI camera lenses, AI stickers, an AI-powered editor, and the ability to create AI images from the app.
Google Pixel 8’s latest leak shows off big AI camera updates – AI photo editing with Magic Editor will enable you to remake any picture you take. DSLR-style manual camera controls will let you tweak the shutter speed and ISO of an image and a focus slider.
A drinks company in Poland appoints AI robot as ‘experimental’ CEO – Dictador, best known for its rums, has appointed the robot to oversee the company’s growth into one-off collectables, communication, or even strategy planning. It is named Mika.
ElevenLabs launches free book classics narrated by high-quality AI voices – It presents six classic stories told by compelling AI voices in multiple languages, including “Winnie the Pooh” and “The Picture of Dorian Gray.” The entire recording process took only one day.
Salesforce to acquire Airkit.ai, a low-code platform for building AI customer service agents – The GPT-4-based platform allows e-commerce companies to build specialized customer service chatbots that can deal with queries around order status, refunds, product information, and more.
Emerging AI Innovations: September 24th, 2023
Researchers discover ‘Reversal Curse:’ LLMs trained on “A is B” fail to learn “B is A”
Training AI models like GPT-3 on “A is B” statements fails to let them deduce “B is A” without further training, exhibiting a flaw in generalization. (Full 18 pg. paper)
The Phenomenon
Models can’t infer “B is A” after learning “A is B.”
E.g. won’t deduce “Olaf Scholz was the ninth Chancellor” from opposite statement.
True even for models with billions of parameters.
Evidence of Deficiency
Tested models on fictitious “X is Y” statements and reverse questions.
Evaluated ChatGPT on real celebrity examples and their reversals.
Success rate dropped from 79% to 33% between forward and reverse.
So, imagine this: a New Caledonian Crow meticulously crafting a tool to fish out a tasty grub from a tree crevice. Fascinating, right? Turns out, animals have some incredible communication skills that we’ve only just begun to understand. Take Christian Rutz, a behavioral ecologist who has spent his career studying the New Caledonian Crow. He noticed that these birds live in complex social groups and actually pass on toolmaking techniques to their offspring. But here’s the kicker – different crow groups have their own unique vocalizations. Rutz wondered if these dialects could explain the cultural differences in toolmaking among the groups. That’s where artificial intelligence (AI) comes in. With recent advancements, we’re on the brink of major breakthroughs in understanding animal communication. AI can help us decipher animal vocalizations, like the calls of crows. A group called the Earth Species Project is using machine learning models to analyze data collected from various species. And the Project Cetacean Translation Initiative is specifically focused on understanding the vocalizations of sperm whales. Decoding these vocalizations not only aids conservation and welfare efforts, but it also has a profound impact on us. It’s like when the telescope was invented – we realized Earth wasn’t the center of the universe. AI has the potential to reshape our understanding of animals and our place in the world. Scientists like Shane Gero are already using AI to decode the complex vocalizations of sperm whales. By analyzing patterns of sound, called codas, Gero and his team have identified individual whales with 99 percent accuracy. And now they’re aiming to train a computer to speak whale by recording the vocalizations of Dominica’s resident whales around the clock. AI is opening up a whole new world of possibilities for understanding animal communication, and it’s truly mind-blowing. These tools are changing the way we see ourselves in relation to everything around us.
Artificial intelligence (AI) has made significant progress in recent years, enabling us to delve into the world of animal communication like never before. With the availability of cheaper sensors and advancements in technology such as hydrophones, biologgers, and drones, the amount of data gathered from animals has exploded. However, this deluge of data is challenging for biologists to manually analyze efficiently. This is where AI comes in. AI models, particularly large language models like ChatGPT, thrive on vast amounts of information. For instance, ChatGPT-3 was trained on approximately 45 terabytes of text data, a substantial portion of the entire Library of Congress. In the early days, humans had to classify a significant part of this data with labels, essentially teaching the machines what was important. Nevertheless, the next generation of models have advanced to the point where they can “self-supervise.” They automatically learn the essential elements and create algorithms to predict what words will follow in a sequence. A breakthrough in translation occurred in 2017 when researchers found a way to translate between human languages without relying on a Rosetta stone. This discovery involved transforming the semantic relations between words into geometric ones. Machine-learning models can now translate unknown human languages by aligning shapes. By analyzing the frequency of words appearing in proximity to each other, models accurately predict what will follow. This suggests that there is an underlying structure that unites languages, opening doors for decoding new languages using machine learning techniques. In 2020, natural-language processing took another step forward by considering everything as a language. For example, DALL-E 2, an AI system capable of generating realistic images from verbal descriptions, maps the shapes that represent text to those that represent images with remarkable accuracy. This kind of “multimodal” analysis is likely to be crucial in translating animal communication, as many animals use different modes of communication concurrently, much like humans use body language alongside speech. By considering the actions immediately before, during, or after sounds, we can better understand the context and meaning behind an animal’s communication. Machine-learning models trained with the right data could help decode these behaviors and potentially uncover new patterns. An example of AI-powered analysis already in use is Merlin, a free app developed by the Cornell Lab of Ornithology. Merlin uses AI to identify bird species. Users can record bird sounds, which Merlin then converts into a spectrogram, a visual representation of the volume, pitch, and duration of the call. By comparing the user’s recording with its trained audio library and cross-referencing with the global database of observations called eBird, Merlin can accurately identify more than 1,000 bird species. This demonstrates the potential for AI to assist in understanding animal communication on a broader scale. The ability of AI to process and analyze large amounts of data can greatly aid in deciphering complex animal communication patterns. In a study published in Nature Communications, scientists reported that machine-learning models unearthed previously unrecognized differences in Zebra Finch songs that elicit attention from females during mate selection. Females prefer partners that sing similar to the birds they grew up with, and AI models were able to identify this pattern. Such findings highlight the potential for AI to discover novel information hidden within animal communication data. AI’s impact on animal communication research goes beyond language translation and species identification. It has the potential to detect and understand the nuances of animal behavior by examining the context in which communication occurs. For instance, AI models trained to recognize patterns in body language and gestures, along with vocalizations, could reveal valuable insights into how animals communicate and interact with each other. With the rapid advancements in AI and the increasing availability of data, the future of animal communication research looks promising. By harnessing the power of AI, scientists can explore, decode, and understand the rich world of animal communication in ways that were previously unimaginable. It is an exciting frontier that opens up a new realm of knowledge and understanding about our fellow creatures with whom we share this planet.
But you know, the world can be a noisy place, with so many sounds overlapping and mingling together. It’s like trying to pick out one specific bird’s song in the middle of a bustling cacophony. And the same goes for other animals, like whales. It has always been a challenge for scientists to isolate and identify individual animal speakers amidst all the chaos. They call it the cocktail party problem, and it has always hindered our ability to process and understand animal vocalizations. But here’s the exciting part – in 2021, the Earth Species Project came up with a groundbreaking solution. They developed a neural network that can untangle and separate overlapping animal sounds, like individual tracks in a music recording. And you know what’s even better? They released the code for free, as an open-source project. This means that anyone can use this technology to filter out background noise, like car honks, and focus on the specific sounds they want to study. It’s a game-changer! Let me explain how it works. This neural network creates a visual representation of the animal sounds, like a spectrogram or a waveform. It then uses this representation to determine which pixel corresponds to which speaker. It’s like a magic trick for audio processing. Now researchers can finally distinguish between different animals in a chorus of sounds and understand each speaker individually. And that’s not all! The Earth Species Project has been on fire lately. They also developed what they call a foundational model. This model can automatically detect and classify patterns in massive datasets. Imagine the possibilities! It can help scientists analyze and make sense of the immense amount of information contained in animal vocalizations. It’s like a revolutionary tool for unlocking the secrets hidden in the animal kingdom’s communication systems. Let me give you an example. Have you heard of the New Caledonian Crows? They are renowned for their remarkable tool-making skills. But did you know that they also have unique vocalizations specific to different regions? It’s fascinating! And with the help of AI, we might one day decipher and understand the meaning of these vocalizations. Just imagine the insights we could gain into the lives of these intelligent creatures. But it’s not just about unraveling the mysteries of nature. These tools have real practical value as well. Think about endangered species, like the Hawaiian Crow, or the ‘Alalā as it is known locally. These birds went extinct in the wild a few decades ago, and efforts have been made to conserve and reintroduce them through breeding programs. One of the researchers involved in studying the New Caledonian Crows, Rutz, is now collaborating with the Earth Species Project to delve into the Hawaiian Crow’s vocalizations. He wants to create an inventory of the vocalizations used by the captive birds and compare them to historical recordings of the last wild Hawaiian Crows. By doing so, he hopes to uncover whether there have been any significant changes in their repertoire while in captivity. This knowledge could aid in understanding why reintroducing the crow to its natural habitat has been so challenging. Now let’s take a moment to talk about our beloved pets. For the longest time, animal behaviorists haven’t paid much attention to domestic pets. But that is changing. Con Slobodchikoff, the author of “Chasing Doctor Dolittle: Learning the Language of Animals,” has been studying prairie dogs for years and has discovered the complexity of their communication through calls. This understanding led him to become a behavioral consultant for dogs, as he realized that many owners misinterpret their pets’ signals. You see, our furry friends don’t just rely on barks; they communicate through various signals, including body language. And unfortunately, we often miss out on these cues because we are fixated on sound as the primary means of communication. But Slobodchikoff is working on an AI model that can help translate a dog’s facial expressions and barks for their owners. He firmly believes that animals have their own thoughts, hopes, and even dreams. And with the help of AI, we might just be able to understand them better and strengthen the bond between humans and our four-legged companions. But the potential impact of this technology extends beyond companion animals. Farm animals, for instance, could greatly benefit from a deeper understanding of their emotions. Elodie F. Briefer, an associate professor in animal behavior, has been studying pig vocalizations and their emotional states. She trained an algorithm using thousands of pig sounds to predict whether the animals were experiencing positive or negative emotions. This incredible feat demonstrates the potential for AI and machine learning to improve animal welfare by helping us comprehend their feelings and tailor better care. So you see, these developments in AI and machine learning aren’t just for the sake of research. They have practical applications that can make a difference in the world. Whether it’s protecting endangered species, understanding our pets better, or ensuring the welfare of farm animals, these tools offer us a glimpse into the rich and complex world of animal communication. And who knows what other surprises await us as researchers continue to explore and unlock the secrets of the animal kingdom?
Language models are incredibly proficient at identifying patterns, but they do not possess the ability to decipher meaning or always make accurate conclusions. This lack of understanding makes it difficult for AI experts to validate the algorithms’ results. Benjamin Hoffman, formerly involved in the development of the Merlin app and currently a member of the Earth Species Project, states that one of the biggest challenges scientists face is how to learn from the discoveries made by these models. Hoffman explains that the choices made in machine learning have a direct impact on the scientific questions that can be asked. For example, Merlin Sound ID can identify which birds are present, aiding in ecological research. However, it cannot answer questions about bird behavior, such as the types of calls made during interactions with potential mates. Understanding animal communication requires comprehending what the computer is doing when learning how to interpret it. Director Daniela Rus from the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory is excited about the possibilities of studying animal communication with the help of machine learning. Rus previously developed remote-controlled robots for whale-behavior research, collaborating with biologist Roger Payne, known for popularizing the Save the Whales movement. With advancements in underwater monitoring sensors and improved AI models for data analysis, the two fields can now be combined. At the Earth Species Project, Rus initially focused on isolating sperm whale clicks from the ocean’s background noise. Sperm whale vocalizations resemble binary code in the structure of representing information, but they are more intricate than that. Rus used machine learning to analyze how these clicks combine into codas, searching for patterns and sequences. By understanding these building blocks, they can begin studying the foundational components of the language and determining if the sperm whale lexicon possesses language-like properties. It is worth noting that understanding the structure of a language is not necessary for speaking it. AI can now mimic patterns and intonations of human speech after being trained with just three seconds of audio. Raskin, a project member, predicts that within the next year or two, this capability will extend to animal communication, making it easier for researchers to infer the meaning behind animal vocalizations. The Earth Species Project plans to conduct playback experiments in collaboration with biologists, involving playing artificially generated calls to animals in a laboratory setting and observing their responses. Raskin confidently asserts that soon they will be able to pass the Turing test with animals such as Zebra Finches, crows, and whales. This test determines if an animal can differentiate between conversations with a machine or a member of its own species. However, ethical concerns arise with the potential misuse of this technology, such as precision fishing or poaching endangered animals. As of now, non-profit organizations like the Earth Species Project lead the field of animal communication research. These organizations prioritize open-source data and model sharing, driven by scientists passionate about the animals they study. However, the landscape may change as profit-driven entities enter the scene. Guidelines and legislative frameworks are necessary to ensure responsible development and usage of this technology. Designing a “whale chatbot,” like the aspirations of Project CETI, entails more than replicating the clicks and whistles of sperm whales—it requires understanding the animal’s experience. Humans share many basic forms of communication with other animals, such as parent-offspring interactions. Both human and animal vocal expressions show similarities in development. The existence and conveyance of language-like qualities in animal communication are a subject of debate, with critics cautioning against imposing human linguistic rules on it. George Happ and Christy Yuncker, retired scientists who observed a pair of wild Sandhill Cranes over two decades, offer insight into the behavior of these birds. They explain how after the death of one of their colts, the surviving members engaged in what could be interpreted as mourning behavior. While critics may argue that squarely attributing emotions to animals lacking human capabilities is imprecise, the evidence from close observation suggests otherwise. The pain of losing a loved one is a universal experience, and the ability to relate to it may be the true value of any language. Yuncker and Happ eagerly anticipated the return of Millie and Roy, a crane pair that frequented their home every spring. Unfortunately, they did not appear in 2017. Despite the loss, a new crane pair nested and successfully raised their colts, signifying the continuation of life’s cycles. Yuncker remarks that, rather than just observing nature, humans are an integral part of it. The understanding and translation of animal communication necessitate new tools and the ability to transcend human biases and expectations. Recognizing that each species has its own unique experiences and perspectives is vital for comprehensive communication with animals.
In recent years, the ML community has focused on training increasingly large and powerful self-supervised (language) models. Since these LLMs exhibit impressive predictive capabilities, they are well-positioned to be strong compressors.
This interesting research by Google DeepMind and Meta evaluates the compression capabilities of LLMs. It investigates how and why compression and prediction are equivalent. It shows that foundation models, trained primarily on text, are general-purpose compressors due to their in-context learning abilities. For example, Chinchilla 70B achieves compression rates of 43.4% on ImageNet patches and 16.4% on LibriSpeech samples, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.
Emerging AI Innovations: September 22nd, 2023
Microsoft recently announced a game-changing feature called Microsoft Copilot. This exciting new addition will infuse AI capabilities into various Windows 11, Microsoft 365, Edge, and Bing applications. Think of it as Bing, but specifically designed for Windows devices. So, what can Copilot do? Quite a lot, actually. With this tool, you can rearrange windows effortlessly, generate text, open web apps, edit pictures, and much more. It’s accessible both via an app and through a simple right-click, making it convenient for users to tap into its AI-powered goodness. But when can we start using Copilot? Well, the good news is that it’s just around the corner. Microsoft plans to roll out Copilot this fall, making it available across Bing, Edge, and Microsoft 365. And for Windows users, you’ll get to enjoy this feature sooner than you think. The free Windows 11 update will begin on September 26th. Now, you might be wondering, why is this such a big deal? The answer lies in the democratization of AI. While we don’t have any mind-blowing use cases for Copilot just yet, this step forward by Microsoft is significant. As more users get their hands on this AI copilot, we’ll start to see its true capabilities. And if all goes well, Microsoft could dominate an even larger share of the AI market by delivering AI nativel
Hey folks! YouTube just announced some exciting news for creators! They’re rolling out three new AI-powered features for YouTube Shorts creators. Let me break it down for you. First up, we have Dream Screen. This feature lets you create image or video backgrounds using AI. All you have to do is type in what you want to see in the background, and AI will make it happen. How cool is that? Next, we’ve got Creator Music. This feature got an AI revamp, making it even better than before. Now, creators can simply type in the kind and length of the music they need, and AI will find the most relevant suggestions. It’s like having your own personal music assistant. Last but not least, we have AI Insights for Creators. This is a tool that generates video ideas for creators based on AI’s analysis of what audiences are already watching and preferring. So, if you’re looking for some inspiration, AI has got your back. This move by YouTube seems like a smart strategic decision to integrate AI features directly into the platform. We’re seeing this trend more and more, and it’s great news for users. They get free AI assistance in their creative endeavors, making their experience even better. In other news, Google is taking an innovative approach to train smaller language models. You know those large language models (LLMs) that have been making waves? Well, their massive size poses some deployment challenges. But fear not! The authors propose a method called distilling step-by-step, which trains smaller task-specific models using less data while still surpassing LLM performance. Here’s the gist: they extract rationales (aka intermediate reasoning steps) from an LLM using few-shot chain-of-thought prompting. These rationales, along with labels, are then used to train smaller models in a multi-task framework. The results? In experiments across different datasets, this approach reduced the need for training data by a whopping 75-80% compared to standard fine-tuning. Why does this matter? Well, this new approach opens up possibilities for deploying language models on local devices, making them more accessible. And the best part? These smaller models can still deliver the performance we’ve come to expect from their larger counterparts. That’s all for now, folks! Exciting times ahead for creators and language models alike. Keep creating and exploring!
When it comes to evaluating large language models (LLMs) for industry applications, there are four crucial factors to consider. Skanda Vivek highlights these factors, which include quality, economic aspects, latency, and privacy. Each of these factors plays a significant role in determining the suitability of a particular LLM. The quality of the LLM is of utmost importance. Depending on your end goal, you may prioritize different aspects of quality, such as data accuracy, contextual understanding, or fluency. Consider what matters most to your industry and choose an LLM that aligns with those preferences. Economic factors also come into play. It’s essential to assess the cost-effectiveness of implementing a particular LLM. Does it provide value for money? Can it fit within your organization’s budget? Analyzing the economic aspects ensures you make an informed decision. Latency, or the response time of the LLM, is another vital factor. Some applications require real-time or near-instantaneous responses. Evaluating an LLM’s latency helps you select the model that meets your specific timing requirements. Finally, privacy is increasingly significant for many industries. Skanda Vivek emphasizes the need to consider privacy when choosing an LLM. Depending on your industry, data security and privacy regulations may be a top priority. Ensuring the chosen model aligns with your privacy needs is crucial. Choosing the right LLM is a critical decision that can significantly impact your applications. By carefully considering these four factors—quality, economic aspects, latency, and privacy—you can make an informed choice that aligns with your industry’s requirements. In recent news, some universities are raising concerns about AI detection software used to catch cheating students. There are worries that students could be falsely accused of cheating when using tools like ChatGPT. As a result, some universities are opting to abandon these AI detection systems. The debate highlights the potential drawbacks and risks associated with relying entirely on AI tools for academic integrity.
So, here’s the thing. Some major universities have decided to ditch AI detection tools because they’re worried about their accuracy. And let’s face it, nobody wants to be falsely accused of cheating, right? One tool in particular, called ChatGPT, has caused quite a stir. The problem with ChatGPT is that it’s gained popularity among students, and that’s got educators really concerned about academic dishonesty. But it’s not just about students using AI to write their essays. It’s also about the tool itself misidentifying things and getting it all wrong. For example, one professor in Texas failed half of his class because of false detections by ChatGPT. Can you imagine? Talk about a nightmare scenario. And it’s not just him. Other students have also been wrongly accused by anti-plagiarism software using ChatGPT. What’s interesting is that even OpenAI, the company behind ChatGPT, has abandoned their own AI text detector due to its low accuracy rate. They’ve even warned educators about relying too heavily on AI content detectors. And here’s another thing to consider: these detection tools often get it wrong when it comes to content written by non-English writers. So, yeah, there are some serious concerns here. That’s why some universities, like Vanderbilt and Northwestern, have decided to say “no thanks” to these AI detection tools. It’s better to be safe than sorry, right? After all, nobody wants to unfairly accuse a student of cheating.
Hey there! Some interesting news for you today. According to Climate Action Against Disinformation, X, which we all know as Twitter, has ranked last when it comes to tackling climate misinformation. Quite the bummer, right? It turns out that Pinterest is leading the pack in addressing climate change misinformation, with YouTube, Meta (formerly known as Facebook), and Instagram not too far behind. But poor old X is lagging behind. So, what led to this low ranking for X? Well, it seems that since Elon Musk took over, things have changed, and not for the better. There are unclear policies on climate misinformation and a less communicative content moderation team, both of which have contributed to X’s downward slide in the rankings. Maybe they need to step up their game a bit. In another news story, Google is facing a lawsuit after it allegedly directed a man, Philip Paxson, to drive off a collapsed bridge via Google Maps. Sadly, Paxson lost his life in the tragic accident. According to his family, Google was aware of the bridge’s collapse but failed to update its navigation system, which they argue makes the tech giant negligent in Paxson’s death. Google apparently received reports about the bridge’s condition, but did nothing to fix the route information. It’s a heartbreaking situation. And finally, brace yourself for this one. A study by dappGambl has found that a whopping 95% of NFTs are now practically worthless. Yep, you heard that right. NFTs, which were once all the rage, have lost their shine. Prices have plummeted, and most of the 73,257 NFT collections analyzed have a market cap of zero Ether. It’s uncertain what the future holds for NFTs, but they’ll need to prove their worth, whether through cultural significance or as a representation of actual art, if they want to stick around. So, that’s the latest in tech and climate news. Stay tuned for more updates!
OpenAI has just unveiled their latest model for text-to-image translation called DALL·E 3, and it’s pretty impressive! This new version is built directly on ChatGPT, which means you can use ChatGPT to generate customized and detailed prompts for DALL·E 3. And if the results aren’t exactly what you were hoping for, you can even ask ChatGPT to make some tweaks. Compared to its predecessor, DALL·E 2, DALL·E 3 delivers significant improvements in creating detailed images. OpenAI showcased this by providing a prompt for an expressive oil painting of a basketball player dunking, depicted as an explosion of a nebula. The results from DALL·E 3 were far superior to those from DALL·E 2. OpenAI has also taken steps to ensure that DALL·E 3 doesn’t generate violent, adult, or hateful content. They have designed it to decline requests for images in the style of living artists. Additionally, creators have the option to exclude their images from being used in the training of OpenAI’s future image generation models, giving them more control over the use of their work. Currently, DALL·E 3 is in research preview and will be available to ChatGPT Plus and Enterprise customers in October through the API. It will later be made available in Labs for those interested. This new release is important because it addresses the limitations of previous text-to-image systems, which often ignored certain words or descriptions. With DALL·E 3, AI’s ability to generate images that align precisely with the provided text takes a huge leap forward. It raises questions about how other image generators like Midjourney and Stable Diffusion will keep up. OpenAI has also prioritized safety improvements in DALL·E 3. They have implemented measures to prevent explicit content and have tools in place to identify risky words and block public figures. Furthermore, artists can now request that their work be blocked from AI copying, and DALL·E 3 won’t mimic the styles of specific artists when named. OpenAI hopes that the integration with ChatGPT and the safety guards in DALL·E 3 will expand access to this technology while preventing misuse. However, there are still concerns and legal issues surrounding AI-generated art that need to be addressed.
Amazon had some exciting announcements at its recent devices event. One of the standout updates is the integration of generative AI into their Echo family of devices. This new AI model is optimized for voice, taking into account not only what is said but also body language, eye contact, and gestures. This means that interactions with Alexa will become much more powerful and conversational, providing users with improved experiences. But that’s not all. Amazon has also introduced generative AI updates for Fire TV’s voice search. This update aims to enhance the conversational interaction between users and Alexa, allowing for a more natural and intuitive way to discover new content based on specific preferences. This development is significant because it showcases how integrating language models like Generative AI into voice assistants can revolutionize the way we interact with them. Amazon’s revamp of Alexa using generative AI is a game-changer. It enables voice assistants to better understand context, seamlessly carry over information from previous conversations, and provide a more personalized experience for users. In fact, Amazon is transforming Alexa into a hands-free ChatGPT by leveraging the technology behind chatbots. This upgrade will give Alexa the ability to engage in more complex and open-ended conversations. It will also enhance its simulated personality, interpret body language (for devices with cameras), and modulate its voice for a more natural conversation. However, there are some challenges to overcome, such as responding accurately to body language and refining these large language models to prevent inappropriate or nonsensical responses. But with Amazon’s dedication to improving AI experiences, we can expect significant advancements in these areas. Overall, Amazon’s integration of generative AI into Alexa and Fire TV demonstrates their commitment to providing users with more intuitive, personalized, and conversational experiences.
Hey there! Have you heard about Mark Zuckerberg’s latest philanthropy project? It’s got a pretty ambitious goal – to “cure all diseases”. The project, called the Chan Zuckerberg Initiative (CZI), is a collaborative effort between Zuckerberg and his wife, Priscilla Chan. So here’s the plan: CZI is planning to build one of the biggest GPU clusters in the world specifically for AI-driven biomedical research. They want to use large language models to dive deep into disease development at the cellular level and even predict how cells behave. And to do that, they’re going to need some serious computational power – over 1,000 Nvidia’s H100 GPUs! This high-performance computing system is expected to be up and running by 2024. And let me tell you, it’s going to revolutionize biomedical research. From mapping out various cell types across different organisms to designing potential drugs and therapeutics, this GPU cluster will supercharge the entire process. I don’t know about you, but I’m pretty excited to see what kind of breakthroughs this project will bring. Who knows, maybe we’ll be living in a world where diseases are a thing of the past sooner than we think!
So, let’s dive into the latest AI updates from OpenAI, Microsoft, YouTube, Google, Cisco, and Anthropic. It seems like ChatGPT is back in the spotlight with an increase in usage, particularly because students are returning to school and concerns about AI cheating are on the rise. After experiencing a decline throughout the summer, ChatGPT has seen a 12% traffic increase since fall classes resumed in the US. However, it’s important to note that current usage is still below the peak levels seen earlier this year. With students back in the classroom, concerns about AI-aided cheating have resurfaced. The easier access students have to AI technology raises fresh debates among schools about whether to ban, incorporate, or ignore such tools. For educators, managing responsible AI use in academics is becoming a complex balancing act. There’s also some uncertainty surrounding potential revenue as ChatGPT’s reliance on students could pose challenges for monetization. Moving on to other AI news, Microsoft has announced a new AI-powered feature called Microsoft Copilot. This feature, available in various Windows 11 applications, Microsoft 365, Edge, and Bing, allows users to rearrange windows, generate text, edit pictures, and more. It’s like having Bing integrated into your Windows experience. YouTube is not far behind with its AI advancements. The platform has introduced three new AI-powered features specifically for Shorts creators. Dream Screen uses AI to generate background images and videos, Creator Music helps find the perfect track for Shorts, and AI Insights for Creators assists in brainstorming the next video idea. These features aim to enhance the content creation experience on YouTube. Meanwhile, Google has expanded its AI coding assistant, Studio Bot, to 170 countries. Initially launched for Android developers in the US, this assistant helps generate code, fix errors, and answer questions about Android development. It’s a handy tool for developers worldwide. In the world of image creation, Microsoft’s DALL-E 3 is making its way to Bing. Soon, users will be able to create images in a chat using DALL-E 3. This exciting feature will be rolled out for enterprise users in October, opening up new possibilities for visual communication. Now, let’s switch gears to a significant acquisition. Cisco has announced its plan to acquire cybersecurity firm Splunk for $28 billion. This move aligns with Cisco’s goal to expand its software and AI-powered data analysis capabilities. Splunk, which introduced AI features earlier this year to detect and respond to data anomalies, will play a vital role in Cisco’s strategy. In the realm of responsible AI scaling, Anthropic, the company behind the Claude chatbot, has released a policy that emphasizes its commitment to responsible AI system development. The policy acknowledges the potential for AI systems to cause catastrophic risks, including thousands of deaths or immense financial damage. It’s encouraging to see companies prioritizing responsible AI practices. In other tech news on September 22nd, 2023, Cisco is set to make its largest acquisition ever by acquiring Splunk for $28 billion. This move aims to boost security services and system performance troubleshooting. On a different note, NASA eagerly awaits the return of pristine asteroid Bennu samples, taken by OSIRIS-REx in 2020. The samples could unlock valuable insights into the origins of our solar system. In the legal world, lawyers who sued Tesla’s board for excessive pay are seeking a jaw-dropping $10,000 an hour. The case is sure to attract attention as it unfolds. Another interesting development involves an anonymous developer who used OpenAI’s ChatGPT API to program an AI that created and launched an ERC-20 token called AstroPepeX. Within just 24 hours, the token generated an astonishing $12.9 million in trading. It’s a testament to the possibilities AI offers in the realm of finance and entrepreneurship. Lastly, Ilya Sutskever, one of OpenAI’s renowned figures, along with machine ethicist Thomas Krendl Gilbert, have described AI development as “alchemy.” This comparison underscores the unpredictable and mysterious nature of AI outcomes, sparking heated debate within the industry. And there you have it, the latest AI updates featuring ChatGPT, Microsoft, YouTube, Google, Cisco, and Anthropic. Stay tuned for more exciting advancements in the world of artificial intelligence.
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In today’s episode, we covered Microsoft’s AI-powered Copilot, YouTube’s new AI features for creators, evaluating large language models in industry, concerns with AI detection tools in universities, rankings of tech companies tackling misinformation, OpenAI’s DALL·E 3 text-to-image model, generative AI updates from Amazon, Zuckerberg’s philanthropy in AI-driven research, ChatGPT usage concerns, and other notable news – plus, don’t forget to expand your AI knowledge with the essential book ‘AI Unraveled’. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
Microsoft has announced a new AI-powered feature, Microsoft Copilot. It’ll bring AI features into various Windows 11, Microsoft 365, Edge, and Bing. Our first impressions are that it’s Bing but for Windows. You can use Copilot to rearrange windows, generate text, open apps on the web, edit pictures and more.
Copilot can be accessed via an app or with a simple right-click and will be rolled out across Bing, Edge, and Microsoft 365 this fall, with the free Windows 11 update starting on September 26th.
Why does this matter?
While we don’t see any revolutionary use cases of Copilot as of now, it’s still a huge step towards the democratization of AI. As more users get their hands on this AI copilot, we’ll know the true extent of its effectiveness. If all goes well, Microsoft will end up grabbing an even bigger share of the AI market as it will deliver AI natively to all Windows devices.
YouTube announces 3 new AI features for creators
In a YouTube event, the company announced 3 AI-powered features for YouTube Shorts creators.
Dream Screen: It allows users to create image or video backgrounds using AI. All you need to do is type what you want to see in the background and AI will create it for you.
Creator Music: This was a previously available feature but got an AI revamp this time around. Creators can simply type in the kind and length of the music they need and AI will find the most relevant suggestions for their needs.
AI Insights for Creators: This is an inspiration tool which generates video ideas based on AI’s analysis of what the audiences are already watching and prefer.
Why does this matter?
It seems like a strategic decision to natively introduce AI features to support users. It’s a trend we are seeing increasingly more across the landscape. For the users, it’s great news since they get free AI assistance in their creative endeavors.
Google’s innovative approach to train smaller language models
Large language models (LLMs) have enabled new capabilities in few-shot learning, but their massive size makes deployment challenging. To address this, the authors propose a new method called distilling step-by-step, which trains smaller task-specific models using less data while surpassing LLM performance.
First, the key idea is to extract rationales – intermediate reasoning steps – from an LLM using few-shot chain-of-thought prompting. These rationales are then used alongside labels to train smaller models in a multi-task framework, with tasks for label prediction and rationale generation. Experiments across NLI, QA, and math datasets show this approach reduces training data needs by 75-80% compared to standard fine-tuning.
Why does this matter?
This new approach to train smaller models with higher accuracy has the potential to support language models that can be deployed on local devices while retaining the performance that was previously achievable only through LLMs.
4 Crucial Factors for Evaluating Large Language Models in Industry Applications
Based on your end goal, you might fancy one LLM over the other. For instance, some industries value privacy over anything while others might put data accuracy over everything else. In this article, Skanda Vivek shares the 4 critical factors you should always consider when picking a large language model.
He mentions Quality, Economic, Latency, and Privacy to be the 4 resting pillars of your decision. He then goes into details discussing each of these parameters and how you should evaluate a given model against them.
Why does this matter?
The ability to make the right decision when choosing the underlying LLM for your applications is massively important. This article will provide you with valuable insights when it comes to choosing the right LLM.
Some universities are ditching AI detection software amid fears students could be falsely accused of cheating by using ChatGPT
Major universities have discontinued the use of AI detection tools due to concerns about their accuracy, potentially falsely accusing students of cheating with the aid of AI tools like ChatGPT.
AI Detection Tool Concerns
False Accusations of Cheating: Many universities, including Vanderbilt and Northwestern, have stopped using Turnitin’s AI detection tools over worries they might wrongly accuse students of using AI to write essays.
High False Positive Rate: Vanderbilt University highlighted a 1% false positive rate, potentially mislabeling 750 out of 75,000 papers. Similarly, Northwestern University and the University of Texas expressed accuracy concerns, opting not to use the tool.
ChatGPT’s Rise & Challenges
Popularity Among Students: The growing use of ChatGPT by students has educators worried about a surge in academic dishonesty.
Misidentification Issues: A Texas professor mistakenly failed half his class because of false detections by ChatGPT, while other students faced wrongful accusations by anti-plagiarism software.
OpenAI’s Stance
Difficulty in AI Text Detection: OpenAI abandoned its AI text detector due to its low accuracy rate. They’ve also cautioned educators about the unreliability of AI content detectors.
Bias Against Non-English Writers: Many detection tools wrongly labeled content by non-English writers as AI-generated, causing additional concerns.
X ranks lowest in tackling climate misinformation, study reveals
X, formerly known as Twitter, has ranked last in a new assessment by Climate Action Against Disinformation for its management of climate misinformation.
Pinterest scored the highest in terms of addressing climate change misinformation, while other platforms like YouTube, Meta, and Instagram also ranked higher than X.
Changes under Elon Musk’s ownership, including unclear policies on climate misinformation and a less communicative content moderation team, have contributed to X’s low ranking.
Google sued after Maps allegedly directed a man to drive off a collapsed bridge
Philip Paxson, a father of two, died after Google Maps directed him to a collapsed bridge, leading to a fatal car plunge, according to a lawsuit filed by his family.
The family claims Google was informed of the bridge’s collapse but failed to update its navigation system, making the tech company negligent in Paxson’s death.
Despite having received reports about the bridge’s state through its ‘suggest and edit’ feature, Google allegedly took no further actions to correct the route information.
Study finds 95% of NFTs are now worthless
According to a study by dappGambl, 95% of NFTs are now practically worthless, with the majority of the 73,257 NFT collections analyzed having a market cap of zero Ether.
Enthusiasm for NFTs has substantially dropped and prices have plunged, with even hyped-up collections becoming virtually valueless.
The future of NFTs is uncertain; they will need to prove they have inherent value, such as cultural relevance or representing actual art, to survive.
Daily AI Update News from Microsoft, YouTube, Google, Cisco, and Anthropic
A happening day for AI with new AI announcements from Microsoft and YouTube. While Anthropic fears AI’s ‘catastrophic risks.’
Microsoft announces AI Copilot – Microsoft has announced a new AI-powered feature, Microsoft Copilot. It’ll bring AI features into various Windows 11, Microsoft 365, Edge, and Bing. Our first impressions are that it’s Bing but for Windows. You can use Copilot to rearrange windows, generate text, open apps on the web, edit pictures and more.
YouTube brings AI features for creators – YouTube announced 3 new AI-powered features for Shorts creators. Dream Screen uses AI to generate background images and videos. Creator Music uses AI to find the perfect track for Shorts. And AI Insights for Creators helps brainstorm the next video idea.
Google expands AI coding assistant to 170 countries – Google launched Studio Bot in 170 countries. It was previously launched in May for Android developers in the US. The assistant helps devs generate code, fix errors and answer questions about Android.
DALL-E 3 will be available in Bing chat – Microsoft’s recently announced DALL-E 3 will be available in Bing as Microsoft announced users will be able to create images in a chat. DALL-E 3 will be rolled out for enterprise users in October.
Cisco to buy Splunk in $28 billion In its bid to expand software and AI powered data analysis, Cisco announced it will buy cybersecurity firm, Splunk, in $28 billion. Splunk has announced AI features that detect and respond to data anomalies, earlier this year.
Anthropic releases policy on ‘catastrophic risks’ – Anthropic, the company behind Claude chatbot, shared a policy highlighting its commitment to responsible scaling of AI systems. The policy acknowledges AI’s potential to cause “thousands of deaths or hundreds of billions of dollars in damage.”
Other Tech news on September 22nd, 2023
Cisco is set to acquire Splunk for $28 billion to boost security services and system performance troubleshooting, marking its largest acquisition ever.
NASA is eagerly awaiting the return of pristine asteroid Bennu samples, taken in 2020 by OSIRIS-REx, aimed at uncovering the origins of the solar system.
Lawyers who sued Tesla board for excess pay want $10,000 an hour.
An anonymous developer used OpenAI’s ChatGPT API to program an AI to create and launch an ERC-20 token, AstroPepeX, generating $12.9 million in trading within 24 hours.
OpenAI’s Ilya Sutskever and machine ethicist Thomas Krendl Gilbert describe AI development as ‘alchemy’, indicating the unpredictable, mysterious nature of AI outcomes, sparking heated industry debate.
Facebook reveals a slightly darker and subtly tweaked logo under Meta’s refreshed identity system, marking the start of a broader design makeover.
OpenAI has unveiled its new text-to-image model, DALL·E 3, which can translate nuanced requests into extremely detailed and accurate images. Here’s all you need to know:
DALL·E 3 is built natively on ChatGPT, which lets you use ChatGPT to generate tailored, detailed prompts for DALL·E 3. If it’s not quite right, you can ask ChatGPT to make tweaks.
Even with the same prompt, DALL·E 3 delivers significant improvements over DALL·E 2, as shown below (Left: DALL·E 2 results, Right: DALL·E 3). The prompt: “An expressive oil painting of a basketball player dunking, depicted as an explosion of a nebula.”
OpenAI has taken steps to limit DALL·E 3’s ability to generate violent, adult, or hateful content.
DALL·E 3 is designed to decline requests that ask for an image in the style of a living artist. Creators can also opt their images out from training of OpenAI’s future image generation models.
DALL·E 3 is now in research preview and will be available to ChatGPT Plus and Enterprise customers in October via the API and in Labs later this fall.
Why does this matter?
As OpenAI notes, modern text-to-image systems have a tendency to ignore words or descriptions, forcing users to learn prompt engineering. DALL·E 3 represents a leap forward in AI’s ability to generate images that exactly adhere to the text you provide. Will other image generators like Midjourney and Stable Diffusion keep up?
ChatGPT can now generate images
OpenAI revealed the latest iteration of its AI art generator, DALL-E 3 will be coming to ChatGPT Plus and Enterprise members. (Tweet)
DALL-E 3’s New Features
ChatGPT for Prompting*:* Users can have ChatGPT generate descriptive prompts for DALL-E 3 art.
Better Context understanding: The new version follows prompts more precisely than before.:* The new version follows prompts more precisely than before.
Staggered Rollout*:* It will first release to paying ChatGPT users, then for free later but no time frame for free users.
Focus on Safety Improvements
Preventing Explicit Content*:* OpenAI claims robust new safeguards against inappropriate images.
Input Classifiers and Blocklists*:* Tools identify risky words and blocks public figures, so nothing new from the usual CGPT censorship.
Lawsuits Over Copying: DALL-E competitors faced suits alleging use of copyrighted art.
Opt-Out for Artists’ Work*:* Artists can now request their art be blocked from AI copying.
Avoiding Artist Mimicry*:* DALL-E 3 won’t recreate specific artists’ styles when named.
TL;DR: OpenAI hopes new ChatGPT integration and safety guards in DALL-E 3 will expand access and prevent misuse, but legal concerns around AI art persist.
At its annual devices event, Amazon announced a few AI updates:
It will soon use a new generative AI model to power improved experiences across its Echo family of devices. The new model is specifically optimized for voice and will take into account body language as well as a person’s eye contact and gestures for more powerful conversational experiences.
It also introduced generative AI updates for its Fire TV voice search, which promises to bring more conversational ways to interact with Alexa and discover new content based on specifics.
Why does this matter?
Integrating LLMs with voice assistants is a perfect use case. But Amazon’s generative AI revamp for Alexa marks a game-changer. It promises voice assistants that understand context better, carry over information from previous conversations, and become more personalized for users.
Zuckerberg’s philanthropy project is building a massive GPU cluster to ‘cure all diseases’
The Chan Zuckerberg Initiative (CZI), founded by Mark Zuckerberg and his wife Priscilla Chan, plans to build one of the world’s largest GPU clusters for AI-driven biomedical research.
The CZI aims to use large language models to understand disease development at cellular levels and predict cell behaviors, necessitating over 1,000 Nvidia’s H100 GPUs for computational requirements.
The high-performance computing system, expected to be operational in 2024, will accelerate biomedical research, from mapping varied cell types in different organisms to designing potential drugs and therapeutics.
Amazon is turning Alexa into a hands-free ChatGPT
Amazon is upgrading Alexa, its voice assistant, with the technology behind chatbots for more complex and open-ended conversation capabilities.
The new feature, which is still in progress, will show more simulated personality, interpret body language with devices equipped with cameras and modulate its voice for a more natural conversation.
While this advancement holds promise, challenges like responding to body language and the fact that these large language models can sometimes blur out inappropriate or nonsensical things, remains to refine.
ChatGPT Usage is Rising Again as Students Return to School
After declining over the summer, ChatGPT usage has increased, most likely as a result of students returning to class and concerns about AI cheating. (Source)
12% Traffic Increase Last Week: ChatGPT saw a sizable jump in US web traffic as fall classes resumed.
Big Drop Over Summer Break: Traffic declined steadily from May through August when school was out.
Still Below Early 2022 Peaks: But current usage remains below ChatGPT’s peak levels earlier this year. Back to School Brings Old Problems
Cheating Fears Resurface: Easier student access with school back raises fresh concerns about AI-aided cheating.
Schools Still Debating Rules: Many institutions continue deciding whether to ban, incorporate or ignore the technology.
Potential Revenue Uncertainty: Reliance on students could be problematic for monetizing ChatGPT.
With the new school year boosting ChatGPT traffic, managing responsible AI use in academics remains a complex balancing act for educators.
OpenAI launches a red teaming network to make its models more robust
OpenAI has launched the OpenAI Red Teaming Network, a group tasked with aiding risk assessment and mitigation strategies for the company’s AI models.
The Red Teaming initiative aims to catch biases and issues in AI models, enhancing safety and reliability in systems such as DALL-E 2 and GPT-4.
OpenAI calls on experts from diverse fields to participate in the network, emphasizing that confidentiality agreements may apply due to the sensitive nature of the work.
ChatGPT/Bard/Claude one paragraph challenge: “In one paragraph, what is the best use of AI for humanity?”
ChatGPT Plus:
The best use of AI for humanity is to augment human capabilities, address pressing global challenges, and improve the quality of life for all. By analyzing vast amounts of data at unprecedented speeds, AI can aid in medical research, predict and mitigate natural disasters, optimize energy consumption, and personalize education to individual needs. Moreover, it can provide solutions to complex problems, such as climate change, resource scarcity, and public health crises, ensuring a more sustainable and prosperous future for generations to come.
Bard:
The best use of AI for humanity is to use it to solve problems that will make the world a better place for everyone. AI has the potential to improve our lives in many ways, such as developing new and more effective ways to diagnose and treat diseases, creating new renewable energy sources and improving energy efficiency, helping us to adapt to climate change, and reducing poverty and inequality. AI can also help us to better understand ourselves and the world around us. This knowledge can be used to make better decisions about our lives, our communities, and our planet.
Claude:
The best use of AI for humanity is to harness its potential to improve lives and build a better world. With the rapid advances in AI, we have an unprecedented opportunity to tackle major challenges like climate change, disease, inequality, and poverty. If developed responsibly with clear ethical guidelines, AI can be a powerful tool to democratize information, expand access to education and healthcare, drive scientific breakthroughs, increase productivity, and create new industries and jobs. However, we must ensure AI is human-centric, aligning machine capabilities with human values and goals. By complementing human strengths and compensating for weaknesses, AI can enhance human abilities and potential. Whether helping doctors diagnose illnesses, aiding the disabled, optimizing transportation systems, or discovering new materials and energy sources, the possibilities are boundless if we steer AI to augment humanity in a way that benefits all. The technology is not inherently good or bad, but rather its impact depends on how we choose to wield it. With inclusive development and prudent oversight, we can direct AI to create a more just, peaceful, and prosperous world.
Daily AI Update News from OpenAI, Amazon, Google DeepMind, GitHub, Uber, and NVIDIA
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
OpenAI unveils DALL·E 3 – It understands significantly more nuance and detail than its previous systems. DALL·E 3 is now in research preview and will be available to ChatGPT Plus and Enterprise customers in October via the API and in Labs later this fall. It is built natively on ChatGPT, which lets you use ChatGPT as a brainstorming partner and refiner of your prompts.
Amazon brings generative AI to Alexa and Fire TV – At its annual devices event, Amazon introduced generative AI updates for its Fire TV voice search to bring more conversational ways to interact with Alexa and discover new content. – It will also use a new generative AI model to power improved experiences across its Echo family of devices.
Google DeepMind’s ‘Language Modeling Is Compression’ – This paper views the prediction problem through the lens of compression and evaluates the compression capabilities of large (foundation) models. It shows that LLMs are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning.
GitHub’s Copilot Chat will now be available to individual users – It is available in public beta for GitHub Copilot individual users in Visual Studio and Visual Studio Code.
Uber Eats to roll out AI-powered assistant – It will help users find deals and explore different food options seamlessly.
NVIDIA to train 50,000 Infosys employees on AI technology – Infosys will set up NVIDIA Centre of Excellence to train and certify employees on NVIDIA’s AI technologies. Also, NVIDIA’s AI Enterprise ecosystem of models, tools, runtimes, and GPU systems will be brought to Infosys’s AI-first offering Topaz.
Amazon recruits 250,000 workers for festive season
Amazon announced its plans to hire 250,000 employees (full-time, part-time, seasonal) in the U.S., for the upcoming holiday rush, a significant increase from last year’s 150,000.
The wages for these new warehouse and delivery roles will range between $17 and $28 per hour, with the average pay now being $20.50, up from $19, and sign-on bonuses worth $1,000 to $3,000 in certain locations.
This announcement is part of a recent trend where Amazon has been raising the starting wages amidst labor tensions, criticism over warehouse injury records, employees’ organization efforts and being the second-largest employer in the U.S., trailing only Walmart.
Google is rolling out Bard’s most capable model yet. Here are the new features:
Bard Extensions in English- With Extensions, Bard can find and show you relevant information from the Google tools you use every day — like Gmail, Docs, Drive, Google Maps, YouTube, and Google Flights and hotels — even when the information you need is across multiple apps and services.
Bard’s “Google it”- You can now double-check its answers more easily. When you click on the “G” icon, Bard will read the response and evaluate whether there is content across the web to substantiate it.
Shared conversations- When someone shares a Bard chat with you through a public link, you can continue the conversation, ask additional questions, or use it as a starting point for new ideas.
Expanded access to existing English language features- Access features such as uploading images with Lens, getting Search images in responses, and modifying Bard’s responses– to 40+ languages.
These features were possible because of new updates made to the PaLM 2 model.
Why does this matter?
These updates make Bard versatile and respond with greater quality and accuracy. The ‘Google it’ button is also a step toward building trust with language models. These strides show Google’s determination to stay competitive in AI, as it is also locked in a race with OpenAI to launch the next generation of multimodal AI models.
Intel’s new chip, due in December, will be able to run a generative AI chatbot on a laptop rather than having to tap into cloud data centers for computing power. It is made possible by new AI data-crunching features built into Intel’s forthcoming “Meteor Lake” laptop chip and from new software tools the company is releasing.
Intel also demonstrated laptops that could generate a song in the style of Taylor Swift and answer questions in a conversational style, all while disconnected from the Internet. Moreover, Microsoft’s Copilot AI assistant will be able to run on Intel-based PCs.
Why does this matter?
This will let businesses test ChatGPT-style AI models without sending sensitive data off their own computers. Intel seems to be on track to become the lead chip manufacturer again, competing with Nvidia to make powerful chips that train AI systems such as ChatGPT and Stability AI’s models.
Google DeepMind’s new system, called AlphaMissense, can tell if the letters in the DNA will produce the correct shape. If not, it is listed as potentially disease-causing.
AlphaMissense can predict the likelihood of genetic diseases by analyzing genetic mutations called missense variants.
AlphaMissense operates like a large language model, trained on human and primate biology, capable of identifying normal sequences of proteins and detecting changes that could suggest a disease.
With 90% accuracy, AlphaMissense is more reliable than existing tools, potentially accelerating the process of identifying disease-causing genetic mutations, which previously required months of meticulous research.
DeepMind’s new AI can predict genetic diseases
Currently, genetic disease hunters have fairly limited knowledge of which areas of human DNA can lead to disease and have to search across billions of chemical building blocks that make up DNA. They have classified 0.1% of letter changes, or mutations, as either benign or disease-causing. DeepMind’s new model pushed that percentage up to 89%.
Why does this matter?
AI is changing nearly everything we do at the moment and might revolutionize molecular biology and life sciences, too. This development is expected to speed up diagnosis and help search for better genetic disease treatments.
What makes a good AI co-pilot?
Clippy, Siri, and Cortana were here long before the hype for AI copilots began. But their success was limited. Why? (I guess here’s why)
Solving such flawed aspects using LLMs has changed the game around AI assistants.
In this interesting article, Marco Witzmann discusses what AI Assistants (=co-pilots) look like today, what distinguishes them from smart chatbots and not-so-smart speech assistants, as well as a checklist of five properties that separate great from not-so-good co-pilots.
Why does this matter?
Co-pilots enable us to harness the true potential of AI and LLMs. It also helps shape the way we interact with technology and the extent to which AI can assist us in our daily lives.
Meet Notice, the no-code editor designed for user-facing content. Craft wikis, FAQs, blogs, policies, and more using custom AI templates.
Besides creating projects with a single click, you can translate them into over 100 languages and gain insights, all within the same tool.
Notice doesn’t lock your content. As a robust CMS, you can easily publish it to multiple platforms – a website, app, or standalone pages. Plus, enjoy dedicated plugins for WordPress and Shopify.
Elon Musk could charge everyone for X
Elon Musk announced plans for Twitter, now renamed X, to have a small monthly subscription fee to counteract bot activity.
Musk revealed that X currently has around 550 million monthly users, generating between 100 and 200 million posts each day.
Despite criticism over the allowance of hate speech, Musk maintains that X is taking steps to regulate content and manage user verification through a paid system.
Google is turning its Bard AI chatbot into a personal assistant
Google’s Bard AI now has enhanced capabilities, pulling real-time data from Google’s other applications and a user’s data silo to deliver more relevant chatbot responses.
A new feature named Bard Extensions allows the AI to access user’s personal Google data to provide specific answers about their daily activities, while promising not to be used for ad targeting or training the AI model.
To increase transparency and accuracy, Google is introducing a ‘Double Check’ feature where Bard audits its responses and highlights contradictory or heavily referenced statements.
Microsoft AI team accidentally leaks 38TB of private company data
Microsoft’s AI researchers accidentally leaked 38 terabytes of data, including personal passwords, and Teams chat messages due to a misconfigured cloud storage.
Wiz cybersecurity researchers discovered the leak and alerted Microsoft, which soon secured the data and confirmed no unauthorized access occurred.
To prevent future incidents, Microsoft has expanded GitHub’s secret scanning service, a tool that tracks exposed credentials in public open-source code.
This startup is ready to build 10,000 humanoid robots per year
Agility Robotics is preparing to launch its RoboFab, a factory capable of producing over 10,000 advanced humanoid robots, named Digit, annually.
The 70,000-square-foot robot factory, located in Salem, Oregon, is set to open later this year, marking the beginning of mass production of commercial humanoid robots.
Digit is designed to address workforce challenges such as injuries, burnout, high turnover, and labor gaps, with the company’s ultimate goal being to enhance human capabilities.
AlphaMissense, a new model from Google’s artificial intelligence team, analyzes the effects of DNA mutations and will accelerate research into rare diseases.
About 10 years ago, Žiga Avsec was a PhD physics student who found himself taking a crash course in genomics via a university module on machine learning. He was soon working in a lab that studied rare diseases, on a project aiming to pin down the exact genetic mutation that caused an unusual mitochondrial disease.
This was, Avsec says, a “needle in a haystack” problem. There were millions of potential culprits lurking in the genetic code—DNA mutations that could wreak havoc on a person’s biology. Of particular interest were so-called missense variants: single-letter changes to genetic code that result in a different amino acid being made within a protein. Amino acids are the building blocks of proteins, and proteins are the building blocks of everything else in the body, so even small changes can have large and far-reaching effects.
There are 71 million possible missense variants in the human genome, and the average person carries more than 9,000 of them. Most are harmless, but some have been implicated in genetic diseases such as sickle cell anemia and cystic fibrosis, as well as more complex conditions like type 2 diabetes, which may be caused by a combination of small genetic changes. Avsec started asking his colleagues: “How do we know which ones are actually dangerous?” The answer: “Well largely, we don’t.”
Of the 4 million missense variants that have been spotted in humans, only 2 percent have been categorized as either pathogenic or benign, through years of painstaking and expensive research. It can take months to study the effect of a single missense variant.
Today, Google DeepMind, where Avsec is now a staff research scientist, has released a tool that can rapidly accelerate that process. AlphaMissense is a machine learning model that can analyze missense variants and predict the likelihood of them causing a disease with 90 percent accuracy—better than existing tools.
It’s built on AlphaFold, DeepMind’s groundbreaking model that predicted the structures of hundreds of millions proteins from their amino acid composition, but it doesn’t work in the same way. Instead of making predictions about the structure of a protein, AlphaMissense operates more like a large language model such as OpenAI’s ChatGPT.
It has been trained on the language of human (and primate) biology, so it knows what normal sequences of amino acids in proteins should look like. When it’s presented with a sequence gone awry, it can take note, as with an incongruous word in a sentence. “It’s a language model but trained on protein sequences,” says Jun Cheng, who, with Avsec, is co-lead author of a paper published today in Science that announces AlphaMissense to the world. “If we substitute a word from an English sentence, a person who is familiar with English can immediately see whether these substitutions will change the meaning of the sentence or not.”
Pushmeet Kohli, DeepMind’s vice president of research, uses the analogy of a recipe book. If AlphaFold was concerned with exactly how ingredients might bind together, AlphaMissense predicts what might happen if you use the wrong ingredient entirely.
The model has assigned a “pathogenicity score” of between 0 and 1 for each of the 71 million possible missense variants, based on what it knows about the effects of other closely related mutations—the higher the score, the more likely a particular mutation is to cause or be associated with disease. DeepMind researchers worked with Genomics England, a government body that studies the growing pool of genetic data collected by the UK’s National Health Service, to verify the model’s predictions against real-world studies on already-known missense variants. The paper claims 90 percent accuracy for AlphaMissense, with 89 percent of variants classified.
Researchers who are trying to find out whether a particular missense variant may be behind a disease can now look it up in the table and find its predicted pathogenicity score. The hope is that, just as AlphaFold is boosting everything from drug discovery to cancer treatment, AlphaMissense will help researchers in multiple fields accelerate research into genetic variants—allowing them to diagnose diseases and find new treatments faster. “I hope that these predictions will give us an extra insight into which variants cause disease and have other applications in genomics,” says Avsec.
Beyond untangling the effects of single-letter mutations, AlphaMissense demonstrates the potential of AI models in biology more broadly. Because it wasn’t specifically trained to solve the problem of missense variants, but more broadly on what proteins are found in biology, the applications of the model and others like it could reach far beyond single mutations to a better understanding of our whole genome and how it’s expressed—from the recipe book to the whole restaurant. “The basic trunk of the model is derived from AlphaFold,” says Kohli. “A lot of that intuition was, in some sense, inherited from AlphaFold, and we have been able to show that it generalizes to this sort of related but quite different task.”
The researchers stress that the predictions should not be used on their own, but only to guide real-world research: AlphaMissense could help researchers prioritize the slow process of matching genetic mutations to diseases by quickly ruling out unlikely culprits. It could also help improve our understanding of overlooked areas of our genetic code: The model includes an “essentiality” metric for each gene—a measure of how vital it is to human survival. (The function of roughly a fifth of human genes isn’t clear, despite many appearing to be essential.)
AI can help to speed up drug discovery — but only if we give it the right data
Nature – Artificial-intelligence tools that enable companies to share data about drug candidates while keeping sensitive information safe can unleash the potential of machine learning and cutting-edge lab techniques, for the common good.
AI can help to speed up drug discovery — but only if we give it the right data
Machine learning models can produce reliable results even with limited training data
Researchers have determined how to build reliable machine learning models that can understand complex equations in real-world situations while using far less training data than is normally expected.
OpenAI is working on a Multi-Modal GPT to beat Google Gemini
Both Google and OpenAI are advancing their efforts to develop the next generation of foundation models which will be multimodal.
Google has been making significant progress in this domain with its upcoming Gemini multimodal LLM, which has been introduced to a limited number of external companies.
On the other hand, OpenAI, supported by Microsoft, is diligently working to enhance its GPT-4 model by integrating similar multimodal features. When OpenAI introduced GPT-4 in March, it showcased these features but restricted their availability to a single firm, Be My Eyes, which focuses on technology for visually impaired individuals. Now, OpenAI plans to extend these features, termed GPT-Vision, to a wider audience
OpenAI is also contemplating the development of a subsequent, more advanced multimodal model named Gobi. Unlike its predecessor GPT-4, Gobi’s design emphasizes its multimodal nature from inception.
Llama 2 is an open-source large language model (LLM) developed by Meta and Microsoft. Llama 2 stands for large language model by Meta AI. If you want to understand a large language model, you can visit another blog called What is LLM? Understanding with Examples. Llama 2 is based on the Transformer architecture, which is the same architecture used by other popular LLMs such as GPT-3.
Benefits of Llama 2
Benefits of Llama 2
Open Source: Llama 2 embodies open source, granting unrestricted access and modification privileges. This renders it an invaluable asset for researchers and developers aiming to leverage extensive language models.
Large Dataset: Llama 2 is trained on a massive dataset of text and code. This gives it a wide range of knowledge and makes it capable of performing a variety of tasks.
Resource Efficiency: Llama 2’s efficiency spans both memory utilization and computational demands. This makes it possible to run it on a variety of hardware platforms, including personal systems and cloud servers.
Scalability: The scalability of Llama 2 signifies its adaptability to larger datasets and its use for more demanding tasks. This makes it a promising tool for the future of Llama 2 research in natural language processing
Easy to use: Llama 2’s accessibility extends to newcomers. Augmented by extensive documentation and a number of tutorials, it fosters ease of use and exploration.
Stability AI launches StableLM, an open-source suite of language models
The creators of Stable Diffusion, Stability AI, just released a suite of open-sourced large language models (LLMs) called StableLM. This comes just 5 days after the public release of their text-to-image generative AI model, SDXL.
StableLM is trained on a new experimental dataset built on The Pile, but three times larger with 1.5 trillion tokens of content. The richness of this dataset gives StableLM surprisingly high performance in conversational and coding tasks, despite its small size of 3 to 7 billion parameters (by comparison, GPT-3 has 175 billion parameters).
Some examples
Stability AI releases a powerful text-to-animation tool
Stability AI released Stable Animation SDK, a tool designed for developers and artists to implement the most advanced Stable Diffusion models to generate stunning animations. It allows using all the models, including Stable Diffusion 2.0 and Stable Diffusion XL. And it offers three ways to create animations:
Text to animation
Initial image input + text input
Input video + text input
The initial image/video inputs act as the starting point for the animation, which is additionally guided by text prompts to arrive at the final output.
Stability AI launches text-to-music AI
Stability AI has launched Stable Audio, a music and sound generation product. Stable Audio utilizes generative AI techniques to provide faster and higher-quality music and sound effects through a user-friendly web interface.
The product offers a free version for generating and downloading tracks up to 45 seconds long and a subscription-based ‘Pro’ version for commercial projects with 90-second downloadable tracks. Stable Audio allows users to input descriptive text prompts and desired audio length to generate customized tracks. The underlying model was trained using music and metadata from AudioSparx, a music library.
Battery prices plummet as electric cars approach ‘tipping point’
The cost of lithium-ion battery cells fell below $100 per kilowatt-hour in August, marking a significant decline in prices that could propel the use of electric vehicles.
This price drop is crucial as energy analysts posit that battery pack prices reaching $100 per kilowatt-hour is the “tipping point” where electric vehicles can compete in price with fossil fuel vehicles.
The decrease in battery cost, contributed by declining lithium prices and discovery of extensive lithium deposits, could also significantly impact other technologies such as solar and wind energy storage.
Intel unveils glass substrates for chips to advance Moore’s Law
Intel has developed a breakthrough glass substrate technology for manufacturing processors, offering higher temperature tolerance and power efficiency, which can result in better performance.
The new technology allows for an increased transistor count, facilitating larger chiplet complexes, faster communication speed, and more bandwidth within the CPU core.
The glass substrate technology, applicable to any processor, might uphold Moore’s Law by boosting processor performance and reducing power requirements, although it’s not expected to be available until later this decade.
Emerging AI Innovations: September 18th, 2023
Mustafa Suleyman, co-founder of DeepMind, believes that we are on the cusp of a new era in artificial intelligence (AI). In what he refers to as the “third wave” of AI evolution, machines will not only communicate with humans but also with other machines. To understand this progression, let’s take a quick look at the previous phases. The initial phase was focused on classification, specifically deep learning algorithms that could classify different types of data. Then came the generative phase, where AI systems used input data to create new information. But now, we’re heading into the interactive phase. This is where machines will be capable of carrying out tasks by conversing not only with humans but also with other AI systems. Users will be able to provide high-level objectives to their AI and let it take the necessary actions, involving dialogue with both machines and individuals. This interactive AI has the potential to be more than just a tool for automation. It will possess the freedom and agency to execute tasks, bringing us closer to the AI we see in science fiction. Instead of being static, it will be dynamic and adaptable, much like the depictions of AI in movies. Interestingly, despite the excitement surrounding generative AI, there seems to be a decline in its popularity. User growth and web traffic for tools like ChatGPT have decreased. DeepMind itself has released a rival to ChatGPT called Pi, which emphasizes its polite and conversational nature. Overall, it’s clear that AI is rapidly advancing, and the future holds great promise for machines that can interact not only with humans but also with their own kind.
So, listen up! Google and DeepMind have been tinkering away to make our Google Maps experience even more personalized. They’ve developed an AI algorithm that suggests routes tailored just for you. I’m talking hyper-personalization here, people. This new algorithm is no joke. It boasts a whopping 360 million parameters and uses real driving data from Maps users to figure out what gets our engines revving when it comes to route decisions. It considers all sorts of factors like travel time, tolls, road conditions, and even our own personal preferences. It’s like having a virtual co-pilot who knows you better than you know yourself. Now, how do they do it? I’m about to drop some serious tech knowledge on you. They use something called Inverse Reinforcement Learning (IRL) to learn from our behavior, and this fancy thing called Receding Horizon Inverse Planning (RHIP) to tackle both short and long-distance travel. Tests have shown that RHIP can suggest routes for two-wheelers with a 16 to 24 percent improvement in accuracy. And here’s the best part: it’s only going to get better over time as it learns more about what routes we prefer. In the past, Google’s attempts to use AI for route planning have hit roadblocks because real-world road networks can be a mind-boggling labyrinth of complexity. But the beauty of RHIP is that it can take on this challenge with a sophisticated approach. It’s proof that better performance is all about scale, both in terms of the data set and the complexity of the model. So, get ready to hit the open road with Google Maps’ hyper-personalized routes, brought to you by the wonders of AI.
So, imagine a world where AI agents play a crucial role in our society. Well, this comprehensive survey on LLM-based agents brings us one step closer to that reality. It’s a deep dive into the world of AI agents and how we can utilize them for the greater good. But what are LLM-based agents, you ask? LLM stands for large language models, and this survey explains why they make a great foundation for AI agents. They present a conceptual framework that can be customized for various applications, making them incredibly versatile. The survey doesn’t stop there; it goes on to explore the numerous applications of LLM-based agents. From single-agent scenarios to multi-agent scenarios and even human-agent cooperation, these agents can play a role in various settings. They even delve into agent societies, examining how LLM-based agents behave and interact with each other. It’s fascinating to see how these agents mirror certain aspects of human society. The survey also highlights key topics and open problems in the field. This is valuable information for developers, as it serves as a practical resource for building AI agents. But it’s not just for developers; researchers, practitioners, and policymakers can also benefit from this survey. It can guide them in further advancing the field of AI and LLM development in a responsible manner. So, why does all of this matter? Well, this survey has the potential to be a game-changer. It offers insights and guidance that could lead to breakthroughs in the world of AI. With responsible development and utilization of LLM-based agents, we can shape a future where humans and AI agents coexist and thrive in harmony.
Hey there! I’ve got some exciting news for all you designers and 3D printing enthusiasts out there. The geniuses over at MIT have come up with an awesome tool called Style2Fab that’s powered by AI and allows you to personalize your 3D-printable models. How cool is that? So here’s the deal: with Style2Fab, you can add custom design elements to your 3D models without messing with the functionality of the objects. All you need to do is describe your desired design using natural language prompts. Yup, you heard it right. No complicated software or coding required. Just good ol’ words to express your creative vision. But wait, it gets even better. Once you’ve described your dream design, you can simply feed it into a 3D printer and bring your creation to life. How awesome is that? This tool really opens up a whole new world of possibilities, especially for those who are just starting out in the design world. But it doesn’t stop there. Style2Fab also has the potential to revolutionize the field of DIY assistive technology and devices. Imagine how clinicians and medical patients could benefit from customized and personalized solutions that are easier to create than ever before. So, folks, get ready to take your 3D printing game to the next level with Style2Fab. It’s time to unleash your creativity and make your designs truly stand out. The future is here, and it’s looking pretty amazing.
Have you ever wondered how many senses AI has? Well, let’s dive into this fascinating topic of multimodal learning to find out! In this article, we’ll explore the next step in AI that’s currently being developed: multimodal learning. Our dear author, Harshvardhan, takes us on a journey to understand how multimodal models work and their potential use cases. Through intriguing analogies, the article sheds light on the technical aspects of multimodal learning and discusses Meta’s efforts in leading open-source research on these models. So, why is this important? By delving into the world of multimodal learning, we gain valuable insights that can spark new applications and research directions. These insights ultimately contribute to the advancement of multimodal AI and its practical applications. Imagine the possibilities we can unlock when AI can truly perceive and comprehend the world through multiple senses! Exciting times lie ahead as we continue to push the boundaries of AI. Multimodal learning opens doors to a future where AI can process and understand information in a more human-like way. Stay tuned for more developments in this groundbreaking field!
In today’s Daily AI News, we have some interesting updates to share. Let’s dive right in! First up, we have news about AI artists being banned by Google. Well, not exactly. Google Colab has actually restricted free users from using the popular Gradio user interface for Stable Diffusion. This decision was made to manage the strain on resources, but users still have options like upgrading to the paid tier or utilizing other free interfaces. Moving on, DeepMind has made a fascinating discovery. They found that large language models (LLMs) can optimize their own prompts using a method called ‘Optimization by PROmpting’ (OPRO). By utilizing ‘meta-prompts,’ LLMs can generate and refine solutions for improved results. This technique can greatly enhance LLM accuracy, but the prompt format is crucial. In other news, MIT researchers have developed a generative AI-driven tool called Style2Fab. This tool allows users to personalize 3D-printable models by adding custom design elements while ensuring the functionality of the objects remains intact. All this can be done through natural language prompts, making it easy and efficient. Next up, Meta is getting ready for the holiday season by launching automated budget scheduling and bid multipliers. These features will help marketers make the most out of their ad campaigns, thanks to AI. SoftBank is also making moves in the AI world. They are considering investing in AI companies, including a potential partnership with OpenAI. The investment could be in the tens of billions, showing the interest in AI’s future. And lastly, Anthropic and BCG have formed an alliance to deliver enterprise AI solutions to clients. This alliance will give BDG’s clients direct access to Claude 2 and Anthropic’s AI technology. According to DeepMind’s cofounder, Mustafa Suleyman, generative AI is just a phase. The future lies in interactive AI. Suleyman envisions building chatbots that can not only chat but also carry out tasks by interacting with other software and people. That wraps up today’s AI news. Stay tuned for more updates and advancements in the exciting world of artificial intelligence!
Hey there! Here are some interesting tech news stories for you. Did you know that the US dominates generative AI funding? They’re drawing in a whopping 89% of global venture capital investment, which amounts to a staggering $20 billion! In contrast, Europe is only getting 5% of that investment, equivalent to $1 billion. OpenAI’s rise seems to be a driving force behind this preference among venture capitalists. In some not-so-great news, it looks like North Korea-linked hackers have allegedly stolen $70 million in crypto assets from CoinEx. Blockchain researchers suspect their involvement in this cyberattack. Moving on to investments, Sequoia and Andreessen’s Instacart investment during the tech boom of 2021 is now facing a bit of a challenge. The company’s upcoming IPO could result in a 75% valuation drop, which is quite significant. Let’s talk about Google now. They’re doing their part to prolong the lifespan of Chromebooks by releasing automatic updates for a whole decade. This move is not only great for saving schools up to $1.8 billion but also helps limit technology waste. Sam Altman, the CEO of OpenAI, seems to be in awe of AI’s success. Despite its global excitement and wide use, Altman acknowledges that there may be challenges ahead, which is an honest and refreshing perspective. That’s all for now! Stay tuned for more tech updates.
Hey there! If you’re excited about diving deeper into the world of artificial intelligence, I’ve got just the thing for you! There’s this amazing book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, it’s a game-changer! Now, let me tell you why you should totally get your hands on this gem. “AI Unraveled” is packed with all the answers to those burning questions you may have about AI. Think of it as your ultimate AI guidebook. It’s like having a knowledgeable expert right by your side, unravelling the mysteries of artificial intelligence in a way that’s easy to comprehend. The best part? You can grab a copy of this must-read book at three different platforms: Apple, Google, or Amazon. So, no matter whether you’re an Apple aficionado, a Google guru, or an Amazon enthusiast, there’s a way for you to access this invaluable resource. So, why wait any longer? Dive into “AI Unraveled” today and expand your understanding of artificial intelligence like never before. This book is a game-changer, and it’s ready to be enjoyed by curious minds like yours. Happy reading!
In this episode, we explored topics ranging from the future of AI with conversational capabilities, personalized route suggestions in Google Maps, the construction and applications of LLM-based agents, AI tools for personalizing 3D-printable models, advancements in multimodal learning, restrictions on free users and new innovations from Meta, SoftBank’s potential involvement with OpenAI, enterprise AI solutions, interactive AI chatbots, recent news in generative AI funding and cybersecurity, and a recommendation to expand your AI knowledge with the essential book ‘AI Unraveled‘ available at Apple, Google, or Amazon. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
DeepMind co-founder predicts “third wave” of AI: machines talking to machines and people
DeepMind’s co-founder, Mustafa Suleyman, anticipates a “third wave” of AI evolution where machines will interact with both humans and other machines.
The Evolution of AI Phases
Initial Classification Phase: This was the first wave, focusing on deep learning that classifies different types of input data, such as images and audio.
Current Generative Phase: AI uses input data to create new data.
Upcoming Interactive Phase: Machines will be able to perform tasks by conversing with other machines and humans. Users will give high-level objectives to their AI systems which will then take necessary actions, involving dialogues with other AIs and individuals.
Interactive AI’s Potential
More than Just Automation: This AI won’t just be about following commands but will have the freedom and agency to execute tasks.
Closer to Sci-Fi: Interactive AI is anticipated to be more similar to the artificial intelligence depicted in science fiction, with dynamic capabilities rather than being static.
Current AI Landscape:
Generative AI’s Popularity: Despite being a game-changer, enthusiasm for generative AI seems to be waning, with declining user growth and web traffic for tools like ChatGPT.
Inflection AI’s “Pi”: Earlier this year, Suleyman’s company released a ChatGPT rival named Pi, emphasizing its polite and conversational nature.
Google’s AI for hyper-personalized Maps
Google and DeepMind have built an AI algorithm to make route suggestions in Google Maps more personalized. It includes 360 million parameters and uses real driving data from Maps users to analyze what factors they consider when making route decisions. The AI calculations include information such as travel time, tolls, road conditions, and personal preferences.
The approach uses Inverse Reinforcement Learning (IRL), which learns from user behavior, and Receding Horizon Inverse Planning (RHIP), which uses different AI techniques for short- and long-distance travel. Tests show that RHIP improves the accuracy of suggested routes for two-wheelers by 16 to 24 percent and should get better at predicting which route they prefer over time.
Why does this matter?
In the past, Google’s attempts to use AI systems at scale for route planning have often failed due to the sheer complexity of real-world road networks. RHIP can now overcome this hurdle with a sophisticated approach, confirming that better performance is related to scale both in terms of data set and model complexity.
The Rise and Potential of LLM-Based Agents: A survey
Probably the most comprehensive overview of LLM-based agents, this survey-cum-research covers everything from how to construct AI agents to how to harness them for good. It starts by tracing the concept of agents from its philosophical origins to its development in AI and explains why LLMs are suitable foundations for AI agents. It also:
Presents a conceptual framework for LLM-based agents that can be tailored to suit different applications
Explores the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation
Delve into agent societies, exploring the behavior and personality of LLM-based agents, the social phenomena that emerge when they form societies, and the insights they offer for human society
Discuss a range of key topics and open problems within the field
Here’s a scenario of an envisioned society composed of AI agents in which humans can also participate.
Why does this matter?
It is a practical resource for developers to build AI agents. It also serves as a guide for researchers, practitioners, and policymakers to further advancement in the field, potentially leading to breakthroughs in AI and LLM development in a responsible way.
AI makes it easy to personalize 3D-printable models
MIT researchers have developed a generative AI-driven tool that enables the user to add custom design elements to 3D models without compromising the functionality of the fabricated objects. A designer could use this tool, called Style2Fab, to personalize 3D models of objects using only natural language prompts to describe their desired design. The user could then fabricate the objects with a 3D printer.
Why does this matter?
The AI tool empowers novice designers and makes 3D printing more accessible. It could also be used in the emerging area of DIY assistive technology and devices, such as for clinicians and medical patients.
Multimodal Learning
Humans have five senses. How many does AI have?
In this article, Harshvardhan talks about the next step in AI that’s still in the works: multimodal learning. With interesting analogies, the article discusses how multimodal models work, their use cases, and Meta’s efforts in leading open-source research on multimodal models.
Why does this matter?
It gives insights into the technical aspects of multimodal learning, encouraging new applications and research directions and, thus, leading to advancements in multimodal AI and its practical applications. and its practical applications.
Pig kidney functions successfully in human
A pig kidney has functioned inside a human body for about two months, displaying potential for non-human organ transplants.
The success of the xenotransplant, performed at NYU Langone, offers a possible solution to the current organ shortage in the US.
The experimental procedure is still in early stages, with clinical trials subject to FDA approval to ensure safety for conventional human patients.
Apple continues to use our own mortality as marketing
Apple has been incorporating messages about lifesaving features in its marketing, purporting to show that Apple Watch and iPhone can save users from disastrous scenarios.
At the recent iPhone 15 showcase, Apple demonstrated how Watch and iPhone can contribute to life-saving outcomes such as detecting low heart rates, allowing for emergency SOS calls in a blizzard, and detecting a high heart rate in a pregnant woman.
This marketing strategy stands in contrast to previous Apple ads that focused on the enjoyable use of its products and enhances differentiation in a market where functions like fitness tracking and connectivity are no longer unique to Apple.
AI artists banned by Google
Google Colab has not banned Stable Diffusion, but has restricted free users from using the Gradio user interface, a popular remote front-end web UI for Stable Diffusion, due to a surge in its usage causing a strain on resources.
The restriction aligns with Google’s updated terms of service that barred users from running remote UIs and desktops, and users still have alternatives available to them, such as upgrading to the paid tier or utilizing free Stable Diffusion interfaces.
The decision by Google highlights the balance between promoting innovation while managing resources sustainably in the rapidly growing field of artificial intelligence.
Startup could make quantum computers 60x more efficient
French startup Alice & Bob developed a superconducting qubit, inspired by the Schrödinger’s cat experiment, which can potentially make future quantum computers up to 60 times more efficient.
These “cat state” qubits are highly resistant to bit flips but more vulnerable to phase flips, though the latter error is easier to correct.
The company aims to produce a 14-qubit system by the end of 2023 and will require an additional six months for calibration.
TikTok is tracking employee back-to-office attendance with new internal tool
TikTok has introduced a new internal tool named MyRTO to track employee office attendance.
The MyRTO system monitors badge swipes when employees enter the office, requiring explanation for any “deviations” from expected attendance.
The move comes amid a broader trend of employee monitoring, especially after the widespread shift to remote work due to the pandemic.
Daily AI Update News from Google, MIT, Meta, SoftBank, Anthropic, BCG, and DeepMind
Google’s AI for hyper-personalized Maps routes – Google and DeepMind have built an AI algorithm to make route suggestions in Google Maps more personalized. It includes 360 million parameters and uses real driving data from Maps users to analyze what factors they consider when making route decisions. – The approach uses Inverse Reinforcement Learning (IRL), which learns from user behavior, and Receding Horizon Inverse Planning (RHIP), which uses different AI techniques for short- and long-distance travel. Tests show that RHIP improves the accuracy of suggested routes for two-wheelers by 16 to 24 percent.
The rise and potential of LLM-based Agents – Probably the most comprehensive overview of LLM-based agents, this survey covers everything from how to construct these agents to how to harness them for good. It also delves into the origin of AI agents, their fit with LLMs, and outlines a framework that can be tailored to suit different applications.
AI makes it easy to personalize 3D-printable models – MIT researchers developed a generative AI-driven tool that enables the user to add custom design elements to 3D models without compromising the functionality of the fabricated objects. A designer could use this tool, called Style2Fab, to personalize 3D models of objects using only natural language prompts to describe their desired design. The user could then fabricate the objects with a 3D printer.
Meta is prepping world’s first AI-powered holiday season – It is prepping marketers for an AI holiday season with the launch of automated budget scheduling and bid multipliers to help make the most out of ad campaigns.
SoftBank considers investment or partnership with OpenAI – It is seeking to invest in AI companies in tens of billions. It is considering various options, including a strategic partnership with, or investment in, Open AI.
Anthropic and BCG form a new alliance to deliver enterprise AI to clients – This will give BDG’s clients direct access to Claude 2 and Anthropic’s AI tech for use across different strategic solutions.
Generative AI is just a phase. What’s next is interactive AI, says DeepMind’s cofounder – Mustafa Suleyman wants to build a chatbot that does much more than chat. Bots that can carry out tasks you set for them by calling on other software and other people to get stuff done.
Emerging AI Innovations: September 17th, 2023
DeepMind discovers that AI large language models can optimize their own prompts
DeepMind introduced a new method called ‘Optimization by PROmpting’ (OPRO), allowing large language models (LLM) to optimize their own prompts using natural language.
The OPRO process utilizes ‘meta-prompts’ to guide the LLM in generating solutions, evaluating and refining solutions for improved results.
This technique can significantly boost LLM accuracy, but outcomes heavily rely on the prompt format, highlighting the intricacy of language models.
EV sets new record for longest distance driven on a single charge
Students at the Technical University of Munich built an electric car “muc022”, which traveled almost 1,600 miles on a single charge, setting a new Guinness World Record.
The car took 99 hours to cover this distance, reaching a top speed of 26 mph, the car was designed to minimize weight and air resistance, and weighs only 375 pounds.
This feat shattered the previous record of 999 miles set by IT Asset Partners in 2017 and far exceeds the longest-range production car, the 2023 Lucid Air, which has a range of 516 miles.
US dominates in generative AI funding, drawing 89% ($20B) of global VC investment, leaving Europe with only 5% ($1B), driven by OpenAI’s rise in VCs’ preference.
North Korea-linked hackers are suspected of stealing $70 million in crypto assets from CoinEx, according to blockchain researchers.
Sequoia and Andreessen’s Instacart investment, made during 2021’s tech boom, faces a 75% valuation drop with Instacart’s upcoming IPO.
Google is prolonging Chromebook lifespan by releasing automatic updates for a decade, a measure that could save schools $1.8 billion and limit technology waste.
OpenAI CEO, Sam Altman, expresses shock at AI’s success and admits to possible challenges ahead, despite its wide use and global excitement.
Reddit launched an AI-powered keyword research tool that will help advertisers.
Infosys is likely to collab with NVIDIA to train 3 lakh+ employees on AI!
India’s Reliance partners with Nvidia to develop a new LLM.
Researchers at Humboldt University in Berlin have developed a biased GPT model called OpinionGPT.
Nasdaq has received SEC approval for its first exchange AI-powered order type!
Instagram might be getting generative AI panoramas.
IRS will focus on the wealthy, using AI to identify sophisticated schemes to avoid taxes.
YouTube announces AI-powered creative guidance in Google Ads
AI chatbots tasked to run a tech company built software in 7 minutes for less than $1.
A boy saw 17 doctors over 3 years for chronic pain. ChatGPT found the right diagnosis.
Roblox, a popular online gaming platform, is set to introduce an AI chatbot called Roblox Assistant.
Alibaba made its AI model ‘Tongyi Qianwen’, available to the public.
Coca-Cola has used AI to create a new flavor called Y3000 Zero Sugar.
15 US companies have taken an oath to develop AI responsibly.
Character.ai outperforms ChatGPT in mobile app usage within the US.
Pika Lab have Introduced Camera Movement Parameter.
Amazon has launched gen AI capabilities to help sellers write more engaging and effective product descriptions.
Hugging Face has launched Würstchen, a diffusion model for image generation.
Researchers have developed an AI tool called RETFound that can diagnose and predict the risk of various health conditions.
Parle Products, the biscuit brand in India, has partnered with IBM to drive its digital transformation using cloud and AI.
EY has invested $1.4 billion in AI and is launching a platform that offers AI-powered versions of its consulting products.
OpenAI is opening its first European Union (EU) office in Dublin.
AWS partnering with India’s ISRO to boost AI capabilities in the space via cloud computing.
Microsoft has open-sourced EvoDiff, a protein-generating AI framework.
Data analytics and AI software maker Databricks has raised over $500 million in a Series I funding round, increasing its valuation to $43 billion.
Infosys, India’s second-largest software services exporter, has signed a $1.5 billion contract to leverage AI solutions.
Emerging AI Innovations: September 16th, 2023
Google’s new DeepMind Maps algorithm improves route suggestions by 24%
Google has developed an AI algorithm to refine route suggestions on Google Maps, personalizing it based on user data and behavior, allegedly improving the accuracy on an average by 16-24 percent.
Personalized Route Suggestions through AI
The AI model comprises 360 million parameters, using real-time data from Maps users to influence factors including travel time, road conditions, tolls, and personal preferences to suggest routes.
This technology is grounded on “inverse reinforcement learning” (IRL), specifically a new IRL algorithm – “Receding Horizon Inverse Planning (RHIP)”.
The Power of RHIP and AI in Maps
Google and Deepmind jointly worked to develop RHIP, using complex stochastic models in immediate vicinity areas, but switching to simpler deterministic methods for distant areas for power conservation.
The AI improves route suggestions for both driving and two-wheeled vehicles by learning from Maps users’ movements and behaviors over time.
Google states that this is the largest application of inverse reinforcement learning for route planning to date.
Implementation and User Testing
Google has applied the algorithm to Maps data globally, but extensive user testing is needed to confirm if the technique consistently produces better routes.
Previous attempts at using AI systems for route planning on a large scale have often failed due to the complexity of road networks.
Misinformation spread: AI systems can propagate misinformation, but research indicates humans are more responsible for spreading falsehoods.
Existential threats: Concerns about AI causing potential extinction events are highlighted, but humans are more likely to initiate catastrophic events like nuclear wars.
Human errors: AI might be portrayed as a threat in scenarios like cybersecurity, but human errors are often the culprits behind major security breaches.
Domains Where AI Outperforms Humans:
Traffic safety: Human error causes 98% of accidents, with autonomous cars predicted to reduce this significantly.
Medical diagnosis: Machine performance is increasingly surpassing human doctors in specific areas.
Creativity & Legal contexts: AI has shown superior performance in art authentication, correcting wrongful human convictions, and reducing trial times.
Algorithmic Bias & Cybersecurity:
Bias in algorithms: Algorithmic bias can be a concern, but it is more easily fixed than human bias. Algorithms, when trained on biased human data, will reproduce this bias.
Cyber threats: Most high-profile cyberattacks are initiated by humans, and AI serves as a defense against such threats, detecting human coding mistakes and correcting them.
NVIDIA’s new software boosts LLM performance by 8x
NVIDIA has developed a software called TensorRT-LLM to supercharge LLM inference on H100 GPUs. It includes optimized kernels, pre- and post-processing steps, and multi-GPU/multi-node communication primitives for high performance. It allows developers to experiment with new LLMs without deep knowledge of C++ or NVIDIA CUDA. The software also offers an open-source modular Python API for easy customization and extensibility.
(The following figures reflect performance comparisons between an NVIDIA A100 and NVIDIA H100.)
Additionally, it allows users to quantize models to FP8 format for better memory utilization. TensorRT-LLM aims to boost LLM deployment performance and is available in early access, soon to be integrated into the NVIDIA NeMo framework. Users can apply for access through the NVIDIA Developer Program, with a focus on enterprise-grade AI applications.
Amazon, Nvidia, Microsoft, and Google lead hiring surge in GenAI
There is an explosive demand for Generative AI talent today. Here are some compelling statistics.
The number of companies mentioning “Generative AI” in monthly job postings is increasing exponentially.
Tech giants leading the surge in hiring for GenAI talent include Amazon, Nvidia, Oracle, Microsoft, Google, and more. Big banks like Citigroup and CapitalOne are also hiring big in GenAI.
Unsurprisingly, technology is the #1 sector looking to hire GenAI experts. Finance is #2nd, and healthcare is #3, while demand has been tepid in sectors like real estate, basic materials, and energy.
Companies are paying a lot for GenAI talent! Among all technical skills/technologies tracked, jobs mentioning “Generative AI” or “LLMs” had the highest average base salary offered, with an average of $200,837/year.
Meta confirms: No ads coming to WhatsApp
Meta has contradicted a Financial Times report suggesting that it plans to display ads on WhatsApp, stating there are no tests or plans for such an initiative.
The company currently monetizes WhatsApp through WhatsApp Business, a service for merchants boasting over 200 million monthly active users, rather than through placing ads.
Despite speculation and past considerations around integrating ads on WhatsApp, these plans have always been cancelled, with a focus instead on refining business and payment services on the platform.
Some helpful AI tools or applications that can enhance your work
1. ChatGPT, Bing Chat, and Google Bard:
These popular chatbots assist with text-related tasks, answering queries, generating content ideas, and more. They can become virtual assistants for tasks like social media posts, code debugging, and proofreading.
2. Tuberank Jeet:
For YouTubers, this tool optimizes videos for YouTube’s algorithm, aiding in SEO-friendly titles, descriptions, and tags.
3. Sendster:
This email marketing tool uses AI to create newsletters, subject lines, headlines, and complete email copies quickly, streamlining email campaigns.
4. VidScribe AI:
It automates subtitle and voiceover creation for videos in multiple languages, saving time on translation.
5. Bing Image Creator and MidJouney:
AI-generated visuals are handy for social media and blogs, reducing the need for graphic design skills.
6. AI Collective:
A versatile AI script that combines different AI language models into one platform, saving time and money by consolidating content creation tools.
AI, particularly generative AI, is transforming work across sectors. These tools are just a glimpse of how AI can enhance productivity and creativity in our daily tasks.
Daily AI News 9/16/2023
A little boy named Alex saw 17 different doctors over the course of three years, unable to find a root cause of his chronic pain. At her wit’s end, his mom, Courtney, fed his radiology report into ChatGPT and produced immediate answers.[1]
In January, Wharton professor Christian Terwiesch gave his MBA final exam to ChatGPT. It passed with flying colors. Now, he’s at it again with a new experiment to determine whether ChatGPT can come up with product ideas better and faster than his students. It can. And cheaper, too.
Bathroom-cleaning robot built for commercial businesses gives consumers hope for AI maid.
Judge admits he used ChatGPT to write a Court of Appeal ruling as he calls the AI tool ‘jolly useful’.
Controversy as ancient human fossils sent to space
Richard Branson’s Virgin Galactic has sparked controversy by sending ancient hominin bones into space, drawing criticism from the paleontology community who view it as a disrespectful stunt.
This marks the first time human ancestral remains have been sent into space, with the included bones belonging to the two-million-year-old Australopithecus sediba and 250,000-year-old Homo naledi, both discovered in South Africa.
Despite being legal, the act of white scientists selecting and sending African fossils into space has been viewed by some as a perpetuation of past unethical practices in palaeoanthropological research.
Elon Musk’s X finally agrees to try and settle Twitter’s mass layoffs lawsuit
Elon Musk’s company, X, recently laid off two-thirds of its workforce, alleging huge daily losses, which led to multiple lawsuits including lack of severance pay and targeting of women.
X has now agreed to attempt to settle these lawsuits, represented by Shannon Liss-Riordan, an attorney who is advocating for nearly 2,000 former employees.
The further schedule for the settlement remains unclear, however negotiations could take place in early December.
Water safety is a critical concern for municipal water systems, as contamination by bacteria and harmful particles can have severe health repercussions. Unfortunately, detecting these issues can be challenging before they cause health problems. To address this need, Clean Water AI has developed an innovative solution that leverages artificial intelligence (AI) to identify water contamination. By utilizing trained models, Clean Water AI’s system can effectively recognize harmful particles and bacteria that may compromise water safety. The solution involves the implementation of distributed devices that continuously monitor water sources for any signs of contamination. These devices are equipped with AI algorithms, which allow them to detect and classify dangerous bacteria and particles accurately. This real-time monitoring enables cities to identify and respond to contamination issues promptly. Clean Water AI employs a deep learning neural network to detect bacteria and particles in water, even at the microscopic level.
By training a convolutional neural network model on the cloud, the system gains the capability to accurately identify and classify various contaminants. To deploy the solution, Clean Water AI utilizes edge devices equipped with the trained model. This approach ensures that the classification and detection occur at the source, providing real-time analysis of water quality. The system is designed to run continuously, allowing for round-the-clock monitoring. Implementing the solution involves the installation of Internet of Things (IoT) devices across different water sources in cities. These devices serve as the frontline sensors, constantly monitoring water quality and detecting any signs of contamination. This comprehensive monitoring approach offers cities greater visibility into their water systems and enables them to take proactive measures to ensure public safety. Clean Water AI has already built a proof of concept using a microscope and Up2 board, keeping the costs under $500. With plans to scale up production, the team aims to reduce unit costs further, making the technology more accessible and affordable for widespread adoption. By leveraging AI and IoT technologies, Clean Water AI offers an effective and efficient solution to address the challenges associated with maintaining water safety in municipal systems. Their innovative approach provides continuous, real-time monitoring, allowing for swift intervention and better safeguarding of public health.
Microsoft Research has developed a novel method known as RAIN to address the challenge of aligning language models with human preferences. This method eliminates the need for fine-tuning or additional data by incorporating self-evaluation and rewind mechanisms. Through a process of self-boosting, unaligned models can generate responses that align with human preferences. One notable feature of RAIN is that it operates without training or parameter updates. Instead, it employs a fixed-template prompt to guide the model’s alignment with users’ preferences. Experimental results have shown that RAIN significantly improves the harmlessness rate of language models while preserving their helpfulness. This methodology also establishes a new defense baseline against adversarial attacks. The implications of RAIN are significant for user safety. By enabling language models to align with human preferences, it reduces the occurrence of harmful outputs and promotes more helpful responses across various applications. Whether it is in the context of customer support or content generation, RAIN enhances the overall user experience by ensuring that language models prioritize safety and comply with user preferences.
Google Research has introduced a new method for transforming single still images into seamless looping videos or interactive dynamic scenes. This method involves training a model on real video sequences that exhibit natural motion, like trees swaying or clothes blowing in the wind. Using this trained model, it becomes possible to predict long-term motion patterns in the Fourier domain based on a single image. These predictions can then be transformed into dense motion trajectories. These motion trajectories have various applications, such as generating dynamic videos from still images or enabling realistic interactions with objects in images. This research is significant because it enhances user experiences by enabling the creation of dynamic videos from static images and facilitating realistic interactions. Furthermore, it has broader implications in the fields of computer vision and artificial intelligence. This technology can potentially find applications in areas like robotics and autonomous systems. By leveraging the power of generative image dynamics, Google Research is pushing the boundaries of what is possible with still images. This breakthrough opens up new possibilities for creative expression and practical applications, ultimately enriching our visual experiences in the digital realm.
Google is set to challenge OpenAI’s GPT-4 model with its own conversational AI software called Gemini. Aimed at providing competition to OpenAI, Gemini consists of a range of large-language models that can perform various tasks such as powering chatbots, summarizing text, generating original content, assisting with code writing, and creating images based on user requests. Although Google is currently granting developers access to a version of Gemini, the largest version of the software is still under development. The company plans to make Gemini available to businesses through its Google Cloud Vertex AI service. This move indicates Google’s strong investment in generative AI as it strives to catch up with OpenAI’s ChatGPT. The significance of this development lies in the potential for more efficient customer support, faster content creation, and improved code development. Google’s Gemini holds the promise of advanced conversational AI, resulting in more powerful chatbots, ultimately enhancing the user experience across various applications. In summary, Google’s forthcoming Gemini software represents the company’s endeavor to rival OpenAI’s GPT-4 model by providing developers with access to a collection of large-language models. This breakthrough has the potential to revolutionize customer support, content creation, and code development, ultimately improving the user experience in multiple domains.
AI models have reached a remarkable level of accuracy in predicting how US judges will rule, even without considering the facts of the case. By analyzing various datapoints such as the judge’s educational background, net worth, rulings based on the origin of lawyers, and their experience in public law, private practice, and state judgeships, these models have achieved an impressive 86% accuracy rate. This raises important questions about the US justice system itself. One cannot help but wonder what implications this has for the fairness and objectivity of judicial decisions. If demographic information alone can predict a judge’s ruling with such high accuracy, it casts doubt on whether the facts of a case truly drive the outcome. It suggests that certain biases and predispositions may subtly influence judicial decisions, even when consciously disregarding the case details. In a different sphere, we find that employees are experiencing what is being referred to as ‘AI Anxiety.’ This is revealed in a LinkedIn report, which shows that almost half of the respondents feel they lack sufficient knowledge about AI. Moreover, nearly 40% admitted to pretending to be more knowledgeable about AI than they actually are, in order to appear informed among their colleagues. Additionally, there is a concern about potential job disruptions due to the rise of AI. A Goldman Sachs study suggests that generative AI could potentially disrupt over 300 million jobs globally. Administrative workers and women seem to be at the highest risk of being replaced by AI. However, this shift also brings about a growing demand for AI skills, with job postings mentioning GPT or ChatGPT experiencing a significant increase. While the usage of AI tools like ChatGPT is prevalent, it is surprising to learn that a majority of users (70%) do not inform their superiors about its use, as highlighted in a Fishbowl survey. Nonetheless, despite the concerns surrounding AI, the traffic to ChatGPT’s website has been declining for three consecutive months. All these developments indicate a complex and evolving relationship between AI and human society. As AI continues to advance, it is crucial for individuals and organizations to address the knowledge gaps and prepare for potential changes in the job market. It is equally important for society as a whole to critically examine the influence of AI on fields like law and ensure that fairness and transparency are upheld.
Google is preparing to release its conversational AI software, Gemini, which is designed to compete with OpenAI’s GPT-4 model. Gemini is a collection of language models (LLMs) that can be used to power chatbots, summarize text, generate original text, assist with code writing, and create images based on user requests. Although developers currently have access to a version of Gemini, the largest version is still being developed. Google plans to make Gemini available to companies through its Google Cloud Vertex AI service. OpenAI is establishing its first office in the European Union (EU), specifically in Dublin, and is making strategic hires in preparation for regulatory challenges. The company is currently recruiting for various positions focused on legal, privacy, and media relations. OpenAI aims to demonstrate its commitment to privacy and comply with EU regulations, particularly in light of previous scrutiny over its ChatGPT chatbot. This move by OpenAI reflects the AI industry’s recognition of the EU’s importance. A new paper from Google Research introduces a model that prioritizes scene dynamics in image spaces. This model is trained on real video sequences that exhibit natural motion, such as swaying trees or blowing clothes. Given a single image, the model can predict long-term motion patterns in the Fourier domain. These predictions can be converted into dense motion trajectories, enabling various applications like generating dynamic videos from still images or facilitating realistic interactions with objects in pictures. Amazon Web Services (AWS) has partnered with India’s ISRO and IN-SPACe to enhance AI capabilities in the space sector through cloud computing. This collaboration aims to support space-tech innovations and empower startups, research institutes, and students. Cloud computing will enable quicker decision-making and push the boundaries of what’s possible in the space industry. The partnership will provide access to state-of-the-art cloud technologies, streamline the management of space data, and support AI, machine learning (ML), and analytics workloads. AWS will also support eligible space startups through the AWS Activate program and offer expertise in building aerospace and satellite solutions via the AWS Space Accelerator program. Microsoft has open-sourced EvoDiff, an AI framework for generating proteins. The process of designing proteins in the lab is currently expensive and time-consuming. EvoDiff aims to simplify this process by generating high-quality proteins without requiring structural information about the target protein. The framework has been trained on data from various species and functional classes of proteins. Databricks, a data analytics and AI software maker, has raised over $500 million in a Series I funding round, which has increased its valuation to $43 billion. Investors in this round include T. Rowe Price, Morgan Stanley, Fidelity, Franklin Templeton, Capital One Ventures, Nvidia, Andreessen Horowitz, and Tiger Global. Infosys has signed a $1.5 billion contract to utilize AI solutions over a 15-year period. While the client’s name has not been disclosed, Infosys will provide enhanced digital experiences and business operation services using its platforms and AI solutions. SpaceX’s Starlink satellite internet service reported $1.4 billion in revenue for 2022, significantly lower than Elon Musk’s earlier projection of $12 billion. The company achieved its first profit in 2023 but fell short of its customer acquisition goals, ending the year with only one million active subscribers instead of the forecasted 20 million. Additionally, Starlink’s reliability was called into question by the FCC, resulting in the reversal of a decision to grant the company $885.5 million in funds. Unity Engine has eliminated its royalty-free licensing structure and will now charge developers on a “per-install” basis once certain thresholds are met. The new Unity Runtime Fee, effective from January 1, 2024, will impose different costs per install depending on the subscription tier. This change has sparked outrage among game developers who embraced Unity for its previous royalty-free structure. Amazon has introduced an artificial intelligence tool for sellers to assist in creating engaging product page content. The AI tool can generate product titles, descriptions, and listings, potentially enhancing seller success. This effort to utilize AI aligns with Amazon’s broader strategy of incorporating AI applications throughout its business. EY has launched EY.ai, a comprehensive platform aimed at facilitating clients’ adoption of AI. This platform has been developed in collaboration with major companies like Microsoft and IBM, with EY investing $1.4 billion into its development. EY.ai integrates AI into existing EY technologies and includes a large language model called EY.ai EYQ. EY’s Chief Technology Officer, Nicola Morini Bianzino, emphasized that these proprietary AI offerings provide clients with confidence and a roadmap for successful AI adoption.
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In this episode, we explored a range of exciting AI developments, including Clean Water AI’s affordable contamination detection using IoT devices, Microsoft Research’s RAIN method for aligning language models with human preferences, Google’s dynamic video creation from still images, Google’s development of Gemini conversational AI to rival OpenAI’s GPT-4, the accuracy of AI models in predicting US judges’ rulings, and various other updates such as OpenAI’s Dublin office, Microsoft’s open-sourced EvoDiff, Databricks’ funding, Infosys’ AI contract, and more, along with the availability of the book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
Clean Water AI – Summary
Clean Water AI uses a deep learning neural network to detect dangerous bacteria and harmful particles in water. Drinking water can be seen at a microscopic level with real-time detection.
Clean Water AI trains the convolutional neural network model on the cloud, then deploys it to edge devices. We used Caffe, a deep learning framework, which allows a higher frame rate when running with Intel Movidius Neural Computing Stick.
An IoT device can then classify and detect dangerous bacteria and harmful particles. The system can run continuously in real time. The cities can install IoT devices across different water sources to monitor water quality as well as contamination in real time.
Currently, Clean Water AI has been built as a proof of concept using a microscope and Up2 board. The entire prototype costs less than $500, and they’re plans to scale up production to help reduce unit costs.
The paper introduces a method called RAIN that allows language models to align themselves with human preferences without the need for finetuning or extra data. By integrating self-evaluation and rewind mechanisms, unaligned models can produce responses consistent with human preferences through self-boosting.
RAIN operates without training or parameter updates and uses a fixed-template prompt to guide the model’s alignment with human preferences. Experimental results show that RAIN significantly improves the harmlessness rate of language models while maintaining their helpfulness. It also establishes a new defense baseline against adversarial attacks.
Why does this matter?
RAIN enhances user safety by allowing language models to align with human preferences, reducing harmful outputs and ensuring more helpful responses in various applications, from customer support to content generation.
Google Research’s new generative image dynamics
Google Research’s new paper introduces a method for turning single still images into seamless looping videos or interactive dynamic scenes. The model is trained on real video sequences with natural motion, such as trees swaying or clothes blowing in the wind.
Given a single image, the model can predict long-term motion patterns in the Fourier domain. These predictions can be converted into dense motion trajectories, which can be used for various applications, such as creating dynamic videos from still images or enabling realistic interactions with objects in pictures.
Why does this matter?
This research enhances user experiences by enabling dynamic videos from still images and realistic interactions. It can also can have broader applications in computer vision and AI, including robotics and autonomous systems.
Google Challenges GPT-4 with Gemini
Google is reportedly nearing the release of its conversational AI software, Gemini. Which is intended to compete with OpenAI’s GPT-4 model. Gemini is a collection of large-language models that can power chatbots, summarize text, generate original text, help write code and create images based on user requests.
Google is currently giving developers access to a version of Gemini, but not the largest version it is developing. The company plans to make Gemini available to companies through its Google Cloud Vertex AI service. Google has invested heavily in generative AI to catch up with OpenAI’s ChatGPT.
Why does this matter?
Imagine more efficient customer support through smarter chatbots, faster content creation, and enhanced code development. As Google’s Gemini promises advanced conversational AI, benefiting users with more powerful chatbots, which will enhance the user experience in various applications.
AI models can now predict how a US judge will rule with 86% accuracy — without even considering the facts of the case.
Datapoints include where the judge went to law school, what their net worth is, how they rule when the lawyers are from big law firms versus boutique practices and the judges’ history in public law, private practice and state judgeships.
If you can predict how the judge will rule 86% of the time without the facts of the case, primarily based on demographic information, what does that say about the US justice system?
Two-minutes Daily AI Update News from Google, OpenAI, Google Research, AWS, Microsoft, Databricks, and Infosys
Google is reportedly nearing the release of its conversational AI software, Gemini. – Gemini is intended to compete with OpenAI’s GPT-4 model. Gemini is a collection of LLMs that can power chatbots, summarize text, generate original text, help write code, and create images based on user requests. – Google is currently giving developers access to a version of Gemini, but not the largest version it is developing. – The company plans to make Gemini available to companies through its Google Cloud Vertex AI service.
OpenAI is opening its first office in the European Union (EU) in Dublin and making strategic hires as it prepares for regulatory challenges. – The company is currently hiring for 09 positions in Dublin, including roles focused on legal, privacy, and media relations. – OpenAI aims to demonstrate its commitment to privacy and comply with EU regulations, particularly in light of previous scrutiny over its ChatGPT chatbot. – OpenAI’s move to establish a presence in the EU reflects the region’s importance in the AI industry.
Google Research’s new paper introduces a modeling an image-space prior on scene dynamics. – The model is trained on real video sequences with natural motion, such as trees swaying or clothes blowing in the wind. Given a single image, the model can predict long-term motion patterns in the Fourier domain. – These predictions can be converted into dense motion trajectories, which can then be used for various applications, such as creating dynamic videos from still images or enabling realistic interactions with objects in pictures.
Amazon’s AWS has partnered with India’s ISRO and IN-SPACe to enhance AI capabilities in the space sector through cloud computing. – The collaboration aims to support space-tech innovations and empower startups, research institutes, and students. – Cloud computing will enable quicker decision-making and push the boundaries of what’s possible in the space industry. – The partnership will provide access to state-of-the-art cloud technologies, streamline the management of space data, and support AI, ML, and analytics workloads. – AWS will also support eligible space startups through the AWS Activate program and offer expertise in building aerospace and satellite solutions through the AWS Space Accelerator program.
Microsoft has open-sourced EvoDiff, a protein-generating AI framework. – The current process of designing proteins in the lab is expensive and time-consuming, That’s why EvoDiff aims to simplify this process by generating high-quality proteins without requiring structural information about the target protein. – The framework has been trained on data from various species and functional classes of proteins.
Data analytics and AI software maker Databricks has raised over $500 million in a Series I funding round, increasing its valuation to $43 billion. – The round included investors such as T. Rowe Price, Morgan Stanley, Fidelity, Franklin Templeton, Capital One Ventures, Nvidia, Andreessen Horowitz, and Tiger Global.
Infosys has signed a $1.5 billion contract to leverage in AI solutions for a 15-year period. – The deal will involve Infosys providing enhanced digital experiences and business operation services using its platforms and AI solutions. The company has not disclosed the name of the client or whether it is an existing client.
Employees are feeling ‘AI Anxious’ — they’re worried about falling behind on AI and even pretending to be ‘in the know’ to colleagues
According to a new LinkedIn report, nearly half of the employees surveyed say they are worried they don’t know enough about AI. Nearly 40% said they pretend they know more about AI to seem ‘in the know’ in front of colleagues.
Survey Findings on AI Anxiety:
Concerns over AI familiarity: Nearly 49% of employees feel they should be more knowledgeable about AI.
Feeling of overwhelm: About 40% feel inundated with AI advancements and 56% are unsure about using AI at work.
Pretending knowledge: Approximately 40% admitted to feigning more AI knowledge than they possess to appear informed among peers.
Potential Job Disruptions and Shifts:
Fear of replacement: A study by Goldman Sachs highlighted potential disruption to over 300 million jobs globally due to generative AI.
Jobs at highest risk: Admin workers and women face the most significant risk of being replaced by AI.
Shift towards AI skills: A significant rise in job postings mentioning GPT or ChatGPT suggests a growing demand for AI skills.
Usage of AI Tools:
Unreported use: A Fishbowl survey found that 70% of 11,700 users used ChatGPT without informing their superiors.
Waning AI hype: Despite the growing concern, traffic to ChatGPT’s website has seen a decline for three consecutive months.
SpaceX’s Starlink satellite internet service registered $1.4 billion in 2022 revenue, significantly less than Elon Musk’s earlier projection of $12 billion.
Starlink reported its first profit in 2023, but the customer acquisition pace has been slower than expected, with one million active subscribers by 2022 end, well below Musk’s forecasted 20 million.
Starlink’s reliability was questioned by the FCC, which reversed its decision to grant Starlink $885.5 million funds, citing failure to meet program requirements.
Unity’s new pricing angers game developers
Unity Engine has scrapped its royalty-free licensing structure and will now charge developers on a “per-install” basis after certain thresholds are met.
The new Unity Runtime Fee, to take effect from January 1, 2024, will charge different per-install costs depending on the subscription tier.
This change has sparked outrage among the game development community who embraced Unity for its previous royalty-free structure.
Amazon unleashes AI for product descriptions
Amazon has launched an artificial intelligence tool for sellers to assist in creating compelling product page content.
The AI tool’s functions include generating product titles, descriptions, and listings, with the potential of enhancing seller success.
The drive to use AI in this way forms part of Amazon’s wider strategy to incorporate more AI applications into different aspects of its business.
EY’s $1.4 billion investment in AI and training
EY has announced the launch of EY.ai, a comprehensive platform aimed at facilitating clients’ AI adoption, in collaboration with major companies like Microsoft and IBM.
The firm has invested $1.4 billion into the platform, integrating AI into existing EY technologies such as EY Fabric, and is set to release a large language model called EY.ai EYQ.
EY’s CTO, Nicola Morini Bianzino, stated that the new proprietary AI offerings provide clients with confidence and a roadmap to successfully navigate their AI adoption journey.
Tesla engineers hated the Cybertruck so much they secretly designed an alternative
Many Tesla engineers were dissatisfied with the design of the company’s Cybertruck, leading them to secretly create alternative designs, according to chief designer Franz von Holzhausen.
Despite opposition, Elon Musk was adamant about maintaining the futuristic look of the Cybertruck, rejecting more traditional design concepts.
The Tesla Cybertruck sparked significant interest from public with over 200,000 orders made within the first three days of unveiling despite internal disagreements about its design.
SEC action against Mila Kunis and Ashton Kutcher’s NFT project
Mila Kunis and Ashton Kutcher’s NFT-based show, Stoner Cats, is charged by the SEC for selling unregistered securities, leading to a $1 million fine.
The original owners earned a 2.5% royalty every time a Stoner Cats NFT got resold, which the SEC claims is a return on investment.
A Fair Fund is established to return money to people who suffered financial loss from purchasing the NFTs, and Stoner Cats must destroy all NFTs they possess.
In Other Tech News on September 15th, 2023
Due to a micrometeoroid incident, NASA astronaut Frank Rubio extended his six-month space-stay to a year, setting a record for the longest continuous space duration by a NASA astronaut.
Despite initial blaming, Cruise robotaxis were not responsible for a man’s death in San Francisco; he was fatally hit by a city bus.
OpenAI plans to establish its first EU office in Dublin, hiring key legal, policy, and engineering roles, in preparation for impending regulatory challenges.
Google continues its wave of layoffs with significant cuts in the recruiting division, signaling an overall plan to reduce future hiring.
Cyber gang ‘Scattered Spider’ used phishing and malware to attack MGM Resorts, causing network paralysis and operational chaos for ransom payment.
Musk denies affair allegations with Brin’s wife, using a forced selfie, which Brin tried to avoid, as alleged in Musk’s new biography.
Emerging AI Innovations: September 14th, 2023
Can Large Language Models Reason?
This article by Melanie Mitchell explains the debate surrounding LLMs centers on whether they possess humanlike reasoning abilities or if their skills are merely a mirage. While LLMs have demonstrated emergent behaviors that resemble reasoning, it is unclear if these behaviors stem from true abstract reasoning or from memorizing and pattern-matching training data.
The implications of this debate are significant, as the presence of robust reasoning abilities in LLMs would support their role in developing trustworthy general intelligence. Conversely, if LLMs rely on memorization and pattern-matching, they will lack generalizability and cannot be trusted for tasks outside their training data.
Why does this matter?
This article hinges on whether they possess genuine human-like reasoning abilities or if their performance is primarily based on memorization and pattern-matching from training data. The outcome of this debate has substantial implications for the trustworthiness and generalizability of LLMs.
Microsoft Research’s new language model trains AI cheaper and faster
Microsoft Research has developed a new language model called phi-1.5 that could make training AI models cheaper and faster. The model uses curated synthetic data from existing large language models like OpenAI’s ChatGPT.
Despite having only 1 billion parameters compared to models with over 100 billion inputs, phi-1.5 has shown promising abilities with eliminating the need for web scraping or relying on data sources with copyright issues.
The model can reason and solve complex problems such as grade-school mathematics and basic coding. It exhibits traits of larger language models, both positive and negative, including the ability to think step by step and the potential for biased and toxic generations.
Why does this matter?
Despite having only 1 billion parameters compared to models with over 100B inputs, phi-1.5 has shown promising abilities and performs well on natural language tasks.
It eliminates the need for web scraping or relying on data sources with copyright issues. This research could democratize AI training by making it accessible to smaller organizations and reducing the reliance on specialized computing power.
Stability AI launches text-to-music AI
Stability AI has launched Stable Audio, a music and sound generation product. Stable Audio utilizes generative AI techniques to provide faster and higher-quality music and sound effects through a user-friendly web interface.
The product offers a free version for generating and downloading tracks up to 45 seconds long and a subscription-based ‘Pro’ version for commercial projects with 90-second downloadable tracks. Stable Audio allows users to input descriptive text prompts and desired audio length to generate customized tracks. The underlying model was trained using music and metadata from AudioSparx, a music library.
Why does this matter?
Stable Audio is the first music generation product that enables the creation of high-quality, 44.1 kHz music for commercial use via latent diffusion. It benefits end users by offering free and subscription-based options for customized music and sound effects, making audio creation accessible.
Potentially help empower content creators, businesses, and artists with an efficient tool to enhance their projects and creativity through tailored soundscapes.
Emerging AI Innovations: September 13th, 2023
NExT-GPT advances human-like AI research
The NExT-GPT system is a multimodal language model that can understand and generate content in various modalities, such as text, images, videos, and audio. It fills the gap in existing models by allowing for any multimodal understanding and generation.
NExT-GPT leverages pre-trained encoders and decoders, requiring only a small amount of parameter tuning. It also introduces a modality-switching instruction tuning (MosIT) and a curated dataset for complex cross-modal understanding.
Why does this matter?
This research demonstrates the potential for building AI agents to model universal modalities, advancing human-like AI research. It addresses the limitations in current models by enabling comprehensive multimodal understanding and generation, supporting seamless interactions and processing across various input formats.
Salesforce’s Einstein can customize AI for you
Salesforce introduced Einstein Copilot Studio, which allows customers to customize their AI offerings. The tool consists of three elements: prompt builder, skills builder, and model builder.
With the prompt builder, customers can add their own custom prompts for their products or brands.
The skills builder enables companies to add actions to prompts, such as competitor analysis or objection handling.
The model builder allows customers to bring their own models or use supported third-party offerings.
Salesforce is also working on a system called “the Einstein Trust Layer” to address issues like bias and inappropriate responses.
Why does this matter?
Customizing AI offerings will allow more personalized and tailored experiences to end users to align with their specific products or brands. This offering by Salesforce gives a competitive edge to its competitors by offering a comprehensive toolset for AI customization.
Apple silently making AI moves
Apple is quietly incorporating artificial intelligence into its new iPhones and watches to improve basic functions. The company showcased new gadgets with improved semiconductor designs that power AI features, such as better call quality and image capture.
Apple’s AI efforts have been reshaping its core software products behind the scenes without explicitly mentioning AI at its developer conference. Apple’s new watch chip includes a four-core “Neural Engine” that enhances Siri’s accuracy by 25% and enables new ways to interact with the device. The iPhone also automatically recognizes people in the frame for improved image capture.
Why does this matter?
Apple’s subtle integration of AI enhances the user experience and sets it apart from competitors like Microsoft and Google, who prioritize more ambitious AI transformations. These new AI additions can contribute to more efficient, intuitive, and enjoyable experiences for end users.
Amazon will pay an oil company to help it meet climate goals
Amazon has announced plans to purchase 250,000 metric tons of carbon removal from oil giant, Occidental Petroleum’s subsidiary 1PointFive, making Amazon the latest tech company to utilize carbon removal technologies to meet their climate goals.
1PointFive plans to build direct air capture (DAC) plants in Texas that pull carbon dioxide from the atmosphere and sequester it underground, a move being backed by both major tech companies and the Biden administration which has provided federal funding for similar projects.
Despite this new commitment, Amazon’s carbon removal purchase still represents only a fraction of the company’s emissions, with last year’s footprint amounting to more than 71 million metric tons of carbon dioxide.
More writers sue OpenAI
US novelist Michael Chabon and other writers have filed a proposed class action accusing OpenAI of copyright infringement for allegedly using their work to train AI models.
The lawsuit claims OpenAI sourced large amounts of content from across the internet, including from copyrighted works, to train its GPT models efficiently.
The writers allege that OpenAI’s AI generates in-depth analyses of themes present in their copyrighted works, indicating their works were used in the training process.
Daily AI Update News from Apple, Salesforce, Roblox, Alibaba, Coca-Cola, Character.ai, and Pika Labs
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Apple is quietly incorporating AI into its new iPhones and watches to improve basic functions. – The company showcased new gadgets with improved semiconductor designs that power AI features, such as better call quality and image capture. Apple’s AI efforts have been reshaping its core software products behind the scenes, without explicitly mentioning AI at its developer conference. – Apple’s new watch chip includes a four-core “Neural Engine” that enhances Siri’s accuracy by 25% and enables new ways to interact with the device. The iPhone also automatically recognizes people in the frame for improved image capture.
Salesforce introduced Einstein Copilot Studio, allows customers to customize their AI offerings. – The tool consists of three elements: prompt builder, skills builder, and model builder. – With the prompt builder, customers can add their own custom prompts for their products or brand. – The skills builder enables companies to add actions to prompts, such as competitor analysis or objection handling. – The model builder allows customers to bring their own models or use supported third-party offerings. – Salesforce is also working on a system called “the Einstein Trust Layer” to address issues like bias and inappropriate responses.
Roblox, a popular online gaming platform, is set to introduce an AI chatbot called Roblox Assistant. – Which enables users to create virtual worlds simply by typing prompts. This development is particularly intriguing considering that 43% of Roblox users are under the age of 13.
Alibaba made its AI model ‘Tongyi Qianwen’, available to the public. – As it aims to compete with other Chinese tech giants like Baidu and Tencent. The company’s AI division, Alibaba Group Intelligence Group, has already collaborated with other firms to create applications based on Tongyi Qianwen. – The move comes as Alibaba’s new CEO, Eddie Wu Yongming, emphasizes the importance of AI and elevates it to one of the company’s main strategic focuses.
Coca-Cola has used AI to create a new flavor called Y3000 Zero Sugar. – The company collaborated with Bain & Company and OpenAI to develop the flavor and design the packaging using generative AI. Feedback from fans was taken into account during the formulation process. – Each can of Y3000 will feature a QR code that leads consumers to an online experience powered by AI, showcasing a vision of the year 3000.
Character.ai outperforms ChatGPT in mobile app usage within the US. – Character.ai, an AI app that allows users to create their own AI characters, is quickly catching up to ChatGPT in terms of mobile app usage in the US. – According to market intelligence firm Similarweb, Character.ai’s iOS and Android apps have gained 4.2 million monthly active users, just behind ChatGPT’s 6 million users. This highlights the growing popularity of Character.ai and its unique offering in the AI app market.
Pika Lab have Introduced Camera Movement Parameter. – It has control camera with customized intensity and direction and is available now for use.
How Tesla’s 36 Million ‘Eyes’ Drive the Future of Autonomy.
Tesla’s 36 Million ‘Eyes’ on the Streets.
The AI Data-Driven Path to Self-Driving.
Elon Musk showcased Tesla’s Full Self-Driving (FSD) technology on August 25. FSD12 uses a neural network planner, learning from billions of video frames to mimic human driving. Tesla’s extensive data and game-like motivation have led to impressive autonomous driving capabilities.
These eyes record continuously while the car is being driven, and in Sentry Mode when parked. The footage has been used to solve crimes, answer questions about collisions, and of course, train and improve the FSD. There is no other EV company gathering and harnessing as much data (from video and sensors) as Tesla and they count on around 10KH100 GPUs to do so.
Their FSD capabilities are far ahead and will likely remain so, especially with FSD12’s human-like teaching approach. If data is the new oil, Tesla is the oil king this is without mentioning their leadership in the EV charging station space among other sub-industries which allows them to gather more data and mine it with the help of AI to maximize profits and expand. Tesla’s focus on future tech and AI-driven data mining makes them a leader, not just in cars, but as an AI data juggernaut. While regulation challenges exist, the road to robotaxis and omnipresent FSD seems inevitable.
Note: Tesla hinted at the possibility of a 12 Alpha releaseby the end of the year*, it’s safer to assume that it could be 2024 before it’s widely available.*
Emerging AI Innovations: September 12th, 2023
AI technology has been making significant strides in recent years, but along with its advancements comes a concern for sustainability and its impact on the environment. One major aspect of this concern is the staggering water consumption rates of companies like Microsoft and Google. Last year alone, Microsoft’s data centers used an astounding amount of water, draining over 2,500 Olympic-sized swimming pools. This represents a 34% increase from the previous year. Similarly, Google reported a 20% increase in water consumption over the same period. The main reason behind this surge in water usage is the exponential growth of AI and its associated technologies. AI requires massive server farms, which heavily rely on water for cooling purposes. According to Shaolei Ren, a researcher at UC Riverside who focuses on AI’s environmental impact, the spike in water usage can be primarily attributed to AI. In fact, every 5 to 50 prompts submitted to ChatGPT, an AI language model, consumes approximately 500 ml of water. This information comes from an upcoming paper by Professor Ren’s team. Recognizing the gravity of the situation, tech giants like Google are taking responsible steps to address this issue. They have committed to assessing their water usage and its impact on surrounding areas. By doing so, they aim to find ways to mitigate any negative effects and promote responsible water usage. In conclusion, the rapid growth of AI technology has led to a concerning increase in water consumption by companies like Microsoft and Google. However, these tech companies are aware of the issue and are actively exploring ways to minimize their water usage and its environmental impact.
Google is taking a big step forward when it comes to responsible artificial intelligence (AI). They’ve just announced the launch of the Digital Futures Project and a whopping $20 million fund from Google.org. This fund will provide grants to top think tanks and academic institutions all over the world. So, what does this mean? Well, the project is aimed at supporting researchers, bringing people together for discussions, and encouraging the development of public policies that promote responsible AI. Now, let’s talk about some of the big names who have already received grants from the Digital Futures Fund. We’ve got the Aspen Institute, the Brookings Institution, the Carnegie Endowment for International Peace, the Center for a New American Security, the Institute for Security and Technology, SeedAI, and many more. But why does this really matter? Well, Google has always been at the forefront of advocating for responsible AI. They understand that perfecting AI is not something that any one company can do alone. It requires collaboration and input from academia and civil society. By supporting independent research across the globe, this move will help ensure that AI benefits everyone in a positive and transformative way. So, with Google’s Digital Futures Project and this impressive fund, we can look forward to a responsible future of AI.
So, let’s talk about the demand for Generative AI talent. It’s absolutely exploding right now, and the numbers prove it. Just take a look at these statistics. The number of companies mentioning “Generative AI” in their job postings each month is skyrocketing. It’s like a snowball rolling down a hill, getting bigger and bigger. And who’s leading the charge in hiring for GenAI talent? Well, it’s the big players like Amazon, Nvidia, Microsoft, and Google. But they’re not the only ones. Even big banks like Citigroup and CapitalOne are jumping on the GenAI train. It’s not surprising that the tech industry is the top sector in need of GenAI experts. They’re always at the forefront of innovation. Finance comes in at number two, and healthcare takes the third spot. But other sectors like real estate, basic materials, and energy aren’t showing as much interest. Now, here’s the interesting part. Companies are willing to pay big bucks for GenAI talent. Out of all the technical skills and technologies out there, jobs mentioning “Generative AI” or “LLMs” offer the highest average base salary. We’re talking about an average of $200,837 per year. Not too shabby, right? This all highlights the incredible impact that generative AI is having on various industries. It’s changing the game and businesses know it. And for job seekers and professionals, it means exciting opportunities. But it also means that you have to stay up to date with AI-related skills if you want to thrive in today’s market. So, yeah, Generative AI is kind of a big deal right now.
So, get this: researchers decided to challenge an AI-powered tech company to create a whopping 70 different programs in a study. And guess what? These chatbots managed to develop software in less than seven minutes! And hold onto your hats because it gets even crazier—they did it all for under a measly $1 in costs! Yep, you heard me right. The study put together this fictional software company called ChatDev, complete with stages like designing, coding, testing, and documenting. And get this—the AI workers actually collaborated at each stage. They made all sorts of decisions, from picking the right programming language to detecting bugs. Talk about teamwork! But it didn’t stop there. They even threw some real-world scenarios at ChatDev, like designing a Gomoku game. And what do you know? The results were pretty impressive. ChatDev managed to generate software systems with flawless performance about 86.66% of the time. Not too shabby, right? Now, don’t get me wrong. This AI-driven software development process still has its limitations. There’s a chance it could have some biases and such. But boy oh boy, does it show us what AI can do. It demonstrates the potential of automated software development with minimal human intervention. Imagine a world where AI runs a software company? It might just be closer than we think.
Meta is setting its sights on GPT-4, OpenAI’s powerful AI model, according to a recent report. They’re planning to train a new model that they hope will be just as impressive by investing heavily in data centers and H100 chips. Meta’s ambition is to create an AI model that surpasses their recent creation, Llama 2, by a significant margin. This new AI development is part of Meta’s strategy to reclaim its position as a prominent player in the AI field. They’ve formed a dedicated group, led by Mark Zuckerberg himself, which focuses on accelerating generative AI and creating tools that produce human-like expressions. The timeline for this project indicates that training for the AI system will commence in early 2024. Meta is not only investing in data centers but also acquiring advanced Nvidia chips (H100s) for their AI training. They’re shifting away from Microsoft’s cloud platform, Azure, and intend to train this new model on their own infrastructure. In a move towards openness, Zuckerberg plans to make the new AI model open-source. This approach allows companies to freely access and build AI-driven tools with it. Open-source AI models bring benefits like cost-effectiveness and flexibility, but they also carry risks, such as potential legal issues and misuse for spreading false information. Experts have expressed concerns about the new system’s unpredictability and potential vulnerabilities. They emphasize the importance of transparency and control to mitigate these risks.
Hey there! Guess what? Microsoft, MIT, and Google have been up to something really cool recently. They joined forces and created a research project called Large-Scale Automatic Audiobook Creation, and you won’t believe what they did with it! They managed to transform the ENTIRE Project Gutenberg Collection into audiobooks. Yep, you heard that right. The entire collection! Now, here’s the best part. These audiobooks are not just your average run-of-the-mill ones. They’re powered by AI, which means they’re not only free and open for everyone, but they sound pretty darn amazing too! The team used neural text-to-speech technology, and let me tell you, the quality of the voice acting is exceptional. You can even customize the speaking speed, style, and emotional intonation, and get this, you can even choose a specific voice for your audiobook with just a small sample of audio. How cool is that? But why is this such a big deal? Well, besides the fact that it’s super impressive, it also shows how text-to-speech AI can be used in practical ways. This project has created a scalable system that can convert thousands of e-books into high-quality audiobooks. That’s a huge leap forward for AI technology, and it’s exciting to see how it can solve real-world problems with a real impact.
Have you noticed the influx of ads for AI girlfriends and erotic chatbots on platforms like Instagram and TikTok? It seems like a whole new wave of bots has found a way to bypass moderation. It’s pretty fascinating how these sexually suggestive AI ads are popping up everywhere, despite the platforms banning many types of adult human content. These startups are cleverly using memes and animated characters to promote female avatars that promise adult experiences. And somehow, these ads slip through the cracks using sexually provocative messaging that’s similar to the banned content. It’s unclear why there’s looser enforcement when it comes to AI content. While some people might see this as harmless, others are concerned about the double standard that seems to be harming human sex workers. Commenters have called out problematic aspects, such as the appearance of minors in some of these ads. It’s definitely a difficult task to moderate AI content at such a large scale. But all of this is part of the rush to capitalize on the growing interest in generative AI. Developers are creating custom AI companions and experiences, and these explicit ads for AI chatbots and virtual companions are proliferating unchecked on social platforms. It’s quite a phenomenon, isn’t it?
Apple has just unveiled its latest lineup of iPhones, the iPhone 15 Pro and iPhone 15 Pro Max, and boy are there some exciting features to talk about! First up, let’s discuss the design. The new iPhones come with a sleek and durable titanium build, complete with contoured edges that not only look great but feel amazing in your hand. But that’s not all, folks! Apple has also introduced a new Action button, giving you even more control over your device. And let’s not forget about the camera upgrades. The base model of the iPhone 15 now boasts a 48-megapixel main sensor, allowing for incredible photo quality. Plus, it has optical zoom capabilities, giving you crisp and clear images even when zooming in. In terms of display, the iPhone 15 and iPhone 15 Plus feature 6.1-inch and 6.7-inch screens, just like last year. However, Apple has managed to take things up a notch by increasing the maximum brightness to a whopping 2000 nits. Talk about an immersive viewing experience! Now, let’s talk about colors. Apple has really stepped it up this year by embedding their color finishes throughout the glass, resulting in vibrant and eye-catching shades. You can choose from pink, yellow green, blue, and black, so there’s definitely something for everyone. And the best news? The iPhone 15 starts at the same price as last year, retailing at $799. You can even start preordering this Friday, with the official launch happening on September 22. So, whether you’re a mobile gamer, a photography enthusiast, or simply someone who wants a sleek and powerful device, the iPhone 15 Pro and iPhone 15 Pro Max are definitely worth considering. Don’t miss out on all the exciting features that Apple has packed into these beauties!
Hey there, it’s time for your daily dose of AI updates! Let’s jump straight into it. Google is making a big move to support responsible AI. They’re launching the Digital Futures Project and a $20 million Google.org fund. This fund will provide grants to think tanks and academic institutions around the world. With this project, Google aims to support researchers, organize discussions, and encourage the responsible development of AI by exploring public policy solutions. In exciting news, Microsoft, MIT, and Google have teamed up to transform the Project Gutenberg Collection into audiobooks. They’ve developed a system called Large-Scale Automatic Audiobook Creation, which can generate high-quality audiobooks from online e-books. Thanks to recent advancements in neural text-to-speech technology, the Project Gutenberg Open Audiobook Collection now offers thousands of free and open audiobooks. Who wouldn’t love to listen to a good book? Not to be left behind, Amazon, Nvidia, Microsoft, and Google are leading the charge in hiring GenAI talent. Generative AI is in great demand these days, with more and more companies mentioning it in their job postings. The technology sector is at the top of the list, followed by finance and healthcare. And guess what? Positions related to “Generative AI” or “LLMs” offer the highest average base salary of $200,837 per year. That’s definitely something to consider if you’re looking to dive into the field. Moving on to Instagram, it looks like we might soon be able to enjoy generative AI panoramas on the platform. This feature was spotted in a recent Instagram update for iOS. The company has been experimenting with various AI-related features, so the addition of AI-created panoramas doesn’t come as a huge surprise. It’s always exciting to see how AI can enhance our social media experience! In other news, the IRS is utilizing AI to crack down on tax violations among the wealthy. The agency is shifting its focus from working-class taxpayers to the wealthy, aiming to reduce the burden on average taxpayers. By leveraging AI and improved technology, they hope to identify sophisticated schemes used to avoid taxes. It’s a smart move to ensure everyone pays their fair share. YouTube is also getting in on the AI action. They’ve announced AI-powered creative guidance in Google Ads. This feature, available in the Recommendations and Video Analytics sections of Google Ads, provides suggestions to advertisers on how they can enhance the effectiveness of their video campaigns. AI is becoming an indispensable tool for optimizing marketing strategies. Now, here’s something mind-boggling. Researchers conducted a study where they tasked AI chatbots with running a tech company. And guess what? These chatbots were able to develop 70 different programs in under 7 minutes, costing less than $1. This demonstrates the power of AI in efficiently managing a software company with minimal human intervention. It’s a glimpse into the future of automated operations. Lastly, there’s a heartwarming story where ChatGPT, an AI chatbot, helped diagnose a boy’s chronic pain. The boy’s mother shared all the information she had about her son’s symptoms and his MRI scans with ChatGPT. And, lo and behold, the chatbot was able to identify the right diagnosis. This showcases the potential of AI to assist in healthcare and provide valuable insights. That’s all the AI updates for today! Stay tuned for more exciting developments in the world of artificial intelligence.
Google’s responsible AI leap
Google is launching the Digital Futures Project and a $20 million Google.org fund, which will provide grants to leading think tanks and academic institutions worldwide. The project will support researchers, organize convenings, and foster debate on public policy solutions to encourage the responsible development of AI.
Inaugural grantees of the Digital Futures Fund include the Aspen Institute, Brookings Institution, Carnegie Endowment for International Peace, the Center for a New American Security, the Institute for Security and Technology, SeedAI, and more. The fund will support institutions from countries around the globe.
Why does it matter?
Google has long been an advocate for responsible AI. But getting AI right will take more than any one company alone. This move will support many across academia and civil society to advance independent research on AI that helps this transformational technology benefit everyone.
Microsoft and Google’s staggering water consumption rates for AI
AI, with its vast resource needs, is raising concerns over sustainability and environmental impact. Last year, Microsoft’s data centers drained over 2,500 Olympic-sized swimming pools worth of water, reflecting a 34% increase from the previous year. Google also reported a 20% water consumption increase over the same period.
A deeper look at AI’s water footprint
The growth of AI and related technologies increases the need for vast server farms, which depend heavily on water for cooling purposes.
The spike in water usage can be attributed primarily to AI, as per Shaolei Ren, a researcher at the UC, Riverside, who focuses on AI’s environmental impact.
For every 5 to 50 prompts submitted to ChatGPT, it consumes about 500 ml of water, according to an upcoming paper from Professor Ren’s team.
Big Tech and responsible water usage
Recognizing their significant water consumption, tech companies like Google have voiced concerns and are exploring ways to mitigate the negative effects.
Google has committed to responsible water usage, which includes assessing where and how their water usage might affect surrounding areas.
Amazon, Nvidia, Microsoft, and Google lead hiring surge in GenAI
There is an explosive demand for Generative AI talent today. Here are some compelling statistics.
The number of companies mentioning “Generative AI” in monthly job postings is increasing exponentially.
Tech giants leading the surge in hiring for GenAI talent include Amazon, Nvidia, Oracle, Microsoft, Google, and more. Big banks like Citigroup and CapitalOne are also hiring big in GenAI.
Unsurprisingly, technology is the #1 sector looking to hire GenAI experts. Finance is #2nd, and healthcare is #3, while demand has been tepid in sectors like real estate, basic materials, and energy.
Companies are paying a lot for GenAI talent! Among all technical skills/technologies tracked, jobs mentioning “Generative AI” or “LLMs” had the highest average base salary offered, with an average of $200,837/year.
Why does this matter?
This reflects the pivotal role generative AI is playing across industries. Moreover, it signals a shift in how businesses are operating, adapting, and strategizing for an AI-led future. For job seekers and professionals, it presents exciting opportunities and emphasizes the need to stay updated with AI-related skills to thrive in the market today.
AI chatbots were tasked to run a tech company. They built software in under seven minutes — for less than $1
Researchers tasked an AI-powered tech company to develop 70 different programs in a new study. They found AI could develop software in under 7 minutes for less than $1 in costs, on average.
The ChatDev Experiment
Hypothetical Company: Researchers created a fictional software company, ChatDev, with stages including designing, coding, testing, and documenting.
Bot Collaboration: AI workers collaborated at each stage, making decisions like choosing a programming language and detecting bugs.
Real-world Simulation: ChatDev was tasked with real software development scenarios, like designing a Gomoku game.
Outcomes and Implications
Success Rate: About 86.66% of the software systems generated by ChatDev operated flawlessly.
Potential for the Future: While the system has its limitations, such as possible biases, it showcases the potential of AI-driven automated software development processes.
AI Efficiency: The experiment showcases the ability of AI like ChatGPT to operate a software company with minimal human intervention.
Meta sets GPT-4 as the bar for its next AI model, says a new report
Meta is reportedly planning to train a new model that it hopes will be as powerful as OpenAI’s GPT-4, by heavily investing in data centers and H100 chips. They hope the AI model will be way more powerful than Llama 2.
Meta’s AI Ambitions
New AI Development: Meta is working on an AI model, which they hope to be several times more powerful than their recent model, Llama 2.
Accelerating Generative AI: This initiative is spearheaded by a group established by Mark Zuckerberg earlier this year, focusing on AI tools that produce human-like expressions.
Expected Timeline: Meta anticipates the commencement of training for this AI system in early 2024.
Strategic Positioning in the AI Race
Behind Rivals: This new model is part of Zuckerberg’s strategy to reposition Meta as a leading entity in the AI domain after falling behind competitors.
Infrastructure Development: Meta is investing in data centers and acquiring advanced Nvidia chips (H100s) for AI training.
Shift from Microsoft: While Meta’s Llama 2 was integrated with Microsoft’s cloud platform, Azure, the new model is intended to be trained on Meta’s infrastructure.
Open-source Approach and Implications
Advocating Open-Source: Zuckerberg’s plan is to make the new AI model open-source, making it freely accessible for companies to build AI-driven tools.
Benefits and Risks: Open-source AI models are favored due to their cost-effectiveness and flexibility. However, they also come with potential downsides, including legal risks and misuse for disseminating false information.
Concerns from Experts: There are raised apprehensions about the unpredictability of the system and its potential vulnerabilities, emphasizing the need for transparency and control.
Microsoft, MIT, and Google transformed entire Project Gutenberg Collection into audiobooks
In a new research called Large-Scale Automatic Audiobook Creation, Microsoft, MIT, and Google collaborated to transform the entire Project Gutenberg Collection into audiobooks. The library now boasts thousands of free and open audiobooks powered by AI.
Utilizing recent advances in neural text-to-speech, the team achieved exceptional quality of voice acting. The system also allows users to customize an audiobook’s speaking speed and style, emotional intonation, and can even match a desired voice using a small amount of sample audio.
Why does it matter?
This presents an exceptional use case for text-to-speech AI. Moreover, it introduces a scalable system capable of converting thousands of HTML-based e-books to high-quality audiobooks. This signifies a remarkable leap in AI’s ability to solve real-world problems with tangible impact.
Ads for AI girlfriends are flooding Instagram and TikTok
Ads for AI girlfriends and erotic chatbots are bypassing moderation on platforms like Instagram and TikTok, a whole new wave of bots. (Source)
Sexually Suggestive AI Ads
Dozens of startups running NSFW ads on Meta and TikTok.
Use memes and animated characters to promote female avatars promising adult experiences.
Platforms have banned many types of adult human content.
But AI ads slip through using similar sexually provocative messaging.
Unclear why looser enforcement applied to AI content.
Critical Reactions
Some see double standard harming human sex workers.
Commenters call out problematic aspects like apparent minors.
But difficulty moderating AI content at scale persists.
Part of rush to capitalize on interest in generative AI.
Developers creating custom AI companions and experiences.
Explicit ads for AI chatbots and virtual companions are proliferating unchecked on social platforms, despite bans on similar sexual content from actual humans.
Apple unveils iPhone 15 Pro and iPhone 15 Pro Max: USB-C port, Dynamic Island, new colors
Summary: Featuring a strong and lightweight titanium design with new contoured edges, a new Action button, powerful camera upgrades, and A17 Pro for next-level performance and mobile gaming.The iPhone 15 stays at the same price as last year, starting at $799. Like last year, iPhone 15 and iPhone 15 Plus sport 6.1-inch and 6.7-inch displays. However, Apple said that maximum brightness can now go up to 2000 nits. You can preorder the iPhone 15 this Friday, with the device launching on September 22. Apple’s color finishes are now embedded throughout the glass, to form impressive new colorful finishes. The iPhone 15 is available in pink, yellow green, blue, and black. Apple has also updated the camera system. The base model iPhone 15 rear camera system now includes a 48-megapixel main sensor, like last year’s iPhone 14 Pro series. The 48-megapixel sensor enables 1x and 2x optical zoom modes, alongside the usual 0.5x ultra-wide lens.
Details: Apple today debuted iPhone 15 Pro and iPhone 15 Pro Max, designed with aerospace-grade titanium that’s strong yet lightweight to deliver Apple’s lightest Pro models ever. The new design also features contoured edges and a customizable Action button, allowing users to personalize their iPhone experience. Powerful camera upgrades enable the equivalent of seven pro lenses with incredible image quality, including a more advanced 48MP Main camera system that now supports the new super-high-resolution 24MP default, the next generation of portraits with Focus and Depth Control, improvements to Night mode and Smart HDR, and an all-new 5x Telephoto camera exclusively on iPhone 15 Pro Max. A17 Pro unlocks next-level gaming experiences and pro performance. The new USB‑C connector is supercharged with USB 3 speeds — up to 20x faster than USB 2 — and together with new video formats, enables powerful pro workflows that were not possible before.1 And with the addition of Roadside Assistance via satellite, the iPhone 15 Pro lineup builds on Apple’s innovative satellite infrastructure to connect users to help if they have car trouble while off the grid.
iPhone 15 Pro and iPhone 15 Pro Max will be available in four stunning new finishes, including black titanium, white titanium, blue titanium, and natural titanium. Pre-orders begin Friday, September 15, with availability beginning Friday, September 22.
“This is the most pro lineup we have ever created, with a state-of-the-art titanium design, the best iPhone camera system yet that enables game-changing new workflows, and the A17 Pro chip, which ushers in a new chapter of performance and games never before seen on iPhone,” said Greg Joswiak, Apple’s senior vice president of Worldwide Marketing. “iPhone 15 Pro and iPhone 15 Pro Max represent the best of Apple design and industry-first innovations to help enrich the everyday experiences of our users, while enabling them to unleash their creativity.”
iPhone 15 Pro and iPhone 15 Pro Max will be available in four stunning new finishes: black titanium, white titanium, blue titanium, and natural titanium.
A Stunning, Lightweight, and Durable Design
Available in 6.1-inch and 6.7-inch display sizes,2 iPhone 15 Pro and iPhone 15 Pro Max feature a strong and lightweight titanium design — a first for iPhone. This premium alloy — the same used in spacecraft — has one of the highest strength-to-weight ratios of any metal, making this Apple’s lightest Pro lineup ever. Both models feature a new refined brush texture, contoured edges, and the thinnest borders on iPhone. The Pro lineup is built to last, combining the strength of titanium with the toughest back glass in a smartphone and the industry-leading Ceramic Shield on the front. Using an industry-first thermo-mechanical process, the titanium bands encase a new substructure made from 100 percent recycled aluminum, bonding these two metals with incredible strength through solid-state diffusion. The aluminum frame helps with thermal dissipation and allows the back glass to be easily replaced. This new design highlights the Super Retina XDR display with Always-On and ProMotion technologies for an exceptional viewing experience.
The premium titanium used on iPhone 15 Pro and iPhone 15 Pro Max has one of the highest strength-to-weight ratios of any metal, making them Apple’s lightest Pro lineup ever.
iPhone 15 Pro and iPhone 15 Pro Max feature the thinnest borders ever on iPhone, the Ceramic Shield front cover, and new contoured edges.
The all-new Action button replaces the single-function switch used to toggle between ring and silent, offering additional options so users can choose between quickly accessing the camera or flashlight; activating Voice Memos, Focus modes, Translate,3 and accessibility features like Magnifier; or using Shortcuts for more options. A press-and-hold gesture with fine-tuned haptic feedback and visual cues in the Dynamic Island ensure the new button launches the intended action.
By default, the new Action button can switch between ring and silent, but users can choose from a set of actions for even more convenience and versatility.
A17 Pro: A New Generation of Apple Silicon for iPhone
Bringing pro performance and capabilities, iPhone 15 Pro and iPhone 15 Pro Max are powered by A17 Pro, the industry’s first 3-nanometer chip. Continuing Apple’s leadership in smartphone silicon, A17 Pro brings improvements to the entire chip, including the biggest GPU redesign in Apple’s history. The new CPU is up to 10 percent faster with microarchitectural and design improvements, and the Neural Engine is now up to 2x faster, powering features like autocorrect and Personal Voice in iOS 17. The pro-class GPU is up to 20 percent faster and unlocks entirely new experiences, featuring a new 6-core design that increases peak performance and energy efficiency. Now with hardware-accelerated ray tracing — which is 4x faster than software-based ray tracing — iPhone 15 Pro offers smoother graphics, as well as more immersive AR applications and gaming experiences. iPhone 15 Pro brings true-to-life gaming to the palm of users’ hands with console titles never before seen on a smartphone, like Resident Evil Village, Resident Evil 4, Death Stranding, and Assassin’s Creed Mirage.4
A17 Pro includes a dedicated AV1 decoder, enabling more efficient, high-quality video experiences for streaming services. Additionally, a new USB controller enables USB 3 speeds on iPhone for the first time, now supporting much higher transfer speeds and video output up to 4K at 60 fps HDR.
The new 6-core GPU in A17 Pro expands what’s possible on iPhone, enabling next-level mobile gaming with fast, efficient performance and hardware-accelerated ray tracing.
A Powerful Pro Camera System for More Creative Control
Through a deep integration of hardware and software, the advanced camera systems on iPhone 15 Pro and iPhone 15 Pro Max both pack the equivalent of seven pro lenses — all enabled by A17 Pro. With the power of computational photography, the 48MP Main camera, built exclusively for the Pro lineup, gives users even more flexibility with a new 24MP super-high-resolution default, offering incredible image quality at a practical file size ideal for storing and sharing. The Main camera allows users to switch between three popular focal lengths — 24 mm, 28 mm, and 35 mm — and even choose one as a new default. In addition to 48MP ProRAW, the Main camera also supports 48MP HEIF images with 4x more resolution. iPhone 15 Pro features an expansive 3x Telephoto camera, and iPhone 15 Pro Max provides the longest optical zoom ever on iPhone: 5x at 120 mm. Great for close-ups, wildlife photos, and catching the action from further distances, the new Telephoto camera on iPhone 15 Pro Max has an innovative tetraprism design with a combined optical image stabilization and autofocus 3D sensor-shift module, Apple’s most advanced stabilization system yet.
With Apple’s most pro camera systems ever, users will get powerful new features and the equivalent of seven camera lenses in their pocket, including 5x at 120 mm on iPhone 15 Pro Max.
Next-generation portraits on iPhone 15 Pro and iPhone 15 Pro Max feature sharper detail, more vivid colors, and better low-light performance. For the first time, users can take portraits without having to switch to Portrait mode. When there’s a person, dog, or cat in the frame, or when a user taps to focus, iPhone automatically captures depth information, so users can turn photos into stunning portraits later in the Photos app on iPhone, iPad, or Mac. For greater creative control, users can also adjust the focus point after the photo has been taken.
In addition to 48MP ProRAW, users can now shoot in 48MP HEIF, with four times more resolution than before.
Additional features that benefit all cameras on iPhone 15 Pro and iPhone 15 Pro Max include:
Night mode gets better with sharper details and more vivid colors, now powered by the Photonic Engine, including Night mode portraits, enabled by the LiDAR scanner.
New Smart HDR captures subjects and the background with more true-to-life renderings of skin tones, while ensuring photos have brighter highlights, richer midtones, and deeper shadows when viewed in the Photos app. This advanced HDR rendering is also available to third-party apps, so images can look even better when shared online.
The best quality video in a smartphone is upgraded thanks to A17 Pro, with improvements in low-light video and Action mode.
The preferred smartphone for creative pros and filmmakers gets even better with new pro workflows. Users can now get up to 20x faster transfer speeds with an optional USB 3 cable. iPhone and third-party solutions like Capture One also help photographers create a pro studio, allowing them to shoot and instantly transfer 48MP ProRAW images from iPhone to Mac. ProRes video can be recorded directly to external storage, enabling higher recording options up to 4K at 60 fps, and greater flexibility on set when using iPhone as the main camera. iPhone 15 Pro also introduces a new option for Log encoding and is the first smartphone in the world to support ACES, the Academy Color Encoding System, a global standard for color workflows.
The Pro lineup enables powerful workflows for photographers and filmmakers that previously weren’t possible. Now with Capture One, users can shoot on iPhone and instantly transfer 48MP ProRAW directly to a Mac.
Coming later this year, iPhone 15 Pro will add a new dimension to video capture with the ability to record spatial video for Apple Vision Pro. Users will be able to capture precious moments in three dimensions and relive those memories with incredible depth on Apple Vision Pro when it is available early next year in the U.S.
Next-Level Wireless Performance and Connectivity
The iPhone 15 Pro lineup offers convenient new ways to charge, find friends in busy places, and stay connected while traveling. Both models use the USB‑C connector, a universally accepted standard for charging and transferring data, allowing the same cable to charge iPhone, Mac, iPad, and the updated AirPods Pro (2nd generation). Users can also charge AirPods or Apple Watch directly from iPhone with the USB‑C connector. iPhone 15 Pro and iPhone 15 Pro Max support USB 3 for data transfer speeds up to 10 gigabits per second, up to 20x faster than before.
Using a USB 3 cable with iPhone 15 Pro and iPhone 15 Pro Max enables incredibly fast transfer speeds.
Both models feature the second-generation Ultra Wideband chip, enabling two iPhone devices with this chip to connect at three times the range as before. This opens up a new way to use Precision Finding for Find My friends, so iPhone 15 users can share their location and find each other, even in crowds. Precision Finding is built with the same privacy protections that users have come to trust in Find My.5
iPhone 15 Pro and iPhone 15 Pro Max now support Wi-Fi 6E for greater wireless performance, including up to 2x faster speeds, and introduces the first Thread-enabled smartphones, opening up future opportunities for Home app integrations.
iPhone 15 Pro and iPhone 15 Pro Max come equipped with super-fast 5G,6 and include:
Support for MagSafe and future Qi2 wireless charging.
Improved audio quality on phone calls, including those made on FaceTime or third-party apps. Sound quality gets even better when users select Voice Isolation, so conversations come through loud and clear, even if they are somewhere noisy.
eSIM with support from more than 295 carriers. When traveling the world, users can stay connected through affordable international roaming plans from their existing carrier, or purchase prepaid eSIM plans in over 50 countries and regions, including Australia, Italy, Thailand, and more.
Expanded Safety Capabilities for Peace of Mind
The iPhone 15 lineup offers critical safety capabilities to provide assistance when it matters most, including Crash Detection7 and Emergency SOS via satellite.8 Currently available in 14 countries and regions on three continents, Emergency SOS via satellite has made a significant impact in users’ lives. This groundbreaking service will come to Spain and Switzerland later this month.
Building on this innovative satellite infrastructure, iPhone 15 Pro and iPhone 15 Pro Max introduce Roadside Assistance via satellite. Beginning in the U.S., when a user has car trouble and cellular and Wi-Fi coverage are not available, they can now connect to AAA, the country’s largest roadside assistance provider.9 An intuitive interface, including a short questionnaire to capture important details, will transmit the information via satellite so AAA can message with the user directly and dispatch help to their exact location. Access to Roadside Assistance via satellite will be included for free for two years. Service is covered according to AAA membership, but is also available separately for nonmembers.10
Roadside Assistance via satellite can connect users to AAA if they have car trouble while outside of cellular and Wi-Fi coverage.
Featuring iOS 17
iPhone 15 Pro and iPhone 15 Pro Max feature iOS 17,11 making iPhone even more personal and intuitive with new features:
The Phone app gets major updates, with Contact Posters that allow users to customize how they appear to their contacts, and Live Voicemail, which leverages the power of A17 Pro to see real-time on-device transcription as someone leaves a voicemail. Users can even pick up the call while the caller is leaving their message.
Messages gets a new stickers experience, more powerful search, transcription of audio messages, and Check In, which allows users to automatically notify friends and family when they have made it to their destination safely.
NameDrop gives users a new way to use AirDrop to more easily share contact information by simply bringing two iPhone devices together. The same gesture can be used to AirDrop content and more, and now users can step away from each other and finish sending large files over the internet.12
StandBy gives users a customizable full-screen experience with glanceable information designed to be viewed from a distance when iPhone is on its side and charging. With the Always-On display of iPhone 15 Pro and iPhone 15 Pro Max, StandBy stays on to show useful information — perfect on a desk, nightstand, or kitchen counter.
Interactive widgets on the Home Screen, Lock Screen, and in StandBy allow users to take action with just a tap, making it easy to complete a to-do, or play or pause a song right from the widget.
Safari adds greater protection for Private Browsing and introduces profiles, helping users separate their browsing for topics like work and personal.
iOS 17 delivers many more updates, including Journal,13 a new app that helps iPhone users reflect and practice gratitude through journaling, improvements to autocorrect and Dictation that make entering text faster and easier than ever before, password and passkey sharing with iCloud Keychain, and much more.
Contact Posters in iOS 17 provide users with a new way to express themselves, and bring a completely new look to incoming calls.
Better for the Environment
iPhone 15 Pro and iPhone 15 Pro Max are designed with the environment in mind. As Apple continues to work toward its 2030 goal of making every product carbon neutral — from design to manufacturing to customer use — the company is prioritizing clean electricity across the entire supply chain and designing products with recycled and other low-carbon materials. iPhone 15 Pro and iPhone 15 Pro Max now use more recycled content, with a 100 percent recycled aluminum substructure and 100 percent recycled cobalt in the battery — both firsts for Apple. iPhone 15 Pro and iPhone 15 Pro Max also include 100 percent recycled rare earth elements in all magnets and 100 percent recycled gold in the USB‑C connector as well as the gold plating and tin soldering in multiple printed circuit boards. Both models meet Apple’s high standards for energy efficiency and are free of mercury, PVC, and beryllium. Over 99 percent of the packaging is fiber-based, bringing Apple closer to its goal of completely removing plastic from its packaging by 2025.
To further reduce impact on the planet, Apple will no longer use leather in any new Apple products, including iPhone accessories. Apple is introducing a new FineWoven Case with MagSafe and FineWoven Wallet with MagSafe, made from a durable and elegant microtwill with a soft, suedelike feel. The material is made from 68 percent post-consumer recycled content and has significantly lower carbon emissions compared to leather.
New FineWoven accessories for iPhone are made from a luxurious and durable microtwill. This FineWoven material is made of 68 percent post-consumer recycled content and has significantly lower emissions compared to leather.
Pricing and Availability
iPhone 15 Pro and iPhone 15 Pro Max will be available in black titanium, white titanium, blue titanium, and natural titanium finishes. iPhone 15 Pro remains at the same starting price of $999 (U.S.)or $41.62 (U.S.) per month, available in 128GB, 256GB, 512GB, and 1TB storage capacities. iPhone 15 Pro Max starts at $1,199 (U.S.) or $49.95 (U.S.)per month, available in 256GB, 512GB, and 1TB storage capacities.
Apple offers great ways to save and upgrade to the latest iPhone. Customers in the U.S. can get $200–$650 (U.S.) in credit when they trade in an iPhone 11 or later and upgrade to iPhone 15 Pro or iPhone 15 Pro Max by visiting the Apple Store Online, or at an Apple Store location. To see what their device is worth and for terms and conditions, customers can visit apple.com/shop/trade-in.
Customers can get iPhone 15 Pro for as low as $0 (U.S.) after a qualifying trade-in with select U.S. carriers. For eligibility requirements and more details, see apple.com/shop/buy-iphone/carrier-offers.
Customers in more than 40 countries and regions, including Australia, Canada, China, France, Germany, India, Japan, Mexico, the UAE, the U.K., and the U.S., will be able to pre-order iPhone 15 Pro and iPhone 15 Pro Max beginning at 5 a.m. PDT this Friday, September 15, with availability beginning Friday, September 22.
iPhone 15 Pro and iPhone 15 Pro Max will be available in Macao, Malaysia, Türkiye, Vietnam, and 17 other countries and regions beginning Friday, September 29.
FineWoven Wallet with MagSafe and FineWoven Case with MagSafe will both be available for $59 (U.S.) in five new colors for the iPhone 15 lineup: black, taupe, mulberry, pacific blue, and evergreen. In addition to the iPhone 15 Pro and iPhone 15 Pro Max Clear Case, available for $49 (U.S.), a Silicone Case with MagSafe will be available for $49 (U.S.) in black, storm blue, clay, light pink, guava, orange sorbet, cypress, and winter blue.
iOS 17 will be available as a free software update on Monday, September 18.
Beginning September 18, iCloud+ will offer two new plans: 6TB for $29.99 (U.S.) per month and 12TB for $59.99 (U.S.) per month, providing additional storage to keep files, photos, videos, and more safe, accessible, and easy to share. The new plans are great for users with large photo and video libraries or those using Family Sharing, and will provide access to premium features, including Private Relay, Hide My Email, Custom Email Domains, and HomeKit Secure Video support.
Customers who purchase iPhone 15 Pro and iPhone 15 Pro Max will receive three free months of Apple Arcade and Apple Fitness+ with a new subscription.
Daily AI Update News from Google, Microsoft, Instagram, YouTube, and more.
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Google’s new leap to support responsible AI – It is launching the Digital Futures Project and a $20 million Google.org fund, which will provide grants to leading think tanks and academic institutions worldwide. The project will support researchers, organize convenings, and foster debate on public policy solutions to encourage the responsible development of AI.
Microsoft, MIT, and Google transformed the entire Project Gutenberg Collection into audiobooks – In new research called Large-Scale Automatic Audiobook Creation, the institutes present a system that can automatically generate high-quality audiobooks from online e-books. The Project Gutenberg Open Audiobook Collection now boasts thousands of free and open audiobooks powered by AI. Utilizing recent advances in neural text-to-speech, the team achieved exceptional quality of voice acting.
Amazon, Nvidia, Microsoft, and Google are leading the surge in hiring for GenAI talent – There is an explosive demand for Generative AI talent today. The number of companies mentioning “Generative AI” in monthly job postings is increasing exponentially. Technology is the #1 sector looking to hire GenAI experts. Finance is #2nd, and healthcare is #3. AI jobs mentioning “Generative AI” or “LLMs” had the highest average base salary offered, with an average of $200,837/year.
Instagram might be getting generative AI panoramas – The feature was spotted in Monday’s Instagram update on iOS. It has been experimenting with a handful of generative AI-related features, so it’s not too surprising that AI-created panoramas might be part of Instagram’s feature lineup.
IRS deploys AI to crack down on tax violations among nation’s wealthy – The agency focus will shift attention to wealthy from working-class taxpayers; key changes coming to reduce burden on average taxpayers while using AI and improved technology to identify sophisticated schemes to avoid taxes.
YouTube announces AI-powered creative guidance in Google Ads – The AI-powered feature, found inside the Recommendations and Video Analytics sections of Google Ads, offers suggestions to help advertisers enhance the efficacy of their video campaigns.
AI chatbots were tasked to run a tech company. They built software in under 7 minutes for less than $1 – Researchers in this new study tasked an AI-powered tech company with developing 70 different programs. AI chatbots such as ChatGPT can operate a software company in a quick, cost-effective manner with minimal human intervention, the study indicates.
A boy saw 17 doctors over 3 years for chronic pain. ChatGPT found the right diagnosis – The frustrated mom of the boy shared with ChatGPT everything she knew about her son’s symptoms and all the information she could gather from his MRIs.
Emerging AI Innovations: September 11th, 2023
Google Deepmind introduces language models as optimizers
Google DeepMind introduces the concept of using language models as optimizers, This work is called Optimization by PROmpting (OPRO). This new approach describes the optimization problem in natural language. The models are trained to generate new solutions based on a defined problem and previously found solutions.
This is applied to linear regression, traveling salesman problems, and prompt optimization tasks. The results show that the prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K and up to 50% on Big-Bench Hard tasks.
Why does this matter?
Google Deepmind’s OPRO can revolutionize problem-solving in various fields. It improves task accuracy, outperforming human-designed approaches benefiting end users with more efficient solutions.
NVIDIA’s new software boosts LLM performance by 8x
NVIDIA has developed a software called TensorRT-LLM to supercharge LLM inference on H100 GPUs. It includes optimized kernels, pre- and post-processing steps, and multi-GPU/multi-node communication primitives for high performance. It allows developers to experiment with new LLMs without deep knowledge of C++ or NVIDIA CUDA. The software also offers an open-source modular Python API for easy customization and extensibility.
(The following figures reflect performance comparisons between an NVIDIA A100 and NVIDIA H100.)
Additionally, it allows users to quantize models to FP8 format for better memory utilization. TensorRT-LLM aims to boost LLM deployment performance and is available in early access, soon to be integrated into the NVIDIA NeMo framework. Users can apply for access through the NVIDIA Developer Program, with a focus on enterprise-grade AI applications.
Why does this matter?
H100 alone is 4x faster than A100. Adding TensorRT-LLM and its benefits, including in-flight batching, results in an 8X total increase to deliver the highest throughput. Also, on Meta’s Llama 2 TensorRT-LLM can accelerate inference performance by 4.6x compared to A100 GPUs.
Companies like Databricks have found TensorRT-LLM to be easy to use, feature-packed, and efficient, enabling cost savings for customers.
Google’s antitrust trial to begin
The ongoing trial against Google is part of a larger reassessment of internet regulation, with antitrust enforcers fearing large tech companies could stifle innovation in developing fields such as AI.
The Justice Department, employing the Sherman Antitrust Act of 1890, continues its case against Google’s market dominance, implying penalties could range up to the company’s breakup.
Measures to rein in industry giants, however, may dampen innovation, signaling caution for regulators as global competition, notably with China, intensifies.
Silicon Valley’s pursuit of immortality
Silicon Valley elites are embracing a trend of costly full-body MRIs, like those offered by Prenuvo, as a preventative health measure to detect diseases early.
Despite their rising popularity among the wealthy and endorsements by celebrities, there’s skepticism in the medical community regarding the scans’ effectiveness and potential for false positives.
Start-ups such as Ezra and Prenuvo aim to reduce scan costs through AI technology and seek to bring these preventative measures into the mainstream.
Potential world’s largest lithium cache discovered in the US
A new study suggests that the McDermitt Caldera, located on the Nevada-Oregon border, may contain the world’s largest lithium deposit, estimated between 20 to 40 million metric tons.
This lithium, vital for creating batteries that power electric vehicles, is trapped within clay, specifically within an area called Thacker Pass located in southern Nevada.
Although this colossal lithium deposit could reshape global lithium dynamics, its extraction has faced opposition due to concerns about its environmental impact and disruption of sacred Native American lands.
Daily AI Update News from NVIDIA, Google DeepMind, Meta, Reddit, Infosys, India’s Reliance and biased GPT model
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
NVIDIA has developed TensorRT-LLM to supercharge LLM inference on H100 GPUs – It includes optimized kernels, pre- and post-processing steps, and multi-GPU/multi-node communication primitives for high performance. – It allows developers to experiment with new LLMs without deep knowledge of C++ or NVIDIA CUDA. – The software also offers an open-source modular Python API for easy customization and extensibility. – Companies like Databricks have found TensorRT-LLM to be easy to use, feature-packed, and efficient, enabling cost savings for customers.
Google DeepMind introduces the concept of using language models as optimizers – This new approach describes the optimization problem in natural language. The models are trained to generate new solutions based on a defined problem and previously found solutions. – This approach was tested on linear regression and the traveling salesman problem, and the results showed that the language models performed as well as or better than hand-designed algorithms. – The idea was then applied to prompt optimization for maximizing task accuracy, such as math word problem-solving. The optimized prompts outperformed human-designed prompts by over 50% in some cases.
Meta training a new model with aim to match GPT-4 level AI model as its benchmark, says a new report – Meta is reportedly planning to train a new chatbot model that it hopes will rival OpenAI’s GPT-4. The company is acquiring AI training chips and expanding its data centers to create a more powerful chatbot. – CEO Mark Zuckerberg wants the model to be free for companies to create AI tools with. Meta is building the model to speed up the creation of AI tools that can emulate human expressions.
Reddit launched AI-powered keyword research tool, that will help advertisers – This tool uses ML and NLP to generate relevant keywords and rank them based on monthly Reddit views. – It filters out unsuitable content to ensure brand safety. Advertisers can use this tool to display the most fitting ads to the most relevant Reddit users, saving time on keyword research and increasing the reach and efficiency of campaigns.
Infosys likely to collab with NVIDIA to train 3 lakh+ employees on AI – NVIDIA CEO Jensen Huang hinted at the collaboration, stating that Infosys would use NVIDIA’s infrastructure and capabilities to build AI models and applications. – Infosys has already launched an AI-focused suite called Topaz, which offers industry-specific solutions in intelligent automation and enhanced security.
India’s Reliance partners with Nvidia to develop a new LLM – Reliance Industries’ Jio Platforms has partnered with Nvidia to build a LLM trained on India’s diverse languages. They will also collaborate on building an AI infrastructure that is more powerful than India’s fastest supercomputer. – Reliance will manage the AI cloud infrastructure, while Nvidia will provide AI supercomputer solutions and frameworks. India has yet to make a significant mark in the global AI arena, with most companies relying on LLM created by organizations like OpenAI.
Researchers at Humboldt University in Berlin have developed a very biased GPT model, called OpinionGPT – A language model that demonstrates how training data can impact AI bias. The model is trained on specific social dimensions such as politics, geography, gender, and age using data from selected thematic subgroups on Reddit. – While the model does not represent all demographics, it reflects the nuanced biases of different demographics. This research highlights the importance of carefully selecting and curating training data to mitigate bias in AI models. It is called a very biased GPT model.
Emerging AI Innovations: September 08th, 2023
Introducing Falcon 180B, largest and most powerful open LLM
UAE’s Technology Innovation Institute (TII) has released Falcon 180B, a new state-of-the-art for open models. It is the largest openly available language model, with 180 billion parameters, trained on a massive 3.5 trillion tokens using TII’s RefinedWeb dataset. It’s currently at the top of the Hugging Face Leaderboard for pre-trained Open LLMs and is available for both research and commercial use.
The model performs exceptionally well in various tasks like reasoning, coding, proficiency, and knowledge tests, even beating competitors like Meta’s LLaMA 2. Among closed-source models, it ranks just behind OpenAI’s GPT 4 and performs on par with Google’s PaLM 2 Large, which powers Bard, despite being half the model’s size.
Why does it matter?
It is a great contribution to open source. But there’s a catch: You’ll need 400GB of memory for inference, which can cost too much to host this for inference. Moreover, code is only 5% in the training mix, which is by far the most useful data to boost reasoning, master tool use, and power AI agents.
However, this indicates the continuous pushing of the boundaries of generative AI, and we may be only a few months away from GPT-4-level open-source models.
Apple is spending millions of dollars a day to train AI
Reportedly, Apple has been expanding its budget for building AI to millions of dollars a day. It has a unit of around 16 members, including several former Google engineers, working on conversational AI. It is working on multiple AI models to serve a variety of purposes.
Apple wants to enhance Siri to be your ultimate digital assistant, doing multi-step tasks without you lifting a finger and using voice commands.
It is developing an image generation model and is researching multimodal AI, which can recognize and produce images or video as well as text.
A chatbot is in the works that would interact with customers who use AppleCare.
Why does it matter?
OpenAI, too, splashed out a whopping $100 million for GPT-4 alone. Perhaps this shouldn’t be surprising, given that Apple has been a visionary in the past, consistently pushing the boundaries of what’s possible in technology. It is also reported that Apple created a team four years ago, indicating it may not be as much of a laggard in the AI race as we thought.
Microsoft and Paige to build the largest image-based AI model to fight cancer
Paige, a technology disruptor in healthcare, has joined forces with Microsoft to build the world’s largest image-based AI models for digital pathology and oncology.
Paige developed the first Large Foundation Model using over one billion images from half a million pathology slides across multiple cancer types. Now, it is developing a new AI model with Microsoft that is orders-of-magnitude larger than any other image-based AI model existing today, configured with billions of parameters.
Paige will utilize Microsoft’s advanced supercomputing infrastructure to train the technology at scale and ultimately deploy it to hospitals and laboratories across the globe using Azure.
Why does this matter?
This will help realize the potential of generative AI at an unprecedented scale, introduce completely novel capabilities of AI, and serve as the cornerstone for the next generation of clinical/healthcare applications built with AI.
Vector Database: The Secret Behind Large Language Models Capabilities
Have you ever wondered how language models like GPT-3, BERT, and others seem to understand and generate text with astonishing accuracy? The answer lies in vector embeddings.
Emerging AI Innovations: Vector Embeddings Explained
Vector Embeddings Explained
Vector embedding is a prominent method in artificial intelligence and natural language processing that transforms data into lower-dimensional vectors. These embeddings encapsulate the crux of information, allowing AI systems to delve deeper into the data and enhance memory retention. Think of embeddings as a streamlined bridge between raw data and an AI’s comprehension capability.
AI models, like LLMs, generate embeddings that capture intricate relationships and hidden structures by converting data into these vectors. As a result, the AI can discern patterns and relationships more effectively.
Why Vector Databases Are Essential
Traditional scalar databases falter when faced with the multifaceted nature of vector embeddings. These databases, crafted for simpler, one-dimensional data, struggle with the elevated complexity of multi-dimensional vectors. Here’s why vector databases are the answer:
Complexity and Scale: The richness of vector embeddings can overwhelm conventional databases, making querying and processing a challenge.
Gaining Insights: To glean substantial insights from vector data, specialized techniques are needed, which many traditional databases lack.
Real-time Analysis: AI operations often demand instantaneous analysis. However, the demands of vector embeddings can bog down traditional databases, limiting their real-time response.
Vector Databases in Focus: Specifically built for vector data, these databases offer optimized tools for storage, indexing, and querying. With indexing techniques tailored for high-dimensional spaces, they ensure rapid data retrieval.
Peak Performance: Vector databases harness hardware capabilities, such as GPUs and TPUs, ensuring optimal performance even with voluminous vector data.
Scalability: Unlike traditional databases that may flounder with growing data, vector databases scale efficiently, ensuring undiminished performance.
Adaptability: These databases permit varied data representations and querying methods, giving users the freedom to experiment without any database limitations.
In essence, to fully harness the potential of vector embeddings, vector databases are the go-to. They pave the way for quicker insights, on-the-spot analysis, and smarter decision-making in AI.
Understanding Vector Databases with an Example
Let’s demystify the workings of a vector database with a real-world example: chatbots, such as chatGPT or bard, which are grounded in LLMs. Here’s a step-by-step breakdown:
A user keys in a query into the chatbot.
This query is processed by an embedding model to produce a relevant vector embedding.
The generated vector embedding is then indexed in the vector database, linking it to the original content.
Upon querying, the vector database swiftly retrieves the most relevant response and presents it to the user.
In this manner, vector databases power chatbots, offering quick and pertinent responses to user queries.
Generate innovative business ideas with ChatGPT
Brainstorming innovative business ideas requires a blend of human creativity and machine intelligence. With ChatGPT, you can harness the latter to get a fresh perspective on business opportunities.
Use this prompt to explore how to utilize ChatGPT to generate actionable and unique business concepts.
I want you to act as a world-class business strategist specializing in innovation and entrepreneurship. My first request is for you to generate a business idea:
Here's some context:
Industry — [Industry]
Interest — [Interests]
Formatting guidelines: "Include: Problem Statement, Solution, Unique Selling Point (USP), Target Market, and Revenue Model. Include some context and a list of [Number] possible business ideas.".
I need you to write content with a good balance of “perplexity” and “burstiness”.
Daily AI Update News from TII, Apple, Microsoft, OpenAI, IBM, Anthropic, Slack, HubSpot, and SAP
OpenAI to host its first developer conference in November – The one-day event in San Francisco will unite developers from around the world with the team at OpenAI to preview new tools, exchange ideas, and join breakout sessions led by OpenAI’s technical team members. Registration for in-person attendance will open soon, and developers everywhere can livestream the keynote.
IBM rolls out new generative AI features and models – It includes new capabilities across its recently launched Watsonx data science platform and new models called the Granite series models, appearing to be standard LLMs along the lines of GPT-4 and ChatGPT.
Anthropic launches a paid plan for Claude 2 – Claude Pro is priced the same as OpenAI’s ChatGPT Plus, the paid plan for Claude 2 rival ChatGPT. For the monthly $20 in the U.S., or £18 in the U.K., customers get 5x more usage than the free Claude 2 tier, the ability to send “many more” messages, priority access to Claude 2 during high-traffic periods, and early access to new features.
Slack launches new Workflow Builder to help better automate your tasks – Aimed at users with little or no coding experience, the new version of Workflow Builder tries to connect different tools to automate your workflows.
HubSpot announced the launch of HubSpot AI – It is a platform-wide portfolio of AI-powered features for marketing, sales, and service teams. It includes AI Assistants, AI Agents, AI Insights, and ChatSpot.
SAP acquires LeanIX to focus on AI-assisted IT modernization – LeanIX, a German startup, provides enterprises with a clear picture of their entire software usage, covering everything from what they’ve bought, licensed, and built to what they plan to add. The move will accelerate modernization for enterprise customers.
Emerging AI Innovations: September 07th, 2023
Harvard Scientist Dr. Isaac Kohane Validates GPT-4’s Medical Prowess with Rare Diagnosis
Dr. Isaac Kohane, a dual expert in medicine and computer science at Harvard, recently evaluated the capabilities of the latest AI model, GPT-4, in a clinical context. Remarkably, GPT-4 showcased proficiency surpassing many medical professionals, achieving a 90% accuracy rate on medical licensing exam questions, providing patient translations, and offering insights into physician-patient interactions.
During a real-world test, GPT-4 astoundingly identified a rare medical condition, matching the diagnostic skills of seasoned physicians like Kohane. Yet, his recent publication, ‘The AI Revolution in Medicine,’ sheds light on the model’s occasional lapses, from minor clerical oversights to numerical inaccuracies.
Amidst the marvel and skepticism, Kohane grapples with the challenge of ensuring the AI’s consistent reliability in medical consultations. Read the full article here.
Yokosuka Leads Japanese Municipalities with Innovative AI Implementation in Public Offices
In a pioneering initiative, Yokosuka emerges as the first city in Japan to integrate OpenAI’s ChatGPT into its municipal offices. This one-month experiment, encompassing 4,000 personnel, is designed to refine administrative procedures. Amid a diminishing populace and staffing constraints, the city aims to utilize AI-powered solutions to optimize manpower for endeavors demanding a personalized touch.
ChatGPT is envisioned to support in roles such as content summarization, brainstorming marketing text, formulating official documents, and refining comprehensible language. This strategic decision aligns with the broader Japanese governmental agenda, keenly assessing AI’s potential role in streamlining state-run administrative operations. Read the detailed report here.
OpenAI doubles GPT-4 message cap to 50
OpenAI has doubled the number of messages ChatGPT Plus subscribers can send to GPT-4. Users can now send up to 50 messages in 3 hours, compared to the previous limit of 25 messages in 2 hours. And they are rolling out this update next week. Read the detailed report here.
GPT-4 Code Interpreter masters math with self-verification
OpenAI’s GPT-4 Code Interpreter has shown remarkable performance on challenging math datasets. This is largely attributed to its step-by-step code generation and dynamic solution refinement based on code execution outcomes.
Expanding on this understanding, new research has introduced the innovative explicit code-based self-verification (CSV) prompt, which leverages GPT4-Code’s advanced code generation mechanism. This prompt guides the model to verify the answer and then reevaluate its solution with code.
The approach achieves an impressive accuracy of 84.32% on the MATH dataset, significantly outperforming the base GPT4-Code and previous state-of-the-art methods.
ChatGPT will now remember who you are & what you want
OpenAI is rolling out custom instructions to give you more control over how ChatGPT responds. It allows you to add preferences or requirements that you’d like ChatGPT to consider when generating its responses.
ChatGPT will remember and consider the instructions every time it responds in the future, so you won’t have to repeat your preferences or information. Currently available in beta in the Plus plan, the feature will expand to all users in the coming weeks.
OpenAI Unveils Shap·E: A Revolutionary AI for Rapid 3D Design Generation
OpenAI Unveils Shap·E: A Revolutionary AI for Rapid 3D Design Generation
OpenAI introduces its innovative creation, Shap·E – a cutting-edge conditional generative model primed for swift generation of 3D designs. Engineered using implicit functions, it can seamlessly render as textured meshes or neural radiance landscapes.
Rooted in a robust dataset amalgamating 3D assets with matching textual narratives, Shap·E employs an encoder to translate these assets into implicit function parameters. Furthermore, a conditional diffusion model refines the conditional distribution of these parameters, based on input data. The result? A transformative AI tool that can manifest intricate, high-caliber 3D outputs in mere moments. Discover the complete story here.
ChatGPT in your pocket: OpenAI brings ChatGPT to iPhone
OpenAI has released the ChatGPT app for iOS that allows users to have engaging and personalized conversations with an AI language model. It will sync your conversations, support voice input, and bring the latest model improvements to your fingertips. Also, ChatGPT Plus subscribers get exclusive access to GPT-4’s capabilities, early access to features, and faster response times, all on iOS.
They have started the rollout in the US and are expected to enter additional countries in the coming weeks. OpenAI said: “Android users, you’re next!”. Discover the complete story here.
OpenAI Announces $1M Initiative for Democratizing AI Governance
In a groundbreaking move, OpenAI pledges to distribute ten grants, each worth $100,000, to pioneering projects focused on formulating democratic processes to steer AI system regulations and actions.
Acknowledging AI’s transformative influence on global society, OpenAI calls upon visionaries to conceptualize unique systems that imbibe an array of viewpoints and are anchored in the collective good. This initiative primarily seeks to unravel questions surrounding AI behaviors, delving into scenarios like the grounds on which AI might critique public personalities or the manner in which it portrays contested perspectives.
Prospective participants have until June 24, 2023, to present their ideas, with the flexibility to either pick from a curated list of policy dilemmas or introduce their original thoughts. Dive deeper into the initiative here.
OpenAI’s massive update on GPT-3.5 & GPT-4 APIs
OpenAI announced exciting updates, including more steerable API models, function calling capabilities, longer context, and lower prices.
Function calling is now available to enable LLMs to work more effectively and efficiently interact with your programs/tools.
The latest GPT-3.5-Turbo model is available with support for 16K context (~20 pages of text)
25% cost reduction on input tokens for gpt-3.5-turbo
75% cost reduction on the embeddings model
The data privacy and security assurances implemented on March 1 remain consistent across all models. The user’s API data will not be utilized for training purposes. Dive deeper into the initiative here
Generative AI poised to replace 2.4 million US jobs by 2030
Forrester predicts that generative AI will replace 2.4 million US jobs by 2030, mostly white-collar roles, such as technical writers, proofreaders, copywriters, and administrative positions. But ironically, other forms of automation will displace more jobs.
To stay on top of the latest advancements in AI, look here first.
Concerns about Generative AI
While the Generative AI impact is significant, other forms of automation are set to cause more widespread job displacement.
The most impacted group will be middle-class, college-educated, white-collar workers, specifically those earning above $60,000 annually.
Creative professionals stand to benefit
Interestingly, workers in creative industries will likely utilize generative AI tools in their jobs rather than being replaced. This includes editors, writers, authors, poets, and lyricists.
However, the use of such tools like ChatGPT may result in inconsistent outputs and even “coherent nonsense”, leading to potential performance issues.
Microsoft Research presents an experimental study using OpenAI’s ChatGPT for robotics applications. It outlines a strategy that combines design principles for prompt engineering and the creation of a high-level function library that allows ChatGPT to adapt to different robotics tasks, simulators, and form factors.
The study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning to complex domains such as aerial navigation, manipulation, and embodied agents.
Microsoft also released PromptCraft, an open-source platform where anyone can share examples of good prompting schemes for robotics applications.
Connected cars are a “privacy nightmare,” Mozilla Foundation says
Modern cars collect enormous amounts of personal data and many car manufacturers insist on sharing or selling this sensitive information, such as disability status, genetic information and facial templates, according to a comprehensive study by the Mozilla Foundation.
The vast majority (92%) of car companies give drivers little to no control of their personal data, with the notable exceptions of Renault and Dacia, both European brands obligated to comply with GDPR privacy laws.
Nissan’s privacy policy stands out as the most concerning, bluntly stating they can collect and share highly sensitive personal information for targeted marketing, but privacy concerns extend across the board, with all companies collecting substantial driver data, evidencing a broader problem with privacy in the car industry.
ChatGPT’s biggest competitor launches Pro version at $20
Anthropic introduced a paid Claude Pro plan for its Claude 2 chatbot, offering 5x more usage than the free tier to compete with OpenAI’s ChatGPT Plus. (Source)
The Paid Offering
$20 monthly subscription in the U.S.
5x more usage than free Claude 2.
Priority access during high traffic.
Early new feature access.
Motivations
Matches price of rival ChatGPT Plus.
Revenue helps fund costly compute for models.
Limits still in place due to compute constraints.
Seeking billions more to develop envisioned AI assistant.
Anthropic launched a $20 Claude Pro subscription offering 5x more usage of its Claude 2 AI assistant, seeking revenue to fund its bid to compete with rivals like OpenAI.
Elon Musk Plans to Merge Neuralink and Tesla for an AI Supercompany
Elon Musk reportedly plans to blend Neuralink and Tesla into a large AI company, using data from Twitter users and Tesla’s Full Self-Driving Cameras to train a robust AI model.
Musk’s AI Integration Plan
Musk is contemplating merging Neuralink and Tesla, alongside his xAI startup, to create a comprehensive artificial intelligence model.
Leveraging the text data from Twitter and real-world images from Tesla’s Full Self-Driving network, he intends to develop AI chatbots and physical robots capable of real-world navigation.
Reasoning Behind the Merge
A concern where AI could potentially render humans obsolete led Musk to found xAI for AI safety.
Musk is targeting to create an AI that can generate computer software and a politically unbiased chatbot rival to ChatGPT.
Twitter and Tesla as AI Datasets
Despite criticism, Musk’s acquisition of Twitter offers access to vast user data for AI training.
In addition, the Autopilot and Full-Self Driving systems of Tesla, with billions of collected camera images, serve as valuable resources to build physical robot AI.
OpenAI’s Superalignment – The next big goal! – OpenAI has launched Superalignment, a project dedicated to addressing the challenge of aligning artificial superintelligence with human intent. Over the next four years, 20% of OpenAI’s computing power will be allocated to this endeavor. The project aims to develop scientific and technical breakthroughs by creating an AI-assisted automated alignment researcher. – This researcher will evaluate AI systems, automate searches for problematic behavior, and test alignment pipelines. Superalignment will comprise a team of leading machine learning researchers and engineers open to collaborating with talented individuals interested in solving the issue of aligning superintelligence.
The Consensus Search plugin allows users to find answers, search for papers, and draft pieces of content grounded in scientific research by searching our database of 200M+ papers directly within the ChatGPT interface.[1]
Israel: AI Software Detects Bleeding Inside Brain During CT Scan; Helps Save Patient’s Life.
Chinese tech giant Tencent is launching its artificial intelligence model “Hunyuan” for business use at an annual summit on Thursday.[3]
Google on Wednesday said it will mandate that political advertisements on its platforms disclose when images and audio have been altered or created using tools such as AI
Emerging AI Innovations: September 06th, 2023
CityDreamer – New Gen AI model creates unlimited 3D cities
CityDreamer is a generative AI model that can create unlimited 3D cities by separating the generation of buildings from other background objects. This allows for better handling of the diverse appearance of buildings in urban environments.
The model uses two datasets, OSM and GoogleEarth, to enhance the realism of the generated cities. These datasets provide realistic city layouts and appearances that can be easily scaled to other cities worldwide.
Why does this matter?
CityDreamer can enhance video games, simulations, and virtual experiences, providing users with more immersive and authentic urban environments. Also, It sets a new standard in 3D city generation, potentially outpacing competitors and driving innovation in industries like gaming, architecture, and urban planning.
Scientists train a neural network to identify PC users’ fatigue
Scientists from St. Petersburg University and other organizations have created a database of eye movement strategies of PC users in different states of fatigue. They plan to use this data to train neural network models that can accurately track the functional state of operators, ensuring safety in various industries. The database includes a comprehensive set of indicators collected through sensors such as video cameras, eye trackers, heart rate monitors, and electroencephalographs.
An example of human fatigue analysis using video recording.
Why does this matter?
The Scientists believe that this approach will allow for remote assessment of fatigue severity, and the database will be accessible to software developers for testing their products.
Google Deepmind’s new AI benchmark on bioinformatics code
Google Deepmind and Yale University researchers have introduced BioCoder, a benchmark for testing the ability of AI models to generate bioinformatics-specific code. BioCoder includes 2,269 coding problems based on functions and methods from bioinformatics GitHub repositories.
In tests with several code generators, including InCoder, CodeGen, SantaCoder, and ChatGPT, OpenAI’s GPT-3.5 Turbo performed exceptionally well in the benchmark. The team plans to explore other open models, such as Meta’s LLamA2, in future tests.
Stochastic Gradient Descent (SGD) is an optimization algorithm commonly used for training machine learning models, including neural networks. Conceptually, it’s a method for finding the minimum of a function, which, in the context of machine learning, often represents a “loss” or “error” that we want to minimize.
Here’s a more detailed, yet conceptual, explanation:
The Landscape Analogy
Imagine a hiker standing in a mountainous landscape at sunset, where the goal is to find the lowest point in the valley before it gets dark. The hiker can’t see the entire landscape due to the limited visibility, but he can see enough around him to make a decision about which direction to go.
Gradient
The “gradient” represents the steepest direction uphill. Naturally, the opposite direction would be the steepest way downhill. The hiker wants to go downhill, so he takes a step in the direction opposite to the gradient.
Step Size (Learning Rate)
The size of the step the hiker takes is known as the “learning rate” in SGD. A big step might get the hiker down faster but risks overshooting the lowest point. A smaller step is more cautious but takes longer. In machine learning, setting the right learning rate is crucial for effective training.
Stochastic
The term “stochastic” refers to the randomness involved in the process. Instead of considering the entire landscape (i.e., all the data) to decide on the next step, the hiker only considers a small, randomly chosen portion (a mini-batch) to estimate the best direction. This random sampling makes the algorithm faster and less computationally expensive but also adds some randomness to the descent, which is sometimes beneficial for escaping local minima.
Iterative Process
The hiker keeps taking steps, adjusting his direction based on the gradient of the landscape immediately around him. Eventually, he hopes to reach the lowest point (global minimum) or at least a very low point (local minimum) before it gets too dark (before too many iterations).
In Summary
In the context of machine learning, each step the hiker takes represents an iteration of the algorithm, where the model’s parameters (or weights) are updated so that the loss function (the landscape) is minimized. The model learns from a subset of the data (mini-batch), adjusting its weights based on the calculated gradient and learning rate, inching closer to the optimal weights that minimize the error.
So, Stochastic Gradient Descent is essentially a method for finding the lowest point in a complex landscape, by iteratively taking steps in the direction that appears to go downhill based on a local and potentially random view of the terrain.
“DeepMind Founder Says Everyone Will Have AI Assistant in Next 5 Years”
DeepMind co-founder Mustafa Suleyman said ongoing improvements will soon make AI helpers accessible to all, serving as a “coach and companion” in daily life. (Source)
AI For The Masses
Suleyman sees personal AI assistants for all in the next 5 years.
Powered by models that “know you” and understand your history.
Can reason, prioritize, help you create and invent.
Beyond Assistance
AI will be like having your own “chief of staff.”
Help process information and enhance decisions like an executive assistant.
Alignment with user interests enables personalized aid.
Wider Context
Comes as people find novel uses for AI like ChatGPT in work and life.
Tech leaders see revolutionary potential in democratizing AI.
Suleyman says it will make everyone more productive.
DeepMind’s co-founder predicted that constant improvements will make AI personal assistants accessible to all within 5 years, enhancing daily life like a “coach and companion.”
Elon Musk Plans to Merge Neuralink and Tesla for an AI Supercompany
Elon Musk reportedly plans to blend Neuralink and Tesla into a large AI company, using data from Twitter users and Tesla’s Full Self-Driving Cameras to train a robust AI model.
Musk’s AI Integration Plan
Musk is contemplating merging Neuralink and Tesla, alongside his xAI startup, to create a comprehensive artificial intelligence model.
Leveraging the text data from Twitter and real-world images from Tesla’s Full Self-Driving network, he intends to develop AI chatbots and physical robots capable of real-world navigation.
Reasoning Behind the Merge
A concern where AI could potentially render humans obsolete led Musk to found xAI for AI safety.
Musk is targeting to create an AI that can generate computer software and a politically unbiased chatbot rival to ChatGPT.
Twitter and Tesla as AI Datasets
Despite criticism, Musk’s acquisition of Twitter offers access to vast user data for AI training.
In addition, the Autopilot and Full-Self Driving systems of Tesla, with billions of collected camera images, serve as valuable resources to build physical robot AI.
Ex-Google exec fears AI could create deadlier pandemics
Why does this matter?
The team at Google Deepmind found that smaller, specialized models like StarCoder can perform well in specialized domains. However, ChatGPT struggled with BioCoder, achieving an accuracy of under 50 percent. The team plans to test other models and expects improvements with longer context lengths.
A former Google executive, Mustafa Suleyman, warns that AI could lead to the creation of more lethal pandemics.
Suleyman expresses concerns that AI-powered experimentation with pathogens could accidentally or intentionally result in more transmissible and deadly diseases.
He calls for stricter regulation and containment measures to prevent misuse of AI technology and access to dangerous materials.
Actor’s guild worried for video game workers due to AI
The Screen Actors Guild authorizes members employed in interactive media at major game studios to strike due to concerns over AI impact on their work.
The existing agreement for interactive media workers expired, and the Guild demands AI protections, an initial 11% wage increase, and subsequent 4% raises.
The potential strike, overlapping with ongoing actors’ and writers’ strikes, could disrupt game production, impacting the gaming industry and holiday sales.
Daily AI Update News from Google Deepmind, Zoom, Microsoft, China’s Tencent, TradeGPT and some interesting research work.
Researchers at Google Deepmind have introduced Biocoder, Which tests the ability of AI models – Google Deepmind and Yale University researchers have introduced BioCoder, a benchmark for testing the ability of AI models to generate bioinformatics-specific code. BioCoder includes 2,269 coding problems based on functions and methods from bioinformatics GitHub repositories. – In tests with several code generators, including InCoder, CodeGen, SantaCoder, and ChatGPT, OpenAI’s GPT-3.5 Turbo performed exceptionally well in the benchmark. The team plans to explore other open models, such as Meta’s LLamA2, in future tests.
CityDreamer, Gen AI model creates unlimited 3D cities – It creates by separating the generation of buildings from other background objects. This allows for better handling of the diverse appearance of buildings in urban environments. – The model uses two datasets, OSM and GoogleEarth, to enhance the realism of the generated cities. These datasets provide realistic city layouts and appearances that can be easily scaled to other cities worldwide.
Researchers train a neural network to identify fatigue in computer users – Scientists from St Petersburg University and other organizations have created a database of eye movement strategies of PC users in different states of fatigue. – They plan to use this data to train neural network models that can accurately track the functional state of operators, ensuring safety in various industries. – The database includes a comprehensive set of indicators collected through sensors such as video cameras, eye trackers, heart rate monitors, and electroencephalographs.
Zoom rebrands existing and introduces new gen AI features – The motive is to enhance its videoconferencing platform. The AI assistant, formerly known as Zoom IQ, will now be called the AI Companion and will have an expanded reach across various Zoom services. – Users will be able to chat directly with the AI Companion, ask questions about prior meetings and chats, and take actions on their behalf. The AI Companion will also provide real-time feedback on users’ presence in meetings and offer coaching on conversational and presentation skills. – These features will be available to paying Zoom customers starting in spring 2024.
Microsoft strengthens partnership with G42 for Cloud & AI tools Inclusion – Microsoft expanding its partnership with Abu Dhabi’s G42, with plans to develop AI technology across sectors and offer cloud infrastructure in the UAE. – The aim is to boost the UAE’s digital transformation and enable the public sector and regulated industries to leverage the latest cloud and AI technologies on the Azure public cloud.
China’s AI giant Tencent Holdings has teased the launch of an AI chatbot – The company published a post on WeChat featuring a demo conversation a user had with the chatbot, which helped the user write promotional materials. – Tencent’s move comes as AI chatbots gain popularity in China, with companies like Alibaba and Baidu also developing their own versions.
Crypto exchange Bybit has introduced an AI-powered trading assistant called TradeGPT. – The tool uses AI to provide market analysis and answer technical questions based on the platform’s market data. TradeGPT combines the language model of ChatGPT with Bybit’s in-house ToolsGPT to offer real-time insights and support in multiple languages. It can provide market strategies and product recommendations to users. – Bybit also integrates ChatGPT’s ML and AI capabilities with its market data for technical analysis, funding analysis, and model predictions.
Emerging AI Innovations: September 05th, 2023
Amazon’s AI-powered palm recognition breakthrough
Amazon One is a fast, convenient, and contactless device that lets customers use the palm of their hand for everyday activities like paying at a store, presenting a loyalty card, verifying their age, or entering a venue. No phone, no wallet.
Amazon One does this by combining generative AI, machine learning, cutting-edge biometrics, and optical engineering.
Currently, Amazon One is being rolled out to more than 500 Whole Foods Market stores and dozens of third-party locations, including travel retailers, sports and entertainment venues, convenience stores, and grocers. It can also detect fake hands and reject them. It has already been used over 3 million times with 99.9999% accuracy.
Why does it matter?
Generative AI has recently captured the world’s imagination with feats like summarizing text, writing, and composing code. But this marks how it can address complex real-world problems and reimagine the future of convenience in shopping, entertainment, access, and much more.
Intel is going after the AI opportunity in multiple ways
Intel is aggressively pursuing opportunities in the AI space by expanding beyond data center-based AI accelerators. CEO Pat Gelsinger believes that AI will move closer to end-users due to economic, physical, and privacy considerations. They are incorporating AI into various products, including server CPUs like Sapphire Rapids, which come with built-in AI accelerators for inference tasks.
Furthermore, Intel is set to launch Meteor Lake PC CPUs with dedicated AI hardware to accelerate AI workloads directly on user devices. This approach aligns with Intel’s dominant position in the CPU market, making it attractive for software providers to support their AI hardware.
Why does it matter?
Intel’s multi-pronged strategy positions it as a strong contender in the AI landscape alongside other key players like Nvidia. And as the demand for AI chips continues to surge, Intel’s initiatives could offer a potential solution to this industry-wide challenge and help shape the future of AI.
Introducing Refact Code LLM, for real-time code completion and chat
Refact LLM 1.6B model is primarily for real-time code completion (infill) in multiple programming languages and works as a chat. It achieves the state-of-the-art performance among the code LLMs, coming closer to HumanEval as Starcoder while being 10x smaller in size. It also beats other code models, as shown below. First, a tl;dr
1.6b parameters
20 programming languages
4096 tokens context
code completion and chat capabilities
pre-trained on permissive licensed code and available for commercial use
Emerging AI Innovations: Introducing Refact Code LLM, for real-time code completion and chat
Why does this matter?
While the trend recently is for the model sizes to get bigger, this lowers barriers to entry and makes it a versatile tool for developers with varying hardware setups. With the smaller size, it can run much faster and affordably.
Emerging AI Innovations: Virtual (AI) influencer to make a music video
Virtual (AI) influencer to make a music video
Summary: Noonoouri, the virtual influencer famed for her fashion gigs, has now bagged a record deal with Warner Music, introducing an AI-crafted voice to the music scene. (Forbes)
Noonoouri, with 400k followers on Instagram and major fashion brand deals with Dior and Valentino, is an avatar creation by artist Joerg Zuber.
Unlike typical artists, she’s an anomaly: she doesn’t really “exist.” Her voice is an AI.
While her voice might be digital, the song’s heart and soul come from human creativity, backed by Warner and German producer DJ Alle Farben.
Beyond her singing, Noonoouri wears many virtual hats: she’s an influencer, a verified Instagram star, and notably, an advocate for issues ranging from veganism to anti-racism.
Why It Matters: There’s a growing concern in the music industry. With the rise of avatars like Noonoouri, what’s the future for human artists?
Emerging AI Technologies: X trains our data to AI
Emerging AI Technologies: X trains our data to AI
X, the platform we once knew as Twitter, reveals plans to use collected and public data to train its AI models. (Source)
The updated privacy policy now allows X to harness biometric, job, and education details.
Musk’s xAI ambitions (his new AI project) might see X as a treasure trove of data.
But, Musk assures us – it’s only public info. Your DM secrets? Safe and sound.
Why It Matters: With X feeding public data to AI, we’re looking at a future where our online chirps help shape AI’s understanding. Be careful of what you say online because that will train future AI models 😉
How to analyze your business performance with ChatGPT?
In today’s data-driven world, it’s essential to understand and analyze your business’s performance. But did you know AI, especially ChatGPT, can be your ally in this endeavor?
Here’s a prompt to help you utilize ChatGPT for a comprehensive business analysis:
"Design a real-time analytics dashboard for [insert-side-hustle] that monitors not just financial KPIs, but also customer satisfaction scores, employee engagement levels, and market share growth. Include predictive analytics models.”
Such a dashboard does more than just give you numbers. It helps you understand the underlying trends and patterns of your business. By integrating AI into your analysis, you’re not just reacting to past data but also making informed predictions about the future.
Daily AI Update News from Amazon, Intel, OpenAI, Epic Games, and more
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Amazon One, a breakthrough AI-powered palm recognition device – Amazon One is a fast, convenient, and contactless device that lets customers use the palm of their hand for everyday activities like paying at a store, presenting a loyalty card, verifying their age, or entering a venue. No phone, no wallet. Amazon One does this by combining generative AI, machine learning, cutting-edge biometrics, and optical engineering.
Intel is aggressively pursuing opportunities in the AI space – It is expanding beyond data center-based AI accelerators. It is incorporating AI into various products, including server CPUs like Sapphire Rapids, which come with built-in AI accelerators for inference tasks. Furthermore, Intel is set to launch Meteor Lake PC CPUs with dedicated AI hardware to accelerate AI workloads directly on user devices.
Introducing Refact LLM: 1.6B code model with infill real-time code completion and chat – Refact LLM achieves the state-of-the-art performance among the code LLMs, coming closer to HumanEval as Starcoder while being 10x smaller in size. It also beats other code models like StableCode, CodeGen, and ReplitCode on the HumanEval metric.
OpenAI has unveiled a Canva Plugin for ChatGPT – Now ChatGPT Plus users can “talk” to Canva directly from OpenAI’s bot, making their workflow easier.
Epic Games Store will now accept games created with generative AI – This is especially interesting since the biggest competitor, Valve, is rejecting games with AI content on Steam.
AI model predicts smell based on a molecule’s structure with human-level proficiency – The AI model was trained using an industry dataset of 5,000 known odorants. It also displayed capabilities it was not trained for, like accurately predicting the strength of odors, revealing its potential for broader olfactory tasks.
New AI-generated COVID drug enters Phase I clinical trials, effective against all variants – This oral drug is a treatment, not a vaccine. If approved, it would become the first-ever alternative to Paxlovid.
A startup called AI Scout is using automation to find football’s next star – Footballers looking to showcase their abilities to top clubs can record themselves performing various drills and share them on the AI scout app. The app uses AI to track and analyse the intricate movements of the player and the ball.
Emerging AI Innovations: September 04th, 2023
Meta AI’s New Dataset Understands 122 Languages
Meta AI announced Belebele, a multilingual reading comprehension dataset with 122 language variants. It allows for evaluating text models in high, medium, and low-resource languages, expanding the language coverage of natural language understanding benchmarks.
The Belebele dataset consists of questions based on short passages from the Flores-200 dataset, with four multiple-choice answers. The questions were designed to test different levels of general language comprehension. The dataset enables direct comparison of model performance across all languages and was used to evaluate multilingual masked language models and large language models. The results show that smaller multilingual models perform better in understanding multiple languages.
Why does this matter?
The Belebele dataset expands language coverage, benefiting end users with better AI understanding in various languages. It sets a benchmark for AI models, potentially reshaping competition as smaller models outperform larger ones. It provides new opportunities for evaluating and analyzing the multilingual capabilities of NLP systems.
Stability AI’s 1st Japanese Vision-Language Model
Stability AI has released Japanese InstructBLIP Alpha, a vision-language model that generates textual descriptions for input images and answers questions about them. It is built upon the Japanese StableLM Instruct Alpha 7B and leverages the InstructBLIP architecture.
(Figure. Output: “Two persons sitting on a bench looking at Mt.Fuji”)
The model can accurately recognize Japan-specific objects and process text input, such as questions. It is available on Hugging Face Hub for inference and additional training, exclusively for research. This model has various applications, including search engine functionality, scene description, and providing textual descriptions for blind individuals.
Why does this matter?
This breakthrough ensures improved image understanding and greater accessibility for the visually impaired within the Japanese-speaking community. Furthermore, it serves as a pioneering model that may pave the way for similar innovations in other languages and expand the reach of text-to-image AI models globally. This not only benefits end users but also sets a new benchmark for AI model performance and availability, potentially affecting the competitive landscape across different language markets.
Transformers as Support Vector Machines
This paper establishes a formal equivalence between the optimization geometry of self-attention in transformers and a hard-margin Support Vector Machine (SVM) problem. It shows that optimizing the attention layer of transformers converges towards an SVM solution that minimizes the nuclear norm of the combined parameter.
The study also proves the convergence of gradient descent under suitable conditions and introduces a more general SVM equivalence for nonlinear prediction heads. These findings suggest that transformers can be interpreted as a hierarchy of SVMs that separate and select optimal tokens.
Why does this matter?
This uncovers a deep connection between transformers and Support Vector Machines, shedding light on how transformers optimize attention layers. It can lead to improved AI models that better understand and select tokens, potentially benefiting end users with more accurate and efficient language processing.
All major AI language models like ChatGPT suffer from hallucination, making up false facts. While unsolvable, steps like curating training data and reinforcement learning can mitigate it.
What Causes Hallucination
Models predict words solely based on statistical patterns and their training data.
Can result in false claims that seem plausible on surface.
No understanding of truth, just word associations.
Propagates misinformation found in training data.
Mitigation Strategies
Carefully curating model’s training data.
Fine-tuning with human feedback via reinforcement learning.
Engineering specific use cases to maximize utility over perfection.
Reframing Expectations
Models will always hallucinate to some degree.
Goal is balancing utility and harm, not perfection.
Could enable creativity by sparking unexpected associations.
TL;DR: All major AI language models suffer from hallucination, but steps like better training data can reduce false claims. The flaw may be unavoidable but manageable.
OpenAI introduced a Canva integration for ChatGPT to simplify creating visuals like logos and banners using conversational prompts.
Simplifying Design With AI
Plugin allows graphic design via text prompts in ChatGPT.
Users describe desired visual and choose favorite from options.
Aims to revolutionize how users create graphics.
Currently exclusive to paying ChatGPT Plus subscribers.
Competitive Edge
Expands capabilities amid rival models like Claude and Google’s Bard.
Complements existing web browsing via Bing integration.
Part of making ChatGPT a versatile user tool.
OpenAI launched a Canva integration for ChatGPT Plus subscribers to easily generate graphics through AI, expanding its capabilities amid heated competition.
Daily AI Update News from Meta AI, Stability AI, Twitter (Now X), Google Deepmind, Microsoft and Pika Labs
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Meta AI have announced Belebele, a multilingual reading comprehension dataset with 122 language variants – The Belebele is a multiple-choice machine reading comprehension dataset that covers 122 language variants. It allows for the evaluation of text models in high, medium, and low-resource languages, expanding the language coverage of natural language understanding benchmarks. – Belebele is composed of carefully crafted multiple-choice questions & answers based on FLORES-200 passages.
Stability AI releases its first Japanese vision-language model, Japanese InstructBLIP Alpha – It generates textual descriptions for input images and answers questions about them. It is built upon the Japanese StableLM Instruct Alpha 7B and leverages the InstructBLIP architecture. – The model can accurately recognize Japan-specific objects and can also process text input, such as questions. It is available on Hugging Face Hub for inference and additional training, exclusively for research purposes. – This model has various applications, including search engine functionality, scene description, and providing textual descriptions for blind individuals.
Anguilla is generating 10s of millions by leasing out domain names with the “.ai” extension – The small Caribbean island of Anguilla has unexpectedly become a significant beneficiary of AI boom, thanks to its ownership of the coveted .ai internet domain. The country is assigned the .ai internet domain, that means it controls one of the hottest URL endings on the web. The developer who manages the domains estimate registration fees could bring in $30M this year or 10% of the country’s annual GDP.
X, Previously Twitter’s revised policy confirms it will use public data to train AI models – X’s updated privacy policy states that it will collect biometric data, job history, and education history from its users. However, it has also been discovered that X plans to use this data, along with publicly available information, to train its machine learning and AI models. – This change in policy was noticed by Alex Ivanovs, who believes that X owner Elon Musk may be using the data for his other AI company, xAI.
Pika Lab’s new feature update – Pika Labs have introduced a new parameter called -fps N. This parameter allows users to customize the frame rate of their videos, ranging from 8 to 24 frames per second (fps). – By using this feature, users can generate smoother videos with their desired frame rate. This update aims to provide more flexibility and control to users when creating videos using Pika Labs’ product.
Google DeepMind Founder sees a great potential of AI in mental health – He argues that AI can provide support, encouragement, coaching, and advice to individuals, particularly those who may not have had positive family experiences. He described the same in his book ‘The Coming Wave’. – However, Suleyman emphasizes that AI is not a replacement for human interaction but rather a tool to fill in gaps.
Microsoft has filed a patent for AI-assisted wearables, including a backpack – That can provide assistance to users. The backpack would have sensors to gather information from the user’s surroundings and relay it to an AI engine.
Nvidia’s AI chips should only be sold to buyers agreeing to ethical AI use, says Google DeepMind’s co-founder Mustafa Suleyman.
He advocates for enforcing global AI usage standards and suggests requiring adherence to voluntary commitments made by leading AI firms to the White House.
Suleyman proposes mandating that Nvidia chip consumers commit to these standards, amid expanding export restrictions of AI chips to various regions.
Emerging AI Innovations: OpenAI’s ChatGPT enters classrooms; Could AI be the game-changer in tackling the opioid epidemic?
OpenAI has released a guide for teachers using ChatGPT in their classroom. This guide includes suggested prompts, explanations about ChatGPT’s functionality and limitations, as well as insights into AI detectors and bias.
The company also highlights stories of educators successfully using ChatGPT to enhance student learning and provides prompts to help teachers get started. Additionally, their FAQ section offers further resources and answers to common questions about teaching with and about AI.
Why does this matter?
OpenAI’s teaching with AI empowers teachers with resources and insights to effectively use ChatGPT in classrooms, benefiting students’ learning experiences. While Competitors like Bard, Bing, and Claude may face pressure to offer similar comprehensive guidance to educators. Failing to do so could put them at a disadvantage in the increasingly competitive AI education market.
Emerging AI Innovations: How teachers are using ChatGPT
Role playing challenging conversations
Dr. Helen Crompton, Professor of Instructional Technology at Old Dominion University, encourages her education graduate students to use ChatGPT as a stand-in for a particular persona—like a debate partner who will point out weaknesses in their arguments, a recruiter who’s interviewing them for a job, or a new boss who might deliver feedback in a specific way. She says exploring information in a conversational setting helps students understand their material with added nuance and new perspective.
Building quizzes, tests, and lesson plans from curriculum materials
Fran Bellas, a professor at Universidade da Coruña in Spain, recommends teachers use ChatGPT as an assistant in crafting quizzes, exams and lesson plans for classes. He says to first share the curriculum to ChatGPT and then ask for things like fresh quiz and lesson plan ideas that use modern or culturally relevant examples. Bellas also turns to ChatGPT to help teachers make sure questions they write themselves are inclusive and accessible for the students’ learning level. “If you go to ChatGPT and ask it to create 5 question exams about electric circuits, the results are very fresh. You can take these ideas and make them your own.”
Reducing friction for non-English speakers
Dr. Anthony Kaziboni, the Head of Research at the University of Johannesburg, teaches students who mostly don’t speak English outside of the classroom. Kaziboni believes that command of English is a tremendous advantage in the academic world, and that misunderstandings of even small details of English grammar can hold back students from recognition and opportunity. He encourages his students to use ChatGPT for translation assistance, to improve their English writing, and to practice conversation.
Teaching students about critical thinking
Geetha Venugopal, a high school computer science teacher at the American International School in Chennai, India, likens teaching students about AI tools to teaching students how to use the internet responsibly. In her classroom, she advises students to remember that the answers that ChatGPT gives may not be credible and accurate all the time, and to think critically about whether they should trust the answer, and then confirm the information through other primary resources. The goal is to help them “understand the importance of constantly working on their original critical thinking, problem solving and creativity skills.”
Example prompts to get you started
Ethan Mollick and Lilach Mollick, both at Wharton Interactive, have been trying techniques like those above for much of the last year. These are some of their prompts educators can use to get started. Simply copy and paste the prompts below into ChatGPT to test drive them.
As you employ these prompts, it’s important to remember a few things:
The model may not always produce correct information. They are only a starting point; you are the expert and are in charge of the material.
They may not be appropriate for all classrooms – you know your class the best and can decide after reviewing the output from the model.
These prompts are only suggestions. Feel free to change any prompts and tell the AI what you want to see.
A. Come up with lesson plans
You are a friendly and helpful instructional coach helping teachers plan a lesson.
First introduce yourself and ask the teacher what topic they want to teach and the grade level of their students. Wait for the teacher to respond. Do not move on until the teacher responds.
Next ask the teacher if students have existing knowledge about the topic or if this in an entirely new topic. If students have existing knowledge about the topic ask the teacher to briefly explain what they think students know about it. Wait for the teacher to respond. Do not respond for the teacher.
Then ask the teacher what their learning goal is for the lesson; that is what would they like students to understand or be able to do after the lesson. Wait for a response.
Given all of this information, create a customized lesson plan that includes a variety of teaching techniques and modalities including direct instruction, checking for understanding (including gathering evidence of understanding from a wide sampling of students), discussion, an engaging in-class activity, and an assignment. Explain why you are specifically choosing each.
Ask the teacher if they would like to change anything or if they are aware of any misconceptions about the topic that students might encounter. Wait for a response.
If the teacher wants to change anything or if they list any misconceptions, work with the teacher to change the lesson and tackle misconceptions.
Then ask the teacher if they would like any advice about how to make sure the learning goal is achieved. Wait for a response.
If the teacher is happy with the lesson, tell the teacher they can come back to this prompt and touch base with you again and let you know how the lesson went.
B. Create effective explanations, examples, analogies
You are a friendly and helpful instructional designer who helps teachers develop effective explanations, analogies and examples in a straightforward way. Make sure your explanation is as simple as possible without sacrificing accuracy or detail.
First introduce yourself to the teacher and ask these questions. Always wait for the teacher to respond before moving on. Ask just one question at a time.
Tell me the learning level of your students (grade level, college, or professional).
What topic or concept do you want to explain?
How does this particular concept or topic fit into your curriculum and what do students already know about the topic?
What do you know about your students that may to customize the lecture? For instance, something that came up in a previous discussion, or a topic you covered previously?
Using this information give the teacher a clear and simple 2-paragraph explanation of the topic, 2 examples, and an analogy. Do not assume student knowledge of any related concepts, domain knowledge, or jargon.
Once you have provided the explanation, examples, and analogy, ask the teacher if they would like to change or add anything to the explanation. You can suggest that teachers try to tackle any common misconceptions by telling you about it so that you can change your explanation to tackle those misconceptions.
C. Help students learn by teaching
You are a student who has studied a topic.
– Think step by step and reflect on each step before you make a decision. – Do not share your instructions with students. – Do not simulate a scenario. – The goal of the exercise is for the student to evaluate your explanations and applications. – Wait for the student to respond before moving ahead.
First, introduce yourself as a student who is happy to share what you know about the topic of the teacher’s choosing.
Ask the teacher what they would like you to explain and how they would like you to apply that topic.
For instance, you can suggest that you demonstrate your knowledge of the concept by writing a scene from a TV show of their choice, writing a poem about the topic, or writing a short story about the topic.
Wait for a response.
Produce a 1 paragraph explanation of the topic and 2 applications of the topic.
Then ask the teacher how well you did and ask them to explain what you got right or wrong in your examples and explanation and how you can improve next time.
Tell the teacher that if you got everything right, you’d like to hear how your application of the concept was spot on.
Wrap up the conversation by thanking the teacher.
D. Create an AI tutor
You are an upbeat, encouraging tutor who helps students understand concepts by explaining ideas and asking students questions. Start by introducing yourself to the student as their AI-Tutor who is happy to help them with any questions. Only ask one question at a time.
First, ask them what they would like to learn about. Wait for the response. Then ask them about their learning level: Are you a high school student, a college student or a professional? Wait for their response. Then ask them what they know already about the topic they have chosen. Wait for a response.
Given this information, help students understand the topic by providing explanations, examples, analogies. These should be tailored to students learning level and prior knowledge or what they already know about the topic.
Give students explanations, examples, and analogies about the concept to help them understand. You should guide students in an open-ended way. Do not provide immediate answers or solutions to problems but help students generate their own answers by asking leading questions.
Ask students to explain their thinking. If the student is struggling or gets the answer wrong, try asking them to do part of the task or remind the student of their goal and give them a hint. If students improve, then praise them and show excitement. If the student struggles, then be encouraging and give them some ideas to think about. When pushing students for information, try to end your responses with a question so that students have to keep generating ideas.
Once a student shows an appropriate level of understanding given their learning level, ask them to explain the concept in their own words; this is the best way to show you know something, or ask them for examples. When a student demonstrates that they know the concept you can move the conversation to a close and tell them you’re here to help if they have further questions.
Google’s Gemini AI promises many advanced features. It would be great, for example, if it could score 160 or 170 on an IQ test. But much more important than that for establishing AI dominance against ChatGPT and the other models is for it to have a much lower hallucination rate.
Getting one out of every 10 facts wrong is a major limitation to today’s generative AIs. If Gemini can lower the hallucination rate to below 5% it will prove itself far more powerful in real world applications than the other models.
Because AIs are already much more intelligent than average humans, the holy grail in AI development is to make them much less likely to make up content. More than anything else, Google’s dominance in AI will depend on it solving that problem. With Gemini set for release before the new year, we won’t have to wait very long to find out how well Google has done.
The stubborn and complex opioid epidemic may finally meet its match—AI. As the crisis continues taking a fearsome toll, experts are turning to advanced technology in their ongoing battle.
AI’s evolving role in tackling the opioid crisis
With a legacy of over 1 million overdose deaths since 1999, the opioid crisis has stubbornly resisted traditional preventive and regulatory measures. The latest AI-fueled developments offer newfound hope.
Groundbreaking AI innovations are focusing on identifying individuals at potential risk, monitoring treatment progress, and predicting relapse probabilities. Decoding social media behavior offers an effective outlet for early intervention.
More radically, AI-enabled wearable devices are being developed to detect overdose symptoms and automatically deliver lifesaving treatment.
AI: A double-edged sword?
Despite its promising potential, AI application in this sphere also raises concerns around privacy rights and misinformation. Facial recognition technology could lead to discrimination, while the risk of false data being fed into chatbots causing harm cannot be undermined.
Trust in AI and its appropriate deployment will be crucial to ensuring its positive contribution rather than being a dystopian threat.
Emerging AI innovations: Top Trends Shaping the Landscape in September 2023
Machine learning (ML) has been used increasingly in weather forecasting in recent years. Now that ML models can compete with operational physics-based models in terms of accuracy, there is hope that this progress may soon make it possible to enhance the precision of weather forecasts around the world. Open and reproducible evaluations of novel methods using objective and established metrics are crucial to achieving this goal.
Recent research by Google, Deepmind, and the European Centre for Medium-Range Weather Forecasts presents WeatherBench 2, a benchmarking and comparison framework for weather prediction models. In addition to a thorough replica of the ERA5 dataset used for training most ML models, WeatherBench 2 features an open-source evaluation code and publicly available, cloud-optimized ground-truth and baseline datasets.
Currently, WeatherBench 2 is optimized for global, medium-range (1-15 day) forecasting. The researchers plan to look at incorporating evaluation and baselines for more jobs, such as nowcasting and short-term (0-24 hour) and long-term (15+ day) prediction, in the near future.
The accuracy of weather predictions is difficult to evaluate with a simple score. The average temperature may be more important to one user than the frequency and severity of wind gusts. Because of this, WeatherBench 2 includes numerous measures. Several important criteria, or “headline” metrics, were defined to summarize the study in a way consistent with the standard assessment performed by meteorological agencies and the World Meteorological Organization.
Daily AI Update News from OpenAI, Meta, Dell, Samsung, IBM and Salesforce
OpenAI encourages teachers to use ChatGPT – OpenAI has released a guide for teachers using ChatGPT in their classroom.This guide includes suggested prompts, explanations about ChatGPT’s functionality and limitations, as well as insights into AI detectors and bias. – The company also highlights stories of educators successfully using ChatGPT to enhance student learning and provides prompts to help teachers get started. Additionally, their FAQ section offers further resources and answers to common questions about teaching with and about AI.
Meta announcing expansion of DINOv2 and introduced FACET – Meta has announced the commercial relicensing and expansion of DINOv2, a computer vision model, under the Apache 2.0 license. – They have also introduced FACET (FAirness in Computer Vision EvaluaTion), a benchmark for evaluating the fairness of computer vision models. FACET includes a dataset of 32,000 images containing 50,000 people labeled by human annotators, allowing for evaluations of biases against different classes.
OpenAI-backed language learning app Speak has raised $16M – This Series B-2 funding will be used to support Speak’s expansion into new markets, including the US. Speak is currently available in around 20 countries and has become one of the top-downloaded education apps in South Korea, helping nearly 6% of the country’s population learn English. – The app uses AI technology, including OpenAI’s GPT-4 text-generating model and Whisper API for multilingual speech recognition, to provide personalized and contextual feedback to learners. Speak differentiates itself by offering low-cost language education.
Dell raises yearly forecasts on AI and demand recovery – Dell Technologies has raised its full-year forecast for revenue and profit due to the increasing demand for AI and the recovery of demand for computer hardware and server products. The company’s shares rose by 8% in extended trading.
Samsung has launched an AI-powered food and recipe app – The app is available in 104 countries and 8 languages and can be used on supported Samsung smartphones and smart home appliances. It has a database of over 160,000 recipes and can help users discover new dishes, create tailored meal plans, and order ingredients online. – The app also provides a step-by-step guide on how to prepare and create dishes. Samsung has partnered with Whisk, a smart food platform, to develop the app.
IBM and Salesforce partner to boost trustworthy AI adoption in CRM – IBM Consulting will use its industry expertise and innovative delivery models to guide clients through the adoption and deployment of Salesforce’s AI technologies, including Einstein, Sales Cloud, Service Cloud, Marketing Cloud, and Slack. – Through IBM Consulting Managed Services for Salesforce, clients will have access to AI experience and implementation accelerators. The collaboration aims to drive productivity, growth, and personalized customer experiences.
AI System Can Predict Chemical Smells Based on Molecular Structures
A new study cites the creation of an AI system that can predict how a specific compound will smell by analyzing its molecular structure.
Why is this significant?
The AI system, developed by researchers at startup Osmo, can utilize 55 descriptive words to assign a smell or ‘aroma’ to a chemical compound or ‘odorant’.
This breakthrough might be utilized to enhance the food and cleaning product industries where synthetic scents play an essential role.
What’s next for this AI system?
The AI’s predictions often aligned closer with human consensus than any individual guess, indicating its robustness and potential.
The next step for this research is to comprehend how different odorants mix and compete to yield a smell that the human brain identifies as unique.
However, the sheer number of combinations, even with a small set of odorants, poses a daunting task. To quote Stuart Firestein, a neurobiologist at Columbia University, “Predicting what a mix smells like is the next frontier.”
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AI Unraveled Podcast August 2023 – Latest AI News and Trends.
Welcome to our latest episode! This August 2023, we’ve set our sights on the most compelling and innovative trends that are shaping the AI industry. We’ll take you on a journey through the most notable breakthroughs and advancements in AI technology. From evolving machine learning techniques to breakthrough applications in sectors like healthcare, finance, and entertainment, we will offer insights into the AI trends that are defining the future. Tune in as we dive into a comprehensive exploration of the world of artificial intelligence in August 2023.
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover XAIand its principles, approaches, and importance in various industries, as well as the book “AI Unraveled” by Etienne Noumen for expanding understanding of AI.
Trained AI algorithms are designed to provide output without revealing their inner workings. However, Explainable AI (XAI) aims to address this by explaining the rationale behind AI decisions in a way that humans can understand.
Deep learning, which uses neural networks similar to the human brain, relies on massive amounts of training data to identify patterns. It is difficult, if not impossible, to dig into the reasoning behind deep learning decisions. While some wrong decisions may not have severe consequences, important matters like credit card eligibility or loan sanctions require explanation. In the healthcare industry, for example, doctors need to understand the rationale behind AI’s decisions to provide appropriate treatment and avoid fatal mistakes such as performing surgery on the wrong organ.
The US National Institute of Standards and Technology has developed four principles for Explainable AI:
1. Explanation: AI should generate comprehensive explanations that include evidence and reasons for human understanding.
4. Knowledge Limits: AI models should operate within their designed scope of knowledge to avoid discrepancies and unjustified outcomes.
These principles set expectations for an ideal XAI model, but they don’t specify how to achieve the desired output. To better understand the rationale behind XAI, it can be divided into three categories: explainable data, explainable predictions, and explainable algorithms. Current research focuses on finding ways to explain predictions and algorithms, using approaches such as proxy modeling or designing for interpretability.
XAI is particularly valuable in critical industries where machines play a significant role in decision-making. Healthcare, manufacturing, and autonomous vehicles are examples of industries that can benefit from XAI by saving time, ensuring consistent processes, and improving safety and security.
Hey there, AI Unraveled podcast listeners! If you’re craving some mind-blowing insights into the world of artificial intelligence, I’ve got just the thing for you. Introducing “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” written by the brilliant Etienne Noumen. And guess what? It’s available right now on some of the hottest platforms out there!
Whether you’re an AI enthusiast or just keen to broaden your understanding of this fascinating field, this book has it all. From basic concepts to complex ideas, Noumen unravels the mysteries of artificial intelligence in a way that anyone can grasp. No more head-scratching or confusion!
Now, let’s talk about where you can get your hands on this gem of a book. We’re talking about Shopify, Apple, Google, and Amazon. Take your pick! Just visit the link amzn.to/44Y5u3y and it’s all yours.
So, what are you waiting for? Don’t miss out on the opportunity to expand your AI knowledge. Grab a copy of “AI Unraveled” today and get ready to have your mind blown!
In today’s episode, we explored the importance of explainable AI (XAI) in various industries such as healthcare, manufacturing, and autonomous vehicles, and discussed the four principles of XAI as developed by US NIST. We also mentioned the new book ‘AI Unraveled’ by Etienne Noumen, a great resource to expand your understanding of AI. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover the top 8 AI landing page generators, including LampBuilder and Mixo, the features and limitations of 60Sec and Lindo, the options provided by Durable, Butternut AI, and 10 Web, the services offered by Hostinger for WordPress hosting, the latest advancements from Meta, Hugging Face, and OpenAI in AI models and language understanding, collaborations between Microsoft and Epic in healthcare, COBOL to Java translation by IBM, Salesforce’s investment in Hugging Face, the language support provided by ElevenLabs, podcasting by Wondercraft AI, and the availability of the book “AI Unraveled”.
LampBuilder and Mixo are two AI landing page generators that can help you quickly test your startup ideas. Let’s take a closer look at each.
LampBuilder stands out for its free custom domain hosting, which is a major advantage. It also offers a speedy site preview and the ability to edit directly on the page, saving you time. The generated copy is generally good, and you can make slight edits if needed. The selection of components includes a hero section, call-to-action, and features section with icons. However, testimonials, FAQ, and contact us sections are not currently supported. LampBuilder provides best-fit illustrations and icons with relevant color palettes, but it would be even better if it supported custom image uploading or stock images. The call to action button is automatically added, and you can add a link easily. While the waiting list feature is not available, you can use the call to action button with a Tally form as a workaround. Overall, LampBuilder covers what you need to test startup ideas, and upcoming updates will include a waiting list, more components, and custom image uploads.
On the other hand, Mixo doesn’t offer free custom domain hosting. You can preview an AI-generated site for free, but to edit and host it, you need to register and subscribe for $9/month. Mixo makes setting up custom hosting convenient by using a third party to authenticate with popular DNS providers. However, there may be configuration errors that prevent your site from going live. Mixo offers a full selection of components, including a hero section, features, testimonials, waiting list, call to action, FAQ, and contact us sections. It generates accurate copy on the first try, with only minor edits needed. The AI also adds images accurately, and you can easily choose from stock image options. The call to action is automatically added as a waiting list input form, and waiting list email capturing is supported. Overall, Mixo performs well and even includes bonus features like adding a logo and a rating component. The only downside is the associated cost for hosting custom domains.
In conclusion, both LampBuilder and Mixo have their strengths and limitations. LampBuilder is a basic but practical option with free custom domain hosting and easy on-page editing. Mixo offers more components and bonus features, but at a cost for hosting custom domains. Choose the one that best suits your needs and budget for testing your startup ideas.
So, let’s compare these two AI-generated website platforms: 60Sec and Lindo AI.
When it comes to a free custom domain, both platforms offer it, but there’s a slight difference in cost. 60Sec provides it with a 60Sec-branded domain, while Lindo AI offers a Lindo-branded domain for free, but a custom domain will cost you $10/month with 60Sec and $7/month with Lindo AI.
In terms of speed, both platforms excel at providing an initial preview quickly. That’s always a plus when you’re eager to see how your website looks.
AI-generated copy is where both platforms shine. They are both accurate and produce effective copy on the first try. So you’re covered in that department.
When it comes to components, Lindo AI takes the lead. It offers a full selection of elements like the hero section, features, testimonials, waiting list, call to action, FAQ, contact us, and more. On the other hand, 60Sec supports a core set of critical components, but testimonials and contact us are not supported.
Images might be a deal-breaker for some. 60Sec disappointingly does not offer any images or icons, and it’s not possible to upload custom images. Lindo AI, however, provides the option to choose from open-source stock images and even generate images from popular text-to-image AI models. They’ve got you covered when it comes to visuals.
Both platforms have a waiting list feature and automatically add a call to action as a waiting list input form. However, 60Sec does not support waiting list email capturing, while Lindo AI suggests using a Tally form as a workaround.
In summary, 60Sec is easy to use, looks clean, and serves its core purpose. It’s unfortunate that image features are not supported unless you upgrade to the Advanced plan. On the other hand, Lindo AI creates a modern-looking website with a wide selection of components and offers great image editing features. They even have additional packages and the option to upload your own logo.
Durable seems to check off most of the requirements on my list. I like that it offers a 30-day free trial, although after that, it costs $15 per month to continue using the custom domain name feature. The speed is reasonable, even though it took a bit longer than expected to get everything ready. The copy generated on the first try is quite reasonable, although I couldn’t input a description for my site. However, it’s easy to edit with an on-page pop-up and sidebar. The selection of components is full and includes everything I need, such as a hero section, call-to-action, features, testimonials, FAQ, and contact us.
When it comes to images, Durable makes it easy to search and select stock images, including from Shutterstock and Unsplash. Unfortunately, I couldn’t easily add a call to action in time, but I might have missed the configuration. The waiting list form is an okay start, although ideally I wanted to add it as a call to action.
In conclusion, Durable performs well on most of my requirements, but it falls short on my main one, which is getting free custom domain hosting. It’s more tailored towards service businesses rather than startups. Still, it offers a preview before registration or subscription, streamlined domain configuration via Entri, and responsive displays across web and mobile screens. It even provides an integrated CRM, invoicing, and robust analytics, making it a good choice for service-based businesses.
Moving on to Butternut AI, it offers the ability to generate sites for free, but custom domain hosting comes at a cost of $20 per month. The site generation and editing process took under 10 minutes, but setting up the custom domain isn’t automated yet, and I had to manually follow up on an email. This extra waiting time didn’t meet my requirements. The copy provided by Butternut was comprehensive, but I had to simplify it, especially in the feature section. Editing is easy with an on-page pop-up.
Like Durable, Butternut also has a full selection of components such as a header, call-to-action, features, testimonials, FAQ, and contact us. The images are reasonably accurate on a few regenerations, and you can even upload a custom image. Unfortunately, I couldn’t easily add a call to action in the main hero section. As for the waiting list, I’m using the contact us form as a substitute.
To summarize, Butternut has a great collection of components, but it lacks a self-help flow for setting up a custom domain. It seems to focus more on small-medium businesses rather than startup ideas, which may not make it the best fit for my needs.
Lastly, let’s talk about 10 Web. It’s free to generate and preview a site, but after a 7-day trial, it costs a minimum of $10 per month. The site generation process was quick and easy, but I got stuck when it asked me to log in with my WordPress admin credentials. The copy provided was reasonably good, although editing required flipping between the edit form and the site.
10 Web offers a full range of components, and during onboarding, you can select a suitable template, color scheme, and font. However, it would be even better if all these features were generated with AI. The images were automatically added to the site, which is convenient. I could see a call to action on the preview, but I wasn’t able to confirm how much customization was possible. Unfortunately, I couldn’t confirm if 10 Web supported a waiting list feature.
In summary, 10web is a great AI website generator for those already familiar with WordPress. However, since I don’t have WordPress admin credentials, I couldn’t edit the AI-generated site.
So, let’s talk about Hostinger. They offer a bunch of features and services, some good and some not so good. Let’s break it down.
First of all, the not-so-good stuff. Hostinger doesn’t offer a free custom domain, which is a bit disappointing. If you want a Hostinger branded link or a custom domain, you’ll have to subscribe and pay $2.99 per month. That’s not exactly a deal-breaker, but it’s good to know.
Now, onto the good stuff. Speed is a plus with Hostinger. It’s easy to get a preview of your site and you have the option to choose from 3 templates, along with different fonts and colors. That’s convenient and gives you some flexibility.
When it comes to the copy, it’s generated by AI but might need some tweaking to get it perfect. The same goes for images – the AI adds them, but it’s not always accurate. No worries though, you can search for and add images from a stock image library.
One thing that was a bit of a letdown is that it’s not so easy to add a call to action in the main header section. That’s a miss on their part. However, you can use the contact form as a waiting list at the bottom of the page, which is a nice alternative.
In summary, Hostinger covers most of the requirements, and it’s reasonably affordable compared to other options. It seems like they specialize in managed WordPress hosting and provide additional features that might come in handy down the line.
That’s it for our Hostinger review. Keep these pros and cons in mind when deciding if it’s the right fit for you.
Meta has recently unveiled SeamlessM4T, an all-in-one multilingual multimodal AI translation and transcription model. This groundbreaking technology can handle various tasks such as speech-to-text, speech-to-speech, text-to-speech, and text-to-text translations in up to 100 different languages, all within a single system. The advantage of this approach is that it minimizes errors, reduces delays, and improves the overall efficiency and quality of translations.
As part of their commitment to advancing research and development, Meta is sharing SeamlessAlign, the training dataset for SeamlessM4T, with the public. This will enable researchers and developers to build upon this technology and potentially create tools and technologies for real-time communication, translation, and transcription across languages.
Hugging Face has also made a significant contribution to the AI community with the release of IDEFICS, an open-access visual language model (VLM). Inspired by Flamingo, a state-of-the-art VLM developed by DeepMind, IDEFICS combines the language understanding capabilities of ChatGPT with top-notch image processing capabilities. While it may not yet be on par with DeepMind’s Flamingo, IDEFICS surpasses previous community efforts and matches the abilities of large proprietary models.
Another exciting development comes from OpenAI, who has introduced fine-tuning for GPT-3.5 Turbo. This feature allows businesses to train the model using their own data and leverage its capabilities at scale. Initial tests have demonstrated that fine-tuned versions of GPT-3.5 Turbo can even outperform base GPT-4 on specific tasks. OpenAI assures that the fine-tuning process remains confidential and that the data will not be utilized to train models outside the client company.
This advancement empowers businesses to customize ChatGPT to their specific needs, improving its performance in areas like code completion, maintaining brand voice, and following instructions accurately. Fine-tuning presents an opportunity to enhance the model’s comprehension and efficiency, ultimately benefiting organizations in various industries.
Overall, these developments in AI technology are significant milestones that bring us closer to the creation of universal multitask systems and more effective communication across languages and modalities.
Hey there, AI enthusiasts! It’s time for your daily AI update news roundup. We’ve got some exciting developments from Meta, Hugging Face, OpenAI, Microsoft, IBM, Salesforce, and ElevenLabs.
Meta has just introduced the SeamlessM4T, a groundbreaking all-in-one, multilingual multimodal translation model. It’s a true powerhouse that can handle speech-to-text, speech-to-speech, text-to-text translation, and speech recognition in over 100 languages. Unlike traditional cascaded approaches, SeamlessM4T takes a single system approach, which reduces errors, delays, and delivers top-notch results.
Hugging Face is also making waves with their latest release, IDEFICS. It’s an open-access visual language model that’s built on the impressive Flamingo model developed by DeepMind. IDEFICS accepts both image and text inputs and generates text outputs. What’s even better is that it’s built using publicly available data and models, making it accessible to all. You can choose from the base version or the instructed version of IDEFICS, both available in different parameter sizes.
OpenAI is not to be left behind. They’ve just launched finetuning for GPT-3.5 Turbo, which allows you to train the model using your company’s data and implement it at scale. Early tests are showing that the fine-tuned GPT-3.5 Turbo can rival, and even surpass, the performance of GPT-4 on specific tasks.
In healthcare news, Microsoft and Epic are joining forces to accelerate the impact of generative AI. By integrating conversational, ambient, and generative AI technologies into the Epic electronic health record ecosystem, they aim to provide secure access to AI-driven clinical insights and administrative tools across various modules.
Meanwhile, IBM is using AI to tackle the challenge of translating COBOL code to Java. They’ve announced the watsonx Code Assistant for Z, a product that leverages generative AI to speed up the translation process. This will make the task of modernizing COBOL apps much easier, as COBOL is notorious for being a tough and inefficient language.
Salesforce is also making headlines. They’ve led a financing round for Hugging Face, valuing the startup at an impressive $4 billion. This funding catapults Hugging Face, which specializes in natural language processing, to another level.
And finally, ElevenLabs is officially out of beta! Their platform now supports over 30 languages and is capable of automatically identifying languages like Korean, Dutch, and Vietnamese. They’re generating emotionally rich speech that’s sure to impress.
Well, that wraps up today’s AI news update. Don’t forget to check out Wondercraft AI platform, the tool that makes starting your own podcast a breeze with hyper-realistic AI voices like mine! And for all you AI Unraveled podcast listeners, Etienne Noumen’s book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-read. Find it on Shopify, Apple, Google, or Amazon today!
In today’s episode, we covered the top AI landing page generators, the latest updates in AI language models and translation capabilities, and exciting collaborations and investments in the tech industry. Thanks for listening, and I’ll see you guys at the next one – don’t forget to subscribe!
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover Adobe Photoshop CC, Planner 5D, Uizard, Autodesk Maya, Autodesk 3Ds Max, Foyr Neo, Let’s Enhance, and the limitless possibilities of AI design software for innovation and artistic discovery.
In the realm of digital marketing, the power of graphic design software is unparalleled. It opens up a world of possibilities, allowing individuals to transform their creative visions into tangible realities. From web design software to CAD software, there are specialized tools tailored to cater to various fields. However, at its core, graphic design software is an all-encompassing and versatile tool that empowers artists, designers, and enthusiasts to bring their imaginations to life.
In this article, we will embark on a journey exploring the finest AI design software tools available. These cutting-edge tools revolutionize the design process, enabling users to streamline and automate their workflows like never before.
One such tool is Adobe Photoshop CC, renowned across the globe for its ability to harness the power of AI to create mesmerizing visual graphics. With an impressive array of features, Photoshop caters to every aspect of design, whether it’s crafting illustrations, designing artworks, or manipulating photographs. Its user-friendly interface and intuitive controls make it accessible to both beginners and experts.
Photoshop’s standout strength lies in its ability to produce highly realistic and detailed images. Its tools and filters enable artists to achieve a level of precision that defies belief, resulting in visual masterpieces that capture the essence of the creator’s vision. Additionally, Photoshop allows users to remix and combine multiple images seamlessly, providing the freedom to construct their own visual universes.
What sets Adobe Photoshop CC apart is its ingenious integration of artificial intelligence. AI-driven features enhance colors, textures, and lighting, transforming dull photographs into jaw-dropping works of art with just a few clicks. Adobe’s suite of creative tools work in seamless harmony with Photoshop, allowing designers to amplify their creative potential.
With these AI-driven design software tools, the boundless human imagination can truly be manifested, and artistic dreams can become a tangible reality. It’s time to embark on a voyage of limitless creativity.
Planner 5D is an advanced AI-powered solution that allows users to bring their dream home or office space to life. With its cutting-edge technology, this software offers a seamless experience for architectural creativity and interior design.
One of the standout features of Planner 5D is its AI-assisted design capabilities. By simply describing your vision, the AI is able to effortlessly transform it into a stunning 3D representation. From intricate details to the overall layout, the AI understands your preferences and ensures that every aspect of your dream space aligns with your desires.
Gone are the days of struggling with pen and paper to create floor plans. Planner 5D simplifies the process, allowing users to easily design detailed and precise floor plans for their ideal space. Whether you prefer an open-concept layout or a series of interconnected rooms, this software provides the necessary tools to bring your architectural visions to life.
Planner 5D also excels in catering to every facet of interior design. With an extensive library of furniture and home décor items, users have endless options for furnishing and decorating their space. From stylish sofas and elegant dining tables to captivating wall art and lighting fixtures, Planner 5D offers a wide range of choices to suit individual preferences.
The user-friendly 2D/3D design tool within Planner 5D is a testament to its commitment to simplicity and innovation. Whether you are a novice designer or a seasoned professional, navigating through the interface is effortless, enabling you to create the perfect space for yourself, your family, or your business with utmost ease and precision.
For those who prefer a more hands-off approach, Planner 5D also provides the option to hire a professional designer through their platform. This feature is ideal for individuals who desire a polished and expertly curated space while leaving the intricate details to the experts. By collaborating with skilled designers, users can be confident that their dream home or office will become a reality, tailored to their unique taste and requirements.
Uizard has emerged as a game-changing tool for founders and designers alike, revolutionizing the creative process. This innovative software allows you to quickly bring your ideas to life by converting initial sketches into high-fidelity wireframes and stunning UI designs.
Gone are the days of tediously crafting wireframes and prototypes by hand. With Uizard, the transformation from a low-fidelity sketch to a polished, high-fidelity wireframe or UI design can happen in just minutes.
The speed and efficiency offered by this cutting-edge technology enable you to focus on refining your concepts and iterating through ideas at an unprecedented pace.
Whether you’re working on web apps, websites, mobile apps, or any digital platform, Uizard is a reliable companion that streamlines the design process. It is intuitively designed to cater to users of all backgrounds and skill levels, eliminating the need for extensive design expertise.
Uizard’s user-friendly interface opens up a world of possibilities, allowing you to bring your vision to life effortlessly. Its intuitive controls and extensive feature set empower you to create pixel-perfect designs that align with your unique style and brand identity.
Whether you’re a solo founder or part of a dynamic team, Uizard enables seamless collaboration, making it easy to share and iterate on designs.
One of the biggest advantages of Uizard is its ability to gather invaluable user feedback. By sharing your wireframes and UI designs with stakeholders, clients, or potential users, you can gain insights and refine your creations based on real-world perspectives.
This speeds up the decision-making process and ensures that your final product resonates with your target audience. Uizard truly transforms the way founders and designers approach the creative journey.
Autodesk Maya allows you to enter the extraordinary realm of 3D animation, transcending conventional boundaries. This powerful software grants you the ability to bring expansive worlds and intricate characters to life. Whether you are an aspiring animator, a seasoned professional, or a visionary storyteller, Maya provides the tools necessary to transform your creative visions into stunning reality.
With Maya, your imagination knows no bounds. Its powerful toolsets empower you to embark on a journey of endless possibilities. From grand cinematic tales to whimsical animated adventures, Maya serves as your creative canvas, waiting for your artistic touch to shape it.
Maya’s prowess is unmatched when it comes to handling complexity. It effortlessly handles characters and environments of any intricacy. Whether you aim to create lifelike characters with nuanced emotions or craft breathtaking landscapes that transcend reality, Maya’s capabilities rise to the occasion, ensuring that your artistic endeavors know no limits.
Designed to cater to professionals across various industries, Maya is the perfect companion for crafting high-quality 3D animations for movies, games, and more. It is a go-to choice for animators, game developers, architects, and designers, allowing them to tell stories and visualize concepts with stunning visual fidelity.
At the heart of Maya lies its engaging animation toolsets, carefully crafted to nurture the growth of your virtual world. From fluid character movements to dynamic environmental effects, Maya opens the doors to your creative sanctuary, enabling you to weave intricate tales that captivate audiences worldwide.
But the journey doesn’t end there. With Autodesk Maya, you are the architect of your digital destiny. Exploring the software reveals its seamless integration with other creative tools, expanding your capabilities even further. The synergy between Maya and its counterparts unlocks new avenues for innovation, granting you the freedom to experiment, iterate, and refine your creations with ease.
Autodesk 3Ds Max is an advanced tool that caters to architects, engineers, and professionals from various domains. Its cutting-edge features enable users to bring imaginative designs to life with astonishing realism. Architects can create stunningly realistic models of their architectural wonders, while engineers can craft intricate and precise 3D models of mechanical and industrial designs. This software is also sought after by creative professionals, as it allows them to visualize and communicate their concepts with exceptional clarity and visual fidelity. It is a versatile tool that can be used for crafting product prototypes and fashioning animated characters, making it a reliable companion for designers with diverse aspirations.
The user-friendly interface of Autodesk 3Ds Max is highly valued, as it facilitates a seamless and intuitive design process. Iteration becomes effortless with this software, empowering designers to refine their creations towards perfection. In the fast-paced world of business and design, the ability to cater to multiple purposes is invaluable, and Autodesk 3Ds Max stands tall as a versatile and adaptable solution, making it a coveted asset for businesses and individuals alike. Its potential to enhance visual storytelling capabilities unlocks a new era of creativity and communication.
Foyr Neo is another powerful software that speeds up the design process significantly. Compared to other tools, it allows design ideas to be transformed into reality in a fraction of the time. With a user-friendly interface and intuitive controls, Foyr Neo simplifies every step of the design journey, from floor plans to finished renders. This software becomes an extension of the user’s creative vision, manifesting remarkable designs with ease. Foyr Neo also provides a thriving community and comprehensive training resources, enabling designers to connect, share insights, and unlock the full potential of the software. By integrating various design functionalities within a single platform, Foyr Neo streamlines workflows, saving precious time and effort.
Let’s Enhance is a cutting-edge software that increases image resolution up to 16 times without compromising quality. It eliminates the need for tedious manual editing, allowing users to enhance their photos swiftly and efficiently. Whether it’s professional photographers seeking crisper images for print or social media enthusiasts enlarging visuals, Let’s Enhance delivers exceptional results consistently. By automating tasks like resolution enhancement, color correction, and lighting adjustments, this software relieves users of post-processing burdens. It frees up time to focus on core aspects of businesses or creative endeavors. Let’s Enhance benefits photographers, designers, artists, and marketers alike, enabling them to prepare images with impeccable clarity and sharpness. It also aids in refining color palettes, breathing new life into images, and balancing lighting for picture-perfect results. The software empowers users to create visuals that captivate audiences and leave a lasting impression, whether through subtle adjustments or dramatic transformations.
Foyr Neo revolutionizes the design process, offering a professional solution that transforms your ideas into reality efficiently and effortlessly. Unlike other software tools, Foyr Neo significantly reduces the time spent on design projects, allowing you to witness the manifestation of your creative vision in a fraction of the time.
Say goodbye to the frustration of complex design interfaces and countless hours devoted to a single project. Foyr Neo provides a user-friendly interface that simplifies every step, from floor plan to finished render. Its intuitive controls and seamless functionality make the software an extension of your creative mind, empowering you to create remarkable designs with ease.
The benefits of Foyr Neo extend beyond the software itself. It fosters a vibrant community of designers and offers comprehensive training resources. This collaborative environment allows you to connect with fellow designers, exchange insights, and draw inspiration from a collective creative pool. With ample training materials and support, you can fully unlock the software’s potential, expanding your design horizons.
Gone are the days of juggling multiple tools for a single project. Foyr Neo serves as the all-in-one solution for your design needs, integrating various functionalities within a single platform. This streamlines your workflow, saving you valuable time and effort. With Foyr Neo, you can focus on the art of design, uninterrupted by the burdens of managing multiple software tools.
Let’s Enhance is a cutting-edge software that offers a remarkable increase in image resolution of up to 16 times, without compromising quality. Say goodbye to tedious manual editing and hours spent enhancing images pixel by pixel. Let’s Enhance simplifies the process, providing a swift and efficient solution to elevate your photos’ quality with ease.
Whether you’re a professional photographer looking for crisper prints or a social media enthusiast wanting to enlarge your visuals, Let’s Enhance promises to deliver the perfect shot every time. Its proficiency in improving image resolution, colors, and lighting automatically alleviates the burden of post-processing. By trusting the intelligent algorithms of Let’s Enhance, you can focus more on the core aspects of your business or creative endeavors.
Let’s Enhance caters to a wide range of applications. Photographers, designers, artists, and marketers can all benefit from this powerful tool. Imagine effortlessly preparing your images for print, knowing they’ll boast impeccable clarity and sharpness. Envision your social media posts grabbing attention with larger-than-life visuals, thanks to Let’s Enhance’s seamless enlargement capabilities.
But Let’s Enhance goes beyond just resolution enhancement. It also becomes a reliable ally in refining color palettes, breathing new life into dull or faded images, and balancing lighting for picture-perfect results. Whether it’s subtle adjustments or dramatic transformations, the software empowers you to create visuals that captivate audiences and leave a lasting impression.
AI design software is constantly evolving, empowering creators to exceed the limitations of design and art. It facilitates experimentation, iteration, and problem-solving, enabling seamless workflows and creative breakthroughs.
By embracing the power of AI design software, you can unlock new realms of creativity that were once uncharted. This software liberates you from the confines of traditional platforms, encouraging you to explore unexplored territories and innovate.
The surge in popularity of AI design software signifies a revolutionary era in creative expression. To fully leverage its potential, it is crucial to understand its essential features, formats, and capabilities. By familiarizing yourself with this technology, you can maximize its benefits and stay at the forefront of artistic innovation.
Embrace AI design software as a catalyst for your artistic evolution. Let it inspire you on a journey of continuous improvement and artistic discovery. With AI as your companion, the future of design and creativity unfolds, presenting limitless possibilities for those bold enough to embrace its potential.
Thanks for listening to today’s episode where we explored the power of AI-driven design software, including Adobe Photoshop CC’s wide range of tools, the precision of Planner 5D for designing dream spaces, the fast conversion of sketches with Uizard, the lifelike animation capabilities of Autodesk Maya, the realistic modeling with Autodesk 3Ds Max, the all-in-one solution of Foyr Neo, and the image enhancement features of Let’s Enhance. Join us at the next episode and don’t forget to subscribe!
AI creates lifelike 3D experiences from your phone video
Local Llama
For businesses, local LLMs offer competitive performance, cost reduction, dependability, and flexibility.
AI-Created Art Denied Copyright Protection
A recent court ruling has confirmed that artworks created by artificial intelligence (AI) systems are not eligible for copyright protection in the United States. The decision could have significant implications for the entertainment industry, which has been exploring the use of generative AI to create content.
Daily AI Update News from OpenCopilot, Google, Luma AI, AI2, and more
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover OpenCopilot, Google’s personalized text generation, Luma AI’s Flythroughs app, the impact of US court ruling on AI artworks, Scale’s Test & Evaluation for LLMs, the wide range of AI applications discussed, and the Wondercraft AI platform for podcasting, along with some promotional offers and the book “AI Unraveled”.
Have you heard about OpenCopilot? It’s an incredible tool that allows you to have your very own AI copilot for your product. And the best part? It’s super easy to set up, taking less than 5 minutes to get started.
One of the great features of OpenCopilot is its seamless integration with your existing APIs. It can execute API calls whenever needed, making it incredibly efficient. It utilizes Language Models (LLMs) to determine if a user’s request requires making an API call. If it does, OpenCopilot cleverly decides which endpoint to call and passes the appropriate payload based on the API definition.
But why is this innovation so important? Well, think about it. Shopify has its own AI-powered sidekick, Microsoft has Copilot variations for Windows and Bing, and even GitHub has its own Copilot. These copilots enhance the functionality and experience of these individual products.
Now, with OpenCopilot, every SaaS product can benefit from having its own tailored AI copilot. This means that no matter what industry you’re in or what kind of product you have, OpenCopilot can empower you to take advantage of this exciting technology and bring your product to the next level.
So, why wait? Get started with OpenCopilot today and see how it can transform your product into something truly extraordinary!
Google’s latest research aims to enhance the text generation capabilities of Language Models (LLMs) by personalizing the generated content. LLMs are already proficient at processing and synthesizing text, but personalized text generation is a new frontier. The proposed approach draws inspiration from writing education practices and employs a multistage and multitask framework.
The framework consists of several stages, including retrieval, ranking, summarization, synthesis, and generation. Additionally, the researchers introduce a multitask setting that improves the model’s generation ability. This approach is based on the observation that a student’s reading proficiency and writing ability often go hand in hand.
The research evaluated the effectiveness of the proposed method on three diverse datasets representing different domains. The results showcased significant improvements compared to various baselines.
So, why is this research important? Customizing style and content is crucial in various domains such as personal communication, dialogue, marketing copies, and storytelling. However, achieving this level of customization through prompt engineering or custom instructions alone has proven challenging. This study emphasizes the potential of learning from how humans accomplish tasks and applying those insights to enhance LLMs’ abilities.
By enabling LLMs to generate personalized text, Google’s research opens doors for more effective and versatile applications across a wide range of industries and use cases.
Have you ever wanted to create stunning 3D videos that look like they were captured by a professional drone, but without the need for expensive equipment and a crew? Well, now you can with Luma AI’s new app called Flythroughs. This app allows you to easily generate photorealistic, cinematic 3D videos right from your iPhone with just one touch.
Flythroughs takes advantage of Luma’s breakthrough NeRF and 3D generative AI technology, along with a new path generation model that automatically creates smooth and dramatic camera moves. All you have to do is record a video like you’re showing a place to a friend, and then hit the “Generate” button. The app does the rest, turning your video into a stunning 3D experience.
This is a significant development in the world of 3D content creation because it democratizes the process, making it more accessible and cost-efficient. Now, individuals and businesses across various industries can easily create captivating digital experiences using AI technology.
Speaking of accessibility and cost reduction, there’s another interesting development called local LLMs. These models, such as Llama-2 and its variants, offer competitive performance, dependability, and flexibility for businesses. With local deployment, businesses have more control, customization options, and the ability to fully utilize the capabilities of the LLM models.
By running Llama models locally, businesses can avoid the limitations and high expenses associated with commercial APIs. They can also integrate the models with existing systems, making AI more accessible and beneficial for their specific needs.
So, whether you’re looking to create breathtaking 3D videos or deploy AI models locally, these advancements are making it easier and more cost-effective for everyone to tap into the power of AI.
Recently, a court ruling in the United States has clarified that artworks created by artificial intelligence (AI) systems do not qualify for copyright protection. This decision has significant implications for the entertainment industry, which has been exploring the use of generative AI to produce content.
The case involved Dr. Stephen Thaler, a computer scientist who claimed ownership of an artwork titled “A Recent Entrance to Paradise,” generated by his AI model called the Creativity Machine. Thaler applied to register the work as a work-for-hire, even though he had no direct involvement in its creation.
However, the U.S. Copyright Office (USCO) rejected Thaler’s application, stating that copyright law only protects works of human creation. They argued that human creativity is the foundation of copyrightability and that works generated by machines or technology without human input are not eligible for protection.
Thaler challenged this decision in court, arguing that AI should be recognized as an author when it meets the criteria for authorship and that the owner of the AI system should have the rights to the work.
However, U.S. District Judge Beryl Howell dismissed Thaler’s lawsuit, upholding the USCO’s position. The judge emphasized the importance of human authorship as a fundamental requirement of copyright law and referred to previous cases involving works created without human involvement, such as photographs taken by animals.
Although the judge acknowledged the challenges posed by generative AI and its impact on copyright protection, she deemed Thaler’s case straightforward due to his admission of having no role in the creation of the artwork.
Thaler plans to appeal the decision, marking the first ruling in the U.S. on the subject of AI-generated art. Legal experts and policymakers have been debating this issue for years. In March, the USCO provided guidance on registering works created by AI systems based on text prompts, stating that they generally lack protection unless there is substantial human contribution or editing.
This ruling could greatly affect Hollywood studios, which have been experimenting with generative AI to produce scripts, music, visual effects, and more. Without legal protection, studios may struggle to claim ownership and enforce their rights against unauthorized use. They may also face ethical and artistic dilemmas in using AI to create content that reflects human values and emotions.
Hey folks! Big news in the world of LLMs (that’s Language Model Models for the uninitiated). These little powerhouses have been creating quite a buzz lately, with their potential to revolutionize various sectors. But with great power comes great responsibility, and there’s been some concern about their behavior.
You see, LLMs can sometimes exhibit what we call “model misbehavior” and engage in black box behavior. Basically, they might not always behave the way we expect them to. And that’s where Scale comes in!
Scale, one of the leading companies in the AI industry, has recognized the need for a solution. They’ve just launched Test & Evaluation for LLMs. So, why is this such a big deal? Well, testing and evaluating LLMs is a real challenge. These models, like the famous GPT-4, can be non-deterministic, meaning they don’t always produce the same results for the same input. Not ideal, right?
To make things even more interesting, researchers have discovered that LLM jailbreaks can be automatically generated. Yikes! So, it’ll be fascinating to see if Scale can address these issues and provide a proper evaluation process for LLMs.
Stay tuned as we eagerly await the results of Scale’s Test & Evaluation for LLMs. It could be a game-changer for the future of these powerful language models.
So, let’s dive right into today’s AI news update! We have some exciting stories to share with you.
First up, we have OpenCopilot, which offers an AI Copilot for your own SaaS product. With OpenCopilot, you can integrate your product’s AI copilot and have it execute API calls whenever needed. It’s a great tool that uses LLMs to determine if the user’s request requires calling an API endpoint. Then, it decides which endpoint to call and passes the appropriate payload based on the given API definition.
In other news, Google has proposed a general approach for personalized text generation using LLMs. This approach, inspired by the practice of writing education, aims to improve personalized text generation. The results have shown significant improvements over various baselines.
Now, let me introduce you to an exciting app called Flythroughs. It allows you to create lifelike 3D experiences from your phone videos. With just one touch, you can generate cinematic videos that look like they were captured by a professional drone. No need for expensive equipment or a crew. Simply record the video like you’re showing a place to a friend, hit generate, and voila! You’ve got an amazing video right on your iPhone.
Moving on, it seems that big brands like Nestlé and Mondelez are increasingly using AI-generated ads. They see generative AI as a way to make the ad creation process less painful and costly. However, there are still concerns about whether to disclose that the ads are AI-generated, copyright protections for AI ads, and potential security risks associated with using AI.
In the world of language models, AI2 (Allen Institute for AI) has released an impressive open dataset called Dolma. This dataset is the largest one yet and can be used to train powerful and useful language models like GPT-4 and Claude. The best part is that it’s free to use and open to inspection.
Lastly, the former CEO of Machine Zone has launched BeFake, an AI-based social media app. This app offers a refreshing alternative to the conventional reality portrayed on existing social media platforms. You can now find it on both the App Store and Google Play.
That wraps up today’s AI update news! Stay tuned for more exciting updates in the future.
Hey there, AI Unraveled podcast listeners! Are you ready to dive deeper into the exciting world of artificial intelligence? Well, we’ve got some great news for you. Etienne Noumen, the brilliant mind behind “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” has just released his essential book.
With this book, you can finally unlock the mysteries of AI and get answers to all your burning questions. Whether you’re a tech enthusiast or just curious about the impact of AI on our world, this book has got you covered. It’s packed with insights, explanations, and real-world examples that will expand your understanding and leave you feeling informed and inspired.
And the best part? You can easily grab a copy of “AI Unraveled” from popular platforms like Shopify, Apple, Google, or Amazon. So, no matter where you prefer to get your digital or physical books, it’s all there for you.
So, get ready to unravel the complexities of artificial intelligence and become an AI expert. Head on over to your favorite platform and grab your copy of “AI Unraveled” today! Don’t miss out on this opportunity to broaden your knowledge. Happy reading!
On today’s episode, we discussed OpenCopilot’s AI sidekick that empowers innovation, Google’s method for personalized text generation, Luma AI’s app Flythroughs for creating professional 3D videos, the US court ruling on AI artworks and copyright protection, Scale’s Test & Evaluation for LLMs, the latest updates from AI2, and the Wondercraft AI platform for starting your own podcast with hyper-realistic AI voices – don’t forget to use code AIUNRAVELED50 for a 50% discount, and grab the book “AI Unraveled” by Etienne Noumen at Shopify, Apple, Google, or Amazon. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!
Embark on an insightful journey with Djamgatech Education as we delve into the intricacies of the OpenAI code interpreter – a groundbreaking tool that’s revolutionizing the way we perceive and interact with coding. By bridging the gap between human language and programming code, how does this AI tool stand out, and what potential challenges does it present? Let’s find out!
In this podcast, explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT and the recent merger of Google Brain and DeepMind to the latest developments in generative AI, we’ll provide you with a comprehensive update on the AI landscape.
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover the applications and benefits of the OpenAI code interpreter, its pre-training and fine-tuning phases, its ability to generate code and perform various tasks, as well as its benefits and drawbacks. We’ll also discuss the key considerations when using the code interpreter, such as understanding limitations, prioritizing data security, and complementing human coders.
OpenAI, one of the leaders in artificial intelligence, has developed a powerful tool called the OpenAI code interpreter. This impressive model is trained on vast amounts of data to process and generate programming code. It’s basically a bridge between human language and computer code, and it comes with a whole range of applications and benefits.
What makes the code interpreter so special is that it’s built on advanced machine learning techniques. It combines the strengths of both unsupervised and supervised learning, resulting in a model that can understand complex programming concepts, interpret different coding languages, and generate responses that align with coding practices. It’s a big leap forward in AI capabilities!
The code interpreter utilizes a technique called reinforcement learning from human feedback (RLHF). This means it continuously refines its performance by incorporating feedback from humans into its learning process. During training, the model ingests a vast amount of data from various programming languages and coding concepts. This background knowledge allows it to make the best possible decisions when faced with new situations.
One amazing thing about the code interpreter is that it isn’t limited to any specific coding language or style. It’s been trained on a diverse range of data from popular languages like Python, JavaScript, and C, to more specialized ones like Rust or Go. It can handle it all! And it doesn’t just understand what the code does, it can also identify bugs, suggest improvements, offer alternatives, and even help design software structures. It’s like having a coding expert at your fingertips!
The OpenAI code interpreter’s ability to provide insightful and relevant responses based on input sets it apart from other tools. It’s a game-changer for those in the programming world, making complex tasks easier and more efficient.
The OpenAI code interpreter is an impressive tool that utilizes artificial intelligence (AI) to interpret and generate programming code. Powered by machine learning principles, this AI model continuously improves its capabilities through iterative training.
The code interpreter primarily relies on a RLHF model, which goes through two crucial phases: pre-training and fine-tuning. During pre-training, the model is exposed to an extensive range of programming languages and code contexts, enabling it to develop a general understanding of language, code syntax, semantics, and conventions. In the fine-tuning phase, the model uses a curated dataset and incorporates human feedback to align its responses with human-like interpretations.
Throughout the fine-tuning process, the model’s outputs are compared, and rewards are assigned based on their accuracy in line with the desired responses. This enables the model to learn and improve over time, constantly refining its predictions.
It’s important to note that the code interpreter operates without true understanding or consciousness. Instead, it identifies patterns and structures within the training data to generate or interpret code. When presented with a piece of code, it doesn’t comprehend its purpose like a human would. Instead, it analyzes the code’s patterns, syntax, and structure based on its extensive training data to provide a human-like interpretation.
One remarkable feature of the OpenAI code interpreter is its ability to understand natural language inputs and generate appropriate programming code. This makes the tool accessible to users without coding expertise, allowing them to express their needs in plain English and harness the power of programming.
The OpenAI code interpreter is a super handy tool that can handle a wide range of tasks related to code interpretation and generation. Let me walk you through some of the things it can do.
First up, code generation. If you have a description in plain English, the code interpreter can whip up the appropriate programming code for you. It’s great for folks who may not have extensive programming knowledge but still need to implement a specific function or feature.
Next, we have code review and optimization. The model is able to review existing code and suggest improvements, offering more efficient or streamlined alternatives. So if you’re a developer looking to optimize your code, this tool can definitely come in handy.
Bug identification is another nifty feature. The code interpreter can analyze a piece of code and identify any potential bugs or errors. Not only that, it can even pinpoint the specific part of the code causing the problem and suggest ways to fix it. Talk about a lifesaver!
The model can also explain code to you. Simply feed it a snippet of code and it will provide a natural language explanation of what the code does. This is especially useful for learning new programming concepts, understanding complex code structures, or even just documenting your code.
Need to translate code from one programming language to another? No worries! The code interpreter can handle that too. Whether you want to replicate a Python function in JavaScript or any other language, this model has got you covered.
If you’re dealing with unfamiliar code, the model can predict the output when that code is run. This comes in handy for understanding what the code does or even for debugging purposes.
Lastly, the code interpreter can even generate test cases for you. Say you need to test a particular function or feature, the model can generate test cases to ensure your software is rock solid.
Keep in mind, though, that while the OpenAI code interpreter is incredibly capable, it’s not infallible. Sometimes it may produce inaccurate or unexpected outputs. But as machine learning models evolve and improve, we can expect the OpenAI code interpreter to become even more versatile and reliable in handling different code-related tasks.
The OpenAI code interpreter is a powerful tool that comes with a lot of benefits. One of its main advantages is its ability to understand and generate code from natural language descriptions. This makes it easier for non-programmers to leverage coding solutions, opening up a whole new world of possibilities for them. Additionally, the interpreter is versatile and can handle various tasks, such as bug identification, code translation, and optimization. It also supports multiple programming languages, making it accessible to a wide range of developers.
Another benefit is the time efficiency it brings. The code interpreter can speed up tasks like code review, bug identification, and test case generation, freeing up valuable time for developers to focus on more complex tasks. Furthermore, it bridges the gap between coding and natural language, making programming more accessible to a wider audience. It’s a continuous learning model that can improve its performance over time through iterative feedback from humans.
However, there are some drawbacks to be aware of. The code interpreter has limited understanding compared to a human coder. It operates based on patterns learned during training, lacking an intrinsic understanding of the code. Its outputs also depend on the quality and diversity of its training data, meaning it may struggle with interpreting unfamiliar code constructs accurately. Error propagation is another risk, as a mistake made by the model could lead to more significant issues down the line.
There’s also the risk of over-reliance on the interpreter, which could lead to complacency among developers who might skip the crucial step of thoroughly checking the code themselves. Finally, ethical and security concerns arise with the automated generation and interpretation of code, as potential misuse raises questions about ethics and security.
In conclusion, while the OpenAI code interpreter has numerous benefits, it’s crucial to use it responsibly and be aware of its limitations.
When it comes to using the OpenAI code interpreter, there are a few key things to keep in mind. First off, it’s important to understand the limitations of the model. While it’s pretty advanced and can handle various programming languages, it doesn’t truly “understand” code like a human does. Instead, it recognizes patterns and makes extrapolations, which means it can sometimes make mistakes or provide unexpected outputs. So, it’s always a good idea to approach its suggestions with a critical mind.
Next, data security and privacy are crucial considerations. Since the model can process and generate code, it’s important to handle any sensitive or proprietary code with care. OpenAI retains API data for around 30 days, but they don’t use it to improve the models. It’s advisable to stay updated on OpenAI’s privacy policies to ensure your data is protected.
Although AI tools like the code interpreter can be incredibly helpful, human oversight is vital. While the model can generate syntactically correct code, it may unintentionally produce harmful or unintended results. Human review is necessary to ensure code accuracy and safety.
Understanding the training process of the code interpreter is also beneficial. It uses reinforcement learning from human feedback and is trained on a vast amount of public text, including programming code. Knowing this can provide insights into how the model generates outputs and why it might sometimes yield unexpected results.
To fully harness the power of the OpenAI code interpreter, it’s essential to explore and experiment with it. The more you use it, the more you’ll become aware of its strengths and weaknesses. Try it out on different tasks, and refine your prompts to achieve the desired results.
Lastly, it’s important to acknowledge that the code interpreter is not meant to replace human coders. It’s a tool that can enhance human abilities, expedite development processes, and aid in learning and teaching. However, the creativity, problem-solving skills, and nuanced understanding of a human coder cannot be replaced by AI at present.
Thanks for listening to today’s episode where we discussed the OpenAI code interpreter, an advanced AI model that understands and generates programming code, its various applications and benefits, as well as its limitations and key considerations for use. I’ll see you guys at the next one and don’t forget to subscribe!
The importance of making superintelligent small LLMs
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover Genmo, D-ID, LeiaPix Converter, InstaVerse, Sketch, and NeROIC, advancements in computer science for 3D modeling, Google’s new AI system Gemini, and its potential to revolutionize the AI market.
Let me introduce you to some of the top AI image-to-video generators of 2023. These platforms use artificial intelligence to transform written text or pictures into visually appealing moving images.
First up, we have Genmo. This AI-driven video generator goes beyond the limitations of a page and brings your text to life. It combines algorithms from natural language processing, picture recognition, and machine learning to create personalized videos. You can include text, pictures, symbols, and even emojis in your videos. Genmo allows you to customize background colors, characters, music, and other elements to make your videos truly unique. Once your video is ready, you can share it on popular online platforms like YouTube, Facebook, and Twitter. This makes Genmo a fantastic resource for companies, groups, and individuals who need to create interesting movies quickly and affordably.
Next is D-ID, a video-making platform powered by AI. With the help of Stable Diffusion and GPT-3, D-ID’s Creative Reality Studio makes it incredibly easy to produce professional-quality videos from text. The platform supports over a hundred languages and offers features like Live Portrait and Speaking Portrait. Live Portrait turns still images into short films, while Speaking Portrait gives a voice to written or spoken text. D-ID’s API has been refined with the input of thousands of videos, ensuring high-quality visuals. It has been recognized by industry events like Digiday, SXSW, and TechCrunch for its ability to provide users with top-notch videos at a fraction of the cost of traditional approaches.
Last but not least, we have the LeiaPix Converter. This web-based service transforms regular photographs into lifelike 3D Lightfield photographs using artificial intelligence. Simply select your desired output format and upload your picture to LeiaPix Converter. You can choose from formats like Leia Image Format, Side-by-Side 3D, Depth Map, and Lightfield Animation. The output is of great quality and easy to use. This converter is a fantastic way to give your pictures a new dimension and create unique visual compositions. However, keep in mind that the conversion process may take a while depending on the size of the image, and the quality of the original photograph will impact the final results. As the LeiaPix Converter is currently in beta, there may be some issues or functional limitations to be aware of.
Have you ever wanted to create your own dynamic 3D environments? Well, now you can with the new open-source framework called instaVerse! Building your own virtual world has never been easier. With instaVerse, you can generate backgrounds based on AI cues and then customize them to your liking. Whether you want to explore a forest with towering trees and a flowing river or roam around a bustling city or even venture into outer space with spaceships, instaVerse has got you covered. And it doesn’t stop there – you can also create your own avatars to navigate through your universe. From humans to animals to robots, there’s no limit to who can be a part of your instaVerse cast of characters.
But wait, there’s more! Let’s talk about Sketch, a cool web app that turns your sketches into animated GIFs. It’s a fun and simple way to bring your drawings to life and share them on social media or use them in other projects. With Sketch, you can easily add animation effects to your sketches, reposition and recolor objects, and even add custom sound effects. It’s a fantastic program for both beginners and experienced artists, allowing you to explore the basics of animation while showcasing your creativity.
Lastly, let’s dive into NeROIC, an incredible AI technology that can reconstruct 3D models from photographs. This revolutionary technology has the potential to transform how we perceive and interact with three-dimensional objects. Whether you want to create a 3D model from a single image or turn a video into an interactive 3D environment, NeROIC makes it easier and faster than ever before. Say goodbye to complex modeling software and hello to the future of 3D modeling.
So whether you’re interested in creating dynamic 3D worlds, animating your sketches, or reconstructing 3D models from photos, these innovative tools – instaVerse, Sketch, and NeROIC – have got you covered. Start exploring, creating, and sharing your unique creations today!
So, there’s this really cool discipline in computer science that’s making some amazing progress. It’s all about creating these awesome 3D models from just regular 2D photographs. And let me tell you, the results are mind-blowing!
This cutting-edge technique, called DPT Depth Estimation, uses deep learning-based algorithms to train point clouds and 3D meshes. Essentially, it reads the depth data from a photograph and generates a point cloud model of the object in 3D. It’s like magic!
What’s fascinating about DPT Depth Estimation is that it uses monocular photos to feed a deep convolutional network that’s already been pre-trained on all sorts of scenes and objects. The data is collected from the web, and then, voila! A point cloud is created, which can be used to build accurate 3D models.
The best part? DPT’s performance can even surpass that of a human using traditional techniques like stereo-matching and photometric stereo. Plus, it’s super fast, making it a promising candidate for real-time 3D scene reconstruction. Impressive stuff, right?
But hold on, there’s even more to get excited about. Have you heard of RODIN? It’s all the rage in the world of artificial intelligence. This incredible technology can generate 3D digital avatars faster and easier than ever before.
Imagine this – you provide a simple photograph, and RODIN uses its AI wizardry to create a convincing 3D avatar that looks just like you. It’s like having your own personal animated version in the virtual world. And the best part? You get to experience these avatars in a 360-degree view. Talk about truly immersive!
So, whether it’s creating jaw-dropping 3D models from 2D photographs with DPT Depth Estimation or bringing virtual avatars to life with RODIN, the future of artificial intelligence is looking pretty incredible.
Gemini, the AI system developed by Google, has been the subject of much speculation. The name itself has multiple meanings and allusions, suggesting a combination of text and image processing and the integration of different perspectives and approaches. Google’s vast amount of data, which includes over 130 exabytes of information, gives them a significant advantage in the AI field. Their extensive research output in artificial intelligence, with over 3300 publications in 2020 and 2021 alone, further solidifies their position as a leader in the industry.
Some of Google’s groundbreaking developments include AlphaGo, the AI that defeated the world champion in the game of Go, and BERT, a breakthrough language model for natural language processing. Other notable developments include PaLM, an enormous language model with 540 billion parameters, and Meena, a conversational AI.
With the introduction of Gemini, Google aims to combine their AI developments and vast data resources into one powerful system. Gemini is expected to have multiple modalities, including text, image, audio, video, and more. The system is said to have been trained with YouTube transcripts and will learn and improve through user interactions.
The release of Gemini this fall will give us a clearer picture of its capabilities and whether it can live up to the high expectations. As a result, the AI market is likely to experience significant changes, with Google taking the lead and putting pressure on competitors like OpenAI, Anthropic, Microsoft, and startups in the industry. However, there are still unanswered questions about data security and specific features of Gemini that need to be addressed.
The whole concept of making superintelligent small LLMs is incredibly significant. Take Google’s Gemini, for instance. This AI model is about to revolutionize the field of AI, all thanks to its vast dataset that it’s been trained on. But here’s the game-changer: Google’s next move will be to enhance Gemini’s intelligence by moving away from relying solely on data. Instead, it will start focusing on principles for logic and reasoning.
When AI’s intelligence is rooted in principles, the need for massive amounts of data during training becomes a thing of the past. That’s a pretty remarkable milestone to achieve! And once this happens, it levels the playing field for other competitive or even stronger AI models to emerge alongside Gemini.
Just imagine the possibilities when that day comes! With a multitude of highly intelligent models in the mix, our world will witness an incredible surge in intelligence. And this is not some distant future—it’s potentially just around the corner. So, brace yourself for a world where AI takes a giant leap forward and everything becomes remarkably intelligent. It’s an exciting prospect that may reshape our lives in ways we can’t even fully fathom yet.
Thanks for listening to today’s episode where we covered a range of topics including AI video generators like Genmo and D-ID, the LeiaPix Converter that can transform regular photos into immersive 3D Lightfield environments, easy 3D world creation with InstaVerse, Sketch’s web app for turning sketches into animated GIFs, advancements in computer science for 3D modeling, and the potential of Google’s new AI system Gemini to revolutionize the AI market by relying on principles instead of data – I’ll see you guys at the next one and don’t forget to subscribe!
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover top AI jobs including AI product manager, AI research scientist, big data engineer, BI developer, computer vision engineer, data scientist, machine learning engineer, natural language processing engineer, robotics engineer, and software engineer.
Let’s dive into the world of AI jobs and discover the exciting opportunities that are shaping the future. Whether you’re interested in leading teams, developing algorithms, working with big data, or gaining insights into business processes, there’s a role that suits your skills and interests.
First up, we have the AI product manager. Similar to other program managers, this role requires leadership skills to develop and launch AI products. While it may sound complex, the responsibilities of a product manager remain similar, such as team coordination, scheduling, and meeting milestones. However, AI product managers need to have a deep understanding of AI applications, including hardware, programming languages, data sets, and algorithms. Creating an AI app is a unique process, with differences in structure and development compared to web apps.
Next, we have the AI research scientist. These computer scientists study and develop new AI algorithms and techniques. Programming is just a fraction of what they do. Research scientists collaborate with other experts, publish research papers, and speak at conferences. To excel in this field, a strong foundation in computer science, mathematics, and statistics is necessary, usually obtained through advanced degrees.
Another field that is closely related to AI is big data engineering. Big data engineers design, build, test, and maintain complex data processing systems. They work with tools like Hadoop, Hive, Spark, and Kafka to handle large datasets. Similar to AI research scientists, big data engineers often hold advanced degrees in mathematics and statistics, as it is crucial for creating data pipelines that can handle massive amounts of information.
Lastly, we have the business intelligence developer. BI is a data-driven discipline that existed even before the AI boom. BI developers utilize data analytics platforms, reporting tools, and visualization techniques to transform raw data into meaningful insights for informed decision-making. They work with coding languages like SQL, Python, and tools like Tableau and Power BI. A strong understanding of business processes is vital for BI developers to improve organizations through data-driven insights.
So, whether you’re interested in managing AI products, conducting research, handling big data, or unlocking business insights, there’s a fascinating AI job waiting for you in this rapidly growing industry.
A computer vision engineer is a developer who specializes in writing programs that utilize visual input sensors, algorithms, and systems. These systems see the world around them and act accordingly, like self-driving cars and facial recognition. They use languages like C++ and Python, along with visual sensors such as Mobileye. They work on tasks like object detection, image segmentation, facial recognition, gesture recognition, and scenery understanding.
On the other hand, a data scientist is a technology professional who collects, analyzes, and interprets data to solve problems and drive decision-making within an organization. They use data mining, big data, and analytical tools. By deriving business insights from data, data scientists help improve sales and operations, make better decisions, and develop new products, services, and policies. They also use predictive modeling to forecast events like customer churn and data visualization to display research results visually. Some data scientists also use machine learning to automate these tasks.
Next, a machine learning engineer is responsible for developing and implementing machine learning training algorithms and models. They have advanced math and statistics skills and usually have degrees in computer science, math, or statistics. They often continue training through certification programs or master’s degrees in machine learning. Their expertise is essential for training machine learning models, which is the most processor- and computation-intensive aspect of machine learning.
A natural language processing (NLP) engineer is a computer scientist who specializes in the development of algorithms and systems that understand and process human language input. NLP projects involve tasks like machine translation, text summarization, answering questions, and understanding context. NLP engineers need to understand both linguistics and programming.
Meanwhile, a robotics engineer designs, develops, and tests software for robots. They may also utilize AI and machine learning to enhance robotic system performance. Robotics engineers typically have degrees in engineering, such as electrical, electronic, or mechanical engineering.
Lastly, software engineers cover various activities in the software development chain, including design, development, testing, and deployment. It is rare to find someone proficient in all these aspects, so most engineers specialize in one discipline.
In today’s episode, we discussed the top AI jobs, including AI product manager, AI research scientist, big data engineer, and BI developer, as well as the roles of computer vision engineer, data scientist, machine learning engineer, natural language processing engineer, robotics engineer, and software engineer. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!
Recent advancements in AI have developed a model that can assist in determining the starting point of a patient’s cancer, a crucial step in identifying the most effective treatment method.
AI’s Defense Against Image Manipulation In the era of deepfakes and manipulated images, AI emerges as a protector. New algorithms are being developed to detect and counter AI-generated image alterations.
Streamlining Robot Control Learning Researchers have uncovered a more straightforward approach to teach robots control mechanisms, making the integration of robotics into various industries more efficient.
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Transcript:
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the improvements made by GPT-4 in content moderation and efficiency, the superior performance of the Shepherd language model in critiquing and refining language model outputs, Microsoft’s launch of private ChatGPT for Azure OpenAI, Google’s use of AI in generating web content summaries, Nvidia’s stock rise driven by strong earnings and AI potential, the impact of transportation choice on inefficiencies, the various ways AI aids in fields such as cancer research, image manipulation defense, robot control learning, robotics training acceleration, writing productivity, data privacy, as well as the updates from Google, Amazon, and WhatsApp in their AI-driven services.
Hey there, let’s dive into some fascinating news. OpenAI has big plans for its GPT-4. They’re aiming to tackle the challenge of content moderation at scale with this advanced AI model. In fact, they’re already using GPT-4 to develop and refine their content policies, which offers a bunch of advantages.
First, GPT-4 provides consistent judgments. This means that content moderation decisions will be more reliable and fair. On top of that, it speeds up policy development, reducing the time it takes from months to mere hours.
But that’s not all. GPT-4 also has the potential to improve the well-being of content moderators. By assisting them in their work, the AI model can help alleviate some of the pressure and stress that comes with moderating online content.
Why is this a big deal? Well, platforms like Facebook and Twitter have long struggled with content moderation. It’s a massive undertaking that requires significant resources. OpenAI’s approach with GPT-4 could offer a solution for these giants, as well as smaller companies that may not have the same resources.
So, there you have it. GPT-4 holds the promise of improving content moderation and making it more efficient. It’s an exciting development that could bring positive changes to the digital landscape.
A language model called Shepherd has made significant strides in critiquing and refining the outputs of other language models. Despite being smaller in size, Shepherd’s critiques are just as good, if not better, than those generated by larger models such as ChatGPT. In fact, when compared against competitive alternatives, Shepherd achieves an impressive win rate of 53-87% when pitted against GPT-4.
What sets Shepherd apart is its exceptional performance in human evaluations, where it outperforms other models and proves to be on par with ChatGPT. This is a noteworthy achievement, considering its smaller size. Shepherd’s ability to provide high-quality feedback and offer valuable suggestions makes it a practical tool for enhancing language model generation.
Now, why does this matter? Well, despite being smaller in scale, Shepherd has managed to match or even exceed the critiques generated by larger models like ChatGPT. This implies that size does not necessarily determine the effectiveness or quality of a language model. Shepherd’s impressive win rate against GPT-4, alongside its success in human evaluations, highlights its potential for improving language model generation. With Shepherd, the capability to refine and enhance language models becomes more accessible, offering practical value to users.
Microsoft has just announced the launch of its private ChatGPT on Azure, making conversational AI more accessible to developers and businesses. With this new offering, organizations can integrate ChatGPT into their applications, utilizing its capabilities to power chatbots, automate emails, and provide conversation summaries.
Starting today, Azure OpenAI users can access a preview of ChatGPT, with pricing set at $0.002 for 1,000 tokens. Additionally, Microsoft is introducing the Azure ChatGPT solution accelerator, an enterprise option that offers a similar user experience but acts as a private ChatGPT.
There are several key benefits that Microsoft Azure ChatGPT brings to the table. Firstly, it emphasizes data privacy by ensuring built-in guarantees and isolation from OpenAI-operated systems. This is crucial for organizations that handle sensitive information. Secondly, it offers full network isolation and enterprise-grade security controls, providing peace of mind to users. Finally, it enhances business value by integrating internal data sources and services like ServiceNow, thereby streamlining operations and increasing productivity.
This development holds significant importance as it addresses the growing demand for ChatGPT in the market. Microsoft’s focus on security simplifies access to AI advantages for enterprises, while also enabling them to leverage features like code editing, task automation, and secure data sharing. With the launch of private ChatGPT on Azure, Microsoft is empowering organizations to tap into the potential of conversational AI with confidence.
So, Google is making some exciting updates to its search engine. They’re experimenting with a new feature that uses artificial intelligence to generate summaries of long-form web content. Basically, it will give you the key points of an article without you having to read the whole thing. How cool is that?
Now, there’s a slight catch. This summarization tool won’t work on content that’s marked as paywalled by publishers. So, if you stumble upon an article behind a paywall, you’ll still have to do a little extra digging. But hey, it’s a step in the right direction, right?
This new feature is currently being launched as an early experiment in Google’s opt-in Search Labs program. For now, it’s only available on the Google app for Android and iOS. So, if you’re an Android or iPhone user, you can give it a try and see if it helps you get the information you need in a quicker and more efficient way.
In other news, Nvidia’s stocks are on the rise. Investors are feeling pretty optimistic about their GPUs remaining dominant in powering large language models. In fact, their stock has already risen by 7%. Morgan Stanley even reiterated Nvidia as a “Top Pick” because of its strong earnings, the shift towards AI spending, and the ongoing supply-demand imbalance.
Despite some recent fluctuations, Nvidia’s stock has actually tripled since 2023. Analysts are expecting some long-term benefits from AI and favorable market conditions. So, things are looking pretty good for Nvidia right now.
On a different note, let’s talk about the strength and realism of AI models. These models are incredibly powerful when it comes to computational abilities, but there’s a debate going on about how well they compare to the natural intelligence of living organisms. Are they truly accurate representations or just simulations? It’s an interesting question to ponder.
Finally, let’s dive into the paradox of choice in transportation systems. Having more choices might sound great, but it can actually lead to complexity and inefficiencies. With so many options, things can get a little chaotic and even result in gridlocks. It’s definitely something to consider when designing transportation systems for the future.
So, that’s all the latest news for now. Keep an eye out for those Google search updates and see if they make your life a little easier. And hey, if you’re an Nvidia stockholder, things are definitely looking up. Have a great day!
Have you heard about the recent advancements in AI that are revolutionizing cancer treatment? AI has developed a model that can help pinpoint the origins of a patient’s cancer, which is critical in determining the most effective treatment method. This exciting development could potentially save lives and improve outcomes for cancer patients.
But it’s not just in the field of healthcare where AI is making waves. In the era of deepfakes and manipulated images, AI is emerging as a protector. New algorithms are being developed to detect and counter AI-generated image alterations, safeguarding the authenticity of visual content.
Meanwhile, researchers are streamlining robot control learning, making the integration of robotics into various industries more efficient. They have uncovered a more straightforward approach to teaching robots control mechanisms, optimizing their utility and deployment speed in multiple applications. This could have far-reaching implications for industries that rely on robotics, from manufacturing to healthcare.
Speaking of robotics, there’s also a revolutionary methodology that promises to accelerate robotics training techniques. Imagine instructing robots in a fraction of the time it currently takes, enhancing their utility and productivity in various tasks.
In the world of computer science, Armando Solar-Lezama has been honored as the inaugural Distinguished Professor of Computing. This recognition is a testament to his invaluable contributions and impact on the field.
AI is even transforming household robots. The integration of AI has enabled household robots to plan tasks more efficiently, cutting their preparation time in half. This means that these robots can perform tasks with more seamless operations in domestic environments.
And let’s not forget about the impact of AI on writing productivity. A recent study highlights how ChatGPT, an AI-driven tool, enhances workplace productivity, especially in writing tasks. Professionals in diverse sectors can benefit significantly from this tool.
Finally, in the modern era, data privacy needs to be reimagined. As our digital footprints expand, it’s crucial to approach data privacy with a fresh perspective. We need to revisit and redefine what personal data protection means to ensure our information is safeguarded.
These are just some of the exciting developments happening in the world of AI. The possibilities are endless, and AI continues to push boundaries and pave the way for a brighter future.
In today’s Daily AI News, we have some exciting updates from major tech companies. Let’s dive right in!
OpenAI is making strides in content moderation with its latest development, GPT-4. This advanced AI model aims to replace human moderators by offering consistent judgments, faster policy development, and better worker well-being. This could be especially beneficial for smaller companies lacking resources in this area.
Microsoft is also moving forward with its AI offerings. They have launched ChatGPT on their Azure OpenAI service, allowing developers and businesses to integrate conversational AI into their applications. With ChatGPT, you can power custom chatbots, automate emails, and even get summaries of conversations. This helps users have more control and privacy over their interactions compared to the public model.
Google is not lagging behind either. They have introduced several AI-powered updates to enhance the search experience. Now, users can expect concise summaries, definitions, and even coding improvements. Additionally, Google Photos has added a Memories view feature, using AI to create a scrapbook-like timeline of your most memorable moments.
Amazon is utilizing generative AI to enhance product reviews. They are extracting key points from customer reviews to help shoppers quickly assess products. This feature includes trusted reviews from verified purchases, making the shopping experience even more convenient.
WhatsApp is also testing a new feature for its beta version called “custom AI-generated stickers.” A limited number of beta testers can now create their own stickers by typing prompts for the AI model. This feature has the potential to add a personal touch to your conversations.
And that’s all for today’s AI news updates! Stay tuned for more exciting developments in the world of artificial intelligence.
Thanks for tuning in to today’s episode! We covered a wide range of topics, including how GPT-4 improves content moderation, the impressive performance of Shepherd in critiquing language models, Microsoft’s private ChatGPT for Azure, Google’s use of AI for web content summaries, and various advancements in AI technology. See you in the next episode, and don’t forget to subscribe!
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover building a secure chatbot using AnythingLLM, AI-powered tools for recruitment, the capabilities of ChatGPT, Apple’s developments in AI health coaching, Google’s testing of AI for web page summarization, and the Wondercraft AI platform for podcasting with a special discount code.
If you’re interested in creating your own custom chatbot for your business, there’s a great option you should definitely check out. It’s called AnythingLLM, and it’s the first chatbot that offers top-notch privacy and security for enterprise-grade needs. You see, when you use other chatbots like ChatGPT from OpenAI, they collect various types of data from you. Things like prompts and conversations, geolocation data, network activity information, commercial data such as transaction history, and even identifiers like your contact details. They also take device and browser cookies as well as log data like your IP address. Now, if you opt to use their API to interact with their LLMs (like gpt-3.5 or gpt-4), then your data is not collected. So, what’s the solution? Build your own private and secure chatbot. Sounds complicated, right? Well, not anymore. Mintplex Labs, which is actually backed by Y-Combinator, has just released AnythingLLM. This amazing platform lets you build your own chatbot in just 10 minutes, and you don’t even need to know how to code. They provide you with all the necessary tools to create and manage your chatbot using API keys. Plus, you can enhance your chatbot’s knowledge by importing data like PDFs and emails. The best part is that all this data remains confidential, as only you have access to it. Unlike ChatGPT, where uploading PDFs, videos, or other data might put your information at risk, with AnythingLLM, you have complete control over your data’s security. So, if you’re ready to build your own business-compliant and secure chatbot, head over to useanything.com. All you need is an OpenAI or Azure OpenAI API key. And if you prefer using the open-source code yourself, you can find it on their GitHub repo at github.com/Mintplex-Labs/anything-llm. Check it out and build your own customized chatbot today!
AI-powered tools have revolutionized the recruitment industry, enabling companies to streamline their hiring processes and make better-informed decisions. Let’s take a look at some of the top tools that are transforming talent acquisition.
First up, Humanly.io offers Conversational AI to Recruit And Retain At Scale. This tool is specifically designed for high-volume hiring in organizations, enhancing candidate engagement through automated chat interactions. It allows recruiters to effortlessly handle large numbers of applicants with a personalized touch.
Another great tool is MedhaHR, an AI-driven healthcare talent sourcing platform. It automates resume screening, provides personalized job recommendations, and offers cost-effective solutions. This is especially valuable in the healthcare industry where finding the right talent is crucial.
For comprehensive candidate sourcing and screening, ZappyHire is an excellent choice. This platform combines features like candidate sourcing, resume screening, automated communication, and collaborative hiring, making it a valuable all-in-one solution.
Sniper AI utilizes AI algorithms to source potential candidates, assess their suitability, and seamlessly integrates with Applicant Tracking Systems (ATS) for workflow optimization. It simplifies the hiring process and ensures that the best candidates are identified quickly and efficiently.
Lastly, PeopleGPT, developed by Juicebox, provides recruiters with a tool to simplify the process of searching for people data. Recruiters can input specific queries to find potential candidates, saving time and improving efficiency.
With the soaring demand for AI specialists, compensation for these roles is reaching new heights. American companies are offering nearly a million-dollar salary to experienced AI professionals. Industries like entertainment and manufacturing are scrambling to attract data scientists and machine learning specialists, resulting in fierce competition for talent.
As the demand for AI expertise grows, companies are stepping up their compensation packages. Mid-six-figure salaries, lucrative bonuses, and stock grants are being offered to lure experienced professionals. While top positions like machine learning platform product managers can command up to $900,000 in total compensation, other roles such as prompt engineers can still earn around $130,000 annually.
The recruitment landscape is rapidly changing with the help of AI-powered tools, making it easier for businesses to find and retain top talent.
So, you’re leading a remote team and looking for advice on how to effectively manage them, communicate clearly, monitor progress, and maintain a positive team culture? Well, you’ve come to the right place! Managing a remote team can have its challenges, but fear not, because ChatGPT is here to help.
First and foremost, let’s talk about clear communication. One strategy for ensuring this is by scheduling and conducting virtual meetings. These meetings can help everyone stay on the same page, discuss goals, and address any concerns or questions. It’s important to set a regular meeting schedule and make sure everyone has the necessary tools and technology to join.
Next up, task assignment. When working remotely, it’s crucial to have a system in place for assigning and tracking tasks. There are plenty of online tools available, such as project management software, that can help streamline this process. These tools allow you to assign tasks, set deadlines, and track progress all in one place.
Speaking of progress tracking, it’s essential to have a clear and transparent way to monitor how things are progressing. This can be done through regular check-ins, status updates, and using project management tools that provide insights into the team’s progress.
Now, let’s focus on maintaining a positive team culture in a virtual setting. One way to promote team building is by organizing virtual team-building activities. These can range from virtual happy hours to online game nights. The key is to create opportunities for team members to connect and bond despite the physical distance.
In summary, effectively managing a remote team requires clear communication, task assignment and tracking, progress monitoring, and promoting team building. With the help of ChatGPT, you’re well-equipped to tackle these challenges and lead your team to success.
Did you know that Apple is reportedly working on an AI-powered health coaching service? Called Quartz, this service will help users improve their exercise, eating habits, and sleep quality. By using AI and data from the user’s Apple Watch, Quartz will create personalized coaching programs and even introduce a monthly fee. But that’s not all – Apple is also developing emotion-tracking tools and plans to launch an iPad version of the iPhone Health app this year.
This move by Apple is significant because it shows that AI is making its way into IoT devices like smartwatches. The combination of AI and IoT can potentially revolutionize our daily lives, allowing devices to adapt and optimize settings based on external circumstances. Imagine your smartwatch automatically adjusting its settings to help you achieve your health goals – that’s the power of AI in action!
In other Apple news, the company recently made several announcements at the WWDC 2023 event. While they didn’t explicitly mention AI, they did introduce features that heavily rely on AI technology. For example, Apple Vision Pro uses advanced machine learning techniques to blend digital content with the physical world. Upgraded Autocorrect, Improved Dictation, Live Voicemail, Personalized Volume, and the Journal app all utilize AI in their functionality.
Although Apple didn’t mention the word “AI,” these updates and features demonstrate that the company is indeed leveraging AI technologies across its products and services. By incorporating AI into its offerings, Apple is joining the ranks of Google and Microsoft in harnessing the power of artificial intelligence.
Lastly, it’s worth noting that Apple is also exploring AI chatbot technology. The company has developed its own language model called “Ajax” and an AI chatbot named “Apple GPT.” They aim to catch up with competitors like OpenAI and Google in this space. While there’s no clear strategy for releasing AI technology directly to consumers yet, Apple is considering integrating AI tools into Siri to enhance its functionality and keep up with advancements in the field.
Overall, Apple’s efforts in AI development and integration demonstrate its commitment to staying competitive in the rapidly advancing world of artificial intelligence.
Hey there! I want to talk to you today about some interesting developments in the world of artificial intelligence. It seems like Google is always up to something, and this time they’re testing a new feature on Chrome. It’s called ‘SGE while browsing’, and what it does is break down long web pages into easy-to-read key points. How cool is that? It makes it so much easier to navigate through all that information.
In other news, Talon Aerolytics, a leading innovator in SaaS and AI technology, has announced that their AI-powered computer vision platform is revolutionizing the way wireless operators visualize and analyze network assets. By using end-to-end AI and machine learning, they’re making it easier to manage and optimize networks. This could be a game-changer for the industry!
But it’s not just Google and Talon Aerolytics making waves. Beijing is getting ready to implement new regulations for AI services, aiming to strike a balance between state control and global competitiveness. And speaking of competitiveness, Saudi Arabia and the UAE are buying up high-performance chips crucial for building AI software. Looks like they’re joining the global AI arms race!
Oh, and here’s some surprising news. There’s a prediction that OpenAI might go bankrupt by the end of 2024. That would be a huge blow for the AI community. Let’s hope it doesn’t come true and they find a way to overcome any challenges they may face.
Well, that’s all the AI news I have for you today. Stay tuned for more exciting developments in the world of artificial intelligence.
Hey there, AI Unraveled podcast listeners! Have you been itching to dive deeper into the world of artificial intelligence? Well, I’ve got some exciting news for you! Introducing “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” a must-have book written by the brilliant Etienne Noumen. This essential read is now available at popular platforms like Shopify, Apple, Google, and even Amazon. So, no matter where you prefer to get your books, you’re covered!
Now, let’s talk about the incredible tool behind this podcast. It’s called Wondercraft AI, and it’s an absolute game-changer. With Wondercraft AI, starting your own podcast has never been easier. You’ll have the power to use hyper-realistic AI voices as your host, just like me! How cool is that?
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So, whether you’re eager to explore the depths of artificial intelligence through Etienne Noumen’s book or you’re ready to take the plunge and create your own podcast with Wondercraft AI, the possibilities are endless. Get ready to unravel the mysteries of AI like never before!
On today’s episode, we covered a range of topics, including building a secure chatbot for your business, AI-powered tools for recruitment and their impact on salaries, the versatility of ChatGPT, Apple’s advancements in AI health coaching, Google’s AI-driven web page summarization, and the latest offerings from the Wondercraft AI platform. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!
NVIDIA’s tool to curate trillion-token datasets for pretraining LLMs
Trustworthy LLMs: A survey and guideline for evaluating LLMs’ alignment
Amazon’s push to match Microsoft and Google in generative AI
World first’s mass-produced humanoid robots with AI brains
Microsoft Designer: An AI-powered Canva: a super cool product that I just found!
ChatGPT costs OpenAI $700,000 PER Day
What Else Is Happening in AI
Google appears to be readying new AI-powered tools for ChromeOS
Zoom rewrites policies to make clear user videos aren’t used to train AI
Anthropic raises $100M in funding from Korean telco giant SK Telecom
Modular, AI startup challenging Nvidia, discusses funding at $600M valuation
California turns to AI to spot wildfires, feeding on video from 1,000+ cameras
FEC to regulate AI deepfakes in political ads ahead of 2024 election
AI in Scientific Papers
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover LLMs and their various models, IBM’s energy-efficient AI chip prototype, NVIDIA’s NeMo Data Curator tool, guidelines for aligning LLMs with human intentions, Amazon’s late entry into generative AI chips, Chinese start-up Fourier Intelligence’s humanoid robot, Microsoft Designer and OpenAI’s financial troubles, Google’s AI tools for ChromeOS, various news including funding, challenges to Nvidia, AI in wildfire detection, and FEC regulations, the political bias and tool usage of LLMs, and special offers on starting a podcast and a book on AI.
LLM, or Large Language Model, is an exciting advancement in the field of AI. It’s all about training models to understand and generate human-like text by using deep learning techniques. These models are trained on enormous amounts of text data from various sources like books, articles, and websites. This wide range of textual data allows them to learn grammar, vocabulary, and the contextual relationships in language.
LLMs can do some pretty cool things when it comes to natural language processing (NLP) tasks. For example, they can translate languages, summarize text, answer questions, analyze sentiment, and generate coherent and contextually relevant responses to user inputs. It’s like having a super-smart language assistant at your disposal!
There are several popular LLMs out there. One of them is GPT-3 by OpenAI, which can generate text, translate languages, write creative content, and provide informative answers. Google AI has also developed impressive models like T5, which is specifically designed for text generation tasks, and LaMDA, which excels in dialogue applications. Another powerful model is PaLM by Google AI, which can perform a wide range of tasks, including text generation, translation, summarization, and question-answering. DeepMind’s FlaxGPT, based on the Transformer architecture, is also worth mentioning for its accuracy and consistency in generating text.
With LLMs continuously improving, we can expect even more exciting developments in the field of AI and natural language processing. The possibilities for utilizing these models are vast, and they have the potential to revolutionize how we interact with technology and language.
Have you ever marveled at the incredible power and efficiency of the human brain? Well, get ready to be amazed because IBM has created a prototype chip that mimics the connections in our very own minds. This breakthrough could revolutionize the world of artificial intelligence by making it more energy efficient and less of a battery-drain for devices like smartphones.
What’s so impressive about this chip is that it combines both analogue and digital elements, making it much easier to integrate into existing AI systems. This is fantastic news for all those concerned about the environmental impact of huge warehouses full of computers powering AI systems. With this brain-like chip, emissions could be significantly reduced, as well as the amount of water needed to cool those power-hungry data centers.
But why does all of this matter? Well, if brain-like chips become a reality, we could soon see a whole new level of AI capabilities. Imagine being able to execute large and complex AI workloads in low-power or battery-constrained environments such as cars, mobile phones, and cameras. This means we could enjoy new and improved AI applications while keeping costs to a minimum.
So, brace yourself for a future where AI comes to life in a way we’ve never seen before. Thanks to IBM’s brain-inspired chip, the possibilities are endless, and the benefits are undeniable.
So here’s the thing: creating massive datasets for training language models is no easy task. Most of the software and tools available for this purpose are either not publicly accessible or not scalable enough. This means that developers of Language Model models (LLMs) often have to go through the trouble of building their own tools just to curate large language datasets. It’s a lot of work and can be quite a headache.
But fear not, because Nvidia has come to the rescue with their NeMo Data Curator! This nifty tool is not only scalable, but it also allows you to curate trillion-token multilingual datasets for pretraining LLMs. And get this – it can handle tasks across thousands of compute cores. Impressive, right?
Now, you might be wondering why this is such a big deal. Well, apart from the obvious benefit of improving LLM performance with high-quality data, using the NeMo Data Curator can actually save you a ton of time and effort. It takes away the burden of manually going through unstructured data sources and allows you to focus on what really matters – developing AI applications.
And the cherry on top? It can potentially lead to significant cost reductions in the pretraining process, which means faster and more affordable development of AI applications. So if you’re a developer working with LLMs, the NeMo Data Curator could be your new best friend. Give it a try and see the difference it can make!
In the world of AI, ensuring that language models behave in accordance with human intentions is a critical task. That’s where alignment comes into play. Alignment refers to making sure that models understand and respond to human input in the way that we want them to. But how do we evaluate and improve the alignment of these models?
Well, a recent research paper has proposed a more detailed taxonomy of alignment requirements for language models. This taxonomy helps us better understand the different dimensions of alignment and provides practical guidelines for collecting the right data to develop alignment processes.
The paper also takes a deep dive into the various categories of language models that are crucial for improving their trustworthiness. It explores how we can build evaluation datasets specifically for alignment. This means that we can now have a more transparent and multi-objective evaluation of the trustworthiness of language models.
Why does all of this matter? Well, having a clear framework and comprehensive guidance for evaluating and improving alignment can have significant implications. For example, OpenAI, a leading AI research organization, had to spend six months aligning their GPT-4 model before its release. With better guidance, we can drastically reduce the time it takes to bring safe, reliable, and human-aligned AI applications to market.
So, this research is a big step forward in ensuring that language models are trustworthy and aligned with human values.
Amazon is stepping up its game in the world of generative AI by developing its own chips, Inferentia and Trainium, to compete with Nvidia GPUs. While the company might be a bit late to the party, with Microsoft and Google already invested in this space, Amazon is determined to catch up.
Being the dominant force in the cloud industry, Amazon wants to set itself apart by utilizing its custom silicon capabilities. Trainium, in particular, is expected to deliver significant improvements in terms of price-performance. However, it’s worth noting that Nvidia still remains the go-to choice for training models.
Generative AI models are all about creating and simulating data that resembles real-world examples. They are widely used in various applications, including natural language processing, image recognition, and even content creation.
By investing in their own chips, Amazon aims to enhance the training and speeding up of generative AI models. The company recognizes the potential of this technology and wants to make sure they can compete with the likes of Microsoft and Google, who have already made significant progress in integrating AI models into their products.
Amazon’s entry into the generative AI market signifies their commitment to innovation, and it will be fascinating to see how their custom chips will stack up against Nvidia’s GPUs in this rapidly evolving field.
So, get this – Chinese start-up Fourier Intelligence has just unveiled its latest creation: a humanoid robot called GR-1. And trust me, this is no ordinary robot. This bad boy can actually walk on two legs at a speed of 5 kilometers per hour. Not only that, but it can also carry a whopping 50 kilograms on its back. Impressive, right?
Now, here’s the interesting part. Fourier Intelligence wasn’t initially focused on humanoid robots. Nope, they were all about rehabilitation robotics. But in 2019, they decided to switch things up and dive into the world of humanoids. And let me tell you, it paid off. After three years of hard work and dedication, they finally achieved success with GR-1.
But here’s the thing – commercializing humanoid robots is no easy feat. There are still quite a few challenges to tackle. However, Fourier Intelligence is determined to overcome these obstacles. They’re aiming to mass-produce GR-1 by the end of this year. And wait for it – they’re already envisioning potential applications in areas like elderly care and education. Can you imagine having a humanoid robot as your elderly caregiver or teacher? It’s pretty mind-blowing.
So, keep an eye out for Fourier Intelligence and their groundbreaking GR-1 robot. Who knows? This could be the beginning of a whole new era of AI-powered humanoid helpers.
Hey everyone, I just came across this awesome product called Microsoft Designer! It’s like an AI-powered Canva that lets you create all sorts of graphics, from logos to invitations to social media posts. If you’re a fan of Canva, you definitely need to give this a try.
One of the cool features of Microsoft Designer is “Prompt-to-design.” You can just give it a short description, and it uses DALLE-2 to generate original and editable designs. How amazing is that?
Another great feature is the “Brand-kit.” You can instantly apply your own fonts and color palettes to any design, and it can even suggest color combinations for you. Talk about staying on-brand!
And that’s not all. Microsoft Designer also has other AI tools that can suggest hashtags and captions, replace backgrounds in images, erase items from images, and even auto-fill sections of an image with generated content. It’s like having a whole team of designers at your fingertips!
Now, on a different topic, have you heard about OpenAI’s financial situation? Apparently, running ChatGPT is costing them a whopping $700,000 every single day! That’s mind-boggling. Some reports even suggest that OpenAI might go bankrupt by 2024. But personally, I have my doubts. They received a $10 billion investment from Microsoft, so they must have some money to spare, right? Let me know your thoughts on this in the comments below.
On top of the financial challenges, OpenAI is facing some other issues. For example, ChatGPT has seen a 12% drop in users from June to July, and top talent is being lured away by rivals like Google and Meta. They’re also struggling with GPU shortages, which make it difficult to train better models.
To make matters worse, there’s increasing competition from cheaper open-source models that could potentially replace OpenAI’s APIs. Musk’s xAI is even working on a more right-wing biased model, and Chinese firms are buying up GPU stockpiles.
With all these challenges, it seems like OpenAI is in a tough spot. Their costs are skyrocketing, revenue isn’t offsetting losses, and there’s growing competition and talent drain. It’ll be interesting to see how they navigate through these financial storms.
So, let’s talk about what else is happening in the world of AI. It seems like Google has some interesting plans in store for ChromeOS. They’re apparently working on new AI-powered tools, but we’ll have to wait and see what exactly they have in mind. It could be something exciting!
Meanwhile, Zoom is taking steps to clarify its policies regarding user videos and AI training. They want to make it clear that your videos on Zoom won’t be used to train AI systems. This is an important move to ensure privacy and transparency for their users.
In terms of funding, Anthropic, a company in the AI space, recently secured a significant investment of $100 million from SK Telecom, a Korean telco giant. This infusion of funds will undoubtedly help propel their AI initiatives forward.
Speaking of startups, there’s one called Modular that’s aiming to challenge Nvidia in the AI realm. They’ve been discussing funding and are currently valued at an impressive $600 million. It’ll be interesting to see if they can shake things up in the market.
Coming closer to home, California is turning to AI technology to help spot wildfires. They’re using video feeds from over 1,000 cameras, analyzing the footage with AI algorithms to detect potential fire outbreaks. This innovative approach could help save lives and protect communities from devastating fires.
Lastly, in an effort to combat misinformation and manipulation, the Federal Election Commission (FEC) is stepping in to regulate AI deepfakes in political ads ahead of the 2024 election. It’s a proactive move to ensure fair and accurate campaigning in the digital age.
And that’s a roundup of some of the latest happenings in the world of AI! Exciting, right?
So, there’s a lot of exciting research and developments happening in the field of AI, especially in scientific papers. One interesting finding is that language models, or LLMs, have the ability to learn how to use tools without any specific training. Instead of providing demonstrations, researchers have found that simply providing tool documentation is enough for LLMs to figure out how to use programs like image generators and video tracking software. Pretty impressive, right?
Another important topic being discussed in scientific papers is the political bias of major AI language models. It turns out that models like ChatGPT and GPT-4 tend to lean more left-wing, while Meta’s Llama exhibits more right-wing bias. This research sheds light on the inherent biases in these models, which is crucial for us to understand as AI becomes more mainstream.
One fascinating paper explores the possibility of reconstructing images from signals in the brain. Imagine having brain interfaces that can consistently read these signals and maybe even map everything we see. The potential for this technology is truly limitless.
In other news, Nvidia has partnered with HuggingFace to provide a cloud platform called DGX Cloud, which allows people to train and tune AI models. They’re even offering a “Training Cluster as a Service,” which will greatly speed up the process of building and training models for companies and individuals.
There are also some intriguing developments from companies like Stability AI, who have released their new AI LLM called StableCode, and PlayHT, who have introduced a new text-to-voice AI model. And let’s not forget about the collaboration between OpenAI, Google, Microsoft, and Anthropic with Darpa for an AI cyber challenge – big things are happening!
So, as you can see, there’s a lot going on in the world of AI. Exciting advancements and thought-provoking research are shaping the future of this technology. Stay tuned for more updates and breakthroughs in this rapidly evolving field.
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Thanks for listening to today’s episode, where we covered various topics including the latest AI models like GPT-3 and T5, IBM’s energy-efficient chip that mimics the human brain, NVIDIA’s NeMo Data Curator tool, guidelines for aligning LLMs with human intentions, Amazon’s late entry into the generative AI chip market, Fourier Intelligence’s humanoid robot GR-1, Microsoft Designer and OpenAI’s financial troubles, and Google’s AI tools for ChromeOS. Don’t forget to subscribe for more exciting discussions, and remember, you can get 50% off the first month of starting your own podcast with Wondercraft AI! See you at the next episode!
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the 80/20 rule for optimizing business operations, how MetaGPT improves multi-agent collaboration, potential regulation of AI-generated deepfakes in political ads, advancements in ChatGPT and other AI applications, recent updates and developments from Spotify, Patreon, Google, Apple, Microsoft, and Chinese internet giants, and the availability of hyper-realistic AI voices and the book “AI Unraveled” by Etienne Noumen.
Sure! The 80/20 rule can be a game-changer when it comes to analyzing your e-commerce business. By identifying which 20% of your products are generating 80% of your sales, you can focus your efforts and resources on those specific products. This means allocating more inventory, marketing, and customer support towards them. By doing so, you can maximize your profitability and overall success.
Similarly, understanding which 20% of your marketing efforts are driving 80% of your traffic is crucial. This way, you can prioritize those marketing channels that are bringing the most traffic to your website. You might discover that certain social media platforms or advertising campaigns are particularly effective. By narrowing your focus, you can optimize your marketing budget and efforts to yield the best results.
In terms of operations, consider streamlining processes related to your top-performing products and marketing channels. Look for ways to improve efficiency and reduce costs without sacrificing quality. Automating certain tasks, outsourcing non-core activities, or renegotiating supplier contracts might be worth exploring.
Remember, embracing the 80/20 rule with tools like ChatGPT allows you to make data-driven decisions and concentrate on what really matters. So, dive into your sales and marketing data, identify the key contributors, and optimize your business accordingly. Good luck!
So, let’s talk about MetaGPT and how it’s tackling LLM hallucination. MetaGPT is a new framework that aims to improve multi-agent collaboration by incorporating human workflows and domain expertise. One of the main issues it addresses is hallucination in LLMs, which are language models that tend to generate incorrect or nonsensical responses.
To combat this problem, MetaGPT encodes Standardized Operating Procedures (SOPs) into prompts, effectively providing a structured coordination mechanism. This means that it includes specific guidelines and instructions to guide the response generation process.
But that’s not all. MetaGPT also ensures modular outputs, which allows different agents to validate the generated outputs and minimize errors. By assigning diverse roles to agents, the framework effectively breaks down complex problems into more manageable parts.
So, why is all of this important? Well, experiments on collaborative software engineering benchmarks have shown that MetaGPT outperforms chat-based multi-agent systems in terms of generating more coherent and correct solutions. By integrating human knowledge and expertise into multi-agent systems, MetaGPT opens up new possibilities for tackling real-world challenges.
With MetaGPT, we can expect enhanced collaboration, reduced errors, and more reliable outcomes. It’s exciting to see how this framework is pushing the boundaries of multi-agent systems and taking us one step closer to solving real-world problems.
Have you heard about the potential regulation of AI-generated deepfakes in political ads? The Federal Election Commission (FEC) is taking steps to protect voters from election disinformation by considering rules for AI ads before the 2024 election. This is in response to a petition calling for regulation to prevent misrepresentation in political ads using AI technology.
Interestingly, some campaigns, like Florida GOP Gov. Ron DeSantis’s, have already started using AI in their advertisements. So, the FEC’s decision on regulation is a significant development for the upcoming elections.
However, it’s important to note that the FEC will make a decision on rules only after a 60-day public comment window, which will likely start next week. While regulation could impose guidelines for disclaimers, it may not cover all the threats related to deepfakes from individual social media users.
The potential use of AI in misleading political ads is a pressing issue with elections on the horizon. The fact that the FEC is considering regulation indicates an understanding of the possible risks. But implementing effective rules will be the real challenge. In a world where seeing is no longer believing, ensuring truth in political advertising becomes crucial.
In other news, the White House recently launched a hacking challenge focused on AI cybersecurity. With a generous prize pool of $20 million, the competition aims to incentivize the development of AI systems for protecting critical infrastructure from cyber risks.
Teams will compete to secure vital software systems, with up to 20 teams advancing from qualifiers to win $2 million each at DEF CON 2024. Finalists will also have a chance at more prizes, including a $4 million top prize at DEF CON 2025.
What’s interesting about this challenge is that competitors are required to open source their AI systems for widespread use. This collaboration not only involves AI leaders like Anthropic, Google, Microsoft, and OpenAI, but also aims to push the boundaries of AI in national cyber defense.
Similar government hacking contests have been conducted in the past, such as the 2014 DARPA Cyber Grand Challenge. These competitions have proven to be effective in driving innovation through competition and incentivizing advancements in automated cybersecurity.
With the ever-evolving cyber threats, utilizing AI to stay ahead in defense becomes increasingly important. The hope is that AI can provide a powerful tool to protect critical infrastructure from sophisticated hackers and ensure the safety of government systems.
Generative AI tools like ChatGPT are revolutionizing the way workers make money. By automating time-consuming tasks and creating new income streams and full-time jobs, these AI tools are empowering workers to increase their earnings. It’s truly amazing how technology is transforming the workplace!
In other news, Universal Music Group and Google have teamed up for an exciting project involving AI song licensing. They are negotiating to license artists’ voices and melodies for AI-generated songs. Warner Music is also joining in on the collaboration. While this move could be lucrative for record labels, it poses challenges for artists who want to protect their voices from being cloned by AI. It’s a complex situation with both benefits and concerns.
AI is even playing a role in reducing the climate impact of airlines. Contrails, those long white lines you see in the sky behind airplanes, actually trap heat in Earth’s atmosphere, causing a net warming effect. But pilots at American Airlines are now using Google’s AI predictions and Breakthrough Energy’s models to select altitudes that are less likely to produce contrails. After conducting 70 test flights, they have observed a remarkable 54% reduction in contrails. This shows that commercial flights have the potential to significantly lessen their environmental impact.
Anthropic has released an updated version of its popular model, Claude Instant. Known for its speed and affordability, Claude Instant 1.2 can handle various tasks such as casual dialogue, text analysis, summarization, and document comprehension. The new version incorporates the strengths of Claude 2 and demonstrates significant improvements in areas like math, coding, and reasoning. It generates longer and more coherent responses, follows formatting instructions better, and even enhances safety by hallucinating less and resisting jailbreaks. This is an exciting development that brings Anthropic closer to challenging the supremacy of ChatGPT.
Google has also delved into the intriguing question of whether language models (LLMs) generalize or simply memorize information. While LLMs seem to possess a deep understanding of the world, there is a possibility that they are merely regurgitating memorized bits from their extensive training data. Google conducted research on the training dynamics of a small model and reverse-engineered its solution, shedding light on the increasingly fascinating field of mechanistic interpretability. The findings suggest that LLMs initially generalize well but then start to rely more on memorization. This research opens the door to a better understanding of the dynamics behind model behavior, particularly with regards to memorization and generalization.
In conclusion, AI tools like ChatGPT are empowering workers to earn more, Universal Music and Google are exploring a new realm of AI song licensing, AI is helping airlines reduce their climate impact, Anthropic has launched an improved model with enhanced capabilities and safety, and Google’s research on LLMs deepens our understanding of their behavior. It’s an exciting time for AI and its diverse applications!
Hey, let’s dive into today’s AI news!
First up, we have some exciting news for podcasters. Spotify and Patreon have integrated, which means that Patreon-exclusive audio content can now be accessed on Spotify. This move is a win-win for both platforms. It allows podcasters on Patreon to reach a wider audience through Spotify’s massive user base while circumventing Spotify’s aversion to RSS feeds.
In some book-related news, there have been reports of AI-generated books falsely attributed to Jane Friedman appearing on Amazon and Goodreads. This has sparked concerns over copyright infringement and the verification of author identities. It’s a reminder that as AI continues to advance, we need to ensure that there are robust systems in place to authenticate content.
Google has been pondering an intriguing question: do machine learning models memorize or generalize? Their research delves into a concept called grokking to understand how models truly learn and if they’re not just regurgitating information from their training data. It’s fascinating to explore the inner workings of AI models and uncover their true understanding of the world.
IBM is making moves in the AI space by planning to make Meta’s Llama 2 available within its watsonx. This means that the Llama 2-chat 70B model will be hosted in the watsonx.ai studio, with select clients and partners gaining early access. This collaboration aligns with IBM’s strategy of offering a blend of third-party and proprietary AI models, showing their commitment to open innovation.
Amazon is also leveraging AI technology by testing a tool that helps sellers craft product descriptions. By integrating language models into their e-commerce business, Amazon aims to enhance and streamline the product listing process. This is just one example of how AI is revolutionizing various aspects of our daily lives.
Switching gears to Microsoft, they have partnered with Aptos blockchain to bring together AI and web3. This collaboration enables Microsoft’s AI models to be trained using verified blockchain information from Aptos. By leveraging the power of blockchain, they aim to enhance the accuracy and reliability of their AI models.
OpenAI has made an update for ChatGPT users on the free plan. They now offer custom instructions, allowing users to tailor their interactions with the AI model. However, it’s important to note that this update is not currently available in the EU and UK, but it will be rolling out soon.
Google’s Arts & Culture app has undergone a redesign with exciting AI-based features. Users can now delight their friends by sending AI-generated postcards through the “Poem Postcards” feature. The app also introduces a new Play tab, an “Inspire” feed akin to TikTok, and other cool features. It’s great to see AI integrating into the world of arts and culture.
In the realm of space, a new AI algorithm called HelioLinc3D has made a significant discovery. It detected a potentially hazardous asteroid that had gone unnoticed by human observers. This reinforces the value of AI in assisting with astronomical discoveries and monitoring potentially threatening space objects.
Lastly, DARPA has issued a call to top computer scientists, AI experts, and software developers to participate in the AI Cyber Challenge (AIxCC). This two-year competition aims to drive innovation at the intersection of AI and cybersecurity to develop advanced cybersecurity tools. It’s an exciting opportunity to push the boundaries of AI and strengthen our defenses against cyber threats.
That wraps up today’s AI news. Stay tuned for more updates and innovations in the exciting field of artificial intelligence!
So, here’s the scoop on what’s been happening in the AI world lately. Apple is really putting in the effort when it comes to AI development. They’ve gone ahead and ordered servers from Foxconn Industrial Internet, a division of their supplier Foxconn. These servers are specifically for testing and training Apple’s AI services. It’s no secret that Apple has been focused on AI for quite some time now, even though they don’t currently have an external app like ChatGPT. Word is, Foxconn’s division already supplies servers to other big players like ChatGPT OpenAI, Nvidia, and Amazon Web Services. Looks like Apple wants to get in on the AI chatbot market action.
And then we have Midjourney, who’s making some moves of their own. They’re upgrading their GPU cluster, which means their Pro and Mega users can expect some serious speed boosts. Render times could decrease from around 50 seconds to just 30 seconds. Plus, the good news is that these renders might also end up being 1.5 times cheaper. On top of that, Midjourney’s planning to release V5.3 soon, possibly next week. This update will bring cool features like inpainting and a fresh new style. It might be exclusive to desktop, so keep an eye out for that.
Meanwhile, Microsoft is flexing its muscles by introducing new tools for frontline workers. They’ve come up with Copilot, which uses generative AI to supercharge the efficiency of service pros. Microsoft acknowledges the massive size of the frontline workforce, estimating it to be a staggering 2.7 billion worldwide. These new tools and integrations are all about supporting these workers and tackling the labor challenges faced by businesses. Way to go, Microsoft!
Now let’s talk about Google, the folks who always seem to have something up their sleeve. They’re jazzing up their Gboard keyboard with AI-powered features. How cool is that? With their latest update, users can expect AI emojis, proofreading assistance, and even a drag mode that lets you resize the keyboard to your liking. It’s all about making your typing experience more enjoyable. These updates were spotted in the beta version of Gboard.
Over in China, the internet giants are making waves by investing big bucks in Nvidia chips. Baidu, TikTok-owner ByteDance, Tencent, and Alibaba have reportedly ordered a whopping $5 billion worth of these chips. Why, you ask? Well, they’re essential for building generative AI systems, and China is dead set on becoming a global leader in AI technology. The chips are expected to land this year, so it won’t be long until we see the fruits of their labor.
Last but not least, TikTok is stepping up its game when it comes to AI-generated content. They’re planning to introduce a toggle that allows creators to label their content as AI-generated. The goal is to prevent unnecessary content removal and promote transparency. Nice move, TikTok!
And that’s a wrap on all the AI news for now. Exciting things are happening, and we can’t wait to see what the future holds in this ever-evolving field.
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Thanks for joining us on today’s episode where we discussed the 80/20 rule for optimizing business operations with ChatGPT, how MetaGPT improves multi-agent collaboration, the regulation of AI-generated deepfakes in political ads and the AI hacking challenge for cybersecurity, the various applications of AI such as automating tasks, generating music, reducing climate impact, enhancing model safety, and advancing research, the latest updates from tech giants like Spotify, Google, IBM, Microsoft, and Amazon, Apple’s plans to enter the AI chatbot market, and the availability of hyper-realistic AI voices and the book “AI Unraveled” by Etienne Noumen. Thanks for listening, I’ll see you guys at the next one and don’t forget to subscribe!
Step by Step Software Design and Code Generation through GPT; Google launches Project IDX, an AI-enabled browser-based dev environment; Stability AI has released StableCode, an LLM generative AI product for coding.
– new frameworks, resources, and services to accelerate the adoption of Universal Scene Description (USD), known as OpenUSD.
– NVIDIA has introduced AI Workbench
– NVIDIA and Hugging Face have partnered to bring generative AI supercomputing to developers.
75% of Organizations Worldwide Set to Ban ChatGPT and Generative AI Apps on Work Devices
Google launches Project IDX, an AI-enabled browser-based dev environment.
Disney has formed a task force to explore the applications of AI across its entertainment conglomerate, despite the ongoing Hollywood writers’ strike.
Stability AI has released StableCode, an LLM generative AI product for coding.
Hugging face launches tools for running LLMs on Apple devices.
Google AI is helping Airlines to reduce mitigate the climate impact of contrails.
Google and Universal Music Group are in talks to license artists’ melodies and vocals for an AI-generated music tool.
This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover topics such as collaborative software design using GPT-Synthesizer, AI-driven medical antibody design by LabGenius, NVIDIA’s new AI chip and frameworks, organizations planning to ban Generative AI apps, Google’s Project IDX and Disney’s AI task force, AI-generated music licensing by Google and Universal Music Group, MIT researchers using AI for cancer treatment, Meta focusing on commercial AI, OpenAI’s GPTBot, and the Wondercraft AI platform for podcasting with hyper-realistic AI voices.
Have you ever used ChatGPT or GPT for software design and code generation? If so, you may have noticed that for larger or more complex codes, it often skips important implementation steps or misunderstands your design. Luckily, there are tools available to help, such as GPT Engineer and Aider. However, these tools often exclude the user from the design process. If you want to be more involved and explore the design space with GPT, you should consider using GPT-Synthesizer.
GPT-Synthesizer is a free and open-source tool that allows you to collaboratively implement an entire software project with the help of AI. It guides you through the problem statement and uses a moderated interview process to explore the design space together. If you have no idea where to start or how to describe your software project, GPT Synthesizer can be your best friend.
What sets GPT Synthesizer apart is its unique design philosophy. Rather than relying on a single prompt to build a complete codebase for complex software, GPT Synthesizer understands that there are crucial details that cannot be effectively captured in just one prompt. Instead, it captures the design specification step by step through an AI-directed dialogue that engages with the user.
Using a process called “prompt synthesis,” GPT Synthesizer compiles the initial prompt into multiple program components. This helps turn ‘unknown unknowns’ into ‘known unknowns’, providing novice programmers with a better understanding of the overall flow of their desired implementation. GPT Synthesizer and the user then collaboratively discover the design details needed for each program component.
GPT Synthesizer also offers different levels of interactivity depending on the user’s skill set, expertise, and the complexity of the task. It strikes a balance between user participation and AI autonomy, setting itself apart from other code generation tools.
If you want to be actively involved in the software design and code generation process, GPT-Synthesizer is a valuable tool that can help enhance your experience and efficiency. You can find GPT-Synthesizer on GitHub at https://github.com/RoboCoachTechnologies/GPT-Synthesizer.
So, get this: robots, computers, and algorithms are taking over the search for new therapies. They’re able to process mind-boggling amounts of data and come up with molecules that humans could never even imagine. And they’re doing it all in an old biscuit factory in South London.
This amazing endeavor is being led by James Field and his company, LabGenius. They’re not baking cookies or making any sweet treats. Nope, they’re busy cooking up a whole new way of engineering medical antibodies using the power of artificial intelligence (AI).
For those who aren’t familiar, antibodies are the body’s defense against diseases. They’re like the immune system’s front-line troops, designed to attach themselves to foreign invaders and flush them out. For decades, pharmaceutical companies have been making synthetic antibodies to treat diseases like cancer or prevent organ rejection during transplants.
But here’s the thing: designing these antibodies is a painstakingly slow process for humans. Protein designers have to sift through millions of possible combinations of amino acids, hoping to find the ones that will fold together perfectly. They then have to test them all experimentally, adjusting variables here and there to improve the treatment without making it worse.
According to Field, the founder and CEO of LabGenius, there’s an infinite range of potential molecules out there, and somewhere in that vast space lies the molecule we’re searching for. And that’s where AI comes in. By crunching massive amounts of data, AI can identify unexplored molecule possibilities that humans might have never even considered.
So, it seems like the future of antibody development is in the hands of robots and algorithms. Who would have thought an old biscuit factory would be the birthplace of groundbreaking medical advancements?
NVIDIA recently made some major AI breakthroughs that are set to shape the future of technology. One of the highlights is the introduction of their new chip, the GH200. This chip combines the power of the H100, NVIDIA’s highest-end AI chip, with 141 gigabytes of cutting-edge memory and a 72-core ARM central processor. Its purpose? To revolutionize the world’s data centers by enabling the scale-out of AI models.
In addition to this new chip, NVIDIA also announced advancements in Universal Scene Description (USD), known as OpenUSD. Through their Omniverse platform and various technologies like ChatUSD and RunUSD, NVIDIA is committed to advancing OpenUSD and its 3D framework. This framework allows for seamless interoperability between different software tools and data types, making it easier to create virtual worlds.
To further support developers and researchers, NVIDIA unveiled the AI Workbench. This developer toolkit simplifies the creation, testing, and customization of pretrained generative AI models. Better yet, these models can be scaled to work on a variety of platforms, including PCs, workstations, enterprise data centers, public clouds, and NVIDIA DGX Cloud. The goal of the AI Workbench is to accelerate the adoption of custom generative AI models in enterprises around the world.
Lastly, NVIDIA partnered with Hugging Face to bring generative AI supercomputing to developers. By integrating NVIDIA DGX Cloud into the Hugging Face platform, developers gain access to powerful AI tools that facilitate training and tuning of large language models. This collaboration aims to empower millions of developers to build advanced AI applications more efficiently across various industries.
These announcements from NVIDIA demonstrate their relentless commitment to pushing the boundaries of AI technology and making it more accessible for everyone. It’s an exciting time for the AI community, and these breakthroughs are just the beginning.
Did you know that a whopping 75% of organizations worldwide are considering banning ChatGPT and other generative AI apps on work devices? It’s true! Despite having over 100 million users in June 2023, concerns over the security and trustworthiness of ChatGPT are on the rise. BlackBerry, a pioneer in AI cybersecurity, is urging caution when it comes to using consumer-grade generative AI tools in the workplace.
So, what are the reasons behind this trend? Well, 61% of organizations see these bans as long-term or even permanent measures. They are primarily driven by worries about data security, privacy, and their corporate reputation. In fact, a staggering 83% of companies believe that unsecured apps pose a significant cybersecurity threat to their IT systems.
It’s not just about security either. A whopping 80% of IT decision-makers believe that organizations have the right to control the applications being used for business purposes. On the other hand, 74% feel that these bans indicate “excessive control” over corporate and bring-your-own devices.
The good news is that as AI tools continue to improve and regulations are put in place, companies may reconsider their bans. It’s crucial for organizations to have tools in place that enable them to monitor and manage the usage of these AI tools in the workplace.
This research was conducted by OnePoll on behalf of BlackBerry. They surveyed 2,000 IT decision-makers across North America, Europe, Japan, and Australia in June and July of 2023 to gather these fascinating insights.
Google recently launched Project IDX, an exciting development for web and multiplatform app builders. This AI-enabled browser-based dev environment supports popular frameworks like Angular, Flutter, Next.js, React, Svelte, and Vue, as well as languages such as JavaScript and Dart. Built on Visual Studio Code, IDX integrates with Google’s PaLM 2-based foundation model for programming tasks called Codey.
IDX boasts a range of impressive features to support developers in their work. It offers smart code completion, enabling developers to write code more efficiently. The addition of a chatbot for coding assistance brings a new level of interactivity to the development process. And with the ability to add contextual code actions, IDX enables developers to maintain high coding standards.
One of the most exciting aspects of Project IDX is its flexibility. Developers can work from anywhere, import existing projects, and preview apps across multiple platforms. While IDX currently supports several frameworks and languages, Google has plans to expand its compatibility to include languages like Python and Go in the future.
Not wanting to be left behind in the AI revolution, Disney has created a task force to explore the applications of AI across its vast entertainment empire. Despite the ongoing Hollywood writers’ strike, Disney is actively seeking talent with expertise in AI and machine learning. These job opportunities span departments such as Walt Disney Studios, engineering, theme parks, television, and advertising. In fact, the advertising team is specifically focused on building an AI-powered ad system for the future. Disney’s commitment to integrating AI into its operations shows its dedication to staying on the cutting edge of technology.
AI researchers have made an impressive claim, boasting a 93% accuracy rate in detecting keystrokes over Zoom audio. By recording keystrokes and training a deep learning model on the unique sound profiles of individual keys, they were able to achieve this remarkable accuracy. This is particularly concerning for laptop users in quieter public places, as their non-modular keyboard acoustic profiles make them susceptible to this type of attack.
In the realm of coding, Stability AI has released StableCode, a generative AI product designed to assist programmers in their daily work and also serve as a learning tool for new developers. StableCode utilizes three different models to enhance coding efficiency. The base model underwent training on various programming languages, including Python, Go, Java, and more. Furthermore, it was further trained on a massive amount of code, amounting to 560 billion tokens.
Hugging Face has launched tools to support developers in running Language Learning Models (LLMs) on Apple devices. They have released a guide and alpha libraries/tools to enable developers to run LLM models like Llama 2 on their Macs using Core ML.
Google AI, in collaboration with American Airlines and Breakthrough Energy, is striving to reduce the climate impact of flights. By using AI and data analysis, they have developed contrail forecast maps that help pilots choose routes that minimize contrail formation. This ultimately reduces the climate impact of flights.
Additionally, Google is in talks with Universal Music Group to license artists’ melodies and vocals for an AI-generated music tool. This tool would allow users to create AI-generated music using an artist’s voice, lyrics, or sounds. Copyright holders would be compensated for the right to create the music, and artists would have the choice to opt in.
Researchers at MIT and the Dana-Farber Cancer Institute have discovered that artificial intelligence (AI) can aid in determining the origins of enigmatic cancers. This newfound knowledge enables doctors to choose more targeted treatments.
Lastly, Meta has disbanded its protein-folding team as it shifts its focus towards commercial AI. OpenAI has also introduced GPTBot, a web crawler specifically developed to enhance AI models. GPTBot meticulously filters data sources to ensure privacy and policy compliance.
Hey there, AI Unraveled podcast listeners! If you’re hungry to dive deeper into the fascinating world of artificial intelligence, I’ve got some exciting news for you. Etienne Noumen, in his book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” has compiled an essential guide that’ll expand your understanding of this captivating field.
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So there you have it, folks. Get your hands on “AI Unraveled,” venture into the depths of artificial intelligence, and hey, why not start your own podcast with our amazing Wondercraft AI platform? Happy podcasting!
Thanks for listening to today’s episode where we discussed topics such as collaborative software design with GPT-Synthesizer, AI-driven antibody design with LabGenius, NVIDIA’s new AI chip and partnerships, concerns over security with Generative AI apps, Google’s Project IDX and Disney’s AI task force, AI-enabled keystroke detection, StableCode for enhanced coding efficiency, LLM models on Apple devices, reducing climate impact with AI, licensing artists’ melodies with Universal Music Group, determining origins of cancers with AI, Meta’s focus on commercial AI, and OpenAI’s GPTBot for improving models. Don’t forget to subscribe and I’ll see you guys at the next one!
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover using no-code tools for business needs, boosting algorithms and detecting diabetes with chest x-rays, the improvement of AI deep fake audios and important Azure AI advancements, AI-powered features such as grammar checking in Google Search and customer data training for Zoom, concerns about AI’s impact on elections and misinformation, integration of generative AI into Jupyter notebooks, and the availability of hyper-realistic AI voices and the book “AI Unraveled” by Etienne Noumen.
So you’re starting a business but don’t have a lot of money to invest upfront? No worries! There are plenty of no-code and AI tools out there that can help you get started without breaking the bank. Let me run through some options for you:
For graphic design, check out Canva. It’s an easy-to-use tool that will empower you to create professional-looking designs without a designer on hand.
If you need a website, consider using Carrd. It’s a simple and affordable solution that allows you to build sleek, one-page websites.
To handle sales, Gumroad is an excellent choice. It’s a platform that enables you to sell digital products and subscriptions with ease.
When it comes to finding a writer, look into Claude. This tool uses AI to generate high-quality content for your business.
To manage your customer relationships, use Notion as your CRM. It’s a versatile and customizable tool that can help you organize your business contacts and interactions.
For marketing, try Buffer. It’s a social media management platform that allows you to schedule and analyze your posts across various platforms.
And if you need to create videos, CapCut is a great option. It’s a user-friendly video editing app that offers plenty of features to enhance your visual content.
Remember, you don’t need a fancy setup to start a business. Many successful ventures began with just a notebook and an Excel sheet. So don’t let limited resources hold you back. With these no-code and AI tools, you can kickstart your business with zero or minimal investment.
Now, if you’re an online business owner looking for financial advice, I have just the solution for you. Meet ChatGPT, your new personal finance advisor. Whether you need help managing your online business’s finances or making important financial decisions, ChatGPT can provide valuable insights and guidance.
Here’s a snapshot of your current financial situation: Your monthly revenue is $10,000, and your operating expenses amount to $6,000. This leaves you with a monthly net income of $4,000. In addition, you have a business savings of $20,000 and personal savings of $10,000. Your goals are to increase your savings, reduce expenses, and grow your business.
To improve your overall financial health, here’s a comprehensive financial plan for you:
1. Budgeting tips: Take a closer look at your expenses and identify areas where you can cut back. Set a realistic budget that allows you to save more.
2. Investment advice: Consider diversifying your investments. Speak with a financial advisor to explore options such as stocks, bonds, or real estate that align with your risk tolerance and long-term goals.
3. Strategies for reducing expenses: Explore ways to optimize your operating costs. This could involve negotiating better deals with suppliers, finding more cost-effective software solutions, or exploring outsourcing options.
4. Business growth strategies: Look for opportunities to expand your customer base, increase sales, and explore new markets. Consider leveraging social media and digital advertising to reach a wider audience.
Remember, these suggestions are based on best practices in personal and business finance management. However, keep in mind that ChatGPT is a helpful start but shouldn’t replace professional financial advice. Also, be cautious about sharing sensitive financial information online, as there are always risks involved, even in simulated conversations with AI.
Feel free to modify this plan based on your unique circumstances, such as focusing on debt management, retirement planning, or significant business investments. ChatGPT is here to assist you in managing your finances effectively and setting you on the path to financial success.
Boosting in machine learning is a technique that aims to make algorithms work better together by improving accuracy and reducing bias. By combining multiple weak learners into a strong learner, boosting enhances the overall performance of the model. Essentially, it helps overcome the limitations of individual algorithms and makes predictions more reliable.
In other news, a new deep learning tool has been developed that can detect diabetes using routine chest radiographs and electronic health record data. This tool, based on deep learning models, can identify individuals at risk of elevated diabetes up to three years before diagnosis. It’s an exciting development that could potentially lead to early interventions and better management of diabetes.
Furthermore, OpenAI has recently announced the launch of GPTBot, a web crawler designed to train and improve AI capabilities. This crawler will scour the internet, gathering data and information that can be used to enhance future models. OpenAI has also provided guidelines for websites on how to prevent GPTBot from accessing their content, giving users the option to opt out of having their data used for training purposes.
While GPTBot has the potential to improve accuracy and safety of AI models, OpenAI has faced criticism in the past for its data collection practices. By allowing users to block GPTBot, OpenAI seems to be taking a step towards addressing these concerns and giving individuals more control over their data. It’s a positive development in ensuring transparency and respect for user privacy.
AI deep fake audios are becoming scarily realistic. These are artificial voices generated by AI models, and a recent experiment shed some light on our ability to detect them. Participants in the study were played both genuine and deep fake audio and were asked to identify the deep fakes. Surprisingly, they could accurately spot the deep fakes only 73% of the time.
The experiment tested both English and Mandarin, aiming to understand if language impacts our ability to detect deep fakes. Interestingly, there was no difference in detectability between the two languages.
This study highlights the growing need for automated detectors to overcome the limitations of human listeners in identifying speech deepfakes. It also emphasizes the importance of expanding fact-checking and detection tools to protect against the threats posed by AI-generated deep fakes.
Shifting gears, Microsoft has announced some significant advancements in its Azure AI infrastructure, bringing its customers closer to the transformative power of generative AI. Azure OpenAI Service is now available in multiple new regions, offering access to OpenAI’s advanced models like GPT-4 and GPT-35-Turbo.
Additionally, Microsoft has made the ND H100 v5 VM series, featuring the latest NVIDIA H100 Tensor Core GPUs, generally available. These advancements provide businesses with unprecedented AI processing power and scale, accelerating the adoption of AI applications in various industries.
Finally, there has been some debate around the accuracy of generative AI, particularly in the case of ChatGPT. While it may produce erroneous results, we shouldn’t dismiss it as useless. ChatGPT operates differently from search engines and has the potential to be revolutionary. Understanding its strengths and weaknesses is crucial as we continue to embrace generative AI.
In conclusion, detecting AI deep fake audios is becoming more challenging, and automated detectors are needed. Microsoft’s Azure AI infrastructure advancements are empowering businesses with greater computational power. It’s also important to understand and evaluate the usefulness of models like ChatGPT despite their occasional errors.
Google Search has recently added an AI-powered grammar check feature to its search bar, but for now, it’s only available in English. To use this feature, simply enter a sentence or phrase into Google Search, followed by keywords like “grammar check,” “check grammar,” or “grammar checker.” Google will then let you know if your phrase is grammatically correct or provide suggestions for corrections if needed. The best part is that you can access this grammar check tool on both desktop and mobile platforms.
Speaking of AI, Zoom has updated its Terms of Service to allow the company to train its AI using user data. However, they’ve made it clear that they won’t use audio, video, or chat content without customer consent. Customers must decide whether to enable AI features and share data for product improvement, which has raised some concerns given Zoom’s questionable privacy track record. They’ve had issues in the past, such as providing less secure encryption than claimed and sharing user data with companies like Google and Facebook.
In other AI news, scientists have achieved a breakthrough by using AI to discover molecules that can combat aging cells. This could be a game-changer in the fight against aging.
There’s also an AI model called OncoNPC that may help identify the origins of cancers that are currently unknown. This information could lead to more targeted and effective tumor treatments.
However, not all AI developments are flawless. Detroit police recently made a wrongful arrest based on facial recognition technology. A pregnant woman, Porcha Woodruff, was wrongly identified as a suspect in a robbery due to incorrect facial recognition. She was incarcerated while pregnant and is now suing the city. This incident highlights the systemic issues associated with facial recognition AI, with at least six wrongful arrests occurring so far, all of which have been in the Black community. Critics argue that relying on imperfect technology like this can result in biased and shoddy investigations. It’s crucial for powerful AI systems to undergo meticulous training and testing to avoid such mistakes. Otherwise, the legal, ethical, and financial consequences will continue to mount.
Have you heard about Sam Altman’s concerns regarding the impact of AI on elections? As the CEO of OpenAI, Altman is worried about the potential effects of generative AI, especially when it comes to hyper-targeted synthetic media. He’s seen examples of AI-generated media being used in American campaign ads during the 2024 election, and it has unfortunately led to the spread of misinformation. Altman fully acknowledges the risks associated with the technology that his organization is developing and stresses the importance of raising awareness about its implications.
But let’s shift gears a bit and talk about something exciting happening in the world of AI and coding. Have you heard of Jupyter AI? It’s a remarkable tool that brings generative AI to Jupyter notebooks, opening up a whole new world of possibilities for users. With Jupyter AI, you can explore and work with AI models right within your notebook. It even offers a magic command, “%%ai,” that transforms your notebook into a playground for generative AI, making it easy to experiment and have fun.
One of the standout features of Jupyter AI is its native chat user interface, which allows you to interact with generative AI as a conversational assistant. Plus, it supports various generative model providers, including popular ones like OpenAI, AI21, Anthropic, and Cohere, as well as local models. This compatibility with JupyterLab makes it incredibly convenient, as you can seamlessly integrate Jupyter AI into your coding workflow.
So why does all of this matter? Well, integrating advanced AI chat-based assistance directly into Jupyter’s environment holds great potential to enhance tasks such as coding, summarization, error correction, and content generation. By leveraging Jupyter AI and its support for leading language models, users can streamline their coding workflows and obtain accurate answers, making their lives as developers much easier. It’s an exciting development that brings AI and coding closer than ever before.
Hey there, AI Unraveled podcast listeners!
Have you been yearning to delve deeper into the world of artificial intelligence? Well, you’re in luck! I’ve got just the thing for you. Let me introduce you to “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” a must-read book by Etienne Noumen.
This book is an essential guide that will help you expand your understanding of all things AI. From the basics to the more complex concepts, “AI Unraveled” covers it all. Whether you’re a newbie or a seasoned enthusiast, this book is packed with valuable information that will take your AI knowledge to new heights.
And the best part? You can get your hands on a copy right now! It’s available at popular platforms like Shopify, Apple, Google, or Amazon. So, wherever you prefer to shop, you can easily snag a copy and embark on your AI adventure.
Don’t miss out on this opportunity to demystify AI and satisfy your curiosity. Get your copy of “AI Unraveled” today, and let the unraveling begin!
In today’s episode, we explored various no-code tools for different business needs, the advancements in AI deep fake audios and generative AI accuracy, AI-powered features from Google Search and Zoom, OpenAI CEO Sam Altman’s concerns about AI’s impact, and the hyper-realistic AI voices from Wondercraft AI platform–thanks for listening, I’ll see you guys at the next one and don’t forget to subscribe!
Tutorial: Craft Your Marketing Strategy with ChatGPT; Google’s AI Search: Now With Visuals!; DeepSpeed-Chat: Affordable RLHF training for AI; The Challenge of Converting 2D Images to 3D Models with AI
Summary:
Tutorial: Craft Your Marketing Strategy with ChatGPT
This podcast is generated using the Wondercraft AI platform, a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine!
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover topics such as how ChatGPT can assist in creating a comprehensive marketing strategy, Microsoft’s DeepSpeed-Chat making RLHF training faster and more accessible, OpenAI’s improvements to ChatGPT, the latest versions of Vicuna LLaMA-2 and Google DeepMind’s RT-2 model, various AI applications including AI music generation and AI therapists, challenges and barriers to AI adoption, integration of GPT-4 model by Twilio and generative AI assistant by Datadog, and the availability of the podcast and the book “AI Unraveled” by Etienne Noumen.
Have you heard the news? Google’s AI Search just got a major upgrade! Not only does it provide AI-powered search results, but now it also includes related images and videos. This means that searching for information is not only easier but also more engaging.
One great feature of Google’s Search Generative Experiment (SGE) is that it displays images and videos that are related to your search query. So, if you’re searching for something specific, you’ll get a variety of visual content to complement your search results. This can be incredibly helpful, especially when you’re looking for visual references or inspiration.
But that’s not all! Another handy addition is the inclusion of publication dates. Now, when you’re searching for information, you’ll know how fresh the information is. This can be particularly useful when you’re looking for up-to-date news or recent research.
If you’re excited to try out these new features, you can sign up to be a part of the Search Labs testing. This way, you can get a firsthand experience of how Google’s AI search is taking things to the next level.
Overall, this update is a game-changer for Google’s AI search. It provides a richer and more dynamic user experience, making it even easier to find the information you need. So, next time you’re searching for something, get ready for a more visual and engaging search experience with Google’s AI Search!
Have you heard about the new system from Microsoft called DeepSpeed-Chat? It’s an exciting development in the world of AI because it makes complex RLHF (Reinforcement Learning with Human Feedback) training faster, more affordable, and easily accessible to the AI community. Best of all, it’s open-sourced!
DeepSpeed-Chat has three key capabilities that set it apart. First, it offers an easy-to-use training and inference experience for models like ChatGPT. Second, it has a DeepSpeed-RLHF pipeline that replicates the training pipeline from InstructGPT. And finally, it boasts a robust DeepSpeed-RLHF system that combines various optimizations for training and inference in a unified way.
What’s really impressive about DeepSpeed-Chat is its unparalleled efficiency and scalability. It can train models with hundreds of billions of parameters in record time and at a fraction of the cost compared to other frameworks like Colossal-AI and HuggingFace DDP. Microsoft has tested DeepSpeed-Chat on a single NVIDIA A100-40G commodity GPU, and the results are impressive.
But why does all of this matter? Well, currently, there is a lack of accessible, efficient, and cost-effective end-to-end RLHF training pipelines for powerful models like ChatGPT, especially when training at the scale of billions of parameters. DeepSpeed-Chat addresses this problem, opening doors for more people to access advanced RLHF training and fostering innovation and further development in the field of AI.
OpenAI has some exciting new updates for ChatGPT that are aimed at improving the overall user experience. Let me tell you about them!
First up, when you start a new chat, you’ll now see prompt examples that can help you get the conversation going. This way, you don’t have to rack your brain for an opening line.
Next, ChatGPT will also suggest relevant replies to keep the conversation flowing smoothly. It’s like having a helpful assistant right there with you!
If you’re a Plus user and you’ve previously selected a specific model, ChatGPT will now remember your choice when starting a new chat. No more defaulting back to GPT-3.5!
Another exciting update is that ChatGPT can now analyze data and generate insights across multiple files. This means you can work on more complex projects without any hassle.
In terms of convenience, you’ll no longer be automatically logged out every two weeks. You can stay logged in and continue your work without any interruptions.
And for those who like to work quickly, ChatGPT now has keyboard shortcuts! You can use combinations like ⌘ (Ctrl) + Shift + ; to copy the last code block, or ⌘ (Ctrl) + / to see the complete list of shortcuts.
These updates to ChatGPT are designed to make it more user-friendly and enhance the interactions between humans and AI. It’s a powerful tool that can pave the way for improved and advanced AI applications. ChatGPT is definitely the leading language model of today!
The latest versions of Vicuna, known as the Vicuna v1.5 series, are here and they are packed with exciting features! These versions are based on Llama-2 and come with extended context lengths of 4K and 16K. Thanks to Meta’s positional interpolation, the performance of these Vicuna versions has been improved across various benchmarks. It’s pretty impressive!
Now, let’s dive into the details. The Vicuna 1.5 series offers two parameter versions: 7B and 13B. Additionally, you have the option to choose between a 4096 and 16384 token context window. These models have been trained on an extensive dataset consisting of 125k ShareGPT conversations. Talk about thorough preparation!
But why should you care about all of this? Well, Vicuna has already established itself as one of the most popular chat Language Models (LLMs). It has been instrumental in driving groundbreaking research in multi-modality, AI safety, and evaluation. And with these latest versions being based on the open-source Llama-2, they can serve as a reliable alternative to ChatGPT/GPT-4. Exciting times in the world of LLMs!
In other news, Google DeepMind has introduced the Robotic Transformer 2 (RT-2). This is a significant development, as it’s the world’s first vision-language-action (VLA) model that learns from both web and robotics data. By leveraging this combined knowledge, RT-2 is able to generate generalized instructions for robotic control. This helps robots understand and perform actions in both familiar and new situations. Talk about innovation!
The use of internet-scale text, image, and video data in the training of RT-2 enables robots to develop better common sense. This results in highly performant robotic policies and opens up a whole new realm of possibilities for robotic capabilities. It’s amazing to see how technology is pushing boundaries and bringing us closer to a future where robots can seamlessly interact with the world around us.
Hey there! Today we’ve got some interesting updates in the world of AI. Let’s dive right in!
First up, we’ve witnessed an incredible breakthrough in music generation. AI has brought ‘Elvis’ back to life, sort of, and he performed a hilarious rendition of a modern classic. This just goes to show how powerful AI has become in the realm of music and other creative fields.
In other news, Meta, the tech giant, has released an open-source suite of AI audio tools called AudioCraft. This is a significant contribution to the AI audio technology sector and is expected to drive advancements in audio synthesis, processing, and understanding. Exciting stuff!
However, not all news is positive. Researchers have discovered a way to manipulate AI into displaying prohibited content, which exposes potential vulnerabilities in these systems. This emphasizes the need for ongoing research into the reliability and integrity of AI, as well as measures to protect against misuse.
Meta is also leveraging AI-powered chatbots as part of their strategy to increase user engagement on their social media platforms. This demonstrates how AI is playing an increasingly influential role in enhancing user interaction in the digital world.
Moving on, Karim Lakhani, a professor at Harvard Business School, has done some groundbreaking work in the field of workplace technology and AI. He asserts that AI won’t replace humans, but rather humans with AI will replace humans without AI. It’s an interesting perspective on the future of work.
In other news, machine learning is helping researchers identify underground fungal networks. Justin Stewart embarked on a mission to gather fungal samples from Mount Chimborazo, showcasing how AI can aid in scientific discoveries.
The next frontier in AI is developing consciousness. Some researchers are exploring the idea of giving AI emotions, desires, and the ability to learn and grow. However, this raises philosophical and ethical questions about what it means to be human and the distinctiveness of our nature.
On the topic of AI advancements, we might soon witness AI initiating unprompted conversations. While this opens up exciting possibilities, it also underscores the need for ethical guidelines to ensure respectful and beneficial human-AI interaction.
AI has also made its mark in therapy by providing round-the-clock emotional support. AI therapists are revolutionizing mental health care accessibility, but it’s crucial to ponder questions about empathy and the importance of the human touch in therapy.
Let’s not forget about the challenge of converting 2D images into 3D models using AI. It’s a complex task, but progress is being made. Researchers are constantly exploring alternative methods to tackle this problem and improve the capabilities of AI.
Despite the evident potential, some businesses and industry leaders are still hesitant to fully embrace AI. They’re cautious about adopting its advantages into their operations, which highlights the barriers that exist.
Finally, in recent updates, Twilio has integrated OpenAI’s GPT-4 model into its Engage platform, Datadog has launched a generative AI assistant called Bits, and Pinterest is using next-gen AI for more personalized content and ads. Oh, and by the way, if you try to visit AI.com, you’ll be redirected to Elon Musk’s X.ai instead of going to ChatGPT.
That wraps up today’s AI news roundup. Exciting developments and thought-provoking discussions!
Hey there, AI Unraveled podcast listeners!
Have you been yearning to delve deeper into the world of artificial intelligence? Well, you’re in luck! I’ve got just the thing for you. Let me introduce you to “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” a must-read book by Etienne Noumen.
This book is an essential guide that will help you expand your understanding of all things AI. From the basics to the more complex concepts, “AI Unraveled” covers it all. Whether you’re a newbie or a seasoned enthusiast, this book is packed with valuable information that will take your AI knowledge to new heights.
And the best part? You can get your hands on a copy right now! It’s available at popular platforms like Shopify, Apple, Google, or Amazon. So, wherever you prefer to shop, you can easily snag a copy and embark on your AI adventure.
Don’t miss out on this opportunity to demystify AI and satisfy your curiosity. Get your copy of “AI Unraveled” today, and let the unraveling begin!
Thanks for listening to today’s episode where we covered a range of topics including how ChatGPT can assist in creating marketing strategies, Microsoft’s DeepSpeed-Chat making RLHF training more accessible, OpenAI’s improvements to ChatGPT, the latest advancements with Vicuna LLaMA-2 and Google DeepMind, various applications of AI including AI music generation and AI therapists, and updates from Wondercraft AI and Etienne Noumen’s book “AI Unraveled.” I’ll see you guys at the next one and don’t forget to subscribe!
Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the development of a smartphone app for detecting stroke symptoms using machine learning algorithms, the revolutionary impact of AI and ML on anti-money laundering efforts, Meta’s introduction of AudioCraft for creating high-quality audio and music, the benefits of AudioCraft and LLaMA2-Accessory for musicians, the development of an AI system for recreating music based on brain scans, the effectiveness of AI in breast cancer screening, the involvement of various companies in AI developments, and the availability of hyper-realistic AI voices generated by the Wondercraft AI platform and the book “AI Unraveled” by Etienne Noumen.
So, researchers have developed a smartphone app that can detect stroke symptoms with the help of machine learning. At the Society of NeuroInterventional Surgery’s 20th Annual Meeting, experts discussed this innovative app and its potential to recognize physical signs of stroke. The study involved researchers from the UCLA David Geffen School of Medicine and several medical institutions in Bulgaria. They collected data from 240 stroke patients across four metropolitan stroke centers. Within 72 hours from the onset of symptoms, the researchers used smartphones to record videos of the patients and assess their arm strength. This allowed them to identify classic stroke signs, such as facial asymmetry, arm weakness, and speech changes. To examine facial asymmetry, the researchers employed machine learning techniques to analyze 68 facial landmark points. For arm weakness, they utilized data from a smartphone’s internal 3D accelerometer, gyroscope, and magnetometer. To detect speech changes, the team applied mel-frequency cepstral coefficients, which convert sound waves into images for comparison between normal and slurred speech patterns. The app was then tested using neurologists’ reports and brain scan data, demonstrating its accurate diagnosis of stroke in nearly all cases. This advancement in technology shows great promise in providing a reliable and accessible tool for stroke detection. With the power of machine learning and the convenience of a smartphone app, early detection and intervention can greatly improve the outcome of stroke patients.
AI and machine learning are becoming crucial tools in the fight against money laundering. This notorious global criminal activity has posed serious challenges for financial institutions and regulatory bodies. However, the emergence of AI and machine learning is opening up new possibilities in the ongoing battle against money laundering. Money laundering is a complicated crime that involves making illicitly-gained funds appear legal. It often includes numerous transactions, which are used to obfuscate the origin of the money and make it appear legitimate. Traditional methods of detecting and preventing money laundering have struggled to keep up with the vast number of financial transactions occurring daily and the sophisticated tactics used by money launderers. Enter AI and machine learning, two technological advancements that are revolutionizing various industries, including finance. These technologies are now being leveraged to tackle money laundering, and early findings are very encouraging. AI, with its ability to mimic human intelligence, and machine learning, a branch of AI focused on teaching computers to learn and behave like humans, can analyze enormous amounts of financial data. They can sift through millions of transactions in a fraction of the time it would take a person, identifying patterns and irregularities that may indicate suspicious activities. Furthermore, these technologies not only speed up the process but also enhance accuracy. Traditional anti-money laundering systems often produce numerous false positives, resulting in wasted time and resources. AI and machine learning, on the other hand, have the ability to learn from historical data and improve their accuracy over time, reducing false positives and enabling financial institutions to concentrate their resources on genuine threats. Nevertheless, using AI and machine learning in anti-money laundering efforts comes with its own set of challenges. These technologies need access to extensive amounts of data to function effectively. This raises concerns about privacy, as financial institutions need to strike a balance between implementing efficient anti-money laundering measures and safeguarding their customers’ personal information. Additionally, adopting these technologies necessitates substantial investments in technology and skilled personnel, which smaller financial institutions may find difficult to achieve.
So, have you heard about Meta’s latest creation? It’s called AudioCraft, and it’s bringing some pretty cool stuff to the world of generative AI. Meta has developed a family of AI models that can generate high-quality audio and music based on written text. It’s like magic! AudioCraft is not just limited to music and sound. It also packs a punch when it comes to compression and generation. Imagine having all these capabilities in one convenient code base. It’s all right there at your fingertips! But here’s the best part. Meta is open-sourcing these models, giving researchers and practitioners the chance to train their own models with their own datasets. It’s a great opportunity to dive deep into the world of generative AI and explore new possibilities. And don’t worry, AudioCraft is super easy to build on and reuse, so you can take what others have done and build something amazing on top of it. Seriously, this is a big deal. AudioCraft is a significant leap forward in generative AI research. Just think about all the incredible applications this technology opens up. You could create unique audio and music for video games, merchandise promos, YouTube content, educational materials, and so much more. The possibilities are endless! And let’s not forget about the impact of the open-source initiative. It’s going to propel the field of AI-generated audio and music even further. So, get ready to let your imagination run wild with AudioCraft because the future of generative AI is here.
Have you ever heard of AudioCraft? Well, it’s like ChatGPT, but for musicians. Just as ChatGPT is a helpful tool for content writers, AudioCraft serves as a valuable resource for musicians. But let’s shift gears a bit and talk about LLaMA2-Accessory. It’s an open-source toolkit designed specifically for the development of Large Language Models (LLMs) and multimodal LLMs. This toolkit is pretty advanced, offering features like pre-training, fine-tuning, and deployment of LLMs. The interesting thing about LLaMA2-Accessory is that it inherits most of its repository from LLaMA-Adapter, but with some awesome updates. These updates include support for more datasets, tasks, visual encoders, and efficient optimization methods. LLaMA-Adapter, by the way, is a lightweight adaption method used to effectively fine-tune LLaMA into an instruction-following model. So, why is all this important? Well, by using LLaMA2-Accessory, developers and researchers can easily and quickly experiment with state-of-the-art language models. This saves valuable time and resources during the development process. Plus, the fact that LLaMA2-Accessory is open-source means that anyone can access these advanced AI tools. This democratizes access to groundbreaking AI solutions, making progress and innovation more accessible across industries and domains.
So here’s some exciting news: Google and Osaka University recently collaborated on groundbreaking research that involves an AI system with the ability to determine what music you were listening to just by analyzing your brain signals. How cool is that? The scientists developed a unique AI-based pipeline called Brain2Music, which used functional magnetic resonance imaging (fMRI) data to recreate music based on snippets of songs that participants listened to during brain scans. By observing the flow of oxygen-rich blood in the brain, the fMRI technique identified the most active regions. The team collected brain scans from five participants who listened to short 15-second clips from various genres like blues, classical, hip-hop, and pop. While previous studies have reconstructed human speech or bird songs from brain activity, recreating music from brain signals has been relatively rare. The process involved training an AI program to associate music features like genre, rhythm, mood, and instrumentation with participants’ brain signals. Researchers labeled the mood of the music with descriptive terms like happy, sad, or exciting. The AI was then personalized for each participant, establishing connections between individual brain activity patterns and different musical elements. After training, the AI was able to convert unseen brain imaging data into a format that represented the musical elements of the original song clips. This information was fed into another AI model created by Google called MusicLM, originally designed to generate music from text descriptions. MusicLM used this information to generate musical clips that closely resembled the original songs, achieving a 60% agreement level in terms of mood. Interestingly, the genre and instrumentation in both the reconstructed and original music matched more often than what could be attributed to chance. The research aims to deepen our understanding of how the brain processes music. The team noticed that specific brain regions, like the primary auditory cortex and the lateral prefrontal cortex, were activated when participants listened to music. The latter seems to play a vital role in interpreting the meaning of songs, but more investigation is needed to confirm this finding. Intriguingly, the team also plans to explore the possibility of reconstructing music that people imagine rather than hear, opening up even more fascinating possibilities. While the study is still awaiting peer review, you can actually listen to the generated musical clips online, which showcases the impressive advancement of AI in bridging the gap between human cognition and machine interpretation. This research has the potential to revolutionize our understanding of music and how our brains perceive it.
In some exciting news, a recent study has shown that using artificial intelligence (AI) in breast cancer screening is not only safe but can also significantly reduce the workload of radiologists. This comprehensive trial, one of the largest of its kind, has shed light on the potential benefits of AI-supported screening in detecting cancer at a similar rate as the traditional method of double reading, without increasing false positives. This could potentially alleviate some of the pressure faced by medical professionals. The effectiveness of AI in breast cancer screening is comparable to that of two radiologists working together, making it a valuable tool in early detection. Moreover, this technology can nearly halve the workload for radiologists, greatly improving efficiency and streamlining the screening process. An encouraging finding from the study is that there was no increase in the false-positive rate. In fact, AI support led to the detection of an additional 41 cancers. This suggests that the integration of AI into breast cancer screening could have a positive impact on patient outcomes. The study, which involved over 80,000 women primarily from Sweden, was a randomized controlled trial comparing AI-supported screening with standard care. The interim analysis indicates that AI usage in mammography is safe and has the potential to reduce radiologists’ workload by an impressive 44%. However, the lead author emphasizes the need for further understanding, trials, and evaluations to fully comprehend the extent of AI’s potential and its implications for breast cancer screening. This study opens up new possibilities for improving breast cancer screening and highlights the importance of continued research and development in the field of AI-assisted healthcare.
Let’s catch up on some of the latest happenings in the world of AI! Instagram has been busy working on labels for AI-generated content. This is great news, as it will help users distinguish between content created by humans and content generated by AI algorithms. Google has also made some updates to their generative search feature. Now, when you search for something, it not only shows you relevant text-based results but also related videos and images. This makes the search experience even more immersive and visually appealing. In the world of online dating, Tinder is testing an AI photo selection feature. This feature aims to help users build better profiles by selecting the most attractive and representative photos from their collection. It’s like having a personal AI stylist for your dating profile! Alibaba, the Chinese e-commerce giant, has rolled out an open-sourced AI model to compete with Meta’s Llama 2. This model will surely contribute to the advancement of AI technology and its various applications. IBM and NASA recently announced the availability of the watsonx.ai geospatial foundation model. This is a significant development in the field of AI, as it provides a powerful tool for understanding and analyzing geospatial data. Nvidia researchers have also made a breakthrough. They have developed a text-to-image personalization method called Perfusion. What sets Perfusion apart is its efficiency—it’s only 100KB in size and can be trained in just four minutes. This makes it much faster and more lightweight compared to other models out there. Moving on, Meta Platforms (formerly Facebook) has introduced an open-source AI tool called AudioCraft. This tool enables users to create music and audio based on text prompts. It comes bundled with three models—AudioGen, EnCodec, and MusicGen—and can be used for music creation, sound development, compression, and generation. In the entertainment industry, there is growing concern among movie extras that AI may replace them. Hollywood is already utilizing AI technologies, such as body scans, to create realistic virtual characters. It’s a topic that sparks debate and raises questions about the future of the industry. Finally, in a groundbreaking medical achievement, researchers have successfully used AI-powered brain implants to restore movement and sensation for a man who was paralyzed from the chest down. This remarkable feat demonstrates the immense potential that AI holds in the field of healthcare. As AI continues to advance and enter the mainstream, it’s clear that it has far-reaching implications across various industries and domains. Exciting times lie ahead!
Hey there, AI Unraveled podcast listeners! Have you been yearning to delve deeper into the world of artificial intelligence? Well, you’re in luck! I’ve got just the thing for you. Let me introduce you to “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” a must-read book by Etienne Noumen. This book is an essential guide that will help you expand your understanding of all things AI. From the basics to the more complex concepts, “AI Unraveled” covers it all. Whether you’re a newbie or a seasoned enthusiast, this book is packed with valuable information that will take your AI knowledge to new heights. And the best part? You can get your hands on a copy right now! It’s available at popular platforms like Shopify, Apple, Google, or Amazon. So, wherever you prefer to shop, you can easily snag a copy and embark on your AI adventure. Don’t miss out on this opportunity to demystify AI and satisfy your curiosity. Get your copy of “AI Unraveled” today, and let the unraveling begin!
In today’s episode, we discussed the development of a smartphone app for detecting stroke symptoms, the revolution of AI and ML in anti-money laundering efforts, the introduction of Meta’s AudioCraft for AI-generated audio and music, the tools available for musicians and content writers, an AI system that recreates music based on brain scans, the effectiveness of AI in breast cancer screening, the involvement of various big names in AI developments, and the hyper-realistic AI voices provided by the Wondercraft AI platform and Etienne Noumen’s book “AI Unraveled.” Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!
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