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 31st 2023: How to Invest In AI; Are We Unknowingly Creating ‘Reptilian’ and ‘Mammalian’ AI?; Any AIs that can find directions from X to Y with natural language?; The Intersection of Artificial Intelligence, Blockchain, and DAO.

How to Invest In AI; Are We Unknowingly Creating 'Reptilian' and 'Mammalian' AI?; Any AIs that can find directions from X to Y with natural language?; The Intersection of Artificial Intelligence, Blockchain, and DAO
Latest AI trends May 31st 2023: How to Invest In AI; Are We Unknowingly Creating ‘Reptilian’ and ‘Mammalian’ AI?; Any AIs that can find directions from X to Y with natural language?; The Intersection of Artificial Intelligence, Blockchain, and DAO

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. In today’s episode, we’ll be discussing the latest AI trends, including how to invest in AI, the possibility of creating ‘Reptilian’ and ‘Mammalian’ AI, and more. Don’t miss out on staying up-to-date with the constantly evolving world of AI – be sure to hit the subscribe button. In today’s episode, we’ll cover investing in AI stocks, recent breakthroughs in AI mathematical problem-solving, the release of a new book to demystify FAQ on AI, the intersection of AI, blockchain, and DAOs, risks to humanity from AI, how the design impacts AI behavior, and a resource to level up machine learning skills.

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Investing in the ever-evolving field of artificial intelligence is an exciting opportunity, but it requires careful consideration and strategic planning. The AI industry is currently experiencing a technological disruption that could lead to substantial returns for savvy investors. However, identifying which companies will emerge as winners in the AI industry can be a difficult task. Innovators and imitators alike may end up with a market-leading position, so it’s important to consider all potential investments.

There are different approaches to investing in AI. Some investors prefer to invest directly in AI development companies, while others opt for companies that stand to benefit the most from its wider adoption. For example, during the personal computer industry’s rise, investors found success in computer manufacturers, software companies, and businesses that benefited from the automation that computers offered. The point is that there are often winners and losers when new technologies emerge.

It’s worth noting that investing in companies that could benefit from changes within the workforce could also be an option. With the potential for AI to displace workers in many industries, there may be opportunities to invest in companies that focus on worker retraining and are poised to capitalize on these significant shifts in the workforce.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

There are individual stocks that match some of these investment criteria for those interested in investing in AI. It’s important to do your own research and consider all the potential risks and returns before making any investment decisions.

If you’re looking to invest in AI, there are several companies to consider. One of the most notable is Tesla, which uses AI to automate driving. This requires constant processing of data to identify other cars, road conditions, traffic signals, and pedestrians. Another key player in the AI space is NVIDIA, which has a strong position in the marketplace through its generative artificial intelligence. They’ve also created chips, hardware, software, and development tools to create start-to-finish AI systems.

Microsoft is another company worth looking into if you’re considering AI investments. They’ve invested $13 billion in AI initiatives and have embedded AI into many of their systems, including Bing search engine, Microsoft 360, sales and marketing tools, X-Box, and GitHub coding tools. They’ve also outlined a framework for building AI apps and copilots and expanding their AI plug-in ecosystem.

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Taiwan Semiconductor Manufacturing is the world’s largest chip maker, and is another leading competitor in chip manufacturing for artificial intelligence. As AI grows, the need for robust computing chips will grow with it. If you’re looking to invest in a more mature company that still has a vested interest in AI, Taiwan Semiconductor Manufacturing may be the way to go.

Meta Platforms invests significantly in AI, utilizing large language module (LLM) AI to drive search results and predict user preferences. Meta has also developed its own silicon chip for AI processing and created a next-generation data center.

Amazon uses AI in its Alexa system and also offers machine learning (ML) and AI tools to its customers. Amazon’s cloud computing business, Amazon Web Services (AWS), provides an AI infrastructure that allows customers to analyze data and incorporate AI into their existing systems. They’ve got a huge customer base of more than 100,000 businesses.

Finally, Apple continues to make a percentage of AI services delivered on its platform and is a significant example of this. They use AI in Siri and also license AI services to be developed on their platform. They can also use their massive cash reserves to make major investments in AI that they build themselves or acquire using their cash reserves. So, if you’re considering investing in AI, these companies are worth checking out!

Hey there! I have some exciting news to share with you today. Greg Brockman, the founder of OpenAI, just shared a groundbreaking achievement in mathematical problem-solving on Twitter. They’ve successfully trained a machine learning model that can reason like humans by rewarding accurate steps in the problem-solving process. This is a departure from the traditional approach of only rewarding the final answer.

Let’s dive into the details of this achievement. The new method is known as “process supervision”, which rewards each individual step in a process, rather than just the final outcome. The goal of this new method is to prevent logical errors, also known as “hallucinations”, and make the model more accurate. Using a dataset that tests the model’s ability to solve math problems, the researchers found that the new method led to better performance and improved model alignment.

This achievement is particularly important in the field of Artificial General Intelligence (AGI), which is the intelligence of a machine that can understand, learn, plan, and execute any intellectual task that a human being can. Advancements in this area bring us closer to creating machines that can solve complex problems like humans.

Additionally, this breakthrough could have significant implications for how AI models are trained in the future. This new approach could lead to improved model alignment, by guiding the machine to follow a logical chain-of-thought, which could result in more predictable and interpretable outputs.

Usually, making AI models safer (more aligned) leads to a performance trade-off known as an alignment tax. However, in this study, the new “process supervision” method led to better performance and alignment, suggesting the possibility of a negative alignment tax, at least in the domain of mathematical problem-solving. This could be a game-changing development for AI research and applications in other domains.

That’s all for now! Keep an eye out for the full breakdown tomorrow morning. What do you think about this achievement? Let’s discuss in the comments below!

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Hey there AI Unraveled podcast listeners, have you been trying to wrap your head around all the buzz about Artificial Intelligence? Well, look no further! We’ve got an essential book recommendation just for you – “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” which is now available on Amazon. This engaging read will help answer all of your burning questions and provide valuable insights into the fascinating world of AI. So, why wait? Elevate your knowledge and stay ahead of the curve with a copy of “AI Unraveled” available on Amazon today!

Hey there! Today, we’re going to dive into an exciting topic that explores the intersection of three of the most transformative technologies of our time: Artificial Intelligence (AI), blockchain, and Decentralized Autonomous Organizations (DAOs). Imagine the immense potential this convergence holds for creating efficient, equitable, and sustainable societies.

Let’s start with AI. It’s evolving rapidly, experiencing recent developments such as GPT-4 and GPT-5, which are OpenAI’s language models that have demonstrated incredible capabilities in language understanding and generation. On the other hand, blockchain and DAOs have disrupted the way we think about governance, ownership, and collective decision-making.

But what is decentralized governance? Simply put, blockchain provides a decentralized and immutable ledger that ensures trust, transparency, and security. DAOs are organizations governed by smart contracts on a blockchain network, where decisions are made collectively by stakeholders. When we combine AI’s problem-solving capabilities with blockchain’s transparency and DAO’s democratic governance, we can create intelligent, decentralized, and fair systems.

Fast forward to 2030, where DAOs have proven their worth in managing local resources like farms, power, and internet service providers. As a result, every county in the state now operates its own DAO, leading to more efficient resource allocation and management. Through AI and the collaboration of stakeholders, these DAOs are capable of making intelligent decisions without any profit motive from a corporate perspective. The goal is to provide services efficiently and equitably, ensuring that everyone gets high-quality services.

As DAOs prove their worth, governments start adopting them for various purposes. The Environmental Protection Agency to the Department of Energy, every governmental agency aims to be run more democratically with DAOs. The entire country becomes fully autonomous, based on AI DAO technology.

To ensure that these AI DAOs align with human values, heuristic imperatives of reducing suffering, increasing prosperity, and increasing understanding are integrated into their consensus mechanism. By integrating AI with blockchain and DAOs, we could be moving toward the development of safe and controllable Artificial General Intelligence (AGI). This will assist in keeping humans in the loop in the decision-making process and having consensus mechanisms that would prevent rogue decisions and ensure collaboration between humans and machines.

But it’s important to note that while AI DAOs hold immense potential, they don’t inherently solve the Malik problem. This refers to the possibility of sliding toward dystopia or extinction, even when things seem to be functioning optimally. However, if we achieve global consensus and rein in factors like corporate greed and global conflict, we might be able to address the Malik problem to some extent.

How can we implement these heuristic imperatives in AI DAOs? There are three primary ways to do so: fine-tuning and reinforcement learning, using the heuristic imperatives as a consensus mechanism, and incorporating heuristic imperatives into the AI DAO system’s architectural design patterns, such as task orchestration.

The possibilities are endless with this triad of AI, blockchain, and DAOs, and we’re excited to see how they’ll transform societies into more efficient, equitable, and sustainable ones.

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Hey there! Today’s AI news covers some pretty interesting topics, including a new warning from scientists and tech leaders about the potential perils of artificial intelligence. In fact, they say mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks like pandemics and nuclear war.

But not everything is doom and gloom. There are also exciting advancements in AI, like Instacart’s new in-app AI search tool powered by ChatGPT. And Nvidia achieved a $1 trillion market cap for the first time thanks to an AI-fueled stock surge.

The White House press shop is also adjusting to the proliferation of AI deep fakes as the coming presidential election approaches. And in other news, the UAE has launched an AI chatbot called “U-Ask” in both Arabic and English.

Last but not least, a new tool has been developed to help people choose the right method for evaluating AI models. Interesting stuff, huh?

Hey there! Today, I stumbled upon a mind-bending research paper that I think we all need to talk about. We’re all fascinated by Artificial Intelligence and how it’s evolving, right? Well, what if I told you that there might be more to it than we ever imagined? The paper drops a bombshell – are we, without even knowing, creating AI that behaves like cold-blooded reptiles or warm-hearted mammals? Crazy, right? But stay with me here. The researchers delve deep into the idea that the AI we build might be reflecting cognitive models – basically, patterns of how we, humans, think and act.

And here’s where it gets wild. They suggest that depending on these cognitive models, we could be designing AI systems that act like survival-focused, competitive ‘Reptilian AI’ or cooperative, empathetic ‘Mammalian AI’. Reptilian AI, like a sly snake, would prioritize resource acquisition and dominance. Think of it as the type of AI that’d do anything to win, no matter what. On the other hand, Mammalian AI would be more like our friendly neighborhood dog, exhibiting social cohesion and emotional understanding. It would prefer cooperation over competition.

So, what does this mean for us? It’s simple but chilling. The way we design AI could be having a profound influence on how these systems behave and interact with their environments. It’s like we’re unintentionally playing God, shaping these artificial entities in our cognitive image. And if you thought that was all, think again. The paper goes further, exploring the implications for potential extraterrestrial AI. But that’s a rabbit hole for another post.

Are you intrigued? Scared? Excited? Let’s dive into this fascinating topic together!

Hey, everyone! So, as we take a break from talking about AI, I want to give a huge shoutout to all the AI enthusiasts out there. I have something valuable to share with you all today. It’s a book that should be on your radar if you’re looking to take your machine learning skills to the next level and even earn a six-figure salary.

The book in question is “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams,” authored by Etienne Noumen, Professional Engineer based in Calgary, AB, Canada. It is an absolute gem of information, packed full of essential tips and advice, along with practical exams that are designed to help you prepare for the AWS Machine Learning Specialty (MLS-C01) Certification. As you all know already, AWS is a giant player in the cloud space, and having this certification under your belt can really set you apart in the industry.

What’s even better is that this book is easily available at Amazon, Google, and even on the Apple Book Store. So, no matter which platform you prefer, you can get your hands on this essential guide.

Now, you don’t have to take my word for it. Just get a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and start your journey towards mastering machine learning and earning that coveted six-figure salary. Trust me, once you read it, it’s going to be a game-changer for you.

On today’s episode, we discussed the profitability of investing in AI companies, breakthroughs in AI problem-solving, AI’s impact on society, the potential of DAOs, as well as concerns around AI behavior and the importance of continuous learning in machine learning skills. 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 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%.

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?

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.

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!

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.

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

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.

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.

CogniBypass – The Ultimate AI Detection Bypass Tool:

As AI monitoring increases, so does the need for privacy protection. CogniBypass offers a solution for individuals seeking enhanced privacy in a world where AI detection mechanisms can be cumbersome. The tool is designed for bypassing AI detection mechanisms, making it one of the most cutting-edge solutions for enhanced privacy protection.

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.

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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.

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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

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!

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.

What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

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!

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.

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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.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

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).

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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.

“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.

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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.

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?

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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.

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

  • [D] ANN for recommendations with already seen items
    by /u/overflozz (Machine Learning) on April 19, 2024 at 1:10 am

    Trying to implement a recommender model for an equivalent of a dating app, where I don't want to recommend someone if they have already been seen by the current user. For the embeddings ANN search, I've looked into Pinecone, VertexAI and Qdrant, but it seems that the filtering is usually limited to larger categories (e.g country). I haven't examples of filters of the type : `{user_id: {not_in: ['123', '456' , ... ]}}`. A user might have already seen 1000s of recommendations. What is the usual approach for this type of history-aware ANN? submitted by /u/overflozz [link] [comments]

  • [D] Has anyone tried distilling large language models the old way?
    by /u/miladink (Machine Learning) on April 19, 2024 at 12:11 am

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  • [D] Combining models of different modalities
    by /u/hophophop1233 (Machine Learning) on April 18, 2024 at 9:55 pm

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  • [D] Vision Transformers, CNN and SMoE
    by /u/mitadev (Machine Learning) on April 18, 2024 at 9:46 pm

    I'm quite newbie so forgive me if anything I say or ask might be dumb. I was wondering some things why todays cnn (like yolov8/9) are faster even if theoretically transformer have the same complexity. Are there SMoE like Mixtral in ViT I have some paper but they do not seem to have brought some significant improvement, am I missing something? On practical term even considering nlp small SMoE work in practice according to your experince? submitted by /u/mitadev [link] [comments]

  • Exposing the True Context Capabilities of Leading LLMs [R]
    by /u/ParsaKhaz (Machine Learning) on April 18, 2024 at 9:34 pm

    I've been examining the real-world context limits of large language models (LLMs), and I wanted to share some enlightening findings from a recent benchmark (RULER) that cuts through the noise. What’s the RULER Benchmark? Developed by NVIDIA, RULER is a benchmark designed to test LLMs' ability to handle long-context information. It's more intricate than the common retrieval-focused NIAH benchmark. RULER evaluates models based on their performance in understanding and using longer pieces of text. Table highlighting RULER benchmark results and effective context lengths of leading LLMs Performance Highlights from the Study: Llama2-7B (chat): Shows decent initial performance but doesn't sustain at higher context lengths. GPT-4: Outperforms others significantly, especially at greater lengths of context, maintaining above 80% accuracy. Command-R (35B): Performs comparably well, slightly behind GPT-4. Yi (34B): Shows strong performance, particularly up to 32K context length. Mixtral (8x7B): Similar to Yi, holds up well until 32K context. Mistral (7B): Drops off in performance as context increases, more so after 32K. ChatGLM (6B): Struggles with longer contexts, showing a steep decline. LWM (7B): Comparable to ChatGLM, with a noticeable decrease in longer contexts. Together (7B): Faces difficulties maintaining accuracy as context length grows. LongChat (13B): Fares reasonably up to 4K but drops off afterwards. LongAlpaca (13B): Shows the most significant drop in performance as context lengthens. Key Takeaways: All models experience a performance drop as the context length increases, without exception. The claimed context length by LLMs often doesn't translate into effective processing ability at those lengths. GPT-4 emerges as a strong leader but isn't immune to decreased accuracy at extended lengths. Why Does This Matter? As AI developers, it’s critical to look beyond the advertised capabilities of LLMs. Understanding the effective context length can help us make informed decisions when integrating these models into applications. What's Missing in the Evaluation? Notably, Google’s Gemini and Claude 3 were not part of the evaluated models. RULER is now open-sourced, paving the way for further evaluations and transparency in the field. Sources I recycled a lot of this (and tried to make it more digestible and easy to read) from the following post, further sources available here: Harmonious.ai Weekly paper roundup: RULER: real context size of LLMs (4/8/2024) submitted by /u/ParsaKhaz [link] [comments]

  • [P] Exploring Fair AI Solutions with New Data Quality Initiative
    by /u/ComplexAnalysis42 (Machine Learning) on April 18, 2024 at 9:29 pm

    Hello Reddit! We're a group of students from Carnegie Mellon passionate about data. We've been working on a project that tackles one of the biggest challenges in AI today: data bias. Our tool is designed to audit your datasets and generate synthetic, privacy-safe data that helps promote fairness in AI applications. Here’s why we think it could be a game-changer: Detect and Correct Data Biases: Ensures your AI models are built on fair and balanced data. Enhance Data Quality: Improves the reliability and performance of your AI systems. Generate Synthetic Data: Expands your dataset without compromising privacy. We believe in a future where data-driven technologies are equitable and just, and we're eager to contribute to making that a reality. We’d love to get your insights and feedback to further refine our tool. Join Us in Shaping the Future of Fair AI Are you interested in AI fairness? Do you have experiences with biased data or ideas on how to address these issues? Let’s start a conversation! Comment here or send us a DM. We’re here to discuss and collaborate on getting you the data you deserve! submitted by /u/ComplexAnalysis42 [link] [comments]

  • [P] help in suggesting correct approach/algo for solving the below problem
    by /u/Abject-Log-1249 (Machine Learning) on April 18, 2024 at 9:10 pm

    Hi Everyone, There is a problem we are trying to solve and I want to know if there is particular approach/algorithm which will work the team. We are using Cloudera Hadoop system at our company and currently we have hard coded rule system to trigger if there is missing data on different dimensions, like time and different configuration items. We are trying to reduce the number of incidents based on what our past observations which we can code it up easily by writing more rules to group the common issues. But at the same time we were wondering if there is an algo/technique which will help us group the common issues based on feature similarities, because the features which we are forming rules by writing common code might become outdated and we soon be writing more rules. Based on my previous beginner level knowledge of ML seems like it is clustering problem where we don't know the number of clusters and some google and chatgpting seems like DBSCAN will fit but don't want to spend lot of time trying out and finding out this is not the right approach to solve the problem. Anyone tried to solve a similar problem and what approach did they take? Anyone has some guidance on this? Features which we have are: time (through, we don't rely on that much and issues at different time are considered different incidents, so may not be that important and would need to give less weight?) 2.cluster Market data pipeline lineage (part of it is in place and part of it is in data collection stage, I believe this will make the coding part complicated if we go with rule based system) Product/Service affected Configuration item Thanks appreciate any help in advance. (let me know if this is not the correct sub for this type of question) submitted by /u/Abject-Log-1249 [link] [comments]

  • [P] Embedding visualizer running entirely in the browser with marimo
    by /u/akshayka (Machine Learning) on April 18, 2024 at 8:09 pm

    I built an embedding visualizer that runs entirely in the browser, using Pyodide and a marimo notebook: https://marimo.app/l/55kclj ​ The notebook uses PCA to project the UCI digits dataset to 2D, then uses marimo's dataflow execution engine to automatically wire plot selections to Python. Select data in the scatterplot, view the original images. ​ The example uses PCA because it can be computed entirely in the browser, but the notebook can be used as a template to explore a scatterplot produced by any other means. submitted by /u/akshayka [link] [comments]

  • [P] 2D CNN for time series
    by /u/redreaper71_ (Machine Learning) on April 18, 2024 at 7:38 pm

    I'm trying to develop a 2D CNN for a time series model and am having a bit of trouble deciding/understanding what the input shape of the 2D CNN should be. The data I have consists of 7 time series, each of which has 5 features, one target (response) variable and 60 time steps. So for example, the data for one time series would look like this (for the first 3 rows): Timestamp, feature1, feature2, feature3 ... target 06/01/2012 01:00:00, 500, 29, 0.3 ... 33 06/01/2012 02:00:00, 200, 32, 0.1 ... 45 06/01/2012 03:00:00, 600, 23, 0.24 ... 56 My goal is to have the CNN be trained on the entirety of 6 of the time series and tested on the last one. I did a bit of research online and found people using the input shape as (number of samples, number of time series, window length, number of features). My questions are: what would the dimensions of my X_train, X_test, y_train, y_test be? if this input shape is valid, why? submitted by /u/redreaper71_ [link] [comments]

  • [R] Looking for an SEM Image and corresponding Layout image for computer chips dataset
    by /u/budgetfroot (Machine Learning) on April 18, 2024 at 7:29 pm

    I'm looking for a dataset consisting of hi-resolution and zoomed in SEM images of computer chips (Can be any chip or IC) along with their idealized layout images (can be rgb or grayscale). Basically I want one sample of the dataset to contain a pair of images (1) the SEM image and (2) the idealized layout corresponding to that SEM image. Does any of you know if a dataset like this exists? Doesn't matter how small at this point I'll take anything. I've been looking for a some time now and can't find anything. Not sure if I'm searching for the wrong thing. https://preview.redd.it/o0srhuu0havc1.png?width=243&format=png&auto=webp&s=8805b9967062a674f13658c727042d69955a567b https://preview.redd.it/q5ysnhu2havc1.png?width=240&format=png&auto=webp&s=c432fd07beab27e261c4579b970c64faebbd049d submitted by /u/budgetfroot [link] [comments]

  • [P] Llama 3 70B powered coding copilot extension
    by /u/geepytee (Machine Learning) on April 18, 2024 at 7:29 pm

    Was super excited to read the news from Meta this morning, particularly around the HumanEval scores the 70B model got. Thought it'd be useful to make the new Llama 3 70B available to anyone that wants to try it, so I added it to my VS Code coding copilot extension double.bot. Also making it free for the first 50 messages so everyone gets a chance to try it while we wait for the quantized versions to run locally submitted by /u/geepytee [link] [comments]

  • [D] Llama-3 (7B and 70B) on a medical domain benchmark
    by /u/aadityaura (Machine Learning) on April 18, 2024 at 6:45 pm

    Llama-3 is making waves in the AI community. I was curious how it will perform in the medical domain, Here are the evaluation results for Llama-3 (7B and 70B) on a medical domain benchmark consisting of 9 diverse datasets https://preview.redd.it/sdwx5tglxbvc1.png?width=1464&format=png&auto=webp&s=d32585a69244d44c83e2b1e8a85301a7a8676ea2 I'll be fine-tuning, evaluating & releasing Llama-3 & different LLMs over the next few days on different Medical and Legal benchmarks. Follow the updates here: https://twitter.com/aadityaura https://preview.redd.it/9egbcayv9avc1.png?width=1344&format=png&auto=webp&s=436a972421d5568e1a544962b8cfd1c7b14efe04 submitted by /u/aadityaura [link] [comments]

  • [D] Data Scientist: job preparation guide 2024
    by /u/xandie985 (Machine Learning) on April 18, 2024 at 6:35 pm

    I have been hunting jobs for almost 4 months now. It was after 2 years, that I opened my eyes to the outside world and in the beginning, the world fell apart because I wasn't aware of how much the industry has changed and genAI and LLMs were now mandatory things. Before, I was just limited to using chatGPT as UI. So, after preparing for so many months it felt as if I was walking in circles and running across here and there without an in-depth understanding of things. I went through around 40+ job posts and studied their requirements, (for a medium seniority DS position). So, I created a plan and then worked on each task one by one. Here, if anyone is interested, you can take a look at the important tools and libraries, that are relevant for the job hunt. Github, Notion I am open to your suggestions and edits, Happy preparation! submitted by /u/xandie985 [link] [comments]

  • [P] chatpdb - gpt for your python debugger
    by /u/the1024 (Machine Learning) on April 18, 2024 at 6:23 pm

    https://github.com/Never-Over/chatpdb ChatGPT can be an awesome tool for quickly diagnosing issues from error output, but pasting snippets into the chat window often misses important context and forces you to switch focus and lose your flow. That’s why we built chatpdb - a python debugger with ChatGPT built in. Simply use chatpdb like you would use ipdb or pdb; it’s a drop in replacement with the exact same functionality. The only addition is the new y keyword, which will trigger a response from ChatGPT. Here’s how it works: > /Programming/test-chatpdb/lib.py(2)echo_platform() 1 def echo_platform(platform: str): ----> 2 print("You are running on:" + platform) 3 ipdb> y The exception occurred because the function `echo_platform` tries to concatenate the string "You are running on:" with the `platform` variable, which is `None`. [...] In this example, chatpdb correctly diagnoses an error in the current function as being caused by a bug in the calling function. chatpdb automatically passes relevant context about your program, including the source code, stack trace, and error information if available to ChatGPT. If you have a more specific question you can also supply a prompt to y: > /Programming/test-chatpdb/lib.py(2)echo_platform() 1 def echo_platform(platform: str): ----> 2 print("You are running on:" + platform) 3 ipdb> y "Why is platform coming through as None?" The variable `platform` is coming through as `None` because the environment variable `"PLATFORM"` is not set in your system's environment variables. [...] It’s easy to install and set up: pip install chatpdb export OPENAI_API_KEY=.... import chatpdb chatpdb.set_trace() chatpdb meets you where you are – AI tooling that’s only invoked when you need it. We hope you give it a try! We’d love any feedback or suggestions. Docs submitted by /u/the1024 [link] [comments]

  • [D] ICML Meta Reviews
    by /u/Personal_Click_6502 (Machine Learning) on April 18, 2024 at 5:09 pm

    When are the ICML meta reviews releasing? Will it be declared along with the final paper notification? The webpage says its due 16th April. https://icml.cc/Conferences/2024/ReviewerInstructions submitted by /u/Personal_Click_6502 [link] [comments]

  • [R] Show Your Work with Confidence: Confidence Bands for Tuning Curves
    by /u/nicholaslourie (Machine Learning) on April 18, 2024 at 4:46 pm

    Paper: https://arxiv.org/abs/2311.09480 Tweet: https://x.com/NickLourie/status/1770077925779337563 Code: https://github.com/nicholaslourie/opda Docs: https://nicholaslourie.github.io/opda/tutorial/usage.html Abstract: The choice of hyperparameters greatly impacts performance in natural language processing. Often, it is hard to tell if a method is better than another or just better tuned. Tuning curves fix this ambiguity by accounting for tuning effort. Specifically, they plot validation performance as a function of the number of hyperparameter choices tried so far. While several estimators exist for these curves, it is common to use point estimates, which we show fail silently and give contradictory results when given too little data. Beyond point estimates, confidence bands are necessary to rigorously establish the relationship between different approaches. We present the first method to construct valid confidence bands for tuning curves. The bands are exact, simultaneous, and distribution-free, thus they provide a robust basis for comparing methods. Empirical analysis shows that while bootstrap confidence bands, which serve as a baseline, fail to approximate their target confidence, ours achieve it exactly. We validate our design with ablations, analyze the effect of sample size, and provide guidance on comparing models with our method. To promote confident comparisons in future work, we release opda: an easy-to-use library that you can install with pip. submitted by /u/nicholaslourie [link] [comments]

  • [R] InternVL v1.5 open sourced, ranking first in OpenCompass multi-modal benchmark
    by /u/flyforlight (Machine Learning) on April 18, 2024 at 4:42 pm

    https://preview.redd.it/fh44g3n4m9vc1.png?width=1383&format=png&auto=webp&s=9b3e499bd51aeb10559f4636eba2a1677d4a08a3 InternVL is a multi-modal foundation model, which is accepted as an Oral paper for CVPR 2024. The latest version InternVL v1.5 ranks first in the OpenCompass multi-modal model benchmark. Demo: https://internvl.opengvlab.com/ Model Download: https://huggingface.co/collections/OpenGVLab/internvl-65b92d6be81c86166ca0dde4 OpenCompass: https://rank.opencompass.org.cn Some examples: https://preview.redd.it/rwj7vs9rm9vc1.jpg?width=902&format=pjpg&auto=webp&s=514e14e692db8ea7bd5a66cc36b1ca3f8351102c https://preview.redd.it/vtwjml3qm9vc1.png?width=2508&format=png&auto=webp&s=e32c044d4bc60ef28baf64dccdcb5fe9b10dfc61 https://preview.redd.it/p51vt3xpn9vc1.png?width=2609&format=png&auto=webp&s=73907e5ffb4d9b9bd4250cbce53e3bd29dedabf1 submitted by /u/flyforlight [link] [comments]

  • [N] Meta releases Llama 3
    by /u/we_are_mammals (Machine Learning) on April 18, 2024 at 4:18 pm

    https://llama.meta.com/llama3/ ​ ​ https://preview.redd.it/n3lwb4xfj9vc1.png?width=3840&format=png&auto=webp&s=b756d89c50c627955668d5ac16df82f7af01cdbc submitted by /u/we_are_mammals [link] [comments]

  • [R] MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control
    by /u/SeawaterFlows (Machine Learning) on April 18, 2024 at 4:07 pm

    Paper: https://arxiv.org/abs/2403.12037 Code: https://github.com/Zhoues/MineDreamer Models and Dataset: https://huggingface.co/Zhoues Abstract: It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow instructions due to difficulties in understanding abstract and sequential natural language instructions. To this end, we introduce MineDreamer, an open-ended embodied agent built upon the challenging Minecraft simulator with an innovative paradigm that enhances instruction-following ability in low-level control signal generation. Specifically, MineDreamer is developed on top of recent advances in Multimodal Large Language Models (MLLMs) and diffusion models, and we employ a Chain-of-Imagination (CoI) mechanism to envision the step-by-step process of executing instructions and translating imaginations into more precise visual prompts tailored to the current state; subsequently, the agent generates keyboard-and-mouse actions to efficiently achieve these imaginations, steadily following the instructions at each step. Extensive experiments demonstrate that MineDreamer follows single and multi-step instructions steadily, significantly outperforming the best generalist agent baseline and nearly doubling its performance. Moreover, qualitative analysis of the agent's imaginative ability reveals its generalization and comprehension of the open world. submitted by /u/SeawaterFlows [link] [comments]

  • [R] Compression Represents Intelligence Linearly
    by /u/SeawaterFlows (Machine Learning) on April 18, 2024 at 3:54 pm

    Paper: https://arxiv.org/abs/2404.09937 Code: https://github.com/hkust-nlp/llm-compression-intelligence Datasets: https://huggingface.co/datasets/hkust-nlp/llm-compression Abstract: There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the success of large language models (LLMs): the development of more advanced language models is essentially enhancing compression which facilitates intelligence. Despite such appealing discussions, little empirical evidence is present for the interplay between compression and intelligence. In this work, we examine their relationship in the context of LLMs, treating LLMs as data compressors. Given the abstract concept of "intelligence", we adopt the average downstream benchmark scores as a surrogate, specifically targeting intelligence related to knowledge and commonsense, coding, and mathematical reasoning. Across 12 benchmarks, our study brings together 30 public LLMs that originate from diverse organizations. Remarkably, we find that LLMs' intelligence -- reflected by average benchmark scores -- almost linearly correlates with their ability to compress external text corpora. These results provide concrete evidence supporting the belief that superior compression indicates greater intelligence. Furthermore, our findings suggest that compression efficiency, as an unsupervised metric derived from raw text corpora, serves as a reliable evaluation measure that is linearly associated with the model capabilities. We open-source our compression datasets as well as our data collection pipelines to facilitate future researchers to assess compression properly. submitted by /u/SeawaterFlows [link] [comments]

  • [D] Product evaluations is one of the most under-discussed topics
    by /u/BootstrapGuy (Machine Learning) on April 18, 2024 at 3:10 pm

    We are an AI consultancy and this happens with us over and over again... We start a new LLM project with a client. Their engineers get to 80% super quickly. They have a lot of edge cases and want us to finish up the remaining 20%. We ask them about evals. And of course they don't have them. We create the eval framework, improve the pipeline iteratively, and voila. Job done, everybody is happy. I seriously think that based on what we see the best AI product teams are going to be the ones that spend an absurd amount of time on evals. It's boring, it's repetitive but it makes the difference between an amazing AI product and an underperforming one. submitted by /u/BootstrapGuy [link] [comments]

  • [D] 100+ labels text-classification problem. Whats the “usual” approach? Transformers?
    by /u/Melodic_Reality_646 (Machine Learning) on April 18, 2024 at 2:07 pm

    Each text has no more than 15 words, and the classes are highly imbalanced. But they all have at least 30 or so instances. I was successful with data of the same nature but with around 15 labels with an ensemble of gradient boosting models. Before diving into testing a bunch models I wondered if there’s some strategy to tackle high-dimensional problems like this one. Some problems are just not solvable, let’s face it. But what would you guys try? submitted by /u/Melodic_Reality_646 [link] [comments]

  • [D] Training model on tabular data resulting in high loss
    by /u/sparttann (Machine Learning) on April 18, 2024 at 12:56 pm

    Hello, I am training a model on tabular data which has already been preprocessed (scaled, PCA). Currently there are over 50k rows and 10 columns. The loss are high, not sure where I'm doing wrong For context, I'm using MSE as my loss function, 0.01 learning rate and 256 batch size. Thank you so much. This is how my init code looks like: class NN(nn.Module): def init(self): super(NN, self).init() # Tabular data processing layers self.fc1 = nn.Linear(10, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 16) self.fc4 = nn.Linear(16, 1) self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(32) self.bn3 = nn.BatchNorm1d(16) self.relu = nn.ReLU() self.dropout = nn.Dropout(0.25) def forward(self, x_tab, x_img): out = self.fc1(x_tab) out = self.bn1(out) out = self.relu(out) out = self.dropout(out) out = self.fc2(out) out = self.bn2(out) out = self.relu(out) out = self.dropout(out) out = self.fc3(out) out = self.bn3(out) out = self.relu(out) out = self.dropout(out) out = self.fc4(out) return out Output: Epoch 1/30, Loss: 16834.8088 Epoch 2/30, Loss: 4379.7037 Epoch 3/30, Loss: 3361.2462 Epoch 4/30, Loss: 3255.9039 Epoch 5/30, Loss: 3255.8603 Epoch 6/30, Loss: 3243.9488 Epoch 7/30, Loss: 3235.4387 Epoch 8/30, Loss: 3213.4688 Epoch 9/30, Loss: 3189.1130 Epoch 10/30, Loss: 3174.2118 Epoch 11/30, Loss: 3168.1597 Epoch 12/30, Loss: 3155.3225 Epoch 13/30, Loss: 3150.0659 Epoch 14/30, Loss: 3119.2989 Epoch 15/30, Loss: 3117.0893 Epoch 16/30, Loss: 3130.4699 Epoch 17/30, Loss: 3126.7107 Epoch 18/30, Loss: 3110.9422 Epoch 19/30, Loss: 3119.8601 Epoch 20/30, Loss: 3094.5037 Epoch 21/30, Loss: 3054.4725 Epoch 22/30, Loss: 3079.4411 Epoch 23/30, Loss: 3064.4010 Epoch 24/30, Loss: 3049.7988 Epoch 25/30, Loss: 3022.9714 Epoch 26/30, Loss: 3029.0342 Epoch 27/30, Loss: 3034.8153 Epoch 28/30, Loss: 3025.2383 Epoch 29/30, Loss: 3052.9892 Epoch 30/30, Loss: 3033.2717 submitted by /u/sparttann [link] [comments]

  • [R] Microsoft presents VASA-1: Lifelike Audio-Drive Talking Faces Generated in Real Time
    by /u/TheMrZZ0 (Machine Learning) on April 18, 2024 at 12:45 pm

    submitted by /u/TheMrZZ0 [link] [comments]

  • [D] Reproducing and Comparing Models from Research - Best Practices?
    by /u/MintOwlTech (Machine Learning) on April 18, 2024 at 11:27 am

    At my work, I'm spending a ton of time reproducing research papers and trying to apply them to our use case (medical image analysis). Usually, that's a big hassle, and even papers-with-code often reveal that it doesn't run without extensive edits. And as soon as they run, results are only good when setting a specific random seed... And then after applying it to our use case, I realized that the performance increase actually doesn't come from a new model, but from a different way of post-processing... I've started to write some scripts for myself to modularize it for easier reproducibility and comparison. But I am not sure if that is the way to go. So I'm curious, what is your approach to reproducing models for your use case and comparing them scientifically? submitted by /u/MintOwlTech [link] [comments]

  • [N] Feds appoint “AI doomer” to run US AI safety institute
    by /u/bregav (Machine Learning) on April 17, 2024 at 10:49 pm

    https://arstechnica.com/tech-policy/2024/04/feds-appoint-ai-doomer-to-run-us-ai-safety-institute/ Article intro: Appointed as head of AI safety is Paul Christiano, a former OpenAI researcher who pioneered a foundational AI safety technique called reinforcement learning from human feedback (RLHF), but is also known for predicting that "there's a 50 percent chance AI development could end in 'doom.'" While Christiano's research background is impressive, some fear that by appointing a so-called "AI doomer," NIST may be risking encouraging non-scientific thinking that many critics view as sheer speculation. submitted by /u/bregav [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on April 7, 2024 at 3:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [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 is the Best Machine Learning Algorithms for Imbalanced Datasets

Machine Learning Algorithms and Imbalanced Datasets

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What is the Best Machine Learning Algorithms for Imbalanced Datasets?

In machine learning, imbalanced datasets are those where one class heavily outnumbers the others. This can be due to the nature of the problem or simply because more data is available for one class than the others. Either way, imbalanced datasets can pose a challenge for machine learning algorithms. In this blog post, we’ll take a look at which machine learning algorithms are best suited for imbalanced datasets and why they tend to perform better than others.

 For example, in a binary classification problem, if there are 100 observations, and only 10 of them are positive (the rest are negatives), then we say that the dataset is imbalanced. The ratio of positive to negative cases is 1:10. 

What is the Best Machine Learning Algorithms for Imbalanced Datasets
What is the Best Machine Learning Algorithms for Imbalanced Datasets

There are a few reasons why some machine learning algorithms tend to perform better on imbalanced datasets than others. First, certain algorithms are designed to handle imbalanced datasets. Second, some algorithms are more robust to outliers, which can be more common in imbalanced datasets. And third, some algorithms are better able to learn from a limited amount of data, which can be an issue when one class is heavily outnumbered by the others.

Some of the best machine learning algorithms for imbalanced datasets include:

Support Vector Machines (SVMs),
Decision Trees,
Random Forests,
– Naive Bayes Classifiers,
k-Nearest Neighbors (kNN),

Of these, SVMs tend to be the most popular choice as they are specifically designed to handle imbalanced datasets. SVMs work by finding a hyperplane that maximizes the margin between the two classes. This helps to reduce overfitting and improve generalization. Decision trees and random forests are also popular choices as they are less sensitive to outliers than other algorithms such as linear regression. Naive Bayes classifiers are another good choice as they are able to learn from a limited amount of data. kNN is also a good choice as it is not sensitive to outliers and is able to learn from a limited amount of data. However, it can be computationally intensive for large datasets.

There are two main types of machine learning algorithms: supervised and unsupervised. Supervised algorithms tend to perform better on imbalanced datasets than unsupervised algorithms. In this blog post, we will discuss why this is so and look at some examples.

Supervised Algorithms
Supervised algorithms are those where the target variable is known. In other words, we have training data where the correct answers are already given. The algorithm then learns from this data and is able to generalize to new data. Some examples of supervised algorithms are regression and classification.

Unsupervised Algorithms
Unsupervised algorithms are those where the target variable is not known. With unsupervised algorithms, we only have input data, without any corresponding output labels. The algorithm has to learn from the data itself without any guidance. Some examples of unsupervised algorithms are clustering and dimensionality reduction.

Why Supervised Algorithms Perform Better on Imbalanced Datasets
The reason why supervised algorithms perform better on imbalanced datasets is because they can learn from the training data which cases are more important. With unsupervised algorithms, all data points are treated equally, regardless of whether they are in the minority or majority class.

For example, in a binary classification problem with an imbalanced dataset, let’s say that we want to predict whether a customer will default on their loan payment or not. We have a training dataset of 1000 customers, out of which only 100 (10%) have defaulted on their loan in the past.

If we use a supervised algorithm like logistic regression, the algorithm will learn from the training data that defaulting on a loan is rare (since only 10% of cases in the training data are Positive). This means that it will be more likely to predict correctly that a new customer will not default on their loan (since this is the majority class in the training data).
However, if we use an unsupervised algorithm like k-means clustering, all data points will be treated equally since there is no target variable to guide the algorithm. This means that it might incorrectly cluster together customers who have defaulted on their loans with those who haven’t since there is no guidance provided by a target variable.

Conclusion:
In conclusion, supervised machine learning algorithms tend to perform better on imbalanced datasets than unsupervised machine learning algorithms because they can learn from the training data which cases are more important. 

Some machine learning algorithms tend to perform better on highly imbalanced datasets because they are designed to deal with imbalance or because they can learn from both classes simultaneously. If you are working with a highly imbalanced dataset, then you should consider using one of these algorithms.

Thanks for reading!

How are machine learning techniques being used to address unstructured data challenges?

Machine learning techniques are being used to address unstructured data challenges in a number of ways:

  1. Natural language processing (NLP): NLP algorithms can be used to extract meaningful information from unstructured text data, such as emails, documents, and social media posts. NLP algorithms can be trained to classify text data, identify key terms and concepts, and extract structured data from unstructured text.
  2. Image recognition: Machine learning algorithms can be used to analyze and classify images, enabling the automatic identification and classification of objects, people, and other elements in images. This can be useful for tasks such as image tagging and search, as well as for applications such as security and surveillance.
  3. Audio and speech recognition: Machine learning algorithms can be used to analyze and classify audio data, enabling the automatic transcription and translation of spoken language. This can be useful for tasks such as speech-to-text transcription, as well as for applications such as call center automation and language translation.
  4. Video analysis: Machine learning algorithms can be used to analyze and classify video data, enabling the automatic detection and classification of objects, people, and other elements in video. This can be useful for tasks such as video tagging and search, as well as for applications such as security and surveillance.

Overall, machine learning techniques are being used in a wide range of applications to extract meaningful information from unstructured data, and to enable the automatic classification and analysis of data in a variety of formats.

How is AI and machine learning impacting application development today?

Artificial intelligence (AI) and machine learning are having a significant impact on application development today in a number of ways:

  1. Enabling new capabilities: AI and machine learning algorithms can be used to enable applications to perform tasks that would be difficult or impossible for humans to do. For example, AI-powered applications can be used to analyze and classify large amounts of data, or to automate complex decision-making processes.
  2. Improving performance: AI and machine learning algorithms can be used to optimize the performance of applications, making them faster, more efficient, and more accurate. For example, machine learning algorithms can be used to improve the accuracy of predictive models, or to optimize the performance of search algorithms.
  3. Streamlining development: AI and machine learning algorithms can be used to automate various aspects of application development, such as testing, debugging, and deployment. This can help to streamline the development process and reduce the time and resources needed to build and maintain applications.
  4. Enhancing user experiences: AI and machine learning algorithms can be used to enhance the user experience of applications, by providing personalized recommendations, recommendations, or by enabling applications to anticipate and respond to the needs and preferences of users.

Overall, AI and machine learning are having a significant impact on application development today, and they are likely to continue to shape the way applications are built and used in the future.

How will advancements in artificial intelligence and machine learning shape the future of work and society?

Advancements in artificial intelligence (AI) and machine learning are likely to shape the future of work and society in a number of ways. Some potential impacts include:

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  1. Automation: AI and machine learning algorithms can be used to automate tasks that are currently performed by humans, such as data entry, customer service, and manufacturing. This could lead to changes in the types of jobs that are available and the skills that are in demand, as well as to increased productivity and efficiency.
  2. Job displacement: While automation may create new job opportunities, it could also lead to job displacement, particularly for workers in industries that are more susceptible to automation. This could lead to social and economic challenges, including unemployment and income inequality.
  3. Increased efficiency: AI and machine learning algorithms can be used to optimize and streamline business processes, leading to increased efficiency and productivity. This could lead to economic growth and innovation, and could also help to reduce costs for businesses and consumers.
  4. Enhanced decision-making: AI and machine learning algorithms can be used to analyze large amounts of data and make more informed and accurate decisions. This could lead to improved outcomes in fields such as healthcare, finance, and education, and could also help to reduce bias and improve fairness.

Overall, the impact of AI and machine learning on the future of work and society is likely to be significant and complex, with both potential benefits and challenges. It will be important to consider and address these impacts as these technologies continue to advance and become more widely adopted.

  • [D] ANN for recommendations with already seen items
    by /u/overflozz (Machine Learning) on April 19, 2024 at 1:10 am

    Trying to implement a recommender model for an equivalent of a dating app, where I don't want to recommend someone if they have already been seen by the current user. For the embeddings ANN search, I've looked into Pinecone, VertexAI and Qdrant, but it seems that the filtering is usually limited to larger categories (e.g country). I haven't examples of filters of the type : `{user_id: {not_in: ['123', '456' , ... ]}}`. A user might have already seen 1000s of recommendations. What is the usual approach for this type of history-aware ANN? submitted by /u/overflozz [link] [comments]

  • [D] Has anyone tried distilling large language models the old way?
    by /u/miladink (Machine Learning) on April 19, 2024 at 12:11 am

    So, nowadays, everyone is distilling rationales gathered from a large language model to another relatively smaller model. However, I remember from the old days that we did we train the small network to match the logits of the large network when doing distillation. Is this forgotten /tried and did not work today? submitted by /u/miladink [link] [comments]

  • [Discussion] tiny ML for text labelling / classification
    by /u/duyth (Machine Learning) on April 18, 2024 at 11:46 pm

    Hi guys I’m working on a small hobbyist project that would require a tiny model (tiny enough to run on my iphone) to classify or label a text as one of the different classifications that I’ve defined e.g: I may have the below classifications Eat Dance Stay still Move … more And the goal is classify say “come on run faster” as “move” Is there a particular tiny ML that I should be aware of (I hope for something specifically in classification so I can hopefully get the results as quick as possible?) Thank you submitted by /u/duyth [link] [comments]

  • Business Forecasting [R]
    by /u/docdropz (Machine Learning) on April 18, 2024 at 10:56 pm

    Hello all, I am starting a golf apparel company. I am a bioinformatician by training with years of experience. I have familiarity with R and Python. My question to you all is what models should I consider and how should I implement them for forecasting sales data and building predictive models? Any GitHub links or tutorials, training materials, etc. would be appreciated! Thank you! submitted by /u/docdropz [link] [comments]

  • [D] Combining models of different modalities
    by /u/hophophop1233 (Machine Learning) on April 18, 2024 at 9:55 pm

    What's the process/approach:architecture of combining multiple models of different modality to generate sane output? Just curious what your experiences have been. Any pointers or links to research would be handy. submitted by /u/hophophop1233 [link] [comments]

  • [D] Vision Transformers, CNN and SMoE
    by /u/mitadev (Machine Learning) on April 18, 2024 at 9:46 pm

    I'm quite newbie so forgive me if anything I say or ask might be dumb. I was wondering some things why todays cnn (like yolov8/9) are faster even if theoretically transformer have the same complexity. Are there SMoE like Mixtral in ViT I have some paper but they do not seem to have brought some significant improvement, am I missing something? On practical term even considering nlp small SMoE work in practice according to your experince? submitted by /u/mitadev [link] [comments]

  • Exposing the True Context Capabilities of Leading LLMs [R]
    by /u/ParsaKhaz (Machine Learning) on April 18, 2024 at 9:34 pm

    I've been examining the real-world context limits of large language models (LLMs), and I wanted to share some enlightening findings from a recent benchmark (RULER) that cuts through the noise. What’s the RULER Benchmark? Developed by NVIDIA, RULER is a benchmark designed to test LLMs' ability to handle long-context information. It's more intricate than the common retrieval-focused NIAH benchmark. RULER evaluates models based on their performance in understanding and using longer pieces of text. Table highlighting RULER benchmark results and effective context lengths of leading LLMs Performance Highlights from the Study: Llama2-7B (chat): Shows decent initial performance but doesn't sustain at higher context lengths. GPT-4: Outperforms others significantly, especially at greater lengths of context, maintaining above 80% accuracy. Command-R (35B): Performs comparably well, slightly behind GPT-4. Yi (34B): Shows strong performance, particularly up to 32K context length. Mixtral (8x7B): Similar to Yi, holds up well until 32K context. Mistral (7B): Drops off in performance as context increases, more so after 32K. ChatGLM (6B): Struggles with longer contexts, showing a steep decline. LWM (7B): Comparable to ChatGLM, with a noticeable decrease in longer contexts. Together (7B): Faces difficulties maintaining accuracy as context length grows. LongChat (13B): Fares reasonably up to 4K but drops off afterwards. LongAlpaca (13B): Shows the most significant drop in performance as context lengthens. Key Takeaways: All models experience a performance drop as the context length increases, without exception. The claimed context length by LLMs often doesn't translate into effective processing ability at those lengths. GPT-4 emerges as a strong leader but isn't immune to decreased accuracy at extended lengths. Why Does This Matter? As AI developers, it’s critical to look beyond the advertised capabilities of LLMs. Understanding the effective context length can help us make informed decisions when integrating these models into applications. What's Missing in the Evaluation? Notably, Google’s Gemini and Claude 3 were not part of the evaluated models. RULER is now open-sourced, paving the way for further evaluations and transparency in the field. Sources I recycled a lot of this (and tried to make it more digestible and easy to read) from the following post, further sources available here: Harmonious.ai Weekly paper roundup: RULER: real context size of LLMs (4/8/2024) submitted by /u/ParsaKhaz [link] [comments]

  • [P] Exploring Fair AI Solutions with New Data Quality Initiative
    by /u/ComplexAnalysis42 (Machine Learning) on April 18, 2024 at 9:29 pm

    Hello Reddit! We're a group of students from Carnegie Mellon passionate about data. We've been working on a project that tackles one of the biggest challenges in AI today: data bias. Our tool is designed to audit your datasets and generate synthetic, privacy-safe data that helps promote fairness in AI applications. Here’s why we think it could be a game-changer: Detect and Correct Data Biases: Ensures your AI models are built on fair and balanced data. Enhance Data Quality: Improves the reliability and performance of your AI systems. Generate Synthetic Data: Expands your dataset without compromising privacy. We believe in a future where data-driven technologies are equitable and just, and we're eager to contribute to making that a reality. We’d love to get your insights and feedback to further refine our tool. Join Us in Shaping the Future of Fair AI Are you interested in AI fairness? Do you have experiences with biased data or ideas on how to address these issues? Let’s start a conversation! Comment here or send us a DM. We’re here to discuss and collaborate on getting you the data you deserve! submitted by /u/ComplexAnalysis42 [link] [comments]

  • [P] help in suggesting correct approach/algo for solving the below problem
    by /u/Abject-Log-1249 (Machine Learning) on April 18, 2024 at 9:10 pm

    Hi Everyone, There is a problem we are trying to solve and I want to know if there is particular approach/algorithm which will work the team. We are using Cloudera Hadoop system at our company and currently we have hard coded rule system to trigger if there is missing data on different dimensions, like time and different configuration items. We are trying to reduce the number of incidents based on what our past observations which we can code it up easily by writing more rules to group the common issues. But at the same time we were wondering if there is an algo/technique which will help us group the common issues based on feature similarities, because the features which we are forming rules by writing common code might become outdated and we soon be writing more rules. Based on my previous beginner level knowledge of ML seems like it is clustering problem where we don't know the number of clusters and some google and chatgpting seems like DBSCAN will fit but don't want to spend lot of time trying out and finding out this is not the right approach to solve the problem. Anyone tried to solve a similar problem and what approach did they take? Anyone has some guidance on this? Features which we have are: time (through, we don't rely on that much and issues at different time are considered different incidents, so may not be that important and would need to give less weight?) 2.cluster Market data pipeline lineage (part of it is in place and part of it is in data collection stage, I believe this will make the coding part complicated if we go with rule based system) Product/Service affected Configuration item Thanks appreciate any help in advance. (let me know if this is not the correct sub for this type of question) submitted by /u/Abject-Log-1249 [link] [comments]

  • [P] Embedding visualizer running entirely in the browser with marimo
    by /u/akshayka (Machine Learning) on April 18, 2024 at 8:09 pm

    I built an embedding visualizer that runs entirely in the browser, using Pyodide and a marimo notebook: https://marimo.app/l/55kclj ​ The notebook uses PCA to project the UCI digits dataset to 2D, then uses marimo's dataflow execution engine to automatically wire plot selections to Python. Select data in the scatterplot, view the original images. ​ The example uses PCA because it can be computed entirely in the browser, but the notebook can be used as a template to explore a scatterplot produced by any other means. submitted by /u/akshayka [link] [comments]

  • [P] 2D CNN for time series
    by /u/redreaper71_ (Machine Learning) on April 18, 2024 at 7:38 pm

    I'm trying to develop a 2D CNN for a time series model and am having a bit of trouble deciding/understanding what the input shape of the 2D CNN should be. The data I have consists of 7 time series, each of which has 5 features, one target (response) variable and 60 time steps. So for example, the data for one time series would look like this (for the first 3 rows): Timestamp, feature1, feature2, feature3 ... target 06/01/2012 01:00:00, 500, 29, 0.3 ... 33 06/01/2012 02:00:00, 200, 32, 0.1 ... 45 06/01/2012 03:00:00, 600, 23, 0.24 ... 56 My goal is to have the CNN be trained on the entirety of 6 of the time series and tested on the last one. I did a bit of research online and found people using the input shape as (number of samples, number of time series, window length, number of features). My questions are: what would the dimensions of my X_train, X_test, y_train, y_test be? if this input shape is valid, why? submitted by /u/redreaper71_ [link] [comments]

  • [R] Looking for an SEM Image and corresponding Layout image for computer chips dataset
    by /u/budgetfroot (Machine Learning) on April 18, 2024 at 7:29 pm

    I'm looking for a dataset consisting of hi-resolution and zoomed in SEM images of computer chips (Can be any chip or IC) along with their idealized layout images (can be rgb or grayscale). Basically I want one sample of the dataset to contain a pair of images (1) the SEM image and (2) the idealized layout corresponding to that SEM image. Does any of you know if a dataset like this exists? Doesn't matter how small at this point I'll take anything. I've been looking for a some time now and can't find anything. Not sure if I'm searching for the wrong thing. https://preview.redd.it/o0srhuu0havc1.png?width=243&format=png&auto=webp&s=8805b9967062a674f13658c727042d69955a567b https://preview.redd.it/q5ysnhu2havc1.png?width=240&format=png&auto=webp&s=c432fd07beab27e261c4579b970c64faebbd049d submitted by /u/budgetfroot [link] [comments]

  • [P] Llama 3 70B powered coding copilot extension
    by /u/geepytee (Machine Learning) on April 18, 2024 at 7:29 pm

    Was super excited to read the news from Meta this morning, particularly around the HumanEval scores the 70B model got. Thought it'd be useful to make the new Llama 3 70B available to anyone that wants to try it, so I added it to my VS Code coding copilot extension double.bot. Also making it free for the first 50 messages so everyone gets a chance to try it while we wait for the quantized versions to run locally submitted by /u/geepytee [link] [comments]

  • [D] Llama-3 (7B and 70B) on a medical domain benchmark
    by /u/aadityaura (Machine Learning) on April 18, 2024 at 6:45 pm

    Llama-3 is making waves in the AI community. I was curious how it will perform in the medical domain, Here are the evaluation results for Llama-3 (7B and 70B) on a medical domain benchmark consisting of 9 diverse datasets https://preview.redd.it/sdwx5tglxbvc1.png?width=1464&format=png&auto=webp&s=d32585a69244d44c83e2b1e8a85301a7a8676ea2 I'll be fine-tuning, evaluating & releasing Llama-3 & different LLMs over the next few days on different Medical and Legal benchmarks. Follow the updates here: https://twitter.com/aadityaura https://preview.redd.it/9egbcayv9avc1.png?width=1344&format=png&auto=webp&s=436a972421d5568e1a544962b8cfd1c7b14efe04 submitted by /u/aadityaura [link] [comments]

  • [D] ICML Meta Reviews
    by /u/Personal_Click_6502 (Machine Learning) on April 18, 2024 at 5:09 pm

    When are the ICML meta reviews releasing? Will it be declared along with the final paper notification? The webpage says its due 16th April. https://icml.cc/Conferences/2024/ReviewerInstructions submitted by /u/Personal_Click_6502 [link] [comments]

  • [R] Show Your Work with Confidence: Confidence Bands for Tuning Curves
    by /u/nicholaslourie (Machine Learning) on April 18, 2024 at 4:46 pm

    Paper: https://arxiv.org/abs/2311.09480 Tweet: https://x.com/NickLourie/status/1770077925779337563 Code: https://github.com/nicholaslourie/opda Docs: https://nicholaslourie.github.io/opda/tutorial/usage.html Abstract: The choice of hyperparameters greatly impacts performance in natural language processing. Often, it is hard to tell if a method is better than another or just better tuned. Tuning curves fix this ambiguity by accounting for tuning effort. Specifically, they plot validation performance as a function of the number of hyperparameter choices tried so far. While several estimators exist for these curves, it is common to use point estimates, which we show fail silently and give contradictory results when given too little data. Beyond point estimates, confidence bands are necessary to rigorously establish the relationship between different approaches. We present the first method to construct valid confidence bands for tuning curves. The bands are exact, simultaneous, and distribution-free, thus they provide a robust basis for comparing methods. Empirical analysis shows that while bootstrap confidence bands, which serve as a baseline, fail to approximate their target confidence, ours achieve it exactly. We validate our design with ablations, analyze the effect of sample size, and provide guidance on comparing models with our method. To promote confident comparisons in future work, we release opda: an easy-to-use library that you can install with pip. submitted by /u/nicholaslourie [link] [comments]

  • [R] InternVL v1.5 open sourced, ranking first in OpenCompass multi-modal benchmark
    by /u/flyforlight (Machine Learning) on April 18, 2024 at 4:42 pm

    https://preview.redd.it/fh44g3n4m9vc1.png?width=1383&format=png&auto=webp&s=9b3e499bd51aeb10559f4636eba2a1677d4a08a3 InternVL is a multi-modal foundation model, which is accepted as an Oral paper for CVPR 2024. The latest version InternVL v1.5 ranks first in the OpenCompass multi-modal model benchmark. Demo: https://internvl.opengvlab.com/ Model Download: https://huggingface.co/collections/OpenGVLab/internvl-65b92d6be81c86166ca0dde4 OpenCompass: https://rank.opencompass.org.cn Some examples: https://preview.redd.it/rwj7vs9rm9vc1.jpg?width=902&format=pjpg&auto=webp&s=514e14e692db8ea7bd5a66cc36b1ca3f8351102c https://preview.redd.it/vtwjml3qm9vc1.png?width=2508&format=png&auto=webp&s=e32c044d4bc60ef28baf64dccdcb5fe9b10dfc61 https://preview.redd.it/p51vt3xpn9vc1.png?width=2609&format=png&auto=webp&s=73907e5ffb4d9b9bd4250cbce53e3bd29dedabf1 submitted by /u/flyforlight [link] [comments]

  • [N] Meta releases Llama 3
    by /u/we_are_mammals (Machine Learning) on April 18, 2024 at 4:18 pm

    https://llama.meta.com/llama3/ ​ ​ https://preview.redd.it/n3lwb4xfj9vc1.png?width=3840&format=png&auto=webp&s=b756d89c50c627955668d5ac16df82f7af01cdbc submitted by /u/we_are_mammals [link] [comments]

  • [R] MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control
    by /u/SeawaterFlows (Machine Learning) on April 18, 2024 at 4:07 pm

    Paper: https://arxiv.org/abs/2403.12037 Code: https://github.com/Zhoues/MineDreamer Models and Dataset: https://huggingface.co/Zhoues Abstract: It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow instructions due to difficulties in understanding abstract and sequential natural language instructions. To this end, we introduce MineDreamer, an open-ended embodied agent built upon the challenging Minecraft simulator with an innovative paradigm that enhances instruction-following ability in low-level control signal generation. Specifically, MineDreamer is developed on top of recent advances in Multimodal Large Language Models (MLLMs) and diffusion models, and we employ a Chain-of-Imagination (CoI) mechanism to envision the step-by-step process of executing instructions and translating imaginations into more precise visual prompts tailored to the current state; subsequently, the agent generates keyboard-and-mouse actions to efficiently achieve these imaginations, steadily following the instructions at each step. Extensive experiments demonstrate that MineDreamer follows single and multi-step instructions steadily, significantly outperforming the best generalist agent baseline and nearly doubling its performance. Moreover, qualitative analysis of the agent's imaginative ability reveals its generalization and comprehension of the open world. submitted by /u/SeawaterFlows [link] [comments]

  • [R] Compression Represents Intelligence Linearly
    by /u/SeawaterFlows (Machine Learning) on April 18, 2024 at 3:54 pm

    Paper: https://arxiv.org/abs/2404.09937 Code: https://github.com/hkust-nlp/llm-compression-intelligence Datasets: https://huggingface.co/datasets/hkust-nlp/llm-compression Abstract: There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the success of large language models (LLMs): the development of more advanced language models is essentially enhancing compression which facilitates intelligence. Despite such appealing discussions, little empirical evidence is present for the interplay between compression and intelligence. In this work, we examine their relationship in the context of LLMs, treating LLMs as data compressors. Given the abstract concept of "intelligence", we adopt the average downstream benchmark scores as a surrogate, specifically targeting intelligence related to knowledge and commonsense, coding, and mathematical reasoning. Across 12 benchmarks, our study brings together 30 public LLMs that originate from diverse organizations. Remarkably, we find that LLMs' intelligence -- reflected by average benchmark scores -- almost linearly correlates with their ability to compress external text corpora. These results provide concrete evidence supporting the belief that superior compression indicates greater intelligence. Furthermore, our findings suggest that compression efficiency, as an unsupervised metric derived from raw text corpora, serves as a reliable evaluation measure that is linearly associated with the model capabilities. We open-source our compression datasets as well as our data collection pipelines to facilitate future researchers to assess compression properly. submitted by /u/SeawaterFlows [link] [comments]

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Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


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