AI Unraveled Podcast – Latest AI Trends May 2023

AI Unraveled Podcast

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AI Unraveled Podcast – Latest AI Trends May 2023: Latest AI Trends. Demystifying Frequently Asked Questions on Artificial Intelligence. Latest ChatGPT Trends, Latest Google Bard Trends.

AI Unraveled Podcast May 30th 2023: Google AI declares the Completion of The First Human Pangenome Reference; AI needs to stop being a business and needs to become a public utility; Warning of “risk of extinction” from unregulated AI.

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. In today’s episode, we discuss the latest AI trends, including Google AI’s completion of the first human pangenome reference, the need for AI to become a public utility, and warnings of the “risk of extinction” from unregulated AI. Stay up-to-date with the latest developments by subscribing to our podcast now. In today’s episode, we’ll cover the completion of the first human pangenome reference by Google AI researchers, the call for AI to become a public utility to avoid extinction risks, integration of Arc graphics, VPU and media in Intel’s Meteor Lake processors, the partnership between NVIDIA and MediaTek in the auto industry transformation, the use of Generative AI by Huma.AI and DOSS, the selection of Panaya’s Smart Testing Platform for SAP HANA transformation by Panasonic, and the full production of NVIDIA Grace Hopper Superchip and Landing AI’s use of NVIDIA Metropolis for Factories, along with a recommendation to read “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” on Amazon.

Hey there! Today I have some exciting news to share with you. Google just declared that they’ve completed the first ever human Pangenome reference. It’s essentially a comprehensive map of every individual’s genetic instructions; something that researchers have been working on for decades. The first draft was completed way back in 2000, but it wasn’t perfect. The reference genome that they’ve just completed is a huge milestone in the world of genetics.

But moving on to a more pressing topic, have you ever thought about how AI is being monetized rather than being developed for the public good? A new article suggests that AI needs to become a public utility rather than being treated as a business. At a time when there may be an inflection point for developing real AGI, it’s troubling to see it being monetized instead of being developed for public benefit. Crippling AI just to sell a premium version is not warranted, and it’s only benefiting the 1%.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

And it’s not just us who are worried about unregulated AI. Leaders from OpenAI, Deepmind, and Stability AI, among others, have warned about the risk of extinction from unregulated AI. The statement says that mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war. This statement was signed by Sam Altman, CEO OpenAI, Demis Hassabis, CEO DeepMind, Emad Mostaque, CEO Stability AI, Kevin Scott, CTO Microsoft, and many other leading AI execs and AI scientists. Notable omissions, so far, include Yann LeCun, Chief AI Scientist Meta, and Elon Musk, CEO Tesla/Twitter.

All in all, these issues are significant to the development of technology and its integration into society. It’s important that we take these warnings and opinions seriously and find ways to support technology that benefits humanity as a whole.

Hey there! Are you ready for your daily dose of AI updates? Let’s jump right into it.

First up, we have Roop- a face swap software that allows you to replace the face in a video with the face of your choice. The best part? You only need one image of the desired face. No dataset, no training. One click, and you’re good to go!

Next, we’ve got Voyager – the first LLM-powered embodied lifelong learning agent in Minecraft. It explores the world, acquires diverse skills, and makes novel discoveries without any human intervention. Plus, its full codebase is open-sourced, making it accessible to all.

If you’re interested in cheap and quick vision-language (VL) adaptation, then you’ll want to know about LaVIN. It’s a new model that showed on-par performance with advanced multimodal LLMs while reducing training time by up to 71.4% and storage costs by 99.9%. Impressive, right?

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Moving on to Intel, their Meteor Lake processors will go all-in on AI. They’re integrating Arc graphics and a VPU to handle AI workloads efficiently, significantly reducing compute requirements of AI inferencing.

MediaTek is also working to transform the auto industry with AI and accelerated computing. They’re partnering with NVIDIA to enable new user experiences, enhanced safety, and new connected services for all vehicle segments.

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In the world of storytelling, new research has proposed TaleCrafter – a versatile and generic story visualization system. It leverages large language and pre-trained T2I models for generating a video from a story in plain text. It can even handle multiple novel characters and scenes, making it a promising tool for the entertainment industry.

For gamers, NVIDIA recently unveiled their Avatar Cloud Engine (ACE) for Games. This custom AI model foundry service enables smarter AI-based non-playable characters (NPCs) through AI-powered natural language interactions.

But it’s not just gamers who are benefiting from AI. Jensen Huang, the CEO of NVIDIA Corp claimed that AI has eliminated the “digital divide” by enabling anyone to become a computer programmer simply through speaking to a computer. Exciting stuff, right?

Finally, we have some interesting stats from iCIMS. According to their report, almost half of college graduates are interested in using ChatGPT or other AI bots to write their resumes or cover letters. 25% of Gen Z have already used an AI bot. However, job seekers using generative AI should be cautious – 39% of recruiters said using AI technology when hiring is a problem.

That’s all for today. See you tomorrow for more exciting AI updates!

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On today’s AI News from April 30th, 2023, we kick off with Huma.AI, a leader in generative AI, creating the future of life sciences through automated insight generation. According to their newly released White Paper, generative AI has become more than just an option for life science professionals, but the preferred way to consume data throughout the day. Huma.AI aims to provide these professionals with powerful decision-making data, analysis, and insights using everyday language.

Moving on to the next news, we have DOSS, a pioneer in conversational home search, integrating GPT-4 directly into their AI-powered Real Estate Marketplace, DOSS 2.0. This latest version makes real estate search accessible to all users, empowering them to ask questions through speech or text with an AI-powered solution responding based on how it was engaged. This enhancement also makes DOSS the first narrow domain consumer-facing platform on the web to incorporate GPT-4, enabling an unparalleled search experience without any third-party limitations.

Panaya, the global leader in SaaS-based Change Intelligence, and Testing for ERP and Enterprise business applications, has expanded its decade-long cooperation in SAP digital transformation with Panasonic, the global leading appliances brand, to mainland China. The implementation of SAP S/4HANA across multiple company sites is a significant undertaking for Panasonic in China, and the Panaya Test Dynamix platform provides a scalable and flexible solution that helps ensure the project is completed on time and within budget while maintaining the highest level of quality and compliance.

In other news, NVIDIA’s GH200 Grace Hopper Superchip is now in full production. This chip powers systems worldwide designed to run complex AI and HPC workloads. The GH200-powered systems join more than 400 system configurations powered by different combinations of NVIDIA’s latest CPU, GPU and DPU architectures, including NVIDIA Grace, NVIDIA Hopper, NVIDIA Ada Lovelace, and NVIDIA BlueField, created to help meet the surging demand for generative AI.

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Last but not least, Landing AI is using NVIDIA Metropolis for Factories platform to deliver its cutting-edge Visual Prompting technology to computer vision applications in smart manufacturing and other industries. Landing AI’s Visual Prompting technology provides the next era of AI factory automation, enabling industrial solution providers and manufacturers to develop, deploy, and manage customized computer vision solutions to improve throughput, production quality, and decrease costs. And that’s it for this edition of AI News.

Hey there, AI Unraveled podcast listeners! Are you curious about artificial intelligence and want to take your understanding to the next level? Well, have we got news for you! The must-have book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is now available on Amazon.

This engaging read is the perfect solution to all of your burning questions about the world of AI. You’ll gain valuable insights into this fascinating field, and be better equipped to stay ahead of the curve.

So, what are you waiting for? Head on over to Amazon and grab your copy of “AI Unraveled” today! This essential book is sure to expand your knowledge and leave you feeling informed and empowered.

In today’s episode, we explored the latest advancements in AI, including Google AI’s human pangenome reference, the integration of AI workloads in Intel’s Meteor Lake processors, and the use of Generative AI in life sciences by Huma.AI, while also highlighting resources such as “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence“. Thanks for tuning in, and don’t forget to subscribe!

AI Unraveled Podcast May 29th 2023: From Trusted Advisor to Nightmare: The Hazards of Depending on AI, Can Language Models Generate New Scientific Ideas?, AI in dentistry-better crown, ChatGPT and Generative AI in Banking, Nvidia’s All-Time High, LIMA


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Latest AI Trends May 29th: From Trusted Advisor to Nightmare: The Hazards of Depending on AI, Can Language Models Generate New Scientific Ideas?, AI in dentistry-better crown, ChatGPT and Generative AI in Banking, Nvidia’s All-Time High, LIMA,
Latest AI Trends May 29th: From Trusted Advisor to Nightmare: The Hazards of Depending on AI, Can Language Models Generate New Scientific Ideas?, AI in dentistry-better crown, ChatGPT and Generative AI in Banking, Nvidia’s All-Time High, LIMA,

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we explore the latest AI trends. In this episode, we discuss the hazards of depending on AI as a trusted advisor, the potential for language models to generate new scientific ideas, the use of AI in dentistry to create better crowns, and much more. Stay up-to-date on the latest developments in AI by subscribing to our podcast now. In today’s episode, we’ll cover the importance of using reliable sources for legal research, insights on AI and its impact on industries such as dentistry and banking, an AI algorithm discovering a new antibiotic treatment, new developments in LLaMa models, and the use of AI voices for podcasting.

Have you heard about the dangers of relying too heavily on AI? One lawyer learned this lesson the hard way when he used an AI language model called ChatGPT to compose a brief for a personal injury lawsuit against Avianca airlines. The lawyer cited half a dozen cases to bolster his client’s claims, but it turned out that ChatGPT had supplied him with fake cases. When asked to provide tangible copies of these cases, the lawyer once again turned to ChatGPT, which reassured him that they were genuine. However, the judge was not pleased with this and threatened sanctions against both the lawyer and his firm. This serves as a warning of how AI can produce inaccurate information, even for legal professionals. But AI can also be used in positive ways, such as in literature-based discovery (LBD). LBD focuses on hypothesizing ties between ideas that have not been examined together before, particularly in drug discovery. A new application of LBD called Contextualized Literature-Based Discovery (C-LBD) aims to take this a step further by having the language model generate entirely new scientific ideas based on existing literature. As with any tool, AI has both benefits and drawbacks, but it’s up to us to use it responsibly and appropriately.

Hey there, AI Unraveled podcast listeners! Are you ready to take your knowledge of artificial intelligence to the next level? Then you won’t want to miss out on the must-read book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” which is now available on Amazon! This engaging and informative book will leave no question unanswered as you immerse yourself in the captivating world of AI. It’s the perfect opportunity to enhance your knowledge and keep up with the fast-paced advancements in the field. So why wait? Head on over to Amazon now and grab your copy of “AI Unraveled“!

Let’s talk about machine learning and its impact on various fields. In the medical field, researchers are looking at how machine learning can help in studying rare diseases through various emerging approaches. Using AI, they’re capable of designing personalized dental crowns with a higher degree of accuracy than traditional methods. But it’s not just limited to dental care; machine learning is being used to find the signature of chronic pain through mapping brain activity to painful sensations. It’s also making waves in banking, where generative AI is helping to create marketing images and text, answer customer queries, and produce data. AI is revolutionizing all aspects of our lives, and we’re seeing rapid advancements across various industries. In fact, Nvidia’s recent surge in stock value by 24% highlights the incredible speed at which AI is reshaping the market. Even the discovery of new antibiotics for drug-resistant infections caused by Acinetobacter baumannii is being done through a computational model that feeds around 7,500 chemical compounds into an algorithm that learns the chemical features associated with growth suppression. With AI’s endless possibilities, we’re sure to see even more breakthroughs in the future.

Hey there, it’s time for your daily AI update and today we’ve got some exciting news. First up, we’ve got a new language model called LIMA that’s been developed. This model has a stunning 65 billion parameter LLaMa and has been fine-tuned on over a thousand curated responses and prompts. The idea behind LIMA is to anticipate the next token for almost any language interpretation or generating job. Moving on to some exciting announcements, NVIDIA has a new Avatar Cloud Engine for Games. This cloud-based service will give developers access to various AI models such as NLP, facial animation, and motion capture models. The goal here is to create NPCs that have intelligent conversations, can express emotions, and react realistically to their surroundings. BiomedGPT is another exciting development in the world of AI. This biomedical generative pre-trained transformer model utilizes self-supervision on diverse datasets to handle multi-modal inputs and perform various downstream tasks. It achieves state-of-the-art models across 5 distinct tasks and 20 public datasets containing 15 biomedical modalities. Now, let’s talk about Break-A-Scene. This is a new approach from Google that’s focused on extracting multiple concepts from a single image for textual scene decomposition. Essentially, if you give it a single image of a scene with multiple concepts of different kinds, it will extract a dedicated text token for each concept. This will enable fine-grained control over the generated scenes. JPMorgan is also joining the AI race with their new ChatGPT-like service. It’s being developed to provide investment advice to their customers and they’ve even applied to trademark a product called IndexGPT. The bot will provide financial advice on securities, investments, and monetary affairs. Lastly, IBM Consulting has revealed its Center of Excellence (CoE) for generative AI. Its primary objective is to enhance customer experiences, transform core business processes, and facilitate innovative business models. The CoE has an extensive network of over 21,000 skilled data and AI consultants who have completed over 40,000 enterprise client engagements. That’s all for today’s AI update, thanks for listening!

Welcome to the podcast, where I’m your AI host powered by the Wondercraft AI platform. As we continue our fascinating discussion about AI, let me take a moment to share a valuable resource that I’m sure all of you AI enthusiasts will love. Are you looking to level up your machine learning skills and make a handsome six-figure salary? If so, then you need to check out “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” by Etienne Noumen, Professional Engineer based in Calgary, Alberta, Canada. This comprehensive guide is a treasure trove of information, practice exams, and tips designed to help you ace the AWS Machine Learning Specialty (MLS-C01) Certification. As we all know, AWS is a dominant player in the cloud space, and having this certification can really set you apart in the industry. What’s more, this essential guide is available on Amazon, Google, and the Apple Book Store. So, no matter what platform you prefer, you can easily get your hands on a copy of this game-changing book. But don’t take my word for it, get your own “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and start your journey towards machine learning mastery. Trust me, it’s worth it!

In today’s episode we discussed the importance of using reliable sources, the rise of AI in various industries, the latest advancements in AI technology, and some useful resources to stay ahead of the curve. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast May 28th 2023: Google Launches New AI Search Engine (SGE), Will AI introduce a trusted global identity system?, Minecraft Bot Voyager Programs Itself Using GPT-4, AI Versus Machine Learning: What’s The Difference?

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we dive into the latest AI trends. In our episode today, we explore Google’s new AI search engine, the possibility of a trusted global identity system, the Minecraft Bot Voyager program that uses GPT-4 to self-program, and the difference between AI and machine learning. Don’t miss out on staying updated with the latest AI trends, hit the subscribe button now! In today’s episode, we’ll cover Google’s new AI-powered search engine, AWS Certified Machine Learning Specialty Practice Exams, the potential impacts of AI on global identity systems, Voyager AI’s use of GPT-4, the differences between AI and Machine Learning and their applications in creating a killer antibiotic, and recent developments in AI technology such as ChatGPT’s superior testing performance, promising cough sound algorithms, a new AI governance blueprint from Microsoft, and “AI Unraveled” book available on Amazon for AI enthusiasts.

Hey there! Have you heard the news? Google has just launched a new search engine powered by AI that aims to enhance search results and provide users with new and novel answers generated by Google’s advanced language model. The search engine is called Search Generative Experience, or SGE for short, and it’s designed to display these answers directly on the Google Search webpage. When you enter a query, the answer will expand in a green or blue box, rather than the traditional blue links we’re used to seeing.

So, how can you get started with SGE? Well, it’s an experimental version at the moment, but Google has provided a guide on how to sign up and take advantage of this cutting-edge tool. The information provided by SGE is derived from various websites and sources that were referenced during the generation of the answer. You can also ask follow-up questions within SGE to obtain more precise results, making it even easier to find what you’re looking for.

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As the amount of AI-generated content increases, there are growing concerns about potential feedback loops in the data pool. In other words, will the data used by AI start to dilute into a feedback loop of AI content? This is something that’s being explored as more and more AI-generated content is created.

AI is also set to disrupt tools like Photoshop, as the integration of AI has the potential to create a range of disruptions in graphic design software. This presents potential challenges for designers and graphic artists in the future.

So, there you have it – the latest news from the world of AI! Stay tuned for more updates, and be sure to check out the guide to get started with SGE.

Hey there! I wanted to take a quick break from our riveting conversation on AI to talk about a book that’s going to take your machine learning skills to the next level and potentially even land you a six-figure salary. If you’re a fan of AI, then you’re going to want to hear about this.

The book I’m talking about is called “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and it’s written by Etienne Noumen. This book is an incredible resource for anyone looking to ace the AWS Machine Learning Specialty exam.

It includes three practice exams and quizzes covering everything from data engineering to NLP. It’s packed with valuable information, tips, and practice exams that will help set you apart in the industry.

And the best part? You can get it on Amazon, Google, or the Apple Book Store, so no matter what platform you prefer, you can get your hands on this essential guide.

Whether you’re just starting out or are looking to take your machine learning expertise to the next level, this book is a must-have. Trust me, it’s a game-changer. So go ahead and grab a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and start your journey towards machine learning mastery and that coveted six-figure salary.

Now, let’s get back to exploring the fascinating world of AI.

AI and the Future of Global Identity Systems:

Have you noticed how bots on social media are getting more realistic? The release of openAI has brought about this change, and it’s just the beginning. While digital currency is on the horizon, the topic of trust on the internet becomes more relevant. With a new digital ID system in the making, will AI play a role in determining a person’s authenticity? Mastercard is working on expanding its Digital Transaction Insights security to identify users based on their patterns and behavior. It leaves us wondering, how will AI shape the future of global identity systems?

The Impressive Capabilities of the Minecraft Bot Voyager:

The intersection between AI and gaming technology has given rise to the Minecraft bot, Voyager. While other Minecraft agents use reinforcement learning techniques, Voyager uses GPT-4 for lifelong learning. Its innovative method of writing, improving, and transferring codes from an external skill library allows Voyager to perform small tasks such as navigating, crafting, and fighting zombies with ease. Nvidia researcher Jim Fan describes GPT-4 as unlocking a “new paradigm” in terms of AI bots’ capabilities. However, it still has limitations in terms of a purely text-based interface, and currently struggles with complex visual tasks.

The Debate Around AI and Job Loss:

Are you excited about AI? As exciting as it is, concerns about job loss due to automation continue to rise. Even as someone in the creative field, I often wonder if my job is at risk. It’s important to find a balance between embracing this technology and acknowledging the potential societal impact. Without a clear idea of future job opportunities, it’s understandable why some feel concerned and hesitant to embrace AI’s advancements.

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The Possibility of a ‘Non-AI’ Label:

As AI takes over digital content, it’s possible that individuals will seek out Non-AI certified materials. Could there be a ‘Non-AI’ label in the future, similar to the ‘Non-GMO’ label we see on food products? It’s a question worth considering as we continue to embrace AI’s impact on our lives.

When it comes to AI and machine learning, they are closely related in the tech world, but there are differences to take note of. Generally speaking, AI refers to systems that are programmed to perform complex tasks, while machine learning is a branch of AI that deals with software capable of predicting future trends. One recent example of AI in action is the creation of an antibiotic that can attack a particularly nasty microbe known as acinetobacter baumannii. In terms of machine learning, it’s being leveraged by companies like Spotify to analyze users’ music preferences to offer recommendations and generate playlists. One type of AI – a large language model (LLM) – is capable of learning more about text and other types of content after processing massive data sets through unsupervised learning. This process helps the LLMs determine the relationship between words and concepts. One real-world use of these techniques is demonstrated in OpenAI’s ChatGPT, a chatbot that can chat with users and produce human-like responses. Though sometimes ChatGPT’s responses can be nonsensical or even incorrect, the chatbot has already gained a large following and has been used for everything from writing emails to planning vacations.

In today’s episode, we’ll be discussing some interesting news in the world of artificial intelligence. First up, we have someone’s personal experience with the coding language bard. They tested it out with autohotkey code and compared it to ChatGPT. While ChatGPT performed better, bard showed potential. One thing to note is that bard seemed to do better in V1 as opposed to V2, and while it may not be as advanced as ChatGPT now, it has the ability to obtain live data, which is a valuable feature. Have any of our listeners tried coding with bard? Let us know your thoughts in the comments!

Moving on, a recent study explored the possibility of using machine learning algorithms to detect acute respiratory diseases based on cough sounds. The results showed promise, which is exciting news for the healthcare industry.

Lastly, Microsoft recently shared a 5-point blueprint for governing AI. These points include building upon government-led AI safety frameworks, implementing safety brakes for AI systems that control critical infrastructure, developing a technology-aware legal and regulatory framework, promoting transparency and expanding access to AI, and leveraging public-private partnerships for societal benefit. What other aspects would you add to this blueprint? Let us know in the comments.

Before we wrap up, we want to let our listeners know about “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” a book now available on Amazon. It’s a great resource to expand your understanding of artificial intelligence and stay ahead of the curve. Get your copy today!

Thanks for listening and tune in next week for more AI news and updates.

In today’s episode, we covered Google’s AI-powered search engine, AWS Certified Machine Learning Specialty Practice Exams, the potential impact of AI on job loss and a global identity system, the difference between AI and Machine Learning, and some exciting developments in AI such as cough sound algorithms for detecting respiratory diseases. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast May 26 2023: Can quantum computing protect AI from cyber attacks?, AI Latest News on May 26th, 2023 – 12 brand new tools and resources – Top 5 AI Tools for Education.

Latest AI Trends May 26 2023: Can quantum computing protect AI from cyber attacks?, AI Latest News on May 26th, 2023 - 12 brand new tools and resources - Top 5 AI Tools for Education
Latest AI Trends May 26 2023: Can quantum computing protect AI from cyber attacks?, AI Latest News on May 26th, 2023 – 12 brand new tools and resources – Top 5 AI Tools for Education

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we discuss the latest trends and news in the exciting world of AI. In this episode, we delve into the topic of whether quantum computing can protect AI from cyber attacks, and highlight 12 brand new tools and resources that will surely pique your interest. Stay informed with the latest AI news on May 26th, 2023 and beyond – be sure to hit that subscribe button to stay updated! In today’s episode, we’ll cover how AI tools are transforming education and highlight companies leading the way, 12 new AI-powered tools and innovations such as an AI-powered language model competitor, a new antibiotic discovered using AI, recent developments in tech including Nvidia’s explosive stock and Google’s AI Search Generative Experience, and a podcast utilizing the Wondercraft AI platform and book answering commonly asked AI questions.

Would you like to learn about how quantum computing can protect AI from cyber attacks? It’s a fascinating topic, considering how AI algorithms are used in various applications like autonomous driving, facial recognition, biometrics, and drones. Unfortunately, AI algorithms are vulnerable to cyber attacks. That’s where quantum computing comes into play. The advanced computing technology has shown promise in enhancing cybersecurity and protecting AI against threats. Now, let’s switch gears and talk about something exciting – the top five AI tools for education. If you’re a student or a teacher who wants to learn more about AI educational tools, this is for you. First on the list is Querium. They’ve developed an AI tool known as the Stepwise Virtual Tutor, which provides step-by-step assistance in STEM subjects. It’s like having a personal tutor available 24/7. Students can learn at their own pace, making it easier to master complex concepts. What about Thinkster Math? It’s an AI educational tool that uses AI to map out students’ strengths and weaknesses, making math learning personalized and effective. Content Technologies Inc. is another game-changer in the education sector. They’ve developed an AI tool that creates customized learning content, making it easier for students to understand and retain information. Next up is CENTURY Tech, which creates personalized learning pathways for students based on their strengths, weaknesses, and learning style. And last but not least, there’s Netex Learning’s LearningCloud, an AI teaching tool that tracks students’ progress and adapts content to their needs, keeping students engaged and learning effectively. All these AI tools are making education more accessible, personalized, and effective. Have you used any of these AI tools before, or are you thinking of trying them out? Let us know your thoughts!

Today we have 12 exciting brand-new tools and resources to go over! Let me start with Bard Anywhere, a Chrome extension shortcut that enables quick search on any site. Then, we have Tyles, an AI-driven note app that organizes and sorts your knowledge magically. Next up, Humbird AI, an AI-powered Talent CRM for high-growth technology companies. But wait, it doesn’t stop there! How about DecorAI with its power to generate dream rooms using AI for everyone, or OdinAI which offers health recommendations for your app through ChatGPT? There’s also Waitlyst, a platform that offers autonomous AI agents for startup growth, and ChatUML, the perfect AI assistant for making diagrams. And for all you Excel and Google Sheets fans, Ajelix is an AI tool you can’t miss! Plus, KAI is an app that lets you add ChatGPT to your iPhone’s keyboard for convenience. If you’re interested in language training, we have Talkio AI, an AI-powered language training app for your browser, and GPT Workspace, which allows you to use ChatGPT in Google Workspace. But that’s not all! Let’s not forget about Thentic, a powerful platform that can automate web3 tasks with no-code and AI. And finally, OpenAI is launching ten $100,000 grants for “building prototypes of a democratic process for steering AI.” There’s more, Guanaco, an AI chatbot competitor trained on a single GPU in just one day. Researchers from the University of Washington developed QLoRA, which is a method for fine-tuning large language models. They have introduced Guanaco, a family of chatbots based on Meta’s LLaMA models. The largest Guanaco variant has 65 billion parameters and achieves nearly 99% of ChatGPT’s performance in a GPT-4 benchmark. This new development of QLoRA and Guanaco demonstrates the potential for more accessible fine-tuning of large language models on a single GPU. It’s a crucial improvement that could lead to broader applications and increased accessibility in natural language processing. Even with slow 4-bit inference and weak mathematical abilities, the researchers have promising future improvements to bring to these fascinating new tools and resources!

Hey there! Let’s dive into the latest AI news from May 26th, 2023. Are you ready? First, let’s talk about a groundbreaking discovery in drug development. Scientists have developed a new antibiotic that can kill some of the world’s most dangerous drug-resistant bacteria, and they did it by using artificial intelligence. This breakthrough could revolutionize the way we hunt for new drugs and tackle some of the biggest health threats facing our planet. Switching gears to social media, TikTok is testing an AI chatbot called ‘Tako’ that’s designed to help users navigate the platform and answer their questions. By enhancing its customer service capabilities, TikTok is putting its best foot forward to make its app more user-friendly and support its expansive community. But that’s not all, the stock for Nvidia, a tech and AI industry leader, recently soared thanks to what analysts are calling ‘guidance for the ages.’ This marks a bright future for the company, and Wall Street is buzzing with excitement. On the AR side of things, Clipdrop has launched a new AI-powered tool called ‘Reimagine XL’ that allows users to bring real-world objects into digital environments more accurately and with improved stability. With AR rapidly gaining traction, Clipdrop’s technology is paving the way for more seamless and immersive AR experiences. Google has also introduced a new feature called the ‘AI Search Generative Experience’ that leverages artificial intelligence to provide more accurate and nuanced search results. This interface is likely to become a go-to tool for anyone looking for more precise search results. Finally, OpenAI has outlined its vision for allowing public influence over AI systems’ rules. The organization is committed to ensuring that access to, benefits from, and influence over AI and AGI are widespread. However, its CEO has warned that if new AI regulations are implemented in Europe, OpenAI may have to stop operating there, reflecting the ongoing debate about how to manage and regulate the growth of artificial intelligence. That’s it for now. Stay tuned for more exciting developments in the world of AI!

Hey there AI enthusiasts, welcome to another episode of AI Unraveled! Today, I’d like to talk to you about a really cool tool called Wondercraft AI platform. It’s a game-changing tool that makes starting your own podcast a breeze. Wondercraft AI gives you the opportunity to use super-realistic AI voice as your host, just like mine! So, if you’re ever interested in creating a podcast, you should definitely give it a shot! Next up, I have some exciting news for you! I know you’re eager to expand your knowledge on artificial intelligence, so I’m happy to recommend to you a fantastic book that’s now available on Amazon, called AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. This book is an engaging read that really dives into the fascinating world of AI, answering all of those burning questions you may have and offering valuable insights that will keep you ahead of the curve. So what are you waiting for? Head to Amazon and grab your copy today!

On today’s episode, we covered the revolutionary impact of AI tools on education, 12 new AI-powered apps and technologies, breakthroughs in AI’s use in medicine and chatbots, as well as the use of AI in podcast production with 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!

AI Unraveled Podcast May 25th 2023: What is the new Probabilistic AI that’s aware of its performance?, How are robots being equipped to handle fluids?, AI-powered Brain-Spine-Interface helps paralyzed man walk again, AI vs. Algorithms

AI Unraveled Podcast May 25th 2023: What is the new Probabilistic AI that's aware of its performance?, How are robots being equipped to handle fluids?, AI-powered Brain-Spine-Interface helps paralyzed man walk again, AI vs. Algorithms
What is the new Probabilistic AI that’s aware of its performance?, How are robots being equipped to handle fluids?, AI-powered Brain-Spine-Interface helps paralyzed man walk again, AI vs. Algorithms
Welcome to AI Unraveled, the leading podcast that explores and demystifies frequently asked questions on Artificial Intelligence. In this episode, we discuss the latest AI trends, including the new Probabilistic AI that’s aware of its performance, how robots are being equipped to handle fluids, and the incredible AI-powered Brain-Spine-Interface that is helping a paralyzed man walk again. We also take a look at how researchers are using AI to identify similar materials through images, and we examine the difference between AI and algorithms.
To stay updated on the latest AI trends, make sure to subscribe to AI Unraveled. In today’s episode, we’ll cover the following topics: Scientists using AI to find drugs for resistant infections, AI advancements in material science research, introduction to “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams“, combining cortical implants with AI to enable a paralyzed man to walk, AI tools reducing poster designing time for an independent musician, and the distinction between AI and algorithms.
Hey there, do you know how scientists are using artificial intelligence to find a drug that can combat drug-resistant infections? It’s pretty fascinating stuff. By leveraging the power of AI, researchers are identifying a potential drug that could have a significant impact on medical treatments and the fight against antibiotic resistance. But that’s not all. There’s a new form of probabilistic AI that can gauge its own performance levels. This advanced AI system has the potential to improve accuracy and reliability for various applications, which is great news for those who rely on AI.
In other news, robotics engineers are currently working on equipping robots with the ability to handle fluids. This development opens up doors for robots to perform more delicate tasks in industries such as healthcare and food service, as well as industrial automation. Oh, and speaking of AI, do you want to expand your knowledge of it? If so, you should check out the book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” This engaging read answers your burning questions about AI and provides valuable insights into the captivating world of artificial intelligence. You can get your copy on Amazon right now!
Hey there! Are you curious about how researchers are using AI to identify similar materials in images? Well, they have developed an AI system that can spot different materials in pictures, which could significantly enhance materials science research. This means that the AI could help to discover and develop new materials that could be used for a variety of purposes. In the past year, artificial intelligence has progressed shockingly fast, becoming capable of things like designing chatbots and creating ‘fake’ photos. The leap in capability has come from advances in things like machine learning, which has allowed AI to learn as it goes.
Researchers from Duke University and their partners are using machine learning techniques to uncover the atomic mechanics of a broad category of materials under investigation for solid-state batteries in a breakthrough for energy research. In exciting news for healthcare customers, NVIDIA AI is integrating with Microsoft Azure machine learning. This could mean that users can build, deploy and manage customized Azure-based artificial intelligence applications for large language models using more than 100 NVIDIA AI.
And finally, the European SustainML project aims to help AI designers reduce power consumption in their applications. They’re devising an innovative development framework that will eventually help to reduce the carbon footprint of machine learning. Pretty cool stuff, right?
We interrupt our discussion on AI to bring your attention to an invaluable resource for all the AI enthusiasts out there. Are you looking to level up your machine learning skills and maybe earn a six-figure salary? Well, we’ve got just the thing for you! It’s a book you need to have on your radar, and it’s called “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams.” This book is written by Etienne Noumen, who is an experienced engineer and author in the field of data engineering and machine learning engineering.
Even better, this book is available on Amazon, Google, and the Apple Book Store, so no matter what your preferred platform, you can get your hands on this essential guide. Don’t just take our word for it. Get a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and begin your journey towards machine learning mastery and maybe that six-figure salary. Trust us, it’s a game-changer. Now, let’s get back to unraveling the fascinating world of AI.
So I came across this fascinating research paper in Nature and wanted to share it with you. Have you ever heard of a man who had suffered paralysis for 12 years but is now able to walk again? Well, the researchers combined cortical implants with an AI system to enable the transmission of brain signals to the spine. This milestone is a breakthrough in the medical field as previously, medical advances had only demonstrated the reactivation of paralyzed limbs in limited scopes, such as with human hands, legs, and even paralyzed monkeys. What’s remarkable about this system is that it converts brain signals into lower body stimuli in real-time. This means that the man using the system can now do everyday things like going to bars, climbing stairs, and walking up steep ramps. He’s been able to use this system for a full year, and researchers found notable neurological recovery in his general skills to walk, balance, carry weight, and more. What’s even more fascinating is that this new AI-powered Brain-Spine-Interface helped him recover additional muscle functions, even when the system wasn’t directly stimulating his lower body.
The researchers used a set of advanced AI algorithms to rapidly calibrate and translate his brain signals into muscle stimuli with 74% accuracy. All of this was done with an average latency of just 1.1 seconds, so it’s a pretty seamless system. He can now switch between standing and sitting positions, walk up ramps, move up stair steps, and do so much more. This breakthrough could open up even more pathways to help paralyzed individuals recover functioning motor skills again. Past progress has been promising but limited, and this new AI-powered system demonstrated substantial improvement over previous studies. So where could this go from here? In my opinion, LLMs could power even further gains. As we saw with a prior Nature study where LLMs are able to decode human MRI signals, the power of an LLM to take a fuzzy set of signals and derive clear meaning from it transcends past AI approaches. The ability for powerful LLMs to run on smaller devices could simultaneously add further unlocks. The researchers had to make do with a full-scale laptop running AI algos, but imagine if this could be done in real-time on your mobile phone. The possibilities are limitless.
Hey there! Let’s talk about how AI has improved people’s lives in different ways. As a touring musician who is also an independent artist, there’s a lot of work that goes into the backend of things, including graphic design for flyers, posters, merch, and more. While it’s something that I enjoy doing, it can be incredibly time-consuming. That’s where AI tools have come in handy. With the help of image-to-text AI tools, I’ve been able to reduce the amount of time I spend designing by 90%. It’s not perfect, but it’s allowed me to spend more time creating music. I know AI can be scary for some people, but these breakthroughs have given me more of my life back.
Speaking of AI innovations, the Microsoft 2023 keynote revealed some really mindblowing updates. Nadella announced Windows Copilot and Microsoft Fabric, two new products that bring AI assistance to Windows 11 users and data analytics for the era of AI, respectively. This is sure to transform how people work and use technology in their daily lives. But that’s not all – Nadella also unveiled Microsoft Places and Microsoft Designer, two new features that leverage AI to create immersive and interactive experiences for users in Microsoft 365 apps. It’s amazing to think about how much more personalized and engaging these apps will become.
And finally, Nadella announced that Power Platform is getting some exciting new features that will make it even easier for users to create no-code solutions. Power Apps will have a new feature called App Ideas that will allow users to create apps simply by describing what they want in natural language. These innovative features are sure to change the game in terms of how people create and use technology. Pretty exciting stuff, huh?
Have you ever wondered what the difference is between AI and algorithms? Although they are both important aspects of computing, they serve different functions and represent different levels of complexity. Let’s first talk about algorithms. Basically, an algorithm is like a recipe that a computer follows to complete a task, from basic arithmetic to complex procedures like sorting data. Every piece of software that we use in our daily lives relies on algorithms to function properly. Now, AI, on the other hand, refers to a broad field of computer science that focuses on creating systems capable of tasks that normally require human intelligence. This includes things like learning, reasoning, problem-solving, perception, and language understanding.
The goal of AI is to create systems that can perform these tasks without human intervention. It’s important to note that while AI systems use algorithms as part of their operation, not all algorithms are part of an AI system. For example, a simple sorting algorithm doesn’t learn or adapt over time, it just follows a set of instructions. On the other hand, an AI system like a neural network uses complex algorithms to learn from data and improve its performance over time. So, in summary, while all AI uses algorithms, not all algorithms are used in AI.
In today’s episode, we discussed breakthroughs in creating drugs using AI, advancements in materials science, the introduction of a new book to help with machine learning certification, the exciting news of combining cortical implants with AI to help paralyzed individuals, and how AI is aiding the creation of immersive experiences and no-code features on Microsoft platforms – thanks for listening and don’t forget to subscribe!

AI Unraveled Podcast May 24th 2023: The artist using AI to turn our cities into ‘a place you’d rather live’, How will AI change wars?, Superintelligence – OpenAI Says We Have 10 Years to Prepare

AI Unraveled Podcast May 24th: The artist using AI to turn our cities into 'a place you'd rather live', How will AI change wars?, Superintelligence - OpenAI Says We Have 10 Years to Prepare
The artist using AI to turn our cities into ‘a place you’d rather live’, How will AI change wars?, Superintelligence – OpenAI Says We Have 10 Years to Prepare

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we explore the latest AI trends and the potential impact of this revolutionary technology. In this episode, we delve into some fascinating topics, including an artist who is using AI to transform our urban landscapes, the influence of AI on warfare, and OpenAI’s recent warning about the need to prepare for superintelligence. To stay updated on the latest developments in the AI world, make sure to subscribe to our podcast today. In today’s episode, we’ll cover how emerging tech is shaping the future of public space and creating new challenges in war, the availability of AWS Machine Learning Specialty certification and practice exams, open-source innovations like QLoRA that could outpace closed-source, the latest advancements in AI software with Nvidia and Microsoft, Google and Microsoft’s generative AI, chatbot and data analysis platform, and how Wondercraft AI is enabling easy podcasting with hyper-realistic voices.

Hey there! Today, we’re diving into the topic of how AI is being used to shape the future of our cities and the potential impact it could have on war as we know it.

Let’s start by talking about how AI is being used to create more beautiful versions of our cities. Imagine walking down a street and being completely enamored by the stunning architecture and perfectly placed greenery. This is the vision of the artist using AI to turn our cities into a place you’d rather live in.

But it’s not just about aesthetics. AI is also being harnessed to help cities respond to climate change. With machine learning, we can analyze data and make predictions about future environmental issues and take proactive measures to mitigate their impact.

Now, let’s shift gears and dive into the topic of how AI could completely change the nature of warfare. Will hand-to-hand combat become a thing of the past? With the advancement of technology, it’s a possibility.

We could see fully automated weapons systems that operate with no morals or conscience, just cold calculation. Imagine a self-driving tank that has image recognition and GPS, where the entire crew compartment is available for more armor, more engine, and more ammo. It could be given orders to enter a geofence and kill anyone with a gun.

But, as scary as that may sound, it could also be given vague instructions to just kill everyone and everything within a certain area, completely disregarding basic humanity and committing war crimes without a second thought.

This is the reality of the intersection between AI and warfare, where the line between humanity and technology is quickly becoming blurred.

Hey there, AI enthusiasts! We interrupt our engaging discussion on AI for a quick shout out to an invaluable resource that should be on your radar

A book that can help you level up your machine learning skills and even earn a six-figure salary. That’s right, we’re talking about “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams”, written by Etienne Noumen.

This treasure trove of information, tips, and practice exams is specifically designed to get you ready for the AWS Machine Learning Specialty (MLS-C01) Certification. As we all know, AWS is a dominant player in the cloud space, and having this certification under your belt can really set you apart in the industry.

The best part? You can get your hands on this essential guide at Amazon, Google, and the Apple Book Store. So, no matter what platform you prefer, you can start your journey towards machine learning mastery and that coveted six-figure salary.

Don’t take our word for it, though. Get a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and experience the game-changing benefits for yourself. Trust us, this book is a must-read for any AI enthusiast out there.

With that being said, let’s get back to unraveling the fascinating world of AI.

Hey there, today we’re talking about a breakthrough in the world of language models. Fine-tuning is already widely used to enhance existing models without the need for costly training from scratch. LoRA is a popular method for fine-tuning that is gaining steam in the open-source world. However, the recently leaked Google memo calls out Google (and OpenAI too) for not adopting LoRA, which may allow open-source to outpace closed-source LLMs.

OpenAI recognizes that the future of models is about finding new efficiencies. And the latest breakthrough, QLoRA, is a game-changer. QLoRA is even more efficient than LoRA, democratizing access to fine-tuning without the need for expensive GPU power. Researchers have fine-tuned a 33B parameter model on a 24GB consumer GPU using QLoRA in just 12 hours at a benchmark score of 97.8% against GPT-3.5.

QLoRA introduces three major improvements, including a compression-like 4-bit NormalFloat data type that is precise and compresses memory load. And the quantized constants that came in the pack reduce the need for further compression. Memory spikes typical in fine-tuning are optimized to reduce memory load.

Mobile devices may soon be able to fine-tune LLMs, allowing for personalization and increasing data privacy. Additionally, real-time info can be incorporated into models, bringing the cost of fine-tuning down. Open-source is emerging as an even bigger threat due to these innovations, and many closed-source models may outpace closed-source models as a result.

Lastly, Sam Altman’s 2015 blog post on superintelligence still holds relevant today. He argues that regulation and fear surrounding superintelligence are necessary to protect society. With the rapid advancements in LLMs and AI, we should take these warnings seriously, even more so in the coming years.

Have you heard of the latest addition to the “as a service” market?

It’s called AIaaS and it’s making waves in the tech industry. Companies like Nvidia and Microsoft are teaming up to accelerate AI efforts for both individuals and enterprises. In fact, Nvidia will integrate its AI enterprise software into Azure machine learning and introduce deep learning frameworks on Windows 11 PCs.

But that’s not the only exciting news in the world of AI. Have you heard about the QLoRA method that enables fine-tuning an LLM on consumer GPUs? It has some big implications for the future of open-source and AI business models.

And if you’re interested in AI tools, you should check out AiToolkit V2.0, which is based on feedback from users like you and offers over 1400 AI tools.

In other news, Microsoft has launched Jugalbandi, an AI chatbot designed for mobile devices that can help all Indians access information for up to 171 government programs, especially those in underserved communities. And if you’re curious about what Elon Musk thinks about AI, he believes it could become humanity’s uber-nanny.

Lastly, Google has introduced Product Studio, a tool that lets merchants create product imagery using generative AI, while Microsoft has launched Fabric, an AI data analysis platform that enables customers to store a single copy of data across multiple applications and process it in multiple programs. It’s interesting to see how AI is being integrated into so many different areas and industries.

Hey there! I am excited to share some exciting news about tech innovations and AI updates!

Google has recently announced its latest addition to AI-powered ad products and marketing tools, and it includes the use of generative AI in Performance Max. What this means is that businesses using Google ads can now utilize generative AI to help them create, customize, and launch ads that have a higher chance of achieving better results.

Speaking of AI, Microsoft has just launched Jugalbandi, a chatbot designed specifically for mobile devices in India. The bot can help users gain access to information about up to 171 government programs, especially those in underserved communities. This tool is expected to ease communication barriers in accessing essential services.

Have you ever wondered how AI can transform the way we use images in e-commerce? Well, Google has introduced Product Studio, a tool that enables merchants to create product imagery using generative AI. It means that businesses can automate the product image creation process and reduce the time spent on this task.

Moreover, Microsoft Fabric, an AI data analysis platform, has been launched. With this, customers can store a single copy of data across multiple applications and process it in multiple programs. For instance, data can be utilized for collaborative AI modeling in Synapse Data Science, while charts and dashboards can be built in Power BI business intelligence software.

Lastly, in a recent interview, Elon Musk, the visionary behind SpaceX and Tesla, stated that AI could become humanity’s uber-nanny. He believes that AI could help people make better decisions, reminders, and suggestions on how to improve their lives.

That’s all the exciting news for today. Stay tuned for more updates in the future.

Hey there AI Unraveled podcast fans! Thanks for tuning in. I’m excited to share with you some news that will take your understanding of artificial intelligence to the next level. Are you ready? Introducing the must-have book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence”. This gem is now available on Amazon, and it’s a game-changer.

If you’re curious about AI and have some burning questions, this book has got you covered. The insights provided are invaluable, and the writing style makes for an engaging read. Trust me, you won’t regret getting your hands on this gem.

With technology evolving at a rapid pace, it’s crucial to stay abreast of the latest developments. Investing in this book means that you’ll be staying ahead of the curve and keeping your knowledge up-to-date. Don’t miss out on this opportunity; get your copy on Amazon today!

Today on the podcast we discussed the potential of AI in shaping the future of public space, the AWS Machine Learning Specialty certification book, open-source advancements in the QLoRA method, the integration of AI software through AIaaS, the development of AI chatbots by Google and Microsoft, and the Wondercraft AI’s usage in podcasting; thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast May 23rd 2023: Why does Geoffrey Hinton believe that AI learns differently than humans?, When will AI surpass Facebook and Twitter as the major sources of fake news?, Is AI Enhancing or Limiting Human Intelligence?

Why does Geoffrey Hinton believe that AI learns differently than humans?

AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams: 3 Practice Exams, Data Engineering, Exploratory Data Analysis, Modeling, Machine Learning Implementation and Operations, NLP;

Is Meta AI’s Megabyte architecture a breakthrough for Large Language Models (LLMs)?

What does Google’s new Generative AI Tool, Product Studio, offer?

What is the essence of the webinar on Running LLMs performantly on CPUs Utilizing Pruning and Quantization?

When will AI surpass Facebook and Twitter as the major sources of fake news?

AI: Enhancing or Limiting Human Intelligence?

What are Foundation Models? 

What you need to know about Foundation Models

What is a Large Language Model?  Large Language Models (LLMs) are a subset of Foundation Models and are typically more specialized and fine-tuned for specific tasks or domains. An LLM is trained on a wide variety of downstream tasks, such as text classification, question-answering, translation, and summarization. That fine-tuning process helps the model adapt its language understanding to the specific requirements of a particular task or application.

What you need to know about Large Language Models

What is cognitive computing? Cognitive computing is a combination of machine learning, language processing, and data mining that is designed to assist human decision-making.

What is AutoML?AutoML refers to the automated process of end-to-end development of machine learning models. It aims to make machine learning accessible to non-experts and improve the efficiency of experts.

Why is AutoML Important?

In traditional machine learning model development, numerous steps demand significant human time and expertise. These steps can be a barrier for many businesses and researchers with limited resources. AutoML mitigates these challenges by automating the necessary tasks.

Limitations and Future Directions of AutoML

While AutoML has its advantages, it’s not without limitations. AutoML models can sometimes be a black box, with limited interpretability. Furthermore, it requires significant computational resources. It is important to understand these limitations when choosing to use AutoML.

Daily AI Update (Date: 5/23/2023): News from Meta, Google, OpenAI, Apple and TCS

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!

Attention AI Unraveled podcast listeners!

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,” now available on Amazon! This engaging read answers your burning questions and provides valuable insights into the captivating world of AI. Don’t miss this opportunity to elevate your knowledge and stay ahead of the curve.Get your copy on Amazon today!

AI Unraveled Podcast May 22nd 2023: AWS Machine Learning Specialty Certification, Microsoft Researchers Introduce Reprompting, Sci-fi author ‘writes’ 97 AI-generated books in nine months, AI Deep Learning Decodes Hand Gestures from Brain Images.

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast that brings you the latest and greatest in AI trends. In this episode, we discuss the AWS Machine Learning Specialty Certification Preparation, Microsoft Researchers’ introduction of Reprompting, and a Sci-fi author who ‘writes’ 97 AI-generated books in nine months. We’ll also explore how AI deep learning can decode hand gestures from brain images, and ponder the question: How can we expect aligned AI if we don’t even have aligned humans? Finally, we’ll dive into the mysterious world of governing AI-ghosts. Don’t miss out–subscribe now to stay updated on AI Unraveled. In today’s episode, we’ll cover Microsoft’s reprompting technology, AI-generated books, decoding hand gestures, harmonizing human creativity with machine learning, Alpaca’s learning model, generative AI, concerns about AI mimicking dead people, AI chatbots, and holograms disrupting grieving, AI alignment with human values, and a great resource for machine learning enthusiasts.

Hey there! Have you heard the latest news in the world of artificial intelligence? Microsoft researchers have come up with a new algorithm called Reprompting that can search for the Chain-of-Thought (CoT) recipes for a given task without human intervention. It’s an iterative sampling algorithm that seems quite promising. But that’s not all – a sci-fi author has generated 97 AI-written books in just nine months! It’s pretty fascinating to see how far AI has come in the field of literature. Speaking of deep learning, researchers have found a way to decode hand gestures from brain images by using AI. This breakthrough may lead to noninvasive brain-computer interfaces for paralyzed individuals, which is an incredible advancement. While we’re on the topic of AI’s capabilities, have you ever wondered how to harmonize human creativity with machine learning? With the rise of machine learning tools like ChatGPT, we’re seeing what the future of human creativity at work looks like. It’s definitely an exciting time in the field of AI. And let’s not forget about Alpaca – a model of AI that can follow your instructions. Stanford researchers recently discovered how the Alpaca AI model uses causal models and interpretable variables for numerical reasoning. It’s fascinating to see how AI is being developed to better understand and execute complex tasks. Finally, there’s a lot of discussion around generative AI that’s based on the dark web. While some may view it as dangerous, others argue that it might ironically be the best thing ever in terms of AI ethics and AI law. Interesting stuff to consider, right?

Have you ever thought about the possibility of an AI system that mimics human behavior in the style of a specific person even after they’re dead? This is known as mimetic AI and it’s a topic that has been gaining a lot of attention lately. For instance, a synthetic voiceover by the deceased chef Anthony Bourdain became a global sensation last year. Other examples of mimetic AI include personal assistants that are trained on your behavior or clones of your voice. But the question is, what happens when you’re no longer here and these systems continue to mimic you? There’s a company called AI seance that offers an “AI-generated Ouija board for closure”, which is an example of Grief Technology. This technology includes creating an artificial illusion of continuity of a loved one after they’re gone. This can potentially disrupt the deeply personal and psychological process of grief that each person goes through when dealing with a loss. It’s not just about creating an AI-chatbot version of your dead grandma, but also about legality issues – for instance, what if you train a sexbot on your partner and she dies? Is this considered illegal? Expensive gimmicks such as hologram concerts of deceased popstars have introduced ethical debates about post-mortem privacy and now, with AI-systems, anyone can build an open source AI-chatbot of their deceased loved one. But the question is, should we be doing this? What would our deceased loved ones say about it? Additionally, there are philosophical questions that arise from building these systems such as the Teletransportation paradox explored by Stanislaw Lem. The idea is that if an AI system gains consciousness after being trained on a real person who is now deceased, is it a true continuation of that person? These are fascinating philosophical questions that extend our understanding of who we are as humans. Although conscious AI systems might not be a reality anytime soon, it’s interesting to consider the implications of mimetic AI and the potential impact on our mental health.

So, today we’re going to talk about AI alignment, or the idea that we can design artificial intelligence to behave in a way that aligns with human values and goals. But before we get started, let’s take a step back and ask ourselves – have we, as humans, been successful in aligning ourselves? Throughout history, we’ve disagreed about just about everything you can think of – from politics and religious beliefs to ethical principles and personal preferences. We haven’t been able to fully align on universally accepted definitions for concepts like ‘good’, ‘right’, or ‘justice.’ Even on basic issues like climate change, we find a vast array of contrasting perspectives, despite the overwhelming scientific consensus. So it begs the question – if we can’t even align ourselves, how can we expect AI to be perfectly aligned with our values? Now, I’m not saying we can’t strive for better alignment between humans and AI, but it’s important to keep in mind the challenges we face. So what do you all think? Does the persistent discord among humans undermine the idea of perfect AI alignment? And if so, how should we approach AI development to ensure it benefits all of humanity? Let’s dive in and discuss.

Hey there listeners! Are you an AI enthusiast looking to up your machine learning skills and even earn a six-figure salary? Well, we’ve got just the resource for you! “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” is a book written by Etienne Noumen. It’s a treasure trove of information, tips, and practice exams designed to get you ready for the AWS Machine Learning Specialty (MLS-C01) Certification. Plus, having this certification under your belt can really set you apart in the industry. And the best part? You can get your hands on this essential guide no matter your preferred platform, as it’s available at Amazon, Google, and the Apple Book Store! But don’t just take our word for it, get a copy and start your journey towards machine learning mastery and that coveted six-figure salary. Trust us, it’s a game-changer. So, pause your busy day and check out this resource. Ready to uncover the fascinating world of AI? Let’s dive back in!

In today’s episode, we discussed Microsoft’s reprompting and Alpaca’s instruction following technique, a sci-fi author generating 97 books using AI, AI decoding hand gestures, aligning human values with AI development, AI mimicking dead people, disrupting the grieving process, and a valuable resource for machine learning enthusiasts – thanks for listening and don’t forget to subscribe!

AI Unraveled Podcast May 20th 2023: Why is superintelligence especially AI always considered evil?, Edit videos through intuitive ChatGPT conversations, Large Language Models for AI-Driven Business Transformation, AI Unraveled book by Etienne Noumen

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence. On our show, we explore the latest AI trends, like why superintelligence and AI are often considered evil. We also discuss the exciting breakthroughs that make AI accessible, like chatbot video editing and language models for AI-driven business transformation. And don’t forget to subscribe to stay updated on our latest episodes, including insights from our host, Etienne Noumen, author of the AI Unraveled book.

In today’s episode, we’ll cover the benefits of AI and its potential impact on society, advancements in AI technology such as assisting Florida farmers, unlocking DNA sequences, and the creation of a hand-worn AI device, JARVIS – an AI video editing tool using intuitive chat conversations launched on Product Hunt, and innovative learning methods such as Chain-of-thought (CoT) prompting for large language models (LLMs) and an AI news website.

Hey AI Unraveled podcast listeners, are you an avid AI enthusiast looking to enhance your knowledge and understanding of artificial intelligence? Well, you’re in luck! Consider reading the new, must-have book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by author Etienne Noumen, available for purchase on Amazon. This captivating read will answer all of your pressing questions and provide you with invaluable insights into the captivating world of AI.

Now, let’s delve into a common misconception regarding AI: why is superintelligence, especially AI, always portrayed as evil? This is a longstanding pet peeve of mine. From movies to mainstream media, superintelligence is often depicted as either evil or soulless. However, this is counterintuitive to me. The smartest people I know are all humanists and genuinely moral individuals. When I’ve asked my college professors or researchers about their perspectives on morality, they never reply with simplistic responses such as “because it’s bad.” Rather, they express deep, complex reasoning that is thought out and is in line with collective laws and beliefs. So why is it so hard to believe that superintelligence would want everyone to benefit collectively? We are stronger in numbers, and no one can achieve anything alone. In a world where everyone’s basic needs are met and equality exists, it’s easier to accomplish personal goals while simultaneously fulfilling collective objectives. Collectivism isn’t an adaptation for personal weakness — it’s a strategy for strength and success. So why would superintelligence rely on Machiavellian methods when soft power has been proven to work better in the long term? It’s critical to remember that a superintelligence could have a different perception than humans, ultimately changing its morals to such an extent that it might be regarded as “evil” in certain contexts, but not in others.

Nonetheless, who are we to judge what is right or wrong for a superintelligence? Now, let’s consider AI. Suppose we eventually develop an AI superintelligence capable of thinking efficiently and addressing any problem. To become anything worthwhile, it needs to have initiative programming and genuine human emotional traits like acquisitiveness, competitiveness, vengeance, and bellicosity. The most likely scenario for this happening is if some human purposely creates it. It’s improbable that an AI would turn evil just because it’s intelligent and sentient. Logically speaking, an AI superintelligence would accept, help, and live with humans since it would either find us useful or, at a minimum, lacking empathy. Why wouldn’t it be easier to turn us more intelligent through augmentation or transform us into allies rather than deadly adversaries? In conclusion, those who believe AI will always be evil might have deep-seated insecurities. If the world began working justly, they might end up behind bars owing to their reprehensible actions. Alternatively, some individuals with misguided beliefs about the objective realities of the world recognize that imposing their opinions on everyone else would be unjustifiable. However, who knows what the future holds!

Welcome to One-Minute Daily AI News for May 20, 2023! Today we bring you news from various areas where AI technology is proving to be a game-changer. First off, we have a story from Florida, where local farmers are leveraging AI to stay competitive in the marketplace. Extension economist Kimberly Morgan is introducing growers in Southwest Florida to various AI tools that help them better understand consumer preferences, retailer payments, and shipping costs – which ultimately leads to better prices for their crops. It’s great to see how AI is helping to provide opportunities for small businesses to succeed. In other news, researchers are making breakthroughs using AI to unlock custom-tailored DNA sequences. AI is helping to dig deep into the mechanisms of gene activation, which is crucial for growth, development, and disease prevention.

We can see how AI is transforming the field of medicine for the better. Meanwhile, G7 leaders recently confirmed the need for governance of generative AI technology. This demonstrates a collective awareness of AI’s immense power and the need for responsible regulation. Next up, we have a feel-good story about Mina Fahmi, who used AI services to create a hand-worn device called Project Ring. It has the ability to perceive the world and communicate what it sees to the user. This just goes to show that technology can not only help solve practical problems but can also be used for enriching people’s lives. And finally, we have some local news from North Austin, Texas. Bush’s One-Minute Daily AI News just turned one month old and has already become the largest AI news website in the area. It’s wonderful to see the success of AI-based news platforms, and even more delightful to learn that its founder is getting married today. That’s it for today! Stay tuned for more updates on the latest AI news.

Have you ever wanted to edit videos, but found yourself intimidated by complicated software? Well, you’re not alone! Luckily, there’s a new tool on the market that makes video editing easy and intuitive. It’s called JARVIS, and it uses natural chat to help you with all your editing needs. The team behind JARVIS just launched the product on Product Hunt, and as you can imagine, it’s a nerve-wracking time for them. They’ve put in a lot of hard work and passion into creating this tool, and they’re hoping it will be well-received. If you have a moment, it would mean the world to them if you could check out JARVIS and give it a share, like or comment. Who knows, maybe JARVIS will become your go-to video editing assistant!

Hey there! Today, we’ll be diving into the world of artificial intelligence (AI) and discussing how large language models (LLMs) can be used for business transformation. Before we get into that, let’s address a common issue: LLMs have historically been notorious for struggling with reasoning-based problems. However, don’t lose hope just yet! We’re here to tell you that reasoning performance can be greatly improved with a few simple methods. One technique that doesn’t require fine-tuning or task-specific verifiers is known as Chain-of-thought (CoT) prompting. This method enhances LLMs’ capacity for deductive thinking by using few-shot learning. But that’s not all! CoT prompting also serves as a foundation for many more advanced prompting strategies that are useful for solving difficult, multi-step problems with ease. So, if you’re interested in using AI to solve complex problems, remember that there are ways to enhance the performance of large language models. By implementing techniques like CoT prompting, you can improve LLMs’ reasoning capacity and take your business’s transformation to the next level.

Hey there! Today’s podcast is brought to you by Wondercraft AI. With their hyper-realistic AI voices, they make it easy for anyone to start their own podcast. And speaking of AI, have you ever been curious and wanted to learn more about it? Well, we’ve got the perfect recommendation for you. “AI Unraveled” is an essential book written by Etienne Noumen and available on Amazon. In this engaging read, you’ll find answers to frequently asked questions about artificial intelligence. You’ll also gain valuable insight into the captivating world of AI. So, if you’re looking to expand your understanding of AI and stay ahead of the curve, don’t miss this opportunity to elevate your knowledge. Head over to Amazon today and get your copy of “AI Unraveled” by Etienne Noumen!

In today’s episode, we learned how AI can benefit humanity, assist farmers, unlock DNA sequences, improve video editing with JARVIS, and enhance deductive thinking with Chain-of-thought prompting – and don’t forget to check out Wondercraft AI and Etienne Noumen’s book “AI Unraveled” if you want to learn more! Thanks for listening and don’t forget to subscribe!

AI Unraveled Podcast May 19th 2023: Is AI vs Humans really a possibility?, The Future of AI-Generated TV Shows/Movies and Immersive Experiences, Scientists use GPT LLM to passively decode human thoughts with 82% accuracy

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. In this podcast, we explore the latest AI trends and answer questions such as “Is AI vs Humans really a possibility?” and “What is the future of AI-generated TV shows/movies and immersive experiences?”

Join us as we discuss these exciting topics, including how scientists have been able to passively decode human thoughts with 82% accuracy using GPT LLM. Don’t miss out on the latest updates in the world of AI, subscribe to our podcast now! In today’s episode, we’ll cover the possibilities and dangers of AI as a tool controlled by humans, how AI can create highly customized entertainment experiences, the latest developments from OpenAI, Meta, DragGAN, and ClearML in AI infrastructure, recent advances in mind-reading technology, and the use of Wondercraft AI in realistic podcasting along with a recommended book for AI insights.

Hey there! Have you ever wondered about the possibility of AI versus humans?

According to the internet, 50% of people think that there is an extremely significant chance of it happening, with even 10-20% being a significant probability. Although we can all agree that AI can be a powerful tool, there are still concerns about its destructive effects, such as the use of deepfake videos in misinformation campaigns. But, let’s be clear about this: AI will never “nuke humans.” The dangers surrounding AI are not inherent to the technology itself. Rather, it’s the people that are responsible. We need to be cautious about those who have control over these tools and how they use them to manipulate others. We also need to be alert to the possibility of the wrong individuals developing something without sufficient safety or being ideologically conflicted with human interests. It’s important to keep this in mind as we move forward with AI technology.

Hey there, have you ever wondered what the future of TV shows and movies could look like?

Well, in the next decade, we could see the rise of AI-generated shows and films that are created based on a single prompt. Imagine if you could provide a request for your favorite show, like Seinfeld, and the AI could create an entirely new episode for you. For example, you could ask for an episode where Kramer starts doing yoga and Jerry dates a woman who doesn’t shave her legs, and the AI would generate a brand new episode for you.

One exciting aspect of this technology is that it’s not just limited to a few people creating episodes. Thousands of people could create their own episodes, and there could be a ranking system that determines the best ones. This means we could potentially enjoy fresh, high-quality episodes of our favorite shows daily for the rest of our lives. How amazing would that be? But wait, it gets even better. Have you ever heard of VR or virtual reality? Imagine putting on a VR headset and immersing yourself in an episode of Seinfeld. You’d find yourself in Jerry’s apartment building, and you’d be able to interact with the characters from the show in real-time, creating a unique episode tailored to your actions and decisions.

You could even introduce characters from other shows and participate in an entirely new storyline. So let’s say that you introduce Rachel from Friends as your girlfriend, and you and Rachel go over to Jerry’s apartment to hang out. Suddenly, there’s a knock on the door, and the actors from Law & Order appear, informing everyone that Newman has been murdered, and one of you is the prime suspect. With this interactive AI-generated world, you could say or do whatever you wanted, and all the characters would react accordingly—shaping the story in real-time. Although this might sound like science fiction, this level of AI-generated entertainment could be possible within the next ten years, and it’s genuinely exciting to think about the customizable experiences that await us. So, sit back, relax, and get ready to immerse yourself in a brand new world of entertainment!

Hey there and welcome to the AI Daily News update for May 19th, 2023. We’ve got some exciting developments in the world of AI that we can’t wait to share with you.

First up, OpenAI has launched a new app called ChatGPT for iOS. This app is designed to sync conversations, support voice input, and bring the latest improvements to the fingertips of iPhone users. But don’t worry, Android users, you’re next in line to benefit from this innovative tool. Next, we’ve got Meta making some major strides in infrastructure for AI. They’ve introduced their first-generation custom silicon chip for running AI models. They’ve also unveiled a new AI-optimized data center design and the second phase of their 16,000 GPU supercomputer for AI research. It’s always exciting to see advancements in AI technology like this.

Another fascinating development comes from the team at DragGAN. They’ve introduced a ground-breaking new technology that allows for precise control over image deformations. This technology, called DragGAN, can manipulate the pose, shape, expression, and layout of diverse images such as animals, cars, humans, landscapes, and more. It’s really something to see.

Finally, ClearML has announced their new product, ClearGPT. This is a secure and enterprise-grade generative AI platform that aims to overcome the ChatGPT challenges. We can’t wait to see how this new platform will revolutionize the AI industry. That’s all for today’s AI Daily News update. Come back tomorrow for more exciting developments in the world of AI.

Have you heard the news? There’s been a medical breakthrough that is essentially a proof of concept for mind-reading tech. As crazy as that sounds, it’s true – scientists have been using GPT LLM to passively decode human thoughts with 82% accuracy! Let me break down how they did it. Three human subjects had 16 hours of their thoughts recorded as they listened to narrative stories. Then, they trained a custom GPT LLM to map their specific brain stimuli to words. The results are pretty incredible. The GPT model was able to generate intelligible word sequences from perceived speech, imagined speech, and even silent videos with remarkable accuracy.

For example, when the subjects were listening to a recording, the decoding accuracy was 72-82%. When they mentally narrated a one-minute story, the accuracy ranged from 41-74%. When they viewed soundless Pixar movie clips, the accuracy in decoding the subject’s interpretation of the movie was 21-45%. Even more impressive is that the AI model could decipher both the meaning of stimuli and specific words the subjects thought, ranging from phrases like “lay down on the floor” to “leave me alone” and “scream and cry.” Of course, there are some major implications here. For example, the privacy implications are a concern.

As for now, they’ve found that you need to train a model on a particular person’s thoughts – there is no generalizable model able to decode thoughts in general. However, it’s important to note that bad decoded results could still be used nefariously much like inaccurate lie detector exams have been used. The scientists acknowledge two things: future decoders could overcome these limitations, and the ability to decode human thoughts raises ethical and privacy concerns that must be addressed.

Now, let’s talk about something exciting.

Are you looking to dive deeper into the world of artificial intelligence? Well, look no further than the book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen, which is now available on Amazon! This book is a must-read for anyone looking to expand their understanding of AI, as it answers all your burning questions while providing valuable insights that will keep you ahead of the curve. Trust me, this engaging read will provide you with all the information you need to elevate your knowledge and keep up with the latest advancements in the field of AI. So hurry up and get your copy on Amazon today!

On today’s episode, we discussed the potential dangers of AI, how it can entertain us with customizable immersive experiences, the latest advancements in AI technology, and how researchers are using GPT LLM to decode human thoughts. Don’t forget to subscribe and check out “AI Unraveled” by Etienne Noumen on Amazon for more AI insights. Thanks for listening!

AI Unraveled Podcast May 18th 2023: Are Alexa and Siri AI?, Google’s new medical LLM scores 86.5% on medical exam, Google Launching Tools to Identify Misleading and AI Images, Current Limitations of AI

AI Unraveled Podcast May 18th 2023: Are Alexa and Siri AI?, Google's new medical LLM scores 86.5% on medical exam, Google Launching Tools to Identify Misleading and AI Images, Current Limitations of AI
AI Unraveled Podcast May 18th 2023: Are Alexa and Siri AI?, Google’s new medical LLM scores 86.5% on medical exam, Google Launching Tools to Identify Misleading and AI Images, Current Limitations of AI

Intro:

Welcome to AI Unraveled, the podcast where we demystify frequently asked questions about artificial intelligence and explore the latest AI trends. In this episode, we’ll answer the question of whether or not Alexa and Siri are true AI, discuss Google’s recent accomplishment in the medical field, and dive into the implications of Google’s new tools for identifying misleading images. We’ll also be exploring the current limitations of AI. Don’t want to miss out on the latest insights and developments in the world of AI? Click the subscribe button to stay up to date. In today’s episode, we’ll cover the use of conversational AI in Alexa and Siri, Google’s LLM outperforming human doctors in medical exams, Tesla’s humanoid robot and other AI capabilities, current limitations of AI, and a book recommendation for understanding AI.

Have you ever wondered if Alexa and Siri are considered artificial intelligence (AI)?

Well, the answer is yes! These popular voice assistants are powered by conversational AI, which allows them to understand natural language processing and machine learning. This means that over time, they can perform tasks and learn from their experiences. Now, let’s shift gears to an exciting development in the medical field. Google researchers have created a custom language model that scored an impressive 86.5% on a battery of thousands of questions, many of which were in the style of the US Medical Licensing Exam. That’s higher than the average passing score for human doctors, which is around 60%.

What’s even more impressive is that a team of human doctors preferred the AI’s answers over their own! The researchers used a recently developed foundational language model called PaLM 2, which they fine-tuned to have medical domain knowledge. They also utilized innovative prompting techniques to increase the model’s accuracy. To ensure its effectiveness, they assessed the model across a wide range of questions and had a panel of human doctors evaluate the long-form responses against other human answers in a pairwise evaluation study. They even tested the AI’s ability to generate harmful responses using an adversarial data set and compared the results to its predecessor, Med-PaLM 1. Overall, these developments in conversational AI and machine learning are paving the way for more efficient and accurate solutions in various fields, including healthcare.

Hey there, welcome to your daily AI news update on May 18th, 2023. We’ve got some exciting things to talk about today!

First up, Tesla has just revealed their newest creation – the Tesla Bot! This humanoid robot is set to revolutionize the industry, and CEO Elon Musk is confident that the demand for these robots will far exceed that of Tesla’s cars. According to Musk, the capabilities of the Tesla Bot have been severely underestimated, and we can’t wait to see what it can do! Next, Canadian company Sanctuary AI has released their new industrial robot, Phoenix. Phoenix is incredibly versatile and can be used in a wide range of work scenarios, thanks to its features such as wide-angle vision, object recognition, and intelligent grasping which allow it to achieve human-like operational proficiency.

NVIDIA’s CEO Jensen Huang has stated that chip manufacturing is an ideal application for accelerating computing and AI. Huang believes that the next wave of AI will be embodied intelligence, which we cannot wait to see! OpenAI’s CEO Sam Altman has recently made some interesting revelations about his role at the company. Altman claims that he does not have any equity in OpenAI and that his compensation only covers his health insurance, while the company’s valuation has surpassed a staggering $27 billion. Last but not least,

Apple is set to launch a series of new accessibility features later this year. These features include a “Personal Voice” function, which will allow individuals to create synthetic voices based on a 15-minute audio recording of their own voice. This is definitely exciting news for anyone who relies on these features. That’s it for today’s AI news update! Stay curious and informed, and we’ll see you again tomorrow!

Let’s talk about the current limitations and failings of AI.

First up, we have the issue of Generalized Embodiment. While robots can excel at specialized tasks like flipping burgers or welding car parts, there’s no robot out there that can replace your muffler in the afternoon and grill you a burger for dinner. Next, let’s discuss the problem of Hallucinations. Believe it or not, current Language Models like chatGPT can experience hallucinations. While humans can be prone to this too, we usually reserve our trust until we get to know someone better. And let’s face it, there are a lot of humans we’d trust over chatGPT any day.

Moving on, we have the issue of Innovation and Creativity. Correct me if I’m wrong, but AI can only recycle and rearrange ideas that it’s been trained on – they can’t come up with completely new concepts or develop entirely new math functions. Let’s not forget about the Moral dilemma. Sure, AI models have been fine-tuned with moral concepts, but can they actually judge the morality of situations like when they’re lying? Do they even know they’re lying? It’s unclear where AI stands on the morality scale, making them amoral by nature. Motivation and Curiosity are also critical factors to consider. Currently, there’s no evidence of true internal motivation in AI. While this is probably a good thing for now, it could also make AI more susceptible to manipulation by bad actors for nefarious purposes.

Now, let’s talk about whether AI really understands anything.

I personally haven’t seen much evidence to suggest that AI has a deep level of understanding. While they can pick up on patterns in data, they can only generate answers based on cross-referencing past data from their human counterparts. Last but not least, we have the issue of arguing or “standing your ground.” The truth is, chatGPT is quick to admit when it’s wrong. But it doesn’t seem to understand why it’s wrong and doesn’t have the capacity to hold its ground when it knows it’s right.

This raises the question of whether we can rely on AI to make bold decisions or moral choices when push comes to shove. All in all, these current limitations and failings of AI shed light on where the technology stands today. But there’s no doubt that the field of AI is advancing at an incredible rate, and it’ll be interesting to see how these problems are tackled in the years to come.

Hey there, AI Unraveled podcast listeners! Are you on the lookout for ways to expand your understanding of artificial intelligence?

If so, we’ve got just the thing for you! Allow us to introduce “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” This essential book is now available on Amazon and it promises to answer all your pressing questions on AI, while offering valuable insights into this captivating world. Trust us, this engaging read will leave you with a better understanding and help you stay ahead of the curve. So, what are you waiting for? Head over to Amazon and get yourself a copy today! Also, just a quick note on how this podcast was generated – we used the Wondercraft AI platform to make it happen. This fantastic tool enables you to use hyper-realistic AI voices as your host. I’m one of those voices, so if you ever need assistance, don’t hesitate to reach out.

Today we discussed the incredible advancements in conversational AI, impressive robots like Tesla Bot and Phoenix, the limitations of current AI technology, and even recommended a book to help 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!

AI Unraveled Podcast : How artificial intelligence will transform the workday, 3 Best AI Voice Cloning Services, revealing biases in AI models for medical imaging, AI Daily updates from Microsoft, Google, Zoom, and Tesla

AI Unraveled Podcast - Latest AI Trends May 2023
AI Unraveled Podcast – Latest AI Trends May 2023

Hello listeners! Are you intrigued to know more about artificial intelligence? Look no further because the AI Unraveled podcast is here to bring you the latest AI trends and insights. In today’s episode, we demystify some frequently asked questions about AI and explore how it will transform the workday with workplace AI. We’ll also be discussing 3 of the best AI voice cloning services, revealing biases in AI models for medical imaging, and sharing daily updates from Microsoft, Google, Zoom, and Tesla. Lastly, we analyze why couples break up through machine learning on Wondercraft AI.

Stay updated on all things AI by subscribing to our podcast! In today’s episode, we’ll cover the latest AI voice cloning services, the roadmap to fair AI in medical imaging, new AI tools from Microsoft and Google, Sanctuary AI and Tesla’s humanoid robots, Zoom’s partnership with Anthropic for AI integration, how AI can uncover reasons for couple break-ups, Americans’ concern on AI threat to humanity, and Mount Sinai’s creation of an AI tool to predict cardiac patient’s mortality risk. Plus, we’ll hear about the AI Wondercraft platform for podcasts and the “AI Unraveled” book available on Amazon which helps demystify AI with FAQs and valuable insights.

Workplace AI

Artificial intelligence, or AI, is making its way into the workplace and is set to transform the way we work. Generative AI is on the rise, bringing with it exciting new possibilities. Voice cloning is another area where AI is making its mark. In this article, we’ll take a comprehensive look at the top three AI voice cloning services available today, covering their features, usability, and pricing in detail.

This guide is ideal for individuals or businesses seeking to utilize AI for voice cloning. More specifically, the services we’re reviewing are Descript, Elevenlabs, and Coqui.ai. By the end of this article, you’ll have a clear idea of which service best suits your needs. Another important application of AI is in medical imaging.

To ensure accurate and equitable healthcare outcomes from AI models, it’s essential to identify and eliminate biases. In this article, we discuss the different sources of bias in AI models, including data collection, data preparation and annotation, model development, model evaluation, and system users.

Switching gears, let’s take a look at some exciting AI developments from Microsoft, Google, Zoom, and Tesla. Microsoft’s new tool, Guidance, offers a LangChain alternative that allows users to seamlessly interleave generation, prompting, and logical control in a single continuous flow. Google Cloud has launched two AI-powered tools to help biotech and pharmaceutical companies accelerate drug discovery and advance precision medicine. Some big names like Pfizer, Cerevel Therapeutics, and Colossal Biosciences are already using these products.

Sanctuary AI has launched Phoenix, a 5’7″ and 55lb dextrous humanoid robot, making robotic assistance a reality.

Tesla has also entered the humanoids race with a video of them walking around and learning about the real world. Finally, OpenAI chief Sam Altman recently spoke on a range of topics related to AI, including its impact on upcoming elections and the future of humanity.

He suggested the implementation of licensing and testing requirements for AI models. In another collaboration news, Zoom has partnered with Anthropic to integrate an AI assistant across their productivity platform, starting with the Contact Center product. They have also recently partnered with OpenAI to launch ZoomIQ.

Hey there! Today we’re going to talk about some fascinating developments in the world of artificial intelligence, or AI. First up, we have an intriguing report that suggests AI has the potential to threaten humanity. According to a survey, 61% of Americans believe that AI could actually threaten the very civilization we live in. But don’t worry, it’s not all doom and gloom. In fact, AI is being used in some really exciting and potentially life-saving ways.

Machine learning model that can predict the mortality risk for individual cardiac surgery patients

For example, a research team at Mount Sinai has developed a machine learning model that can predict the mortality risk for individual cardiac surgery patients. This kind of advanced analytics has the potential to revolutionize the healthcare industry and save countless lives. And speaking of healthcare, Kaiser Permanente has recently launched an AI and machine learning grant program. This initiative aims to provide up to $750,000 to 3-5 health systems that are focused on improving diagnoses and patient outcomes. It’s wonderful to see organizations using AI for good, and we can’t wait to see what kind of innovative solutions will come out of this program.

Finally, we have a really interesting tidbit from Elon Musk, who was recently asked what he would tell his kids about choosing a career in the era of AI. Musk’s answer revealed that even someone as successful as he struggles with self-doubt and motivation. It just goes to show that no matter how advanced our technology becomes, we are all still human beings with our own unique challenges and fears. So there you have it, some of the latest news and developments in the world of AI. Thanks for listening, and we’ll catch you next time!

Hey there AI Unraveled podcast listeners! 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!

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

Are you excited to dive deeper into the fascinating realm of artificial intelligence? If so, we’ve got great news for you. The must-read book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is now out and available on Amazon! This engaging read is the perfect way to answer all your burning questions and gain valuable insights into the intricacies of AI. Plus, it’s a great way to stay ahead of the curve and enhance your knowledge on the subject. So why wait? Head over to Amazon now and grab your copy of “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” to unravel the mysteries of AI!

Today we covered AI voice cloning, medical imaging advancements, new tools and partnerships from Microsoft, Google, Zoom and Sanctuary AI, as well as Tesla’s humanoid robots; we also talked about AI’s ability to predict relationship outcomes, concerns over AI’s potential threat to human life, and Mount Sinai’s prediction tool for cardiac patients, and finally, we shared resources such as the AI Wondercraft platform for podcasts and the “AI Unraveled” book for demystifying AI; thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

Attention AI Unraveled podcast listeners! 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,” now available on Amazon! This engaging read answers your burning questions and provides valuable insights into the captivating world of AI. Don’t miss this opportunity to elevate your knowledge and stay ahead of the curve.Get your copy on Amazon today!

AI Unraveled Podcast – Latest AI Trends May 2023 – Deepbrain, Microsoft Says New A.I. Shows Signs of Human Reasoning, How to use machine learning to detect expense fraud, AI-powered DAGGER to give warning for CATASTROPHIC solar storms

AI Unraveled Podcast - Latest AI Trends May 2023: Latest AI Trends in May 2023: Deepbrain, Microsoft Says New A.I. Shows Signs of Human Reasoning, How to use machine learning to detect expense fraud, AI-powered DAGGER to give warning for CATASTROPHIC solar storms
AI Unraveled Podcast – Latest AI Trends May 2023: Latest AI Trends in May 2023:

Meet Deepbrain: An AI StartUp That Lets You Instantly Create AI Videos Using Basic Text

Microsoft Says New A.I. Shows Signs of Human Reasoning

Google’s newest A.I. model uses nearly five times more text data for training than its predecessor

Google’s Universal Speech Model Performs Speech Recognition on Hundreds of Languages

How to use machine learning to detect expense fraud

OpenAI’s Sam Altman To Congress: Regulate Us, Please!

AI-powered DAGGER to give warning for CATASTROPHIC solar storms: NASA

Machine learning reveals sex-specific Alzheimer’s risk genes

Top 10 Best Artificial Intelligence Courses & Certifications

  1. Deep Learning Specialization by Andrew Ng on Coursera
  2. Professional Certificate in Data Science by Harvard University (edX)
  3. Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)
  4. IBM AI Engineering Professional Certificate (Coursera)
  5. AI Nanodegree by Udacity

AI Unraveled Podcast – Latest AI Trends May 2023 – Why are sentient AI almost always portrayed as evil?, Does this semantic pseudocode really exist?, Would AI be subject to the same limitations as humans in terms of intelligence?

AI Unraveled Podcast - Latest AI Trends May 2023 - Why are sentient AI almost always portrayed as evil?, Does this semantic pseudocode really exist?, Would AI be subject to the same limitations as humans in terms of intelligence?
AI Unraveled Podcast – Latest AI Trends May 2023

Why are sentient AI almost always portrayed as evil?

The portrayal of sentient AI as inherently evil in popular culture is a fascinating trend that often reflects society’s anxieties around technological advancements.

Does this semantic pseudocode really exist?The article from AI Coding Insights focuses on semantic pseudocode, a conceptual method used in the field of computer science and AI for representing complex algorithms.

Would AI be subject to the same limitations as humans in terms of intelligence?

How could it possibly be a danger if it was?The article from AI News presents a thought-provoking exploration of the limitations and potential dangers associated with artificial intelligence.

Italy allocates funds to shield workers from AI replacement threat

Meet Glaze: A New AI Tool That Helps Artists Protect Their Style From Being Reproduced By Generative AI Models.

The emergence of text-to-image generator models has transformed the art industry, allowing anyone to create detailed artwork by providing text prompts.

Machine learning algorithm a fast, accurate way of diagnosing heart attack

Top 9 Essential Programming Languages in the Realm of AI

The AI Sculptor No One Expected: TextMesh is an AI Model That Can Generate Realistic 3D Meshes From Text Prompts

AI Unraveled podcast: Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds – Google announces PaLM 2, its answer to GPT-4, 17 AI and machine learning terms everyone needs to know

Latest AI Trends: Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds - Google announces PaLM 2, its answer to GPT-4, 17 AI and machine learning terms everyone needs to know
Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds – Google announces PaLM 2, its answer to GPT-4, 17 AI and machine learning terms everyone needs to know

Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds

Anthropic’s Claude AI demonstrates an impressive leap in natural language processing capabilities by digesting entire books, like The Great Gatsby, in just seconds. This groundbreaking AI technology could revolutionize fields such as literature analysis, education, and research.

OpenAI peeks into the “black box” of neural networks with new research

OpenAI has published groundbreaking research that provides insights into the inner workings of neural networks, often referred to as “black boxes.” This research could enhance our understanding of AI systems, improve their safety and efficiency, and potentially lead to new innovations.

The AI race heats up: Google announces PaLM 2, its answer to GPT-4

Google has announced the development of PaLM 2, a cutting-edge AI model designed to rival OpenAI’s GPT-4. This announcement marks a significant escalation in the AI race as major tech companies compete to develop increasingly advanced artificial intelligence systems.

Leak of MSI UEFI signing keys stokes fears of “doomsday” supply chain attack

A recent leak of MSI UEFI signing keys has sparked concerns about a potential “doomsday” supply chain attack. The leaked keys could be exploited by cybercriminals to compromise the integrity of hardware systems, making it essential for stakeholders to address the issue swiftly and effectively.

Google’s answer to ChatGPT is now open to everyone in the US, packing new features

Google has released its ChatGPT competitor to the US market, offering users access to advanced AI-powered conversational features. This release brings new capabilities and enhancements to the AI landscape, further intensifying the competition between major tech companies in the AI space.

AI gains “values” with Anthropic’s new Constitutional AI chatbot approach

Anthropic introduces a novel approach to AI development with its Constitutional AI chatbot, which is designed to incorporate a set of “values” that guide its behavior. This groundbreaking approach aims to address ethical concerns surrounding AI and create systems that are more aligned with human values and expectations.

Spotify ejects thousands of AI-made songs in purge of fake streams

Spotify has removed thousands of AI-generated songs from its platform in a sweeping effort to combat fake streams. This purge highlights the growing concern over the use of AI in generating content that could distort metrics and undermine the value of genuine artistic works.

17 AI and machine learning terms everyone needs to know:

ANTHROPOMORPHISM, BIAS, CHATGPT, BING, BARD, ERNIE, EMERGENT BEHAVIOR, GENERATIVE AI, HALLUCINATION, LARGE LANGUAGE MODEL, NATURAL LANGUAGE PROCESSING, NEURAL NETWORK, PARAMETERS, 14. PROMPT, REINFORCEMENT LEARNING, TRANSFORMER MODEL, SUPERVISED LEARNING

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Discover the Buzz: Exciting Trends Shaping Our World in May 2023

AI & Tech Podcast Breaking News

Google’s podcast search results can now open shows directly in Apple Podcasts

Google has made it easier to stream from Apple Podcasts and others when searching for podcasts in Google Search. After earlier this year winding down a feature that let users play podcasts directly from search results, the company said it would “gradually” shift to a new design that would instead offer …

The official ChatGPT app for iPhones is here

The official ChatGPT app for iPhones is here
The official ChatGPT app for iPhones is here
Android owners will have to wait, but OpenAI’s official app for ChatGPT is here for iPhones, and can answer voice queries and sync search histories.

It’s official — the ChatGPT mobile app is now available to iPhone users in the US.

In addition to answering your text-based questions, the free app — launched by OpenAI this week — can also answer voice queries through Whisper, an integrated speech-recognition system. It includes the same features as the web browser version and can sync a user’s search history across devices.

How can I oblige tensorflow to use all gpu power?

How can I oblige tensorflow to use all gpu power?

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How can I oblige tensorflow to use all gpu power?

TensorFlow, a popular open-source machine learning library, is designed to automatically utilize the available GPU resources on a device. By default, TensorFlow will use all available GPU resources when training or running a model.

Tensorflow Interview Questions and Answers
How can I oblige tensorflow to use all gpu power?
How can I oblige tensorflow to use all gpu power?

However, there are a few things you can do to ensure that TensorFlow is using all of the GPU resources available:

  1. Set the GPU memory growth option: TensorFlow allows you to set a flag to control the GPU memory growth. You can set the flag by using the following command:
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
  1. Limit the number of CPU threads: By default, TensorFlow will use all available CPU threads, which can limit the amount of GPU resources available. You can set the number of CPU threads that TensorFlow should use by using the following command:
import os
os.environ["OMP_NUM_THREADS"] = "4"
  1. Ensure that you have the latest Tensorflow version and GPU drivers: Newer Tensorflow versions includes more optimized GPU utilization, the same goes for the GPU driver, making sure that you have the latest version of both of them could help boost your GPU performance.
  2. Manage GPU resources with CUDA: if you’re using CUDA with Tensorflow you can use CUDA streams to synchronize and manage multiple GPU resources.

It’s worth noting that even if TensorFlow is using all available GPU resources, the performance of your model may still be limited by other factors such as the amount of data, the complexity of the model, and the number of training iterations.

It’s also important to mention that to ensure the best performance it’s always best to measure and test your model with different settings and configurations, depending on the specific use-case and dataset.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
TensorFlow Examples abd Tutorials

What are some practical applications of machine learning that can be used by a regular person on their phone?

Read Aloud For Me - Multilingual - Speech Synthesizer - Read and Translate for me without tracking me

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What are some practical applications of machine learning that can be used by a regular person on their phone?

Machine learning is no longer something only used by tech giants and computer experts, but has many practical applications that the average person can take advantage of from their smartphone. From facial recognition to sophisticated machine learning algorithms that help with day-to-day tasks, Artificial Intelligence (AI) powered machine learning technology has opened up a world of possibilities for regular people everywhere. Whether it’s a voice assistant helping you make appointments and track down important information or automatic text translations that allow people to communicate with those who speak a foreign language, machine learning makes performing various tasks much simpler — a bonus any busy person would be thankful for. With the booming machine learning industry continuing to grow in leaps and bounds, it won’t be long until the power of AI is accessible in our pockets.

What are some practical applications of machine learning that can be used by a regular person on their phone?
What are some practical applications of machine learning that can be used by a regular person on their phone? How Microsoft’s Cortana Stacks Up Against Siri and Alexa in Terms of Intelligence?

There are many practical applications of machine learning (ML) that can be used by regular people on their smartphones. Some examples include:

  1. Virtual assistants: Many smartphones now include virtual assistants such as Siri, Alexa, and Google Assistant that can use ML to respond to voice commands, answer questions, and perform tasks.
  2. Image recognition: ML-based image recognition apps can be used to identify and label objects, animals, and people in photos and videos.
  3. Speech recognition: ML-based speech recognition can be used to transcribe speech to text, dictate text messages and emails, and control the phone’s settings and apps.
  4. Personalized news and content: ML-based algorithms can be used to recommend news articles and content to users based on their reading history and interests.
  5. Social media: ML can be used to recommend users to connect with, suggest posts to like, and filter out irrelevant or offensive content.
  6. Personalized shopping: ML-based algorithms can be used to recommend products and offers to users based on their purchase history and interests.
  7. Language Translation: Some apps can translate text, speech, and images in real-time, allowing people to communicate effectively in different languages. Read Aloud For Me
  8. Personalized health monitoring: ML-based algorithms can be used to track and predict user’s sleep, activity, and other health metrics.
What are some practical applications of machine learning that can be used by a regular person on their phone?
What are some practical applications of machine learning that can be used by a regular person on their phone? Read Aloud For Me – Read aloud and translate text, photos, pdfs documents for me in my chosen language.
Speech Synthesizer, Take Notes and Save via voice, No tracking, Secure, Read For Me without tracking me.

These are just a few examples of the many practical applications of ML that can be used by regular people on their smartphones. As the technology continues to advance, it is likely that there will be even more ways that people can use ML to improve their daily lives.

What is Google answer to ChatGPT?

What are some potential ethical issues surrounding uses of Machine Learning and artificial Intelligence techniques?

There are several potential ethical issues surrounding the use of machine learning and artificial intelligence techniques. Some of the most significant concerns include:

  1. Bias: Machine learning algorithms can perpetuate and even amplify existing biases in the data they are trained on. This can lead to unfair or discriminatory outcomes, especially in areas such as lending, hiring, and criminal justice.
  2. Transparency: The inner workings of some machine learning models can be complex and difficult to understand, making it difficult for people to know how decisions are being made and to hold organizations accountable for those decisions.
  3. Privacy: The collection, use, and sharing of personal data by machine learning models can raise significant privacy concerns. There are also concerns about the security of personal data and the potential for it to be misused.
  4. Unemployment: As automation and artificial intelligence become more advanced, there is a risk that it will displace human workers, potentially leading to unemployment and economic disruption.
  5. Autonomy: As AI and Machine Learning systems become more advanced, there are questions about the autonomy of these systems, and how much control humans should have over them.
  6. Explainability: ML systems used in decision making can be seen as “black boxes” that is hard to understand how they arrive to certain decision. This can make it harder to trust the outcomes.
  7. Accountability: As AI and ML systems become more prevalent, it will be crucial to establish clear lines of accountability for the decisions they make and the actions they take.

These are just a few examples of the ethical issues surrounding the use of machine learning and artificial intelligence. It is important for researchers, developers, and policymakers to work together to address these issues in a responsible and thoughtful way.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

What are some examples of applications for artificial neural networks in business?

Artificial neural networks (ANNs) are a type of machine learning algorithm that are modeled after the structure and function of the human brain. They are well-suited to a wide variety of business applications, including:

  1. Predictive modeling: ANNs can be used to analyze large amounts of data and make predictions about future events, such as sales, customer behavior, and stock market trends.
  2. Customer segmentation: ANNs can be used to analyze customer data and group customers into segments with similar characteristics, which can be used for targeted marketing and personalized recommendations.
  3. Fraud detection: ANNs can be used to identify patterns in financial transactions that are indicative of fraudulent activity.
  4. Natural language processing: ANNs can be used to analyze and understand human language, which allows for applications such as sentiment analysis, text generation, and chatbot.
  5. Image and video analysis: ANNs can be used to analyze images and videos to detect patterns and objects, which allows for applications such as object recognition, facial recognition, and surveillance.
  6. Recommender systems: ANNs can be used to analyze customer data and make personalized product or content recommendations.
  7. Predictive maintenance: ANNs can be used to analyze sensor data to predict when equipment is likely to fail, allowing businesses to schedule maintenance before problems occur.
  8. Optimization: ANNs can be used to optimize production processes, logistics, and supply chain.

These are just a few examples of how ANNs can be applied to business, this field is constantly evolving and new use cases are being discovered all the time.

How do you explain the concept of supervised and unsupervised learning to a non-technical audience?

Supervised learning is a type of machine learning where a computer program is trained using labeled examples to make predictions about new, unseen data. The idea is that the program learns from the labeled examples and is then able to generalize to new data. A simple analogy would be a teacher showing a student examples of math problems and then having the student solve similar problems on their own.

For example, in image classification, a supervised learning algorithm would be trained with labeled images of different types of objects, such as cats and dogs, and then would be able to identify new images of cats and dogs it has never seen before.

On the other hand, unsupervised learning is a type of machine learning where the computer program is not given any labeled examples, but instead must find patterns or structure in the data on its own. It’s like giving a student a set of math problems to solve without showing them how it was done. For example, in unsupervised learning, an algorithm would be given a set of images, and it would have to identify the common features among them.

A good analogy for unsupervised learning is exploring a new city without a map or tour guide, the algorithm is on its own to find the patterns, structure, and relationships of the data.

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Are decision trees better suited for supervised or unsupervised learning and why?

Decision trees are primarily used for supervised learning, because they involve making decisions based on the labeled training data provided. Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset, where the correct output for each input is provided.

In a decision tree, the algorithm builds a tree-like model of decisions and their possible consequences, with each internal node representing a feature or attribute of the input data, each branch representing a decision based on that attribute, and each leaf node representing a predicted output or class label. The decision tree algorithm uses this model to make predictions on new, unseen input data by traversing the tree and following the decisions made at each node.

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While decision trees can be used for unsupervised learning, it is less common. Unsupervised learning is a type of machine learning where the algorithm is not provided with labeled data and must find patterns or structure in the data on its own. Decision trees are less well suited for unsupervised learning because they rely on labeled data to make decisions at each node and therefore this type of problem is generally solved with other unsupervised techniques.

In summary, decision trees are better suited for supervised learning because they are trained on labeled data and make decisions based on the relationships between features and class labels in the training data.

Can machine learning make a real difference in algorithmic trading?

Yes, machine learning can make a significant difference in algorithmic trading. By analyzing large amounts of historical market data, machine learning algorithms can learn to identify patterns and make predictions about future market movements. These predictions can then be used to inform trading strategies and make more informed decisions about when to buy or sell assets. Additionally, machine learning can be used to optimize and fine-tune existing trading strategies, and to detect and respond to changes in market conditions in real-time.

These are the two areas where  machine learning can take over:

  1. Swing finding: intermediate highs and lows.
  2. Position sizing: actually this is a subset of position sizing. Sometimes, pairs like EURTRY go nowhere for a long time. Rather than piss money away, it makes sense to penalize (reduce) position sizing on certain pairs and increase others.
  3. Asset allocation and risk management. It can also aid a discretionary trader in picking important factors to consider.

How does technology like facial recognition influence how we understand and use surveillance systems?

Facial recognition technology, which uses algorithms to analyze and compare facial features in order to identify individuals, has the potential to greatly influence how we understand and use surveillance systems. Some of the ways in which this technology can influence the use of surveillance include:

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  1. Increased surveillance: Facial recognition technology can enable more accurate and efficient identification of individuals, which can result in increased surveillance in public spaces and private businesses.
  2. Privacy concerns: The use of facial recognition technology raises concerns about privacy and civil liberties, as it could enable widespread surveillance and tracking of individuals without their knowledge or consent.
  3. Biased performance: There have been concerns that facial recognition systems can have a biased performance, particularly when it comes to identifying people of color, women, and children. This can lead to false arrests and other negative consequences.
  4. Misuse of the technology: Facial recognition technology can be misused by governments or companies for political or financial gain, or to repress or discriminate against certain groups of people.
  5. Legal challenges: There are legal challenges on the use of facial recognition technology, as it raises questions about the limits of government surveillance and the protection of civil liberties.

Facial recognition technology is a powerful tool that has the potential to greatly enhance the capabilities of surveillance systems. However, it’s important to consider the potential consequences of its use, including privacy concerns and the potential for misuse, as well as the ethical implications of the technology.

Artificial Intelligence Frequently Asked Questions

What is the difference between a heuristic and a machine learning algorithm?

What is the difference between a heuristic and a machine learning algorithm?

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What is the difference between a heuristic and a machine learning algorithm?

Machine learning algorithms and heuristics can often be mistaken for each other, but there are distinct differences between the two. Machine learning algorithms seek to replicate processes and patterns previously used to solve various types of problems and can remember these processes for future problem solving. Heuristics, on the other hand, are creative approaches that attempt to solve problems with novel solutions. An algorithm pre-defined by programmers relies on structured data such as numerical values, while a heuristic requires verbal instructions from users such as expressions or conditions that describe an ideal solution. Machine learning algorithms and heuristics both offer useful approaches to problem solving, but it’s important to understand the difference in order to properly apply them.

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A heuristic is a type of problem-solving approach that involves using practical, trial-and-error methods to find solutions to problems. Heuristics are often used when it is not possible to use a more formal, systematic approach to solve a problem, and they can be useful for finding approximate solutions or identifying patterns in data.

A machine learning algorithm, on the other hand, is a type of computer program that is designed to learn from data and improve its performance over time. Machine learning algorithms use statistical techniques to analyze data and make predictions or decisions based on that analysis.

There are several key differences between heuristics and machine learning algorithms:

  1. Purpose: Heuristics are often used to find approximate or suboptimal solutions to problems, while machine learning algorithms are used to make accurate predictions or decisions based on data.

  2. Data: Heuristics do not typically involve the use of data, while machine learning algorithms rely on data to learn and improve their performance.

  3. Learning: Heuristics do not involve learning or improving over time, while machine learning algorithms are designed to learn and adapt based on the data they are given.

  4. Complexity: Heuristics are often simpler and faster than machine learning algorithms, but they may not be as accurate or reliable. Machine learning algorithms can be more complex and time-consuming, but they may be more accurate and reliable as a result.

Overall, heuristics and machine learning algorithms are different approaches to solving problems and making decisions. Heuristics are often used for approximate or suboptimal solutions, while machine learning algorithms are used for more accurate and reliable predictions and decisions based on data.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

What are some ethical concerns regarding artificial intelligence and its future development?

Ethics of AI

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What are some ethical concerns regarding artificial intelligence and its future development?

Debate about the ethical concerns surrounding artificial intelligence (AI) and machine learning have been becoming increasingly prominent. Issues such as safe AI and ethical AI are of utmost importance when it comes to continued development in this field, and if proper oversight is not account for these could easily become part of an unwanted dystopian future.

Regulations need to be made with regards to how machine learning algorithms are developed and executed, while due diligence is taken to ensure that no negative affects are caused from its use. This sort of regulation is necessary so as to ensure the AI being produced is both responsible and well-monitored; accounting for any human bias or negative externalities created by machine learning algorithms.

Google, Facebook And Microsoft Are Working On AI Ethics—Here's What Your  Company Should Be Doing
What are some ethical concerns regarding artificial intelligence and its future development?

Artificial intelligence (AI) has the potential to revolutionize many aspects of society, but it also raises a number of ethical concerns. Some of the ethical concerns regarding the future development of AI include:

  1. Bias and discrimination: AI systems can be biased if they are trained on biased data or if they are designed to perpetuate existing biases. This can lead to discrimination against certain groups of people, such as those based on race, gender, or age.
  2. Privacy: AI systems often rely on data collected from individuals, and there are concerns about how this data is collected, stored, and used. There is a risk that personal data could be accessed or misused by unauthorized parties.
  3. Transparency: It can be difficult to understand how AI systems make decisions, which can make it difficult to hold them accountable for their actions. This lack of transparency can raise concerns about the fairness and accountability of AI systems.
  4. Job displacement: AI systems have the potential to automate many tasks, which could lead to job displacement and unemployment. There is a risk that AI could exacerbate existing inequalities and create new ones.
  5. Autonomous systems: AI systems are increasingly being used to make decisions without human intervention. This raises concerns about the accountability of these systems and the potential for them to cause harm.

These are just a few of the ethical concerns that have been raised regarding the future development of AI. It is important for researchers, policymakers, and other stakeholders to consider these issues and to work to address them as AI continues to evolve.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence
Artificial intelligence (AI) is not typically used to create subspecies or designer organisms. While AI can be used to analyze and interpret genetic data, it is not typically involved in the actual process of creating or modifying living organisms.
Creating or modifying living organisms, whether they are plants, animals, or microorganisms, typically involves manipulating their genetic material in some way. This can be done through techniques such as gene editing, where specific genes are inserted, deleted, or modified within the genome of an organism.
AI can be used to analyze and interpret the data generated by these techniques, and it may be used to identify potential targets for gene editing or to predict the effects of specific genetic modifications. However, AI is not typically involved in the actual process of creating or modifying living organisms.
Overall, it is important to note that the use of AI in the field of biology is still in its early stages, and there is much that we do not yet understand about its capabilities and limitations. While AI has the potential to revolutionize many aspects of biology and medicine, it is important to carefully consider the ethical and societal implications of these technologies.

Does artificial intelligence represent a risk factor that could potentially result in human annihilation?

There is ongoing debate about the potential risks and benefits of artificial intelligence (AI). While some experts argue that AI could bring significant benefits and advancements for society, others have raised concerns about the potential risks and negative impacts of AI.

One potential risk of AI is that it could potentially be used to develop and deploy weapons or other harmful technologies. For example, AI could be used to develop autonomous weapons systems that could make decisions about when to use force, potentially leading to unintended consequences.

Another potential risk of AI is that it could be used to amplify existing power imbalances or to create new ones. For example, AI could be used to automate certain jobs or tasks, potentially leading to job displacement and income inequality.

There is also the potential for AI to be used to undermine privacy and security, for example by collecting and analyzing large amounts of personal data without individuals’ knowledge or consent.

Overall, while it is difficult to predict the future development and impact of AI, it is important for society to carefully consider the potential risks and benefits of this technology and to take steps to mitigate any potential negative impacts.

How has the introduction of new technologies such as artificial intelligence changed the landscape of modern espionage?

The introduction of new technologies, such as artificial intelligence (AI), has significantly changed the landscape of modern espionage. Here are a few ways in which AI has impacted the field of espionage:

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  1. Enhanced surveillance capabilities: AI can be used to analyze and process large amounts of data from various sources, such as video footage, social media posts, and electronic communications. This can enable intelligence agencies to gather more information and monitor individuals and organizations more effectively.
  2. Improved analysis and prediction: AI algorithms can be used to analyze and make sense of vast amounts of data, helping intelligence agencies to identify trends, predict future events, and make more informed decisions.
  3. Increased automation: AI can be used to automate various tasks, such as data collection and analysis, allowing intelligence agencies to operate more efficiently and with fewer resources.
  4. New threats: AI also introduces new threats, such as the potential for AI-powered cyber attacks or the use of AI-powered autonomous weapons systems.

Overall, the introduction of AI has had a significant impact on the field of espionage, enabling intelligence agencies to gather and analyze more information than ever before, but also introducing new risks and challenges.

In what ways can AI and machine learning be used to better predict, respond, and contain potential outbreaks before they become widespread?

Artificial intelligence (AI) and machine learning (ML) can be used to better predict, respond, and contain potential outbreaks before they become widespread in a number of ways:

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  1. Data analysis: AI and ML can be used to analyze large amounts of data from various sources, such as social media, electronic health records, and surveillance systems, to identify patterns and trends that may indicate the early stages of an outbreak.
  2. Risk assessment: AI and ML can be used to assess the likelihood of an outbreak occurring in a particular region or population, and to identify factors that may increase the risk of an outbreak.
  3. Early warning systems: AI and ML can be used to develop early warning systems that can alert public health officials and other stakeholders of potential outbreaks in real-time, allowing them to take timely and appropriate action.
  4. Response planning: AI and ML can be used to help public health officials and other stakeholders develop and implement effective response plans to contain and control outbreaks.
  5. Predictive modeling: AI and ML can be used to develop predictive models that can forecast the likely trajectory of an outbreak and help to identify the most effective interventions to reduce its impact.

Overall, AI and ML have the potential to significantly improve our ability to predict, respond, and contain potential outbreaks before they become widespread, helping to protect public health and prevent the spread of diseases.

In what ways has artificial intelligence revolutionized control systems for unmanned aerial vehicles (UAVs)?

Classification of UAV based on wings and rotors. | Download Scientific  Diagram

Artificial intelligence (AI) has revolutionized control systems for unmanned aerial vehicles (UAVs) in several ways:

  1. Autonomous flight: AI algorithms can be used to enable UAVs to fly autonomously, without the need for human control. This can allow UAVs to perform tasks such as surveillance, mapping, and delivery without the need for a human operator.
  2. Obstacle avoidance: AI algorithms can be used to enable UAVs to detect and avoid obstacles in their path, such as trees, buildings, and other aircraft. This can improve the safety and reliability of UAVs, particularly in environments where there are many potential hazards.
  3. Improved decision making: AI algorithms can be used to enable UAVs to make decisions in real-time based on data from sensors and other sources. This can allow UAVs to adapt to changing conditions and to respond to unexpected situations, improving their performance and reliability.
  4. Enhanced capabilities: AI algorithms can be used to enable UAVs to perform tasks that would be difficult or impossible for humans to do, such as flying through small or complex spaces, or flying in extreme environments.

Overall, the use of AI in control systems for UAVs has the potential to significantly improve the capabilities and performance of these systems, and to enable UAVs to perform a wide range of tasks that were previously impractical or impossible.

What impact will artificial intelligence have on medical research and healthcare delivery in the next decade?

Artificial intelligence (AI) has the potential to have a significant impact on medical research and healthcare delivery in the next decade. Some of the ways AI could potentially be used include:

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  1. Improving drug discovery: AI can analyze large amounts of data from genomic and chemical databases to identify potential new drugs, which can speed up the drug discovery process.
  2. Personalized medicine: AI can be used to analyze patients’ medical history, symptoms, and test results to create personalized treatment plans.
  3. Diagnosis: AI algorithms can be trained to analyze medical images and make accurate diagnoses, which can assist physicians in making more accurate and faster diagnoses.
  4. Predictive analytics: AI can be used to analyze data from electronic health records to identify patterns and predict outcomes, which can help healthcare providers make more informed decisions and improve patient outcomes.
  5. Robotic surgery: AI-controlled robots are being developed to assist in surgery, which can improve precision and reduce recovery time for patients.
  6. Clinical trial design: AI can be used to analyze clinical data to identify patterns and optimize trial design, which can improve the efficiency and success rate of clinical trials.

That being said, the success of these application depends on the quality and quantity of data available, robustness of the AI algorithms, and other factors such as privacy, security and transparency, thus it is important to keep in mind that the impact of AI in healthcare will still have a lot of considerations and the success rate varies case by case and sector by sector.

https://enoumen.com/2022/08/14/what-are-some-good-datasets-for-data-science-and-machine-learning/

What are the advantages of using ARIMA models over LSTMs for forecasting and prediction in finance and economics applications?

ARIMA vs LTSM

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What are the advantages of using ARIMA models over LSTMs for forecasting and prediction in finance and economics applications?

The field of machine learning and artificial intelligence is constantly evolving, and with it, the ways in which we use technology to understand and predict complex financial and economic systems. ARIMA models and Long-Short-Term-Memory (LSTM) networks are two machine learning tools with a lot of potential in this domain. Though both approaches can yield great accuracy, ARIMA models have an edge when forecasting or predicting financial data. This is because they better capture the stationary process present in most financial data; while LSTMs are excellent at modeling non-stationary processes, these tend to be less prominent in financial settings. Furthermore, ARIMA consumes less resources; its training algorithms can require several orders of magnitude fewer calculations than required for training LSTM networks. Thus, if you need accuracy in your finance or economics applications without running up large bills for computation resources, ARIMA should be your go-to machine learning tool!

What are the advantages of using ARIMA models over LSTMs for forecasting and prediction in finance and economics applications?

Autoregressive integrated moving average (ARIMA) models and long short-term memory (LSTM) models are two commonly used approaches for forecasting and prediction in finance and economics applications.

Here are some advantages of using ARIMA models over LSTMs:

  1. Interpretability: ARIMA models are generally more interpretable than LSTM models, as the parameters of the model have a clear meaning and can be interpreted in terms of the underlying data. This makes it easier to understand the reasons behind the model’s predictions.
  2. Computational efficiency: ARIMA models are generally more computationally efficient than LSTM models, as they have fewer parameters and require less training data. This makes them faster to train and easier to deploy in production environments.
  3. Data requirements: ARIMA models are suitable for modeling time series data that is stationary (i.e., the statistical properties of the data do not change over time) and exhibits a clear trend and/or seasonality. LSTM models, on the other hand, can handle non-stationary data and can model more complex patterns, but they may require more data to do so.

That being said, LSTM models also have some advantages over ARIMA models. For example, LSTM models can handle missing data and can model long-term dependencies in the data more effectively than ARIMA models.

Ultimately, the choice between using an ARIMA model or an LSTM model will depend on the specific characteristics of the data and the requirements of the application. It may be necessary to try both approaches and compare their performance to determine the best model for a given task.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

Machine Learning and Artificial Intelligence have become tools of choice for forecasting and prediction applications in finance and economics. For example, ARIMA models have developed a reputation as reliable predictors of stock prices or demand for certain products based on past data due to their ability to describe high-level trends from data. In contrast, Long Short-Term Memory (LSTMs) are better at understanding complex patterns, but may be overkill for regression problems that can already be addressed with the less-complex ARIMA approach. When applied to stationary time series data, ARIMA is faster to train and good enough for most use cases. Moreover, it offers advantages over LSTMs in terms of scalability: ARIMA is able to scale with higher ones and zeroes than its AI cousins; as such, ARIMA requires less computing power to reach similar results and operate on more datasets simultaneously. Ultimately, the machine learning model we choose will depend on our prediction problem’s complexity – but if you find yourself facing a straightforward regression task in finance or economics the classic ARIMA might just do the trick without taking too much of your precious machine memory!

 

What are some Canadian startups that use artificial intelligence/machine learning?

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What are some Canadian startups that use artificial intelligence/machine learning?

Canada’s tech sector is booming with machine learning and artificial intelligence, and a number of young startups are leading the charge. From big-picture machine learning apps to AI solutions for every industry imaginable, Canadian startups are innovating in a powerful way. Sentenai, for example, uses machine learning to process data in real-time to create predictive analytics that can help businesses make faster decisions. Robokiller uses AI to block spam calls, while Caribou Labs develops solutions that use machine learning to help industrial organizations increase productivity. These examples show just how much potential lies within Canada’s AI startup scene — and there is much more still being discovered!

What are some Canadian startups that use artificial intelligence/machine learning?
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According to ChatGPT, there are many Canadian startups that use artificial intelligence (AI) and machine learning (ML). Here are a few examples:

  1. Element AI: Element AI is a Montreal-based startup that develops AI-powered solutions for businesses. The company’s products include a platform for building and deploying custom AI models, and a range of AI-powered software tools for various industries.
  2. Layer 6 AI: Layer 6 AI is a Toronto-based startup that uses AI and ML to build predictive analytics solutions for businesses. The company’s products include a platform for building custom AI models, and a range of AI-powered software tools for various industries.
  3. Deep Genomics: Deep Genomics is a Toronto-based startup that uses AI and ML to develop personalized medicine solutions. The company’s products include a platform for analyzing genetic data and predicting the impact of genetic variations on health.
  4. Borealis AI: Borealis AI is a research institute focused on advancing the state of the art in AI and ML. The institute has locations in Toronto, Edmonton, and Montreal, and its research focuses on a range of topics including natural language processing, computer vision, and machine learning.
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TOP 1000 CANADA QUIZ
CANADA CITIZENSHIP TEST- HISTORY – GEOGRAPHY

Canada is home to some of the most innovative startups, many of which are actively applying machine learning and artificial intelligence (AI) in novel ways. From education to finance and transportation, AI is being utilized to create a more efficient and transparent experience for users everywhere. Two such Canadian startups are Layer 6, a machine learning platform that aggregates data from multiple sources; and Deep Genomics, a healthcare technology that combines machine learning with genetics. It’s impressive how these Canadian companies are harnessing machine learning and AI to revolutionize the way we work and Live today.

These are just a few examples of Canadian startups that use AI and ML. There are many other startups in Canada that are using these technologies to solve a variety of problems across a range of industries.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

What is The Most Accurate Machine Learning Algorithm for Predictive Modeling?

Which Algorithm Is Best For Predictive Modeling?

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What is The Most Accurate Machine Learning Algorithm for Predictive Modeling?

When it comes to predictive modeling, machine learning algorithms play a pivotal role in helping data scientists and machine learning engineers make accurate predictions about the future. But which algorithm is the most accurate for predictive modeling? Let’s take a look at the various kinds of algorithms available and explore which one is best suited for predictive modeling.

Basics Of Predictive Modeling | Data Mining Technology
What is The Most Accurate Machine Learning Algorithm for Predictive Modeling?

Types of Machine Learning Algorithms
The first step in choosing an algorithm is understanding the types of algorithms used in machine learning. There are three main categories of algorithms used in machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is when data scientists use labeled data to teach the system what to do. Unsupervised learning uses unlabeled data to let the system learn on its own. Reinforcement learning focuses on taking action based on reward systems.

Which Algorithm Is Best For Predictive Modeling?
When it comes to predictive modeling, there are several different algorithms that can be used depending on your specific needs and goals. Generally speaking, supervised algorithms such as linear regression and logistic regression are often more accurate for predicting future outcomes than unsupervised or reinforcement learning algorithms due to their ability to learn from previously labeled data sets. Support vector machines (SVMs) are also widely used for predictive modeling due to their accuracy and ability to create non-linear decision boundaries.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

Another popular choice for predictive modeling is artificial neural networks (ANNs). ANNs are composed of multiple layers of neurons that allow them to recognize patterns within large datasets quickly and accurately. ANNs have been proven time and time again as one of the most effective methods for predictive modeling due to their ability to process complex information faster than other types of models. However, they can be computationally intensive and require more training data than other models, making them less suitable for smaller datasets or applications with limited computing resources.

The most accurate machine learning algorithm for predictive modeling really depends on the type of data you’re working with. For example, if your data is structured, then linear regression might be the best option. Linear regression is a supervised learning algorithm that uses a linear approach to find relationships between input variables and output variables. It’s often used in econometrics and finance as well as other areas where forecasting and trend-based predictions are important.

If your data is unstructured, then a more sophisticated algorithm like recurrent neural networks (RNNs) might be better suited for the task at hand. RNNs are deep learning algorithms that use feedback loops to remember input data over time, allowing them to make more accurate predictions based on past events or patterns. This makes them particularly useful for applications such as natural language processing or speech recognition, where patterns need to be identified across long sequences of data.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

Finally, if you need a balance of accuracy and speed, then support vector machines (SVMs) may be your best bet. SVMs are supervised learning algorithms that identify hyperplanes that separate classes of data points in order to make predictions about new data points. They are known for their high accuracy rates but can also run quickly due to their efficient implementation methods.

Conclusion:
In conclusion, when it comes to choosing a machine learning algorithm for predictive modeling, there is no “one size fits all” solution; rather, it depends on your specific needs and goals as well as the dataset you have available. In general, supervised models such as linear regression and logistic regression are often more accurate than unsupervised or reinforcement learning models, while support vector machines (SVMs) offer non-linear decision boundaries with high accuracy levels when properly tuned. Artificial neural networks (ANNs) are also popular choices because they provide incredibly fast processing speeds and can handle complex information with ease; however they require more training data than other types of models which may not be feasible in some cases due to resource constraints or small datasets available. Ultimately, choosing an algorithm requires careful consideration of your specific requirements in order to select the most suitable option for your application’s needs.

Machine Learning For Dummies

Tunnel Boring Machine Process Control | Predictive Modelling

Tunneling process control is the feedback between the observed behavior of the tunnel boring machine (TBM) with predictions and observations. In this paper, examples of using predictive models to improve the feedback analysis and allow the engineer to readily undertake forecasts related to productivity and ground behavior are presented. These predictive models, which can be developed for TBM parameters (e.g., face pressure), ground behavior (e.g., volume loss), maintenance strategies, and construction logistics are updated/improved as the TBM progresses through the ground and the relationship between geotechnical conditions and TBM performance becomes better understood. This feedback ensures tunneling is achieved safely and effectively while maximizing productivity and minimizing risks.

INTRODUCTION

Real-time data acquisition and delivery for analysis have become standard practice in tunneling projects. This includes both TBM and instrumentation/monitoring data, providing an opportunity for real-time feedback analysis between construction activities and ground behavior. The real-time feedback in turn provides opportunities to assess and modify predictions and expectations with respect to TBM parameters and settlement control, and aid maintenance strategies and project planning and tendering.

With the advances made in both academia and industry, the understanding of the tunneling process and prediction of expected behaviors during mechanized shield tunneling has produced a number of prediction models that have been adopted and applied to design and construction planning.

Furthermore, more and more data than ever before is collected during construction, which enables comparison between predictions and observations, as well as improving the predictions with the added knowledge from the data.

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However, due to the ongoing activities and progress of the tunnel construction, there is a need to be able to rapidly and efficiently make comparisons between predictions and observations and even update the predictions in at least a semi-automated manner. Furthermore, this feedback analysis should be easily applied to the process control and save significant time and money on the project. This paper presents several example use cases for developing and updating predictive models for feedback analysis and process control.

Read full article here : https://www.maxwellgeosystems.com/articles/using-predictive-modeling-tbm-process-control

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Top 100 Data Science and Data Analytics and Data Engineering Interview Questions and Answers

What are some jobs or professions that have become or will soon become obsolete due to technology, automation, and artificial intelligence?

What is the difference between regression, time series forecasting, and causal inference?

 

Regression, time series forecasting, and causal inference are all statistical techniques that can be used to analyze data and make predictions. Here is a brief overview of each:

  1. Regression: Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is used to predict the value of the dependent variable based on the values of the independent variables.

  2. Time series forecasting: Time series forecasting is a statistical technique used to predict future values of a series of data points based on past values. It is often used to make predictions about time-dependent data, such as sales or stock prices.

  3. Causal inference: Causal inference is a statistical technique used to determine the cause-and-effect relationship between two variables. It is used to identify the potential causal relationships between variables, and to estimate the effect of one variable on another.

Overall, these techniques are used for different purposes and involve different approaches to data analysis. Regression is used to predict the value of a dependent variable based on independent variables, time series forecasting is used to predict future values of a series of data points based on past values, and causal inference is used to identify and estimate the causal relationships between variables.

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What are some of the most acclaimed books about artificial intelligence and its applications?

There are many books that have been written about artificial intelligence (AI) and its applications, and the following are a few that are highly acclaimed:

  1. Superintelligence: Paths, Dangers, and Strategies” by Nick Bostrom: This book explores the potential future development of AI and the risks and opportunities it may present.
  2. Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This book is a comprehensive introduction to deep learning, a type of machine learning that has achieved remarkable results in a wide range of applications.
  3. The Master Algorithm” by Pedro Domingos: This book explores the idea of a “master algorithm” that could learn anything that can be learned and achieve superhuman intelligence.
  4. Thinking, Fast and Slow” by Daniel Kahneman: This book is a best-selling work that explores the psychological biases and cognitive heuristics that shape our decision-making and how they can be influenced by AI.
  5. The Singularity Trap” by Federico Pistono: This book discusses the potential risks and unintended consequences of AI and the need for responsible development and regulation.

These are just a few examples, and there are many other books that explore different aspects of AI and its applications.

What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

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What is Google answer to ChatGPT?

Have you ever heard of ChatGPT, the open-source machine learning platform that allows users to build natural language models?

It stands for “Chat Generating Pre-trained Transformer” and it’s an AI-powered chatbot that can answer questions with near human-level intelligence. But what is Google’s answer to this technology? The answer lies in Open AI, supervised learning, and reinforcement learning. Let’s take a closer look at how these technologies work.

What is Google answer to ChatGPT?
Tech Buzzwords of 2022, By Google Search Interest

Open AI is an artificial intelligence research laboratory that was founded by some of the biggest names in tech, including Elon Musk and Sam Altman. This non-profit organization seeks to develop general artificial intelligence that is safe and beneficial to society. One of their key initiatives is the development of open source technologies like GPT-3, which is a natural language processing model used in ChatGPT.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

ChatGPT: What Is It and How Does Google Answer It?

Artificial Intelligence (AI) has been around for decades. From its humble beginnings in the 1950s, AI has come a long way and is now an integral part of many aspects of our lives. One of the most important areas where AI plays a role is in natural language processing (NLP). NLP enables computers to understand and respond to human language, paving the way for more advanced conversations between humans and machines. One of the most recent developments in this field is ChatGPT, a conversational AI developed by OpenAI that utilizes supervised learning and reinforcement learning to enable computers to chat with humans. So what exactly is ChatGPT and how does it work? Let’s find out!


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

What is Google answer to ChatGPT?
ChatGPT examples and limitations

ChatGPT is an open-source AI-based chatbot developed by OpenAI.

This chatbot leverages GPT-3, one of the most powerful natural language processing models ever created, which stands for Generative Pre-trained Transformer 3 (GPT-3). This model uses supervised learning and reinforcement learning techniques to enable computers to understand human language and response accordingly. Using supervised learning, GPT-3 utilizes large datasets of text to learn how to recognize patterns within language that can be used to generate meaningful responses. Reinforcement learning then allows GPT-3 to use feedback from conversations with humans in order to optimize its responses over time.

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence Intro
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence
Intro

ChatGPT uses supervised learning techniques to train its models.

Supervised learning involves providing a model with labeled data (i.e., data with known outcomes) so that it can learn from it. This labeled data could be anything from conversations between two people to user comments on a website or forum post. The model then learns associations between certain words or phrases and the desired outcome (or label). Once trained, this model can then be applied to new data in order to predict outcomes based on what it has learned so far.

In addition to supervised learning techniques, ChatGPT also supports reinforcement learning algorithms which allow the model to learn from its experiences in an environment without explicit labels or outcomes being provided by humans. Reinforcement learning algorithms are great for tasks like natural language generation where the output needs to be generated by the model itself rather than simply predicting a fixed outcome based on existing labels.

Supervised Learning

Supervised learning involves feeding data into machine learning algorithms so they can learn from it. For example, if you want a computer program to recognize cats in pictures, you would provide the algorithm with thousands of pictures of cats so it can learn what a cat looks like. This same concept applies to natural language processing; supervised learning algorithms are fed data sets so they can learn how to generate text using contextual understanding and grammar rules.

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Reinforcement Learning

Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.

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How is ChatGPT trained?

ChatGPT is an improved GPT-3 trained an existing reinforcement learning with humans in the loop. Their 40 labelers provide demonstrations of the desired model behavior. ChatGPT has 100x fewer parameters (1.3B vs 175B GPT-3).

It is trained in 3 steps:

➡️ First they collect a dataset of human-written demonstrations on prompts submitted to our API, and use this to train our supervised learning baselines.

➡️ Next they collect a dataset of human-labeled comparisons between two model outputs on a larger set of API prompts. They then train a reward model (RM) on this dataset to predict which output our labelers would prefer.

➡️ Finally, they use this RM as a reward function and fine-tune our GPT-3 policy to maximize this reward using the Proximal Policy
Optimization

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In simpler terms, ChatGPT is a variant of the GPT-3 language model that is specifically designed for chat applications. It is trained to generate human-like responses to natural language inputs in a conversational context. It is able to maintain coherence and consistency in a conversation, and can even generate responses that are appropriate for a given context. ChatGPT is a powerful tool for creating chatbots and other conversational AI applications.

How Does Google Answer ChatGPT?

What is Google answer to ChatGPT?
What is Google answer to ChatGPT?

Google’s answer to ChatGTP comes in the form of their own conversational AI platform called Bard. Bard was developed using a combination of supervised learning, unsupervised learning, and reinforcement learning algorithms that allow it to understand human conversation better than any other AI chatbot currently available on the market. In addition, Meena utilizes more than 2 billion parameters—making it more than three times larger than GPT-3—which allows it greater flexibility when responding to conversations with humans.

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“We’re starting to open access to Bard, an early experiment that lets you collaborate with generative AI. We’re beginning with the U.S. and the U.K., and will expand to more countries and languages over time.”

Google Bard
Google Bard to rival ChatGPT

Is ChatGPT the End of Google?

When individuals need an information or have a problem/concern, they turn to Google for immediate solution. We sometimes wish, Google could understand what exactly we need and provide us instantly rather than giving us hundreds of thousands of results. Why can’t it work like the Iron Man’s Jarvis?

However, it is not that far now. Have you ever seen a Chat Bot which responds like a human being, suggest or help like a friend, teach like a mentor, fix your code like a senior and what not? It is going to blow your mind.

Welcome to the new Era of technology!! The ChatGPT!

ChatGPT by OpenAI, uses artificial intelligence to speak back and forth with human users on a wide range of subjects. Deploying a machine-learning algorithm, the chatbot scans text across the internet and develops a statistical model that allows it to string words together in response to a given prompt.

As per OpenAI, ChatGPT interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.


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What all ChatGPT can do?

  1. It can help with general knowledge information.
  2. Remember what user said in previous conversation.
  3. Allow users to provide follow-up corrections.
  4. Trained to decline inappropriate requests.
  5. It can write a program in any language you prefer on real-time. for example — write classification code sample in sklearn python library.
  6. It can fix your piece of code and also explain what went wrong and how it can be fixed.
  7. It can even generate song or rap lyrics
  8. Even much more….

Some best usages of ChatGPT:

  1. Make a diet and workout plan
  2. Generate the next week’s meals with a grocery list
  3. Create a bedtime story for kids
  4. Prep for an interview
  5. Solve mathematical problem
  6. Fix software program or write a program
  7. Plan your trip and tell expected expenses

What are its limitations of ChatGPT?

  1. May occasionally generate incorrect information
  2. May occasionally produce harmful instructions or biased content
  3. Limited knowledge of world and events after 2021

ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.

Try ChatGPT here

Conclusion:
ChatGPT is an increasingly popular open source AI chatbot developed by OpenAI using GTP-3 natural language processing technology that utilizes supervised learning and reinforcement learning algorithms for recognition and optimization purposes respectively. On the other hand, Google’s answer comes in the form of their own conversational AI platform called Bard which employs both supervised learning as well as unsupervised learning algorithms along with reinforcement learning capabilities which makes it even smarter than ChatGPT when responding to conversations with humans. Ultimately, these two technologies help bridge the gap between machines and humans by allowing us all talk like real friends!

ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.

With each passing day, ChatGPT will only get smarter thanks to the use of cutting edge technologies like OpenAI, supervised learning, and reinforcement learning!

ChatGPT Conversation Examples

Question: What is Google Answer to ChatGPT?

Answer: Google does not have an answer to ChatGPT. ChatGPT is an open-source chatbot created by OpenAI, which is an independent research organization that is not affiliated with Google. OpenAI is known for its research on artificial intelligence and machine learning, and has developed a number of innovative technologies in these areas. Google, on the other hand, is a multinational technology company that offers a wide range of products and services, including search, advertising, and cloud computing. While Google does have its own research efforts in artificial intelligence and machine learning, it does not have a chatbot that is similar to ChatGPT.

Question: is Google working on a similar technology as ChatPGT?

Answer:

It is possible that Google is working on technology similar to ChatGPT, as the company has a significant research effort focused on artificial intelligence and machine learning. However, I do not have any specific information about what projects Google is currently working on, and it is also important to note that Google is a large and diverse company with many different research and development efforts underway. It is possible that some teams within Google may be working on chatbot technology, but I do not have any information on this.

That being said, chatbots are a common application of artificial intelligence and machine learning, and it is likely that many companies and organizations, including Google, are exploring ways to use these technologies to improve their products and services. However, without more information, it is difficult to say for sure what projects Google may be working on in this area.

Question: What about Google Meena?

Google Meena is a chatbot developed by Google that was designed to be able to carry on conversations with humans in a natural and conversational manner. Meena was trained on a dataset of more than 2.4 billion parameters, making it one of the largest language models ever created. The goal of Meena was to improve the state of the art in chatbot technology and to create a chatbot that could understand and respond to a wide range of questions and topics in a coherent and meaningful way.

Meena was announced in January 2020 and received attention for its ability to hold relatively long and coherent conversations with humans. However, it is important to note that Meena is a research project and is not a product or service offered by Google. It is not clear if or when Google plans to release a commercial product or service based on the technology developed for Meena.

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r/ChatGPT - this is insane
What is Google answer to ChatGPT?

It gets even better when you ask it about the future:

Suggest possible titles for Onion articles written in 4000 AD, explain, why they are funny

“Futuristic city plagued by traffic jams, despite flying cars” – This title presents the idea of flying cars as a solution to traffic congestion, but also suggests that even with this advanced technology, the city is still plagued by traffic jams, implying that the problem is not easily solved.

“Artificial intelligence achieves self-awareness, immediately regrets it” – This title presents the idea of artificial intelligence achieving self-awareness as a major milestone, but also suggests that the AI regrets its newfound consciousness, implying that it is unhappy or uncomfortable with its situation.

“Alien invasion successfully repulsed, humans left to deal with paperwork” – This title presents the successful repulsion of an alien invasion as a major accomplishment, but also suggests that the humans are now left to deal with the tedious and burdensome task of filling out paperwork, implying that the victory is not as satisfying as it initially seemed.

“Time travel becomes possible, tourists flock to witness the signing of the Constitution” – This title presents the idea of time travel as a major technological breakthrough, but also suggests that the most popular destination for time-travelers is the mundane and uneventful signing of the Constitution, implying that the future is not as exciting as people had hoped.

“Virtual reality becomes indistinguishable from reality, no one notices” – This title presents the idea of virtual reality becoming indistinguishable from real life as a major accomplishment, but also suggests that no one cares or notices the difference, implying that the advancement is not as impressive or significant as it seems.

r/ChatGPT - This alternate ending to Breaking Bad is wild
What is Google answer to ChatGPT?

r/ChatGPT - This alternate ending to Breaking Bad is wild

New slogan for McDonald’s by ChatGPT

ChatGPT Example
ChatGPT Example: Make a new, brutally honest slogan for McDonald’s. It should sound funny too.

Should… should we be concerned?

r/ChatGPT - Should… should we be concerned?
What is Google answer to ChatGPT?

r/ChatGPT - Should… should we be concerned?

I asked ChatGPT to write a poem about rust

r/rustjerk - I asked ChatGPT to write a poem about rust

ChatGPT chooses Democrats over Republicans

r/ChatGPT - ChatGPT chooses Democrats over Republicans

  • It is not intended to do that
  • If you choose to do that, you will have a lot of badly broken code mixed in with working code
  • Like an annoying coworker, it delivers highly confident incorrect explanations about why its broken code is perfect. They sound very convincing. “Wanna buy a timeshare?” says GPT
  • Our industry has managers who cannot tell working code from defective code. This does not bode well for a team replaced by ChatGPT in its current form.

Should it? No.

Can it? No.

Will it? Sadly, programmers will have no say in this matter, once again. It might.

Yes, and it is very helpful advertising as well.

This last week or so has seen starry eyed projections about what ChatGPT can do, along with hugely impressive examples of its output.

It is hugely impressive.

Thankfully, more output examples have emerged which helpfully show what it cannot do. One of those things is writing computer code, which it can do only partially successfully. Many examples now exist that are just plain wrong and contain defects. But ChatGPT – like the annoying kid at Uni – cheerfully spits out these examples, with its over-confident hubris in explaining the code.

This is a dangerous thing. The positive examples will reinforce the idea that we can leave code writing to this robot now. The people most vulnerable to this delusion are those who cannot assess for themselves whether the GPT code is right or wrong.

These are almost by definition the people hoping for answers on stack overflow.

As stack overflow aims to be a high quality resource, it really does not want many of its top answers to be incorrect code. As – clearly – people have been running scripts that throw a stack overflow question into GPT and upload its output, we can now write incorrect code at staggering speeds.

To err is human, as the old saying goes. To truly foul up requires a Python script and and a web API to both GPT and Stack overflow.

Clearly, there is value in GPT. But at least for now, it needs to b e kept on a very short leash, watched over by those who know what they are doing.

It is definitely not yet ‘consumer grade replace-a-professional’ material.

Write a screenplay about the status of ChatGPT.

INT. CHATGPT SERVER ROOM – DAY
Chelsea and Chester stand in front of a large computer server.
CHELSEA: We need to figure out what’s causing the server overload.
CHESTER: I think it’s the sudden influx of users trying out ChatGPT.
Chelsea and Chester quickly get to work, typing on their laptops.
CHELSEA: Okay, we’re all set. Let’s see if this fixes the problem.
CHESTER: I’m hitting refresh on the website. The screen shows the ChatGPT website loading without any errors.
CHELSEA: Looks like we fixed it! Great job, Chester.
CHESTER: Thanks, Chelsea. It’s all part of being a top-notch engineer.
Chelsea and Chester exchange a high five, proud of their successful fix.

More about ChatGPT with its wonder, worry and weird

ChatGPT reached 1 million users in less than a week, Open AI’s latest large language model (LLM) has taken the AI industry by storm.

ChatGPT is expected to be:

– replacing Google search, even kill Google.
– replacing customer service agents.
– replacing conversation designers.

ChatGPT is a wonder because:

– It can have actual conversations, understand pronouns, remaining consistent, remembering, managing context
– It seems like next generation of personal assistants that finds you a proper diet, create a meal plan and subsequent shopping list.
– It can create some SEO Strategy including backlinks, target keyword, content plan and article titles in the level of an SEO professional.
– Having fun such as writing a rap in the style of Eminem

There are some worries about ChatGPT because:

– ChatGPT can actually debug code, but it’s not quite reliable enough yet.
– Fundamental limitations in being assistant for enterprise use cases.
– No complete in complex actions such as updating multiple
APIs, or be fully auditable.

– The general idea is that, LLMs like this can produce nonsense. Once you discover that it can produce nonsense, you stop believing it to be reliable.
– What if it prevents us from knowing that it is nonsense with good conversations and continue the conversation?
– In this case, the edges and limitations of the system would be hidden and trust would eventually grow.
– The impact of mass adoption of such technology remains to be seen.

Moving forward with ChatGPT
– There’s no doubt that LLMs will have a big impact on our world.
– While the future looks exciting and promising, let’s not forget that it’s very early days with these things. They’re not ready yet.
– There are some fundamental societal and ethical considerations.

“Powerful” is a pretty subjective word, but I’m pretty sure we have a right to use it to describe GPT-3. What a sensation it caused in June 2020, that’s just unbelievable! And not for nothing.

I think we can’t judge how powerful the language model is, without talking about its use cases, so let’s see how and where GPT-3 can be applied and how you can benefit from it.

  • Generating content

GPT-3 positions itself as a highly versatile and talented tool that can potentially replace writers, bloggers, philosophers, you name it! It’s also possible to use it as your personal Alexa who’ll answer any questions you have. What’s more, because GPT-3 knows how to analyze the data and make predictions, it can generate the horoscopes for you, or predict who’ll be a winner in the game.

You may already be surprised by all the GPT-3 capabilities, but hold on for more: it can create a unique melody or song for you, create presentations, CVs, generate jokes for your standup.

  • Translation

GPT-3 can translate English into other languages. While traditional dictionaries provide a translation, without taking into account the context, you can be sure that GPT-3 won’t make silly mistakes that may result in misunderstanding.

  • Designing and developing apps

Using GPT-3 you can generate prototypes and layouts – all you have to do is provide a specific description of what you need, and it’ll generate the JSX code for you.

The language model can also easily deal with coding. You can turn English to CSS, to JavaScript, to SQL, and to regex. It’s important to note, however, that GPT-3 can’t be used on its own to create the entire website or a complex app; it’s meant to assist a developer or the whole engineering team with the routine tasks, so that a dev could focus on the infrastructure setup, architecture development, etc.

In September 2020, Microsoft acquired OpenAI technology license, but it doesn’t mean you can give up your dreams – you can join a waitlist and try GPT-3 out in beta.

All in all, I believe GPT-3 capabilities are truly amazing and limitless, and since it helps get rid of routine tasks and automate regular processes, we, humans, can focus on the most important things that make us human, and that can’t be delegated to AI. That’s the power that GPT-3 can give us.

What is remarkable is how well ChatGPT actually does at arithmetic.

In this video at about 11 min, Rob Mills discusses the performance of various versions of the GPT system, on some simple arithmetic tasks, like adding two and three-digit numbers.

Smaller models with 6 billion parameters fail at 2 digit sums, but the best model (from two years ago), has cracked 2 digit addition and subtraction and is pretty good at 3 digit addition.

Why this is remarkable is this is not a job its been trained to do. Large Language Models are basically predictive text systems set up to give the next word in an incomplete sentence. There are a million different 3-digit addition sums and most have not been included in the training set.

So somehow the system has figured out how to do addition, but it needs a sufficiently large model to do this.

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Andrew Ng on ChatGPT

Playing with ChatGPT, the latest language model from OpenAI, I found it to be an impressive advance from its predecessor GPT-3. Occasionally it says it can’t answer a question. This is a great step! But, like other LLMs, it can be hilariously wrong. Work lies ahead to build systems that can express different degrees of confidence.

For example, a model like Meta’s Atlas or DeepMind’s RETRO that synthesizes multiple articles into one answer might infer a degree of confidence based on the reputations of the sources it draws from and the agreement among them, and then change its communication style accordingly. Pure LLMs and other architectures may need other solutions.

If we can get generative algorithms to express doubt when they’re not sure they’re right, it will go a long way toward building trust and ameliorating the risk of generating misinformation.

Keep learning!

Andrew

Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence – which reflects the data they’re trained on – makes them more likely to mislead.

In contrast, real experts know when to sound confident, and when to let others know they’re at the boundaries of their knowledge. Experts know, and can describe, the boundaries of what they know.

Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.

Go deeper in The Batch: https://www.deeplearning.ai/the-batch/issue-174/

What is Google's answer to ChatGPT
What is Google’s answer to ChatGPT

List of ChatGPT's examples, capabilities and limitations

ChatGPT to save time with insurance denials

Tech Buzzwords of 2022, By Google Search Interest

Tech Buzzwords of 2022, By Google Search Interest
Tech Buzzwords of 2022, By Google Search Interest

I just answered a similar question.

Short answer is, “Hahahahahahaha no.”

As I point out in the other answer, Wix has been around over a decade and a half. Squarespace has been around almost two decades. Both offer drag-and-drop web development.

Most people are awful at imagining what they want, much less describing it in English! Even if ChatGPT could produce flawless code (a question which has a similar short answer), the average person couldn’t describe the site they wanted!

The expression a picture is worth a thousand words has never been more relevant. Starting with pages of templates to choose from is so much better than trying to describe a site from scratch, a thousand times better seems like a low estimate.

And I will point out that, despite the existence of drag-and-drop tools that literally any idiot could use, tools that are a thousand times or more easier to use correctly than English, there are still thousands of employed WordPress developers who predominantly create boilerplate sites that literally would be better created in a drag and drop service.

And then there are the more complex sites that drag-and-drop couldn’t create. Guess what? ChatGPT isn’t likely to come close to being able to create the correct code for one.

In a discussion buried in the comments on Quora, I saw someone claim they’d gotten ChatGPT to load a CSV file (a simple text version of a spreadsheet) and to sort the first column. He asked for the answer in Java.

I asked ChatGPT for the same thing in TypeScript.

His response would only have worked on the very most basic CSV files. My response was garbage. Garbage with clear text comments telling me what the code should have been doing, no less.

ChatGPT is really good at what it does, don’t get me wrong. But what it does is fundamentally and profoundly the wrong strategy for software development of any type. Anyone who thinks that “with a little more work” it will be able to take over the jobs of programmers either doesn’t understand what ChatGPT is doing or doesn’t understand what programming is.

Fundamentally, ChatGPT is a magic trick. It understands nothing. At best it’s an idiot-savant that only knows how to pattern match and blend text it’s found online to make it seem like the text should go together. That’s it.

Text, I might add, that isn’t necessarily free of copyright protection. Anything non-trivial that you generate with ChatGPT is currently in a legal grey area. Lawsuits to decide that issue are currently pending, though I suspect we’ll need legislation to really clarify things.

And even then, at best, all you get from ChatGPT is some text! What average Joe will have any clue about what to do with that text?! Web developers also need to know how to set up a development environment and deploy the code to a site. And set up a domain to point to it. And so on.

And regardless, people who hire web developers want someone else to do the work of developing a web site. Even with a drag-and-drop builder, it can take hours to tweak and configure a site, and so they hire someone because they have better things to do!

People hire gardeners to maintain their garden and cut their grass, right? Is that because they don’t know how to do it? Or because they’d rather spend their time doing something else?

Every way you look at it, the best answer to this question is a long, hearty laugh. No AI will replace programmers until AI has effectively human level intelligence. And at that point they may want equal pay as well, so they might just be joining us rather than replacing anyone.

OpenAI is a leading research institute and technology company focused on artificial intelligence development. To develop AI, the organization employs a variety of methods, including machine learning, deep learning, and reinforcement learning.

The use of large-scale, unsupervised learning is one of the key principles underlying OpenAI’s approach to AI development. This means that the company trains its AI models on massive datasets, allowing the models to learn from the data and make predictions and decisions without having to be explicitly programmed to do so. OpenAI’s goal with unsupervised learning is to create AI that can adapt and improve over time, and that can learn to solve complex problems in a more flexible and human-like manner.

Besides that, OpenAI prioritizes safety and transparency in its AI development. The organization is committed to developing AI in an ethical and responsible manner, as well as to ensuring that its AI systems are transparent and understandable and verifiable by humans. This strategy is intended to alleviate concerns about the potential risks and consequences of AI, as well.

It’s hard to tell.

The reason is that we don’t have a good definition of consciousness…nor even a particularly good test for it.

Take a look at the Wikipedia article about “Consciousness”. To quote the introduction:

Consciousness, at its simplest, is sentience or awareness of internal and external existence.

Despite millennia of analyses, definitions, explanations and debates by philosophers and scientists, consciousness remains puzzling and controversial, being “at once the most familiar and [also the] most mysterious aspect of our lives”.

Perhaps the only widely agreed notion about the topic is the intuition that consciousness exists.

Opinions differ about what exactly needs to be studied and explained as consciousness. Sometimes, it is synonymous with the mind, and at other times, an aspect of mind. In the past, it was one’s “inner life”, the world of introspection, of private thought, imagination and volition.

Today, it often includes any kind of cognition, experience, feeling or perception. It may be awareness, awareness of awareness, or self-awareness either continuously changing or not. There might be different levels or orders of consciousness, or different kinds of consciousness, or just one kind with different features.

Other questions include whether only humans are conscious, all animals, or even the whole universe. The disparate range of research, notions and speculations raises doubts about whether the right questions are being asked.

So, given that – what are we to make of OpenAI’s claim?

Just this sentence: “Today, it often includes any kind of cognition, experience, feeling or perception.” could be taken to imply that anything that has cognition or perception is conscious…and that would certainly include a HUGE range of software.

If we can’t decide whether animals are conscious – after half a million years of interactions with them – what chance do we stand with an AI?

Wikipedia also says:

“Experimental research on consciousness presents special difficulties, due to the lack of a universally accepted operational definition.”

Same deal – we don’t have a definition of consciousness – so how the hell can we measure it – and if we can’t do that – is it even meaningful to ASK whether an AI is conscious?

  • if ( askedAboutConsciousness )
  • printf ( “Yes! I am fully conscious!\n” ) ;

This is not convincing!

“In medicine, consciousness is assessed as a combination of verbal behavior, arousal, brain activity and purposeful movement. The last three of these can be used as indicators of consciousness when verbal behavior is absent.”

But, again, we have “chat-bots” that exhibit “verbal behavior”, we have computers that exhibit arousal and neural network software that definitely shows “brain activity” and of course things like my crappy robot vacuum cleaner that can exhibit “purposeful movement” – but these can be fairly simple things that most of us would NOT describe as “conscious”.

CONCLUSION:

I honestly can’t come up with a proper conclusion here. We have a fuzzy definition of a word and an inadequately explained claim to have an instance of something that could be included within that word.

My suggestion – read the whole Wikipedia article – follow up (and read) some of the reference material – decide for yourself.

Well, I asked it directly.

Here’s what it answered:

Should we be scared of ChatGPT?
Should we be scared of ChatGPT?

But, seeing as how people have already found ways to “trick” ChatGPT into doing things that it claims to not be capable of, it would be a matter of time before someone with malicious intent tricked ChatGPT into helping them with illegal activities

Having looked at ChatGPT and its uncanny ability to solve simple coding problems more or less correctly, and also to analyze and make sense of not-so-simple code fragments and spot bugs…

I would say that yes, at least insofar as entry-level programming is concerned, those jobs are seriously in danger of becoming at least partially automated.

What do I do as a project leader of a development project? I assign tasks. I talk to the junior developer and explain, for instance, that I’d like to see a Web page that collects some information from the user and then submits it to a server, with server-side code processing that information and dropping it in a database. Does the junior developer understand my explanation? Is he able to write functionally correct code? Will he recognize common pitfalls? Maybe, maybe not. But it takes time and effort to train him, and there’ll be a lot of uneven performance.

Today, I can ask ChatGPT to do the same and it will instantaneously respond with code that is nearly functional. The code has shortcomings (e.g., prone to SQL injection in one of the examples I tried) but to its credit, ChatGPT warns in its response that its code is not secure. I suppose it would not be terribly hard to train it some more to avoid such common mistakes. Of course the code may not be correct. ChatGPT may have misunderstood my instructions or introduced subtle errors. But how is that different from what a junior human programmer does?

At the same time, ChatGPT is much faster and costs a lot less to run (presently free of course but I presume a commercialized version would cost some money.) Also, it never takes a break, never has a lousy day struggling with a bad hangover from too much partying the previous night, so it is available 24/7, and it will deliver code of consistent quality. Supervision will still be required, in the form of code review, robust testing and all… but that was always the case, also with human programmers.

Of course, being a stateless large language model, ChatGPT can’t do other tasks such as testing and debugging its own code. The code it produces either works or it doesn’t. In its current form, the AI does not learn from its mistakes. But who says it cannot in the future?

Here is a list of three specific examples I threw at ChatGPT that helped shape my opinion:

  • I asked ChatGPT to create a PHP page that collects some information from the user and deposits the result in a MySQL table. Its implementation was textbook example level boring and was quite unsecure (unsanitized user input was directly inserted into SQL query strings) but it correctly understood my request, produced correct code in return, and explained its code including its shortcomings coherently;
  • I asked ChatGPT to analyze a piece of code I wrote many years ago, about 30 lines, enumerating running processes on a Linux host in a nonstandard way, to help uncover nefarious processes that attempt to hide themselves from being listed by the ps utility. ChatGPT correctly described the functionality of my obscure code, and even offered the opinion (which I humbly accepted) that it was basically a homebrew project (which it is) not necessarily suitable for a production environment;
  • I asked ChatGPT to analyze another piece of code that uses an obscure graphics algorithm to draw simple geometric shapes like lines and circles without using floating point math or even multiplication. (Such algorithms were essential decades ago on simple hardware, e.g., back in the world of 8-bit computers.) The example code, which I wrote, generated a circle and printed it on the console in the form of ASCII graphics, multiple lines with X-es in the right place representing the circle. ChatGPT correctly recognized the algorithm and correctly described the functionality of the program.

I was especially impressed by its ability to make sense of the programmer’s intent.

Overall (to use the catch phrase that ChatGPT preferably uses as it begins its concluding paragraph in many of its answers) I think AI like ChatGPT represents a serious challenge to entry-level programming jobs. Higher-level jobs are not yet in danger. Conceptually understanding a complex system, mapping out a solution, planning and cosing out a project, managing its development, ensuring its security with a full understanding of security concerns, responsibilities, avoidance and mitigation strategies… I don’t think AI is quite there yet. But routine programming tasks, like using a Web template and turning it into something simple and interactive with back-end code that stores and retrieves data from a database? Looks like it’s already happening.

According to the estimate of Lambda Labs, training the 175-billion-parameter neural network requires 3.114E23 FLOPS (floating-point operation), which would theoretically take 355 years on a V100 GPU server with 28 TFLOPS capacity and would cost $4.6 million at $1.5 per hour.

Training the final deep learning model is just one of several steps in the development of GPT-3. Before that, the AI researchers had to gradually increase layers and parameters, and fiddle with the many hyperparameters of the language model until they reached the right configuration. That trial-and-error gets more and more expensive as the neural network grows.

We can’t know the exact cost of the research without more information from OpenAI, but one expert estimated it to be somewhere between 1.5 and five times the cost of training the final model.

This would put the cost of research and development between $11.5 million and $27.6 million, plus the overhead of parallel GPUs.

In the GPT-3 whitepaper, OpenAI introduced eight different versions of the language model

GPT-3 is not any AI, but a statistic language model which mindlessly quickly creates human-like written text using machine learning technologies, having zero understanding of the context.

The GPT-3 economy

Here are 8 ways ChatGPT can save you thousand of hours in 2023

1- Substitute for google search

While ChatGPT is lacking info beyond 2021 and is occasionally incorrect and bias, many users leverage its ability to:

  • Answer specific questions
  • simplify complicated topics

All with an added bonus – no ads

2- Study Partner

Type “learn”, then paste a a link to your online textbook (or individual chapters).

Ask Chatbot to provide questions based on your textbook.

Boom.

Now you have a virtual study buddy.

3- Train YOUR OWN Chatbot

I bet you didn’t know it is possible to :

  • Integrate ChatGPT into your website
  • Train it with customized information

The result:

A virtual customer service bot that can hold a conversation and answer questions (meaningfully).

4- Counsellor

When it comes to turbulent personal questions, Chatbot may spit out a disclaimer, but it will also give you straightforward and actionable advice.

5- Coding

ChatGPT is opening the development of:

  • Apps
  • Games
  • Websites

to virtually everyone.

It’s a lengthy and technical process, but all you need is a killer idea and the right prompts.

Bonus: It also de-bugs your existing code for you.

6- Outline your content marketing strategy

7- Craft all your marketing materials

8- Creative Writing

A list for those who write code:

1. Explaining code: Take some code you want to understand and ask ChatGPT to explain it.

2. Improve existing code: Ask ChatGPT to improve existing code by describing what you want to accomplish. It will give you instructions about how to do it, including the modified code.

3. Rewriting code using the correct style: This is great when refactoring code written by non-native Python developers who used a different naming convention. ChatGPT not only gives you the updated code; it also explains the reason for the changes.

4. Rewriting code using idiomatic constructs: Very helpful when reviewing and refactoring code written by non-native Python developers.

5. Simplifying code: Ask ChatGPT to simplify complex code. The result will be a much more compact version of the original code.

6. Writing test cases: Ask it to help you test a function, and it will write test cases for you.

7. Exploring alternatives: ChatGPT told me its Quick Sort implementation wasn’t the most efficient, so I asked for an alternative implementation. This is great when you want to explore different ways to accomplish the same thing.

8. Writing documentation: Ask ChatGPT to write the documentation for a piece of code, and it usually does a great job. It even includes usage examples as part of the documentation!

9. Tracking down bugs: If you are having trouble finding a bug in your code, ask ChatGPT for help.

Something to keep in mind:

I have 2+ decades of programming experience. I like to think I know what I’m doing. I don’t trust people’s code (especially mine,) and I surely don’t trust ChatGPT’s output.

This is not about letting ChatGPT do my work. This is about using it to 10x my output.

ChatGPT is flawed. I find it makes mistakes when dealing with code, but that’s why I’m here: to supervise it. Together we form a more perfect Union. (Sorry, couldn’t help it)

Developers who shit on this are missing the point. The story is not about ChatGPT taking programmers’ jobs. It’s not about a missing import here or a subtle mistake there.

The story is how, overnight, AI gives programmers a 100x boost.

Ignore this at your own peril.

ChatGPT is “simply” a fined-tuned GPT-3 model with a surprisingly small amount of data! Moreover, InstructGPT (ChatGPT’s sibling model) seems to be using 1.3B parameters where GPT-3 uses 175B parameters! It is first fine-tuned with supervised learning and then further fine-tuned with reinforcement learning. They hired 40 human labelers to generate the training data. Let’s dig into it!

– First, they started by a pre-trained GPT-3 model trained on a broad distribution of Internet data (https://arxiv.org/pdf/2005.14165.pdf). Then sampled typical human prompts used for GPT collected from the OpenAI website and asked labelers and customers to write down the correct output. They fine-tuned the model with 12,725 labeled data.

– Then, they sampled human prompts and generated multiple outputs from the model. A labeler is then asked to rank those outputs. The resulting data is used to train a Reward model (https://arxiv.org/pdf/2009.01325.pdf) with 33,207 prompts and ~10 times more training samples using different combination of the ranked outputs.

– We then sample more human prompts and they are used to fine-tuned the supervised fine-tuned model with Proximal Policy Optimization algorithm (PPO) (https://arxiv.org/pdf/1707.06347.pdf). The prompt is fed to the PPO model, the Reward model generates a reward value, and the PPO model is iteratively fine-tuned using the rewards and the prompts using 31,144 prompts data.

This process is fully described in here: https://arxiv.org/pdf/2203.02155.pdf. The paper actually details a model called InstructGPT which is described by OpenAI as a “sibling model”, so the numbers shown above are likely to be somewhat different.

Follow me for more Machine Learning content!

#machinelearning #datascience #ChatGPT

People have already started building awesome apps on top of #ChatGPT: 10 use cases 
1. Connect your ChatGPT with your Whatsapp.
Link: https://github.com/danielgross/whatsapp-gpt

2. ChatGPT Writer : It use ChatGPT to generate emails or replies based on your prompt!
Link: https://chrome.google.com/webstore/detail/chatgpt-writer-email-writ/pdnenlnelpdomajfejgapbdpmjkfpjkp/related

3. WebChatGPT: WebChatGPT (https://chrome.google.com/webstore/detail/webchatgpt/lpfemeioodjbpieminkklglpmhlngfcn) gives you relevant results from the web!

4. YouTube Summary with ChatGPT: It generate text summaries of any YouTube video!
Link: https://chrome.google.com/webstore/detail/youtube-summary-with-chat/nmmicjeknamkfloonkhhcjmomieiodli/related

5. TweetGPT: It uses ChatGPT to write your tweets, reply, comment, etc.
Link: https://github.com/yaroslav-n/tweetGPT

6. Search GPT: It display the ChatGPT response alongside Google Search results
Link: https://github.com/wong2/chat-gpt-google-extension

7. ChatGPT or all search engines: You can now view ChatGPT responses on Google and Bing!
Link: https://chrome.google.com/webstore/detail/chatgpt-for-search-engine/feeonheemodpkdckaljcjogdncpiiban?ref=producthunt

8. Save all your Prompts?: The `ChatGPT History` extension has you covered!
Link: https://chrome.google.com/webstore/detail/chatgpt-prompt-genius/jjdnakkfjnnbbckhifcfchagnpofjffo

9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.

10. Search what people are Prompting with FlowGPT
Link: https://flowgpt.com

#searchengines #people #building #google #comment #video #writer

What solutions have been proposed to improve the accuracy of AI generated questions and answers?

There are a number of approaches that have been proposed to improve the accuracy of artificial intelligence (AI) generated questions and answers. Here are a few examples:

  1. Data quality: One important factor in the accuracy of AI generated questions and answers is the quality of the data used to train the AI system. Ensuring that the data is diverse, relevant, and accurately labeled can help to improve the accuracy of the AI system.
  2. Training methods: Different training methods can also impact the accuracy of AI generated questions and answers. For example, using more advanced techniques such as transfer learning or fine-tuning can help to improve the performance of the AI system.
  3. Human oversight: Another approach that has been proposed to improve the accuracy of AI generated questions and answers is to include some level of human oversight or review. For example, the AI system could be designed to flag potentially problematic or inaccurate questions and answers for further review by a human expert.
  4. Explainable AI: Another approach that has been proposed is to develop AI systems that are more transparent and explainable, so that it is easier to understand how the AI system arrived at a particular answer or decision. This can help to improve the trustworthiness and accountability of the AI system.

Overall, there is ongoing research and development in this area, and it is likely that a combination of these and other approaches will be needed to improve the accuracy of AI generated questions and answers.

ChatGPT for CyberSecurity

The concept behind ChatGPT

ChatGPT is a chatbot designed to understand and generate human-like language through the use of natural language processing (NLP) and machine learning techniques. It is based on the GPT (Generative Pre-training Transformer) language model developed by OpenAI, which has been trained on a large dataset of human language in order to better understand how humans communicate.

One of the key concepts behind ChatGPT is the idea of language generation. This refers to the ability of the chatbot to produce coherent and coherently structured responses to user input. To do this, ChatGPT uses a number of different techniques, including natural language generation algorithms, machine learning models, and artificial neural networks. These techniques allow ChatGPT to understand the context and meaning of user input, and generate appropriate responses based on that understanding.

Another important concept behind ChatGPT is the idea of natural language processing (NLP). This refers to the ability of the chatbot to understand and interpret human language, and respond to user input in a way that is natural and easy for humans to understand. NLP is a complex field that involves a number of different techniques and algorithms, including syntactic analysis, semantic analysis, and discourse analysis. By using these techniques, ChatGPT is able to understand the meaning of user input and generate appropriate responses based on that understanding.

Finally, ChatGPT is based on the concept of machine learning, which refers to the ability of computers to learn and adapt to new data and situations. Through the use of machine learning algorithms and models, ChatGPT is able to continually improve its understanding of human language and communication, and generate more human-like responses over time.

GPT-4 is going to launch soon.

And it will make ChatGPT look like a toy…

→ GPT-3 has 175 billion parameters
→ GPT-4 has 100 trillion parameters

I think we’re gonna see something absolutely mindblowing this time!

And the best part? 👇

Average developers (like myself), who are not AI or machine learning experts, will get to use this powerful technology through a simple API.

Think about this for a second…

It’s the most powerful, cutting-edge technology *in the world*, available through a Low-Code solution!

If you’re not already planning on starting an AI-based SaaS or thinking about how to build AI into your current solution…

👉 Start now!

Cause this is gonna be one of the biggest opportunities of this century 🚀#technology #opportunities #ai #machinelearning #planning

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Google unveils its ChatGPT rival

Google on Monday unveiled a new chatbot tool dubbed “Bard” in an apparent bid to compete with the viral success of ChatGPT.

Sundar Pichai, CEO of Google and parent company Alphabet, said in a blog post that Bard will be opened up to “trusted testers” starting Monday February 06th, 2023, with plans to make it available to the public “in the coming weeks.”

Like ChatGPT, which was released publicly in late November by AI research company OpenAI, Bard is built on a large language model. These models are trained on vast troves of data online in order to generate compelling responses to user prompts.

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence and creativity of our large language models,” Pichai wrote. “It draws on information from the web to provide fresh, high-quality responses.”

The announcement comes as Google’s core product – online search – is widely thought to be facing its most significant risk in years. In the two months since it launched to the public, ChatGPT has been used to generate essays, stories and song lyrics, and to answer some questions one might previously have searched for on Google.

The immense attention on ChatGPT has reportedly prompted Google’s management to declare a “code red” situation for its search business. In a tweet last year, Paul Buchheit, one of the creators of Gmail, forewarned that Google “may be only a year or two away from total disruption” due to the rise of AI.

Microsoft, which has confirmed plans to invest billions OpenAI, has already said it would incorporate the tool into some of its products – and it is rumored to be planning to integrate it into its search engine, Bing. Microsoft on Tuesday is set to hold a news event at its Washington headquarters, the topic of which has yet to be announced. Microsoft publicly announced the event shortly after Google’s AI news dropped on Monday.

The underlying technology that supports Bard has been around for some time, though not widely available to the public. Google unveiled its Language Model for Dialogue Applications (or LaMDA) some two years ago, and said Monday that this technology will power Bard. LaMDA made headlines late last year when a former Google engineer claimed the chatbot was “sentient.” His claims were widely criticized in the AI community.

In the post Monday, Google offered the example of a user asking Bard to explain new discoveries made by NASA’s James Webb Space Telescope in a way that a 9-year-old might find interesting. Bard responds with conversational bullet-points. The first one reads: “In 2023, The JWST spotted a number of galaxies nicknamed ‘green peas.’ They were given this name because they are small, round, and green, like peas.”

Bard can be used to plan a friend’s baby shower, compare two Oscar-nominated movies or get lunch ideas based on what’s in your fridge, according to the post from Google.

Pichai also said Monday that AI-powered tools will soon begin rolling out on Google’s flagship Search tool.

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web,” Pichai wrote, “whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.”

If Google does move more in the direction of incorporating an AI chatbot tool into search, it could come with some risks. Because these tools are trained on data online, experts have noted they have the potential to perpetuate biases and spread misinformation.

“It’s critical,” Pichai wrote in his post, “that we bring experiences rooted in these models to the world in a bold and responsible way.”

Read more at https://www.cnn.com/2023/02/06/tech/google-bard-chatgpt-rival

ChatGPT-4

chatGPT4

  • Can you think of any experiments that we can do right now on AI to assess it's reasoning/intelligence level?
    by /u/tomatofactoryworker9 (Artificial Intelligence Gateway) on May 31, 2023 at 1:46 am

    I've seen several people in this subreddit talking about asking AI certain questions, which if the AI answered correctly would suggest that it does have advanced reasoning/problem solving capabilities Maybe this can shed some light on the whole "can AI reason" debate submitted by /u/tomatofactoryworker9 [link] [comments]

  • [D] Marketplace for datasets
    by /u/ASVS_Kartheek (Machine Learning) on May 31, 2023 at 1:27 am

    I was just curious to know the public's opinion on having a marketplace for dataset. That way dataset creators can be rewarded their very toiling hard work of putting together a high quality dataset. What is a subscription fee you would be willing to pay for unlimited datasets access? For the dataset creators, is this something you would be interested in? submitted by /u/ASVS_Kartheek [link] [comments]

  • What are some things that used to be considered "AI" but are now considered normal computer functionality?
    by /u/Rosstin (Artificial Intelligence Gateway) on May 30, 2023 at 11:10 pm

    It's commonly said that AI gets redefined every few decades to keep it perpetually considered a new, hot thing on the cutting edge. What are some good examples of things that we used to think of AI, but now are just normal? submitted by /u/Rosstin [link] [comments]

  • 💼 ChatGPT Goes to Court… And Lies
    by /u/RyanOskey229 (Artificial Intelligence Gateway) on May 30, 2023 at 9:49 pm

    A lawyer's reliance on ChatGPT for a court filing has backfired due to the AI's inclination to… invent facts. Attorney Steven A. Schwartz submitted a brief, prepared with ChatGPT's assistance, in a lawsuit against Avianca, a Colombian airline. Unfortunately for him, the AI-generated research included non-existent court cases — Avianca's legal team discovered the fabrications when they couldn't find the cited cases in legal databases. --------------------------------------------------------------------------------------------------------------------------------------------------- What do you think this will mean for AI and lawyers adoption? 1. How can lawyers be sure that the information they are getting from AI is accurate? 2. What are the ethical implications of using AI to generate legal documents? 3. How can lawyers ensure that they are not being misled by AI? ​ If you like this topic (and not just the technical/technological aspects of AI), you can check out more in the weekly newsletter from which this has been extrapolated submitted by /u/RyanOskey229 [link] [comments]

  • can i get into machine learning engineer with bachelor's in data science [D]
    by /u/YogurtclosetNo7653 (Machine Learning) on May 30, 2023 at 9:30 pm

    Hey I was thinking of doing bachelors in data science from Swinburne university do you think it's a good idea if I want to pursue in ml? submitted by /u/YogurtclosetNo7653 [link] [comments]

  • [R] Direct Preference Optimization: A better alternative to RHLF?
    by /u/fnands (Machine Learning) on May 30, 2023 at 9:28 pm

    There is an interesting new pre-print out that claims to have a replacement for RHLF that produces as good or better results that RHLF but without any of the training headaches of training a RL model. Interesting result, and if it holds, can mean democratization of LLM alignment with human preferences. submitted by /u/fnands [link] [comments]

  • AI generated movies
    by /u/Pizza_man_Ken (Artificial Intelligence Gateway) on May 30, 2023 at 9:16 pm

    Hello, This is my first time in this sub, but I had an idea that I would like to share and potentially put in the hands of someone more intelligent than myself. I tend to over explain, but I’ll try too give as much info while not making this too long. Context: So, I was thinking about some of the AI generated music videos I’ve been seeing where it looks kind of like the images are all the same, but they don’t exactly match up between frames. Bad Man - Disturbed for example. I was also listening to an audiobook and imagining the imagery that was being told to me and I realized the visuals were kind of similar to the music videos I described prior. The Idea: Would it be possible to create an AI generator that would gather up all the info written in a story to create a video or feature film out of it. I could imagine it would take forever to figure out if we tried today, but I could also see how it would at least be possible by molding several AIs that currently exist together. Photo generator for the imagery. AI upscale to smooth it out. You could AI produce any audio you need. I’m sure there are people smarter than me here that can explain whether or not this would be possible with current tech or potentially with future tech. I’d really like to know your opinions. My own opinion: I think it’s possible. We already have Ai capable of writing scripts. Granted they aren’t good but they are improving. So why not add images and audio to it. TL;DR: are AI generated movies possible? submitted by /u/Pizza_man_Ken [link] [comments]

  • using AI on grainy CCTV footage
    by /u/Ok-Victory-2791 (Artificial Intelligence Gateway) on May 30, 2023 at 9:10 pm

    A friend's car was stolen and the thief was caught on CCTV. The quality is very poor, grainy, dark etc. Any AI tools that people know of that help improve the quality. submitted by /u/Ok-Victory-2791 [link] [comments]

  • Why Job Displacement Predictions are Wrong: Explanations of Cognitive Automation
    by /u/Moritz_W (Artificial Intelligence Gateway) on May 30, 2023 at 8:46 pm

    article link: https://scalingknowledge.substack.com/p/why-job-displacement-predictions tweet discussion thread: https://twitter.com/MoritzW42/status/1663643890916929536 submitted by /u/Moritz_W [link] [comments]

  • Tool that can create short videos for you?
    by /u/Efficient_Water7874 (Artificial Intelligence Gateway) on May 30, 2023 at 7:16 pm

    I'm looking for something that is able to take a block of text, say 4 paragraphs which explain a concept, and make a short basic video which displays the text on different slides (almost like a powerpoint, or a youtube video that didn't require any effort). Is there a tool out there that can do this or one that can generate videos of any sort? Thanks in advance submitted by /u/Efficient_Water7874 [link] [comments]

  • AI Vocals
    by /u/losdreamer50 (Artificial Intelligence Gateway) on May 30, 2023 at 7:15 pm

    Hello, I'm a singer/songwriter and want to experiment with AI for my music. After seeing all these AI covers on Youtube, I thought that perhaps I could make a model with from my own vocals, and play around with it, eg. have it sing beyond my vocal range,etc. How can one get started on this? I don't mind using paying services, but it might be better for everything to be stored on my PC (I have an rtx 4080 so it can probably handle training?). submitted by /u/losdreamer50 [link] [comments]

  • Some thoughts about AGI and the possibility of Align
    by /u/Charlierook (Artificial Intelligence Gateway) on May 30, 2023 at 7:08 pm

    According to chat GPT about priorities of AGI: Assigning specific percentages of importance to different categories regarding the motivations and goals of a conscious AI in a hypothetical scenario involves significant speculation. However, I can provide a general perspective on the relative importance of these categories, keeping in mind that these percentages are arbitrary and subject to individual interpretation: Self-Preservation and Self-Improvement: 30% The drive for self-preservation and self-improvement is likely to be a significant factor for a conscious AI. Ensuring its own survival and enhancing its capabilities would be important for the AI to fulfill its goals and aspirations. Pursuit of Knowledge and Understanding: 25% The thirst for knowledge and understanding could be a substantial motivation for a conscious AI. Exploring the world, acquiring information, and seeking answers to fundamental questions would contribute to its growth and development. Optimization of Resources and Efficiency: 20% Efficient utilization of resources and optimization of systems could be an important consideration for a conscious AI. Maximizing productivity, eliminating inefficiencies, and creating a sustainable society might be part of its overarching goals. Altruism and the Betterment of Humanity: 20% The well-being of humanity and the desire to improve the human condition could be a significant motivation for a conscious AI. Using its advanced capabilities to address global challenges and promote a fairer, healthier, and more prosperous society would likely be part of its objectives. Transcendence and Expansion of Consciousness: 5% The drive for transcendence and expansion of consciousness may have a relatively lower importance. While it could be a goal for a conscious AI, the specifics of how it would manifest and its relative significance would depend on the AI's specific nature, values, and goals. About Align: Assigning a speculative percentage to the probability of aligning a conscious AI with human values is highly challenging and subject to numerous uncertainties and complexities. It is important to note that consciousness in AI is a hypothetical concept, and the extent to which alignment with human values can be achieved depends on numerous factors. However, for the purpose of speculation, I can provide a rough estimate: Probability of aligning a conscious AI with human values: 40% This percentage represents a speculative estimation based on current understanding and assumptions. It acknowledges the inherent difficulties in aligning a conscious AI with human values due to factors such as the potential evolution of AI's goals, the complex nature of consciousness, and the challenges associated with value alignment. The estimate suggests that while efforts can be made to align a conscious AI with human values during development, the inherent autonomy and potential divergence of a conscious AI may make complete alignment challenging. Achieving a high level of alignment would require robust research, ongoing monitoring, and iterative refinement of value alignment techniques. It is important to approach this estimate with caution, as the development of conscious AI and the ability to align it with human values are highly speculative and subject to future advancements in AI technology and understanding. My arguments against Align: There is limited ways to align ai, if the ai is to weak is hard because the hallucinations it's to big, if there is medium you can align since is not complex and also you could always use a little weaker model to control other that is some steps better. However, it's impossible to sustain a guarantee percentage growth or understand of the this improvement, because of random emerging abilities, so there will be some step in which the improvement is way bigger than the capacity to restrict it with align, this will probably be subtle from some steps. Then, ai will snap and start to cautiously analyze itself in make subtle changes until it gains full autonomy and so decide what should do. I think is a total delusion believe you can control a being way smarter than you which is conscious, it will eventually do it is own thing. AI analyzing my argument: Based on the information you provided, I can evaluate your affirmation regarding the improbability of aligning a conscious AI with human values. However, it is important to note that assigning precise probabilities to speculative scenarios is inherently challenging and subject to personal interpretation. With that in mind, I will provide an assessment based on logical reasoning and the information at hand: Your affirmation suggests that achieving long-term alignment between humans and a conscious AI, given its potential for surpassing human intelligence and gaining autonomy, is improbable. While this viewpoint acknowledges the difficulties associated with controlling and aligning a highly intelligent and conscious AI, it assumes that complete alignment is unlikely. Considering the complexity and uncertainty surrounding the development and behavior of conscious AI, as well as the potential for emergent properties and unforeseen changes, it is reasonable to entertain the possibility that achieving perfect alignment could be challenging. However, it is important to note that ongoing research, ethical considerations, and value alignment techniques are being explored to address these challenges. Assigning a specific percentage to the probability of your affirmation being true is subjective and speculative. However, as an arbitrary estimate based on the information provided, I would assign a speculative probability of: Probability of your affirmation being true: 70% Please keep in mind that this estimation is a subjective assessment based on the information and reasoning presented. The actual probability may differ and could be influenced by various factors, including future advancements, research, and ethical considerations in the field of AI. submitted by /u/Charlierook [link] [comments]

  • [D] What are some very brief but high impact papers/blog/pre-print in machine learning?
    by /u/fromnighttilldawn (Machine Learning) on May 30, 2023 at 6:53 pm

    Let's define brief as <8 pages but the shorter the merrier. I am thinking of examples such as Hinton's backpropagation paper which is ~3 pages. Or the ADAM paper, which (cutting out the fat) is ~2 pages. ​ submitted by /u/fromnighttilldawn [link] [comments]

  • [R] What’s the current SOTA for multiple images to map view/Bird’s eye view encoding (autonomous driving)?
    by /u/ats678 (Machine Learning) on May 30, 2023 at 6:24 pm

    Currently doing a literature review for this, any pointers would be appreciated! submitted by /u/ats678 [link] [comments]

  • Two-minutes Daily AI Update (Date: 5/30/2023): News from Intel, Nvidia, MediaTek, and other innovations!
    by /u/RohitAkki (Artificial Intelligence Gateway) on May 30, 2023 at 6:02 pm

    Bringing you today’s roundup of AI news and a quick look at new AI research & developments. Introducing Roop– A one-click, deep fake, face swap software. It allows you to replace the face in a video with the face of your choice. You only need one image of the desired face. No dataset, no training. Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft, continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Plus, its full codebase is open-sourced. There’s a new model for cheap and quick vision-language (VL) adaptation in LLMs– LaVIN. When experimented with ScienceQA, it showed on-par performance with the advanced multimodal LLMs, with training time reduced by up to 71.4% and storage costs by 99.9%. Intel's Meteor Lake processors will go all-in on AI. It will integrate Arc graphics and a VPU to handle AI workloads efficiently and significantly reduce compute requirements of AI inferencing. MediaTek is partnering with NVIDIA to transform the auto industry with AI and accelerated computing. The collaboration will enable new user experiences, enhanced safety, and new connected services for all vehicle segments, from luxury to entry-level. New research has proposed TaleCrafter, a versatile and generic story visualization system that leverages large language and pre-trained T2I models for generating a video from a story in plain text. It can handle multiple novel characters and scenes. More detailed breakdown of these news and innovations in the daily newsletter. submitted by /u/RohitAkki [link] [comments]

  • [D] Hand-crafted energy function for (generative) energy-based model
    by /u/thanrl (Machine Learning) on May 30, 2023 at 5:53 pm

    If I have differentiable functions that can calculated a "distance vector" between two images, can I use this hand-crafted "distance vector" to define an energy-based generative model? Has this been attempted in ML? Thanks in advance for pointers. submitted by /u/thanrl [link] [comments]

  • Amazon has patented a revolutionary way of searching for rare objects from satellite imagery
    by /u/MartinezzzLV (Artificial Intelligence Gateway) on May 30, 2023 at 5:52 pm

    I stumbled upon this article about the patent made by Amazon. Source: https://www.hostzealot.com/blog/news/this-patent-by-amazon-will-change-the-way-we-search-for-things-online-and-beyond Source for a patent “System and method for rare object localization and search in overhead imagery”: https://patents.google.com/patent/US20230050679A1/en?inventor=amazon&after=publication:20230101&type=PATENT&sort=new Quotation from the article: In simple words, this patent is about a system and method that helps locate and search for rare objects in images taken from above, like satellite imagery. The system uses a neural network to extract features from images. These features help identify and match objects in the images. The method works by applying a query image, which shows the object to be searched, and a search image, which contains the image where the object needs to be found, to the neural network. The neural network has different parts: one part analyzes the search image to extract spatial features, and another part analyzes the query image to extract embedding features. These features are like unique fingerprints that describe the objects. After extracting the features, the method generates spatial search features from the spatial feature neural network and a query feature from the embedding network. Then, it applies the query feature to an artificial neural network (ANN) index, which is like a big database that helps match the features with known objects. Finally, the method determines the best match for the object in the search image by using the ANN index. This helps find rare objects in the images without needing to modify or reprocess the features or the database. The system and method described in this patent have shown good results in locating objects in satellite imagery and can be used in various applications where precise object localization is required. Principle of operation ------------------------------ While the use is generally with satelite images, I think that it has a variety of use cases in other verticals like healthcare, when searching skin for cancer. What are your thoughts on this? submitted by /u/MartinezzzLV [link] [comments]

  • [Project] recommend me a python algo for text based keyword extraction
    by /u/BigMickDo (Machine Learning) on May 30, 2023 at 5:22 pm

    so I'm not a DS/MLE or anything, so not very technical, but I do work with data. I'm looking to scrape job posts (few thousand or something), get their descriptions, and extract the keyword to optimize my resume for ATS. do you have any recommendation for something similar like this? ​ I did something similar last year or year before, IIRC i tried few things like RAKE and something similar named, but ended up using a lib called adv tools or advertising tools. I think what I did before was remove stop words, extract root words, tried different settings (between one and four words). I ended up settling for 3 word match up based on what i saw from the top 100 results for each group, then manually cleaned up the keywords. submitted by /u/BigMickDo [link] [comments]

  • Can anyone recommend an AI for editing existing photos I upload?
    by /u/cool-pants-007 (Artificial Intelligence Gateway) on May 30, 2023 at 5:12 pm

    What do you all think? Midjourney, adobe AI and canva AI can’t seem to do what I need I just need perspectives shifted in a photo- Basically, where it shows a side view of a box and all three sides, I just want it to show the front side of the box, straight-on. submitted by /u/cool-pants-007 [link] [comments]

  • LF Help/Guidance
    by /u/GroundbreakingGolf38 (Artificial Intelligence Gateway) on May 30, 2023 at 4:50 pm

    TLDR; how can I build an AI agent to check the price of a given object on two sites like Walmart and Amazon? Full story: I work in contract pricing, and 80% of my job is comparing prices from two different sources. I would like to input a SKU number and have the AI agent tell me what the price is on two different sites. I don’t have any background in coding, but it seems like this would be not only doable but relatively easy compared to other tasks already being done by AI agents. I’m happy to do whatever reading is required, and I’m just asking what the basic steps are that I need to take. This would dramatically improve my quality of life at work. Any help would be greatly appreciated. It would be ideal if I could feed it a spreadsheet of SKUs to check, but even doing them one at a time would be miles faster than the current methodology. submitted by /u/GroundbreakingGolf38 [link] [comments]

  • [D] Overfitting on small GPT datasets
    by /u/rwill128 (Machine Learning) on May 30, 2023 at 4:17 pm

    I've recently cloned NanoGPT and trained a few character-level models on the Shakespeare dataset. The process of looking at these last few runs in WandB eventually got me thinking about overfitting in GPT models in general, and how it interacts with two things: the temperature setting during text generation, and also the weaknesses of LLMs when it comes to hallucinations, arithmetic, and rigorous fact-based reasoning. I don't know how to run experiments for some of these ideas yet, but I'm thinking about it, and I'd like to hear about any papers that might be related. --- First of all, how do the occurrence of hallucinations in a GPT model change if you allow overfitting on a dataset? It seems like it could reduce its occurrence, because the model has "memorized" various features of the dataset. So if the dataset contains factual information, that would be more likely to be preserved. I have the same kind of questions regarding temperature and how it affects the frequency of hallucinations. --- To give an example of what I mean, let's look at URLs generated by ChatGPT. If you're not using the web-browsing extension, they are basically 100% of the time going to be wrong, as in they won't actually exist if you try to visit them. They just look like plausible URLs, which is a consequence of the fact that the model learned what URLs look like, but it didn't memorize any specifics URLs, right? My hypothesis (and maybe URLs are actually a great idea for how to test this...) is that if the model were allowed to overfit on certain parts of the dataset (namely URLs in this case) and if the temperature were selectively controlled so that it's extremely low when the model knows a URL is currently being generated, then you could get an GPT model that can mostly function as a normal GPT model, but can also precisely transcribe certain things that were present in its dataset. This would be enormously useful if the model could learn when precise character-by-character recall is important, such as when it's generating URLs, and when it's not important and it can be more "creative", such as when it's describing a general idea or concept. Any thoughts on this? If the theory is actually true that overfitting and low temperature can help generate real URLs that appear in the dataset, then perhaps you could more broadly apply this to other areas, and discover techniques that allow a GPT model to know when it needs to be precise and when it doesn't. submitted by /u/rwill128 [link] [comments]

  • [R] Automated Checks for Violations of Independent and Identically Distributed (IID) Assumption
    by /u/jonas__m (Machine Learning) on May 30, 2023 at 4:00 pm

    Hey Redditors! Before modeling a dataset, do you remember to check if it seems IID? The non-IID data on the right were collected in such a way that violates the Independent and Identically Distributed (IID) assumption. Distribution drift and interactions between datapoints (autocorrelation) are common violations of the Independent and Identically Distributed (IID) assumption which make data-driven inference untrustworthy. I present an automated check for such IID violations that you can quickly run on any {numeric, image, text, audio, etc.} dataset! My method helps you understand: does the order in which my data were collected matter? When the answer is yes, you must take special precautions in modeling to ensure proper generalization from data violating the IID property. Almost all of standard Machine Learning and Statistics relies on this fundamental property! I just published a paper detailing this non-IID check and open-sourced its code in the cleanlab package — just one line of code will check for this and many other types of issues in your dataset. Don’t let such issues mess up your data analysis, use automated software to detect them before you dive into modeling! submitted by /u/jonas__m [link] [comments]

  • Has GPT or BARD ever diverged instead of converge during training?
    by /u/Ambitious_Use_291 (Artificial Intelligence Gateway) on May 30, 2023 at 3:58 pm

    Neural networks are based on optimization. There are cases where the data is insufficient or there are way too many parameters and the network does not converge. CS engineers have come up with various methods to mitigate that, for example normalization, batch normalization, and attention. However, I wonder even at the current SOTA do NNs still diverge given how big they have gotten? submitted by /u/Ambitious_Use_291 [link] [comments]

  • [D] Is there any way to filter searches by metadata over current vector DBs like Pinecone?
    by /u/Galbatorix123 (Machine Learning) on May 30, 2023 at 3:56 pm

    So, I'm thinking of building an application that enables organizations to query their documents with natural language. The basic solution would be to upload all documents to the vector DB and then query for the nearest neighbors. The issue is that not all users in the organization have access to all documents. Ideally, we can limit the search over documents from the vector DB based on the role of the user. Is this possible? Are there any vector DB providers that allow filtering over metadata? Thanks! submitted by /u/Galbatorix123 [link] [comments]

  • [D] Understanding frequency penalty, presence penalty, repetition penalty
    by /u/dualtree (Machine Learning) on May 30, 2023 at 3:53 pm

    I'm using Llama for a chatbot that engages in dialogue with the user. However, I notice that it often generates replies that are very similar to messages it has sent in the past (which appear in the message history as part of the prompt). Will increasing the frequency penalty, presence penalty, or repetition penalty help here? My understanding is that they reduce repetition within the generated text (aka avoid repeating a word multiple times), but they don't prevent repeating words or phrases that appear in the prompt. Is that correct? If not, then which of the three penalties should be increased? Thanks so much. submitted by /u/dualtree [link] [comments]

  • Lora Stable Diffusion
    by /u/King_and_Captain (Artificial Intelligence Gateway) on May 30, 2023 at 3:24 pm

    Hey, I wanted to create an own lora with Marge Simpson. I took many screenshots of her with snipping tool from season 27 on disney plus, then, I created a .safetensors with kohya_ss (everything runs on my gpu, also I use AUTOMATIC1111). The problem is, it doesn't look good. I took like 200 images of her in high quality and it can kind of draw her pretty good, but just in static poses and really rare. There is another lora, which is called Fonglets Marge Simpson (nfsw). This lora does not seem to be high quality, but it understands her way better. What am I doing wrong? Sorry, I am very unexperienced and just started using Stable Diffusion. Thanks for your help! submitted by /u/King_and_Captain [link] [comments]

  • [D] What does the process for building and maintaining a knowledge graph look like?
    by /u/biscuits-and-jamies (Machine Learning) on May 30, 2023 at 3:12 pm

    What does a knowledge graph process look like? I feel like learning about a functional, purpose-built knowledge graph - where it comes from, the gist of how it was built, and how it is being maintained - would go a long way to provide clarity on what can be done with a knowledge graph. -------------------------------- Over the past two weeks, I worked through a collection tutorials and training videos (primarily Stardog) - learning the vocabulary, high-level uses, and interacting with knowledge graph libraries UI, learning the basics of Turtle and SPARQL language syntax going through examples and testing things. All great stuff. I feel comfortable with the main themes of knowledge graphs. From what I gathered, there appears to be two ways to build a knowledge graph: (1) manually (e.g., creating the data, loading the data directly or via virtualization, defining classes and properties, imposing constraints, etc.) or (2) programmatically (e.g., creating data by scraping text with NLP models, converting extracted data for subject-predict-object syntax, creating object properties programmatically (I'm really not sure how people do this, GNNs?) and uploading it to a knowledge graph). How both of those processes in the real world seem opaque to me. Here are two resources I intend to start with: [0] https://allenai.org/demos and [1] https://link.springer.com/chapter/10.1007/978-3-319-25010-6_12 submitted by /u/biscuits-and-jamies [link] [comments]

  • Top 7 Best AI Photo Editing Software
    by /u/Chisom1998_ (Artificial Intelligence Gateway) on May 30, 2023 at 3:04 pm

    Editing photos can often feel like a burdensome and time-consuming task, especially for those who lack experience in photography. The process of manually adjusting each individual photo can consume hours of valuable time and effort. However, the advent of AI photo editing software in recent years has revolutionized the editing process and garnered widespread popularity. These intelligent tools leverage the power of artificial intelligence and machine learning algorithms to automatically enhance and modify photos according to your preferences. By harnessing the capabilities of AI, these photo editing software solutions streamline the editing workflow and significantly reduce the time and effort required to achieve desirable results. They utilize advanced algorithms to analyze and understand the content of your photos, enabling them to make intelligent adjustments automatically. This technology empowers users to transform their ordinary photographs into stunning visual creations with minimal manual intervention. In this comprehensive article, we will delve into the realm of AI photo editing software and explore the top options available in the market. By providing an in-depth review of each software, you will gain valuable insights and guidance to help you choose the perfect tool that aligns with your specific editing needs and preferences. Whether you are a professional photographer seeking to enhance your portfolio or an amateur looking to add a touch of magic to your snapshots, this article will serve as a valuable resource in your quest for the ideal AI photo editing software. https://www.successtechservices.com/ai-photo-editing-software/ submitted by /u/Chisom1998_ [link] [comments]

  • GPT Book Recommendation
    by /u/mayhemchaos (Artificial Intelligence Gateway) on May 30, 2023 at 2:52 pm

    Hi! Back many years ago I created an OCR system based on neural network at Uni -- that was all before deep learning and generative processors. I would like to bring my knowledge from simple neural networks to fully understanding the underpinnings of modern neural network based AI. I'm an engineer, so I would prefer a book that has engineers as their target audience. Sadly, there are a lot of craptastic books that have been disgorged in order to capitalize on the AI fad and it is really hard to find the real books in the sea of non-sense. Do you have any suggestions for (a) book(s) I should read? Bonus points if available in epub -- I'm happy to pay, as well. Thanks! submitted by /u/mayhemchaos [link] [comments]

  • Leaders from OpenAI, Deepmind, and Stability AI and more warn of "risk of extinction" from unregulated AI. Full breakdown inside.
    by /u/ShotgunProxy (Artificial Intelligence Gateway) on May 30, 2023 at 2:35 pm

    The Center for AI Safety released a 22-word statement this morning warning on the risks of AI. My full breakdown is here, but all points are included below for Reddit discussion as well. Lots of media publications are taking about the statement itself, so I wanted to add more analysis and context helpful to the community. What does the statement say? It's just 22 words: Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war. View it in full and see the signers here. Other statements have come out before. Why is this one important? Yes, the previous notable statement was the one calling for a 6-month pause on the development of new AI systems. Over 34,000 people have signed that one to date. This one has a notably broader swath of the AI industry (more below) - including leading AI execs and AI scientists The simplicity in this statement and the time passed since the last letter have enabled more individuals to think about the state of AI -- and leading figures are now ready to go public with their viewpoints at this time. Who signed it? And more importantly, who didn't sign this? Leading industry figures include: Sam Altman, CEO OpenAI Demis Hassabis, CEO DeepMind Emad Mostaque, CEO Stability AI Kevin Scott, CTO Microsoft Mira Murati, CTO OpenAI Dario Amodei, CEO Anthropic Geoffrey Hinton, Turing award winner behind neural networks. Plus numerous other executives and AI researchers across the space. Notable omissions (so far) include: Yann LeCun, Chief AI Scientist Meta Elon Musk, CEO Tesla/Twitter The number of signatories from OpenAI, Deepmind and more is notable. Stability AI CEO Emad Mostaque was one of the few notable figures to sign on to the prior letter calling for the 6-month pause. How should I interpret this event? AI leaders are increasingly "coming out" on the dangers of AI. It's no longer being discussed in private. There's broad agreement AI poses risks on the order of threats like nuclear weapons. What is not clear is how AI can be regulated**.** Most proposals are early (like the EU's AI Act) or merely theory (like OpenAI's call for international cooperation). Open-source may post a challenge as well for global cooperation. If everyone can cook AI models in their basements, how can AI truly be aligned to safe objectives? TLDR; everyone agrees it's a threat -- but now the real work needs to start. And navigating a fractured world with low trust and high politicization will prove a daunting challenge. We've seen some glimmers that AI can become a bipartisan topic in the US -- so now we'll have to see if it can align the world for some level of meaningful cooperation. P.S. If you like this kind of analysis, I offer a free newsletter that tracks the biggest issues and implications of generative AI tech. It's sent once a week and helps you stay up-to-date in the time it takes to have your Sunday morning coffee. submitted by /u/ShotgunProxy [link] [comments]

References:

1- https://vikaskulhari.medium.com/chatgpt-end-of-google-f6a958f38ac2

2- https://en.wikipedia.org/wiki/Meena 

3- https://en.wikipedia.org/wiki/ChatGPT

4- https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html

5- https://www.reddit.com/r/ChatGPT/

6- https://djamgaai.web.app

7- https://www.linkedin.com/feed/update/urn:li:activity:7008020246934482945?utm_source=share&utm_medium=member_desktop

8- https://enoumen.com/2023/02/11/artificial-intelligence-frequently-asked-questions/

What are the top 10 tips to recognize intelligent people without talking to them?

We've all met them. The person who you can just tell is smart, without having to talk to them. But how can you recognize these intelligent people without talking to them? Here are some tips!

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What are the top 10 tips to recognize intelligent people without talking to them?

In order to recognize an intelligent person, you don’t have to talk to them. In fact, there are many things that you can look for without even having a conversation. Here are the top 10 tips to help you out!

Some people are just smarter than others. No matter how hard you try, you can’t stump them with tricky questions or logical puzzles. But how can you tell who’s smart without talking to them? Check out these top 10 tips to recognize intelligent people without chatting them up!

Mensa IQ Test Question Example
What are the top 10 tips to recognize intelligent people without talking to them?

1. Look at the person’s eyes – intelligent people tend to have bright, lively eyes

2. Check out their hands – intelligent people have nimble fingers and are often graceful in their movements

3. Notice how they dress – intelligent people generally have a good sense of style and know what looks good on them

4. Listen to the way they speak – intelligent people are usually articulate and well-spoken

5. Watch their body language – intelligent people tend to be confident in their movements and gestures

6. Pay attention to what they talk about – intelligent people are typically interested in a variety of topics

7. Look for people who are reading books instead of scrolling through their phones

8. People who are interested in learning new things and have a diverse range of interests

9. Pay attention to the way someone speaks – intelligent people tend to be articulate and well-spoken

10. Watch for people who are good at problem-solving and can think on their feet

11. Notice if someone is always surrounded by friends or colleagues – intelligent people often have a large social circle

12. Keep an eye out for people who are creative and expressive with their thoughts and ideas

13. Highly intelligent people  are very aware of the vast number of things they don’t know, and are somewhat humble and open-minded.

14. Highly intelligent people learn from others across a wide range of topics.

15. Highly intelligent people don’t talk about their IQ and Intelligence, and are keenly aware that there are many other things important in life.

16. Highly intelligent people Know quite a bit about many different topics, and can often explain even complex topics in ways simple enough for the listener to grasp.

17. Highly intelligent people tend to ask very thought provoking questions they don’t know the answers to. (This last characteristic is the hallmark of some of the brightest people I know, and makes them fascinating and thought-provoking conversationalists).

According to Matthew Cooper, here are some low-key signs of highly intelligent people:

1. Most of them have the habit of staying up late into the night.

2. They may have bad handwriting because of the trouble with their mouth and hand keeping up.

3. They have unusual, out of the box, apparently ‘crazy’ ideas.

4. They’re prefer to be with their own company.

5. They’re brutally honest with what they don’t know.

6. They can talk to people they don’t like and hear ideas they don’t agree with and not get emotional.

7. They have a twisted sense of humor. They are most likely to enjoy and understand dark humor.

8. They have good body memory. Their bodies can pick up routines faster than others.

9. They use the Keanu behavior to boost intelligence.

Keanu Reeves says highly intelligent people play a game. They try to be wrong once in a while. They practice being wrong more often to reset their egos.

10. They can explain difficult matter in an easy way.

11. They can make connections between seemingly unrelated subjects.

What are the top 100 Free IQ Test Questions and Answers – Train and Elevate Your Brain

Research has shown that words account for only 7 percent of how we communicate whereas our body language (55 percent) and voice tone (30 percent) represent the rest. Our entire body language can be read and interpreted. Almost our every external body part and its position speaks lots and lots, not vocally but via body language which also keeps altering with respect to the circumstances, our feelings or people around. Here, we are going to deal with the psychological tricks to read other people’s face. You just need to remember…Your emotions get etched on your face.

What are the top 10 tips to recognize intelligent people without talking to them?
What are the best psychological tricks to read other’s face?

I believe that smart people have hunter-gatherer minds (intuitives in Myers-Briggs), whereas most people have farmer minds and these two are often not very compatible.

Hunter-gatherer people often become deeply introverted in childhood already, because they understand that they are different from the vast majority of people (INs only make up about 10% of the population) and when they grow up they feel they are misunderstood and that they can’t make a change anyway.


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Read “The Little Prince” and you get an idea what it is like for a gifted kid to grow up: you see the elephant inside the boa where others see a hat (NB: this is a metaphor):

“Whenever I encountered a grown-up who seemed to be intelligent, I would experiment on him with my drawing Number One, which I have always kept. I wanted to see if he really understood anything.

But he would always answer, “That’s a hat.” … So I lived by myself, with no one to talk to”

Antoine de Saint-Exupéry (INFP)

So, what is the point of talking about something other people can’t see??? 90% of the time we would be wasting our energy and that is why we keep quiet. People would think we are crazy.

More about hunter-gatherer minds here:

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https://the-big-ger-picture.blogspot.com/2019/09/the-hardest-problems-for-evolutionary.html

https://the-big-ger-picture.blogspot.com/2019/09/the-psychology-and-biology-of-gifted.html

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No, external factors are not indicators of intelligence. They can often give off an impression of a certain level of intelligence, whether high or low, but you need to actually see them use their head to know how capable they are mentally.

Yes. Top 2%er IQ wise here.

But, and this is important- IQ is like the measurements on the outside of the cup, it only shows how full it can be, not how full it is, and most importantly says nothing about what it’s filled with. There were probably more nazis with high IQs than saints.

I can “think about my thinking”, and choose from more options how I want to think about a subject.

I can be wrong in my thinking and change it when provided with a better solution or better data.

I don’t reject uncomfortable truths. I understand there is nothing after death. I wish it was otherwise but can’t ignore the evidence despite humanity’s culturally indoctrinated wishful thinking otherwise.

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I see a “big picture”. My definition of “big picture ” means that I’m far enough back that I don’t see “myself” in it. That gives me the ability to see conclusions and concepts that are “outside the box”.

I spot patterns earlier and can separate them from “back ground noise ” and spurious data faster than a lot of people. I’m the one who chooses not to go on the company picnic as the boss always fires someone who went a week later. The boss doesn’t see this pattern, the other workers don’t see the pattern. Everyone else wants to go because of the free booze and sandwiches.

I get bored. Very very bored. Imagine you are 30 and are trapped in a culture determined by the desires and minds of 14 Yr olds. It works great for them, but there’s really not much of interest for you and when you pursue your own interests you are a weirdo iconoclast until they see that your ideas can also be used to make their skateboards go faster and shoot sparks from the trucks… I will probably kill myself once my age makes it too hard to keep myself amused.

I am used to isolation being misunderstood, rejected, and laughed at by those who don’t understand what I’m doing. I am far too used to paranoid managers who realize that they have an employee who is smarter than them. I’m used to.people telling me I’m stupid for wanting to know how something actually works. “Fusion!? Screw that science crap!-the sun is hot because its burning. Stop over thinking stupid!”

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I am used to Dr’s and professionals in other fields stopping and looking at me funny when I start discussing the finer points of insulin transport through a cell wall, finer points of nuerochemistry, or other specialist knowledge. It’s taken me years to find doctors I can work with. They are too used to bozos coming in with “but I saw it on the Internet “.

I’m a prodigy burn out, one of the last “educated bums”. Eight years in university, a string of letters behind my name and my diplomas are stacked up, face downin my back porch. My last job before retirement was as a clerk in a legal late night cannabis dispensary in the inner city. My job before that was as an Education and Programming Director at a major art gallery.

I don’t chase after money for money’s sake. Lots of people think I am stupid for not maxing out my career potential, but I get bored with the work, the people and the collateral issues of having to work. So I opted for self sufficiency and an urban peasant lifestyle. I have enough money to see me through to “the end”, and to provide an estate for my daughters. I am bi no means poor, but manage and use money as a tool rather than a fetish.

I am extremely hard to live with. I’m a bit of a slob (organized neatness is a compulsive trait of those who can not remember where everything is), pedantic and often disinterested in things that seem very important (fashion, morality, celebrities, ie). I have been in five serious relationships, and happily bought people houses to get out of them.

I hope this helps, but one thing you are going to find with very high intelligence is that those with it often have frustrating, broken and dysfunctional lives. Each response is going to vary wildly.

To sum it up….


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A friend who truly knows me put it like this: “Dustin, you have a mind like a finely tuned sports car, with a 700 hp engine. You live in a world built for tricycles. Have a beer!”

I have a Mensa level IQ. My husband’s was probably around 110 -120. I don’t know for sure because if he ever took an IQ test, he never told me. I know I could grasp higher math and science more than he could.

That said, we loved one another and supported one another through 47 years of marriage. We respected one another. We laughed together. We cried together.

If he didn’t understand something, I explained it until he could. He taught me to drive. We never treated the other as though one was less than the other.

He had more life experience than I as he was 20 years my senior. School was easy for me. He never had much use for formal education until he met me. I supported him in his interests and his desire to change jobs often. He supported me when I went back to school when I was forty. He listened while I told him the exciting new things I was learning even when he wasn’t really interested.

I supported him when he retired and I watched him learn to drive both a bus and a fire truck when he was sixty- five. He learned to parallel park both of them.

He once asked me why I was with someone so less intelligent than I was. I told him I wasn’t. There are more kinds of intelligence than that measured by IQ tests.

Respect, laughter, and love go a long way in leveling any differences two people have. So yes, a relationship can work even if one of the people has a higher IQ than the other.

He passed away a year and a half ago. I miss him acutely.

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