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In Yuval Noah Harari's new book "Nexus: A Brief History of Information Networks from the Stone Age to AI," the bestselling author and historian looks at the unique challenges that artificial intelligence represents for our time. Harari joins the show.Originally aired on September 16, 2024
?The real problem of humanity is the following: we have paleolithic emotions; medieval institutions; and god-like technology.? - E.O. Wilson
It's no secret that AI is controversial today. Judging by some of the chaos it's caused, there's good reason to think AI seems to ruin everything it touches. But what about the flipside? What good is AI actually doing in the world? It's a question I don't hear asked much so today we'll find out. Note: Reinforcement learning etc are all included in this conversation.
OpenAI?s new O1 AI model is transforming industries like coding, healthcare, and education with advanced reasoning capabilities that surpass previous AI models. However, users should avoid asking the O1 model about its internal thought processes, as OpenAI has restricted access to this information to prevent potential risks. This groundbreaking AI is pushing the boundaries of artificial intelligence by excelling at complex problem-solving, paving the way toward autonomous AI and the possibility of AGI.🔍 Key Topics Covered: - OpenAI?s new O1 model and how it surpasses human experts in reasoning and problem-solving - The hidden reasoning process behind O1 and why OpenAI restricts access to its full logic - How O1 is revolutionizing industries like healthcare, coding, and science with deep, multi-step reasoning - Safety measures and privacy concerns as OpenAI moves closer to autonomous AI and AGI - The future of AI as OpenAI transitions from reasoning to AI agents capable of acting without user input 🎥 What You?ll Learn: - How OpenAI?s O1 model is changing the landscape of AI by outperforming previous models - Why OpenAI hides O1?s full reasoning process and how this affects users? interaction with AI - Insights into how O1 is solving complex challenges in fields like mathematics, research, and coding 📊 Why This Matters: OpenAI?s O1 model is at the forefront of AI innovation, offering unprecedented reasoning capabilities that push AI closer to autonomy and AGI. This advancement isn?t just about faster responses; it?s transforming industries, speeding up scientific breakthroughs, and potentially reshaping our relationship with technology. The rise of autonomous AI has profound implications for the future of problem-solving and decision-making in every field.DISCLAIMER: This video discusses the latest developments in AI and reasoning technologies. Viewer discretion is advised for sensitive topics surrounding AI safety and ethical concerns. The creators are not legal professionals, and the content is for educational and informational purposes only.
Artificial intelligence just seems to keep growing and growing and growing, fueled by largely unregulated access to massive amounts of free, publicly available data. But unfortunately for our future robot overlords, that data has begun drying up as websites and organizations have begun restricting access to their information. What does this mean for the AI industry? Let?s take a look.
Links From Todays Video:https://arxiv.org/pdf/2408.0331400:00 - Introduction and background on LLMs02:15 - Test time compute explained03:26 - Scaling model parameters strategy04:48 - Scaling vs optimizing test time compute05:53 - Key concepts from DeepMind research06:39 - Verifier reward models07:45 - Adaptive response updating09:04 - Compute optimal scaling strategy10:28 - Math benchmark for testing11:30 - Palm 2 models used12:36 - Core techniques tested14:16 - Search methods used15:05 - Results and comparisons15:27 - OpenAI's O1 model comparison15:58 - Conclusion and future implication
o1 is different, and even sceptics are calling it a 'large reasoning model'. But why is it so different, and why does that say about the future? When models are rewarded for correctness of answers, not just harmlessness or predicting the next word. But does even o1 lack spatial reasoning? How did the White House react yesterday? And did Ilya Sutskever warn of o1 getting ... 'creative'?Chapters: 00:00 ? Intro01:04 ? How o1 Works (The 3rd Paradigm)03:10 ? We Don?t Need Human Examples (OpenAI)03:54 ? How o1 Works (Temp 1 Graded)06:28 ? Is This Reasoning? 08:48 ? Personal Announcement11:27 ? Hidden, serial Thoughts? 13:11 ? Memorized Reasoning?15:40 ? 10 Facts 2021 Paper on Verifiers: https://arxiv.org/pdf/2110.14168Let?s Verify Step By Step: https://arxiv.org/pdf/2305.20050DeepMind Not Far Behind: https://arxiv.org/pdf/2211.14275Chain of Thought for Serial Problems: https://arxiv.org/pdf/2402.12875
00:00 - Introduction and Danny Zhou's statement00:38 - Explanation of chain of thought prompting01:41 - Importance and limitations of Transformers03:01 - Breakdown of the groundbreaking claim04:16 - Intermediate reasoning tokens explained05:44 - Constant depth sufficiency discussion06:55 - How this changes AI understanding07:42 - Viral post and AGI implications08:59 - Detailed analysis of AGI claims10:39 - Significance of the research findings11:44 - Transformers' versatility and future implications12:26 - Closing thoughts and call to action
00:00 - Introduction02:30 - AI in healthcare04:39 - Software development demo09:43 - AI timeline discussion15:05 - AGI timeline analysis17:28 - AGI to superintelligence progression19:46 - Deep learning and scaling20:35 - Future scientific predictions22:02 - Potential AI downsides23:45 - Comparison of past and present views24:40 - Recent funding discussion
Google DeepMind's latest innovation, SCoRe (Self-Correction via Reinforcement Learning), enables AI models to fix their own mistakes without human intervention. This new method allows AI systems to improve performance across various domains, including math and coding, by learning from errors and making more meaningful corrections. SCoRe significantly enhances the accuracy and efficiency of AI models, reducing reliance on external supervision and improving results in real-world applications.🔍 Key Topics Covered:- How Google DeepMind's SCoRe method teaches AI to correct its own mistakes - The revolutionary process that allows AI to improve without human help using reinforcement learning - The incredible results of applying SCoRe to complex tasks like math and coding - How this self-correction method enhances AI performance across different fields and real-world scenarios - The significance of this breakthrough in reducing the need for human oversight in AI development 🎥 What You?ll Learn:- How SCoRe enables AI models to self-correct and improve their problem-solving accuracy - Insights into the two-stage process behind SCoRe, including meaningful corrections and multi-turn reinforcement learning - The impressive improvements in AI performance on mathematical reasoning and coding tasks through self-correction 📊 Why This Matters:SCoRe represents a major advancement in AI development, allowing models to learn from their own mistakes and achieve greater efficiency. This breakthrough reduces the reliance on external systems or human intervention, making AI more practical and scalable for real-world applications. By enhancing AI?s ability to self-correct, DeepMind's SCoRe opens up new possibilities in areas like software development, research, and complex multi-step tasks.DISCLAIMER: This video discusses advanced AI concepts related to self-correction and reinforcement learning. Viewer discretion is advised for those unfamiliar with technical AI topics. The content is for educational and informational purposes based on the latest research.
What is model distillation? Well today I will explain the concept and how it is applied to make SoTA cheaper for people to access. Model with names like Turbo, fast, lightning, are usually processed with distillation. It can also be used in some other interesting aspects, eg. step distillation or hide your model structures.
Fei-Fei Li and Justin Johnson are pioneers in AI. While the world has only recently witnessed a surge in consumer AI, our guests have long been laying the groundwork for innovations that are transforming industries today.In this episode, a16z General Partner Martin Casado joins Fei-Fei and Justin to explore the journey from early AI winters to the rise of deep learning and the rapid expansion of multimodal AI. From foundational advancements like ImageNet to the cutting-edge realm of spatial intelligence, Fei-Fei and Justin share the breakthroughs that have shaped the AI landscape and reveal what's next for innovation at World Labs.If you're curious about how AI is evolving beyond language models and into a new realm of 3D, generative worlds, this episode is a must-listen.Timestamps: 00:00 - Spatial Intelligence: A New Frontier01:38 - Scaling AI: The Impact of ImageNet on Computer Vision06:56 - The Role of Compute09:16 - Data as the Key Driver17:01 - Defining AI?s Ultimate Goal18:58 - What is Spatial Intelligence? Unlocking 3D Understanding in AI26:35 - Comparing Models: Spatial Intelligence vs. Language-Based AI29:41 - 1D vs. 3D32:39 - Building Immersive Worlds with Spatial Intelligence 35:11 - From Static Scenes to Dynamic Worlds37:42 - The Future of VR and AR40:42 - Creating Deep Tech Platforms44:26 - Building a World-Class Team45:54 - Measuring Success: Milestones in Spatial Intelligence
A company stole my voice using AI. I called them out, and they responded.Contents:00:00 - What happened?01:11 - Do not let the sun go down on your rat02:58 - Elecrow's response06:55 - Judging the response08:03 - AI voice cloning 10109:09 - Consent for ethical AI cloning12:26 - When life gives you lemons...
Open AI's latest o1 model released just last week and has now broken nearly every prior ceiling test and Benchmark0:07for human intelligence surpassing PhD level experts across the board and now0:13even outperforms humans on psychological tests for levels of self-awareness and0:18reasoning we're on The Cutting Edge with this one folks and most people really have no clue of just how far and how0:25fast things are moving right now when it comes to artificial intelligence but we are going to get you all up to speed with it
Artificial intelligence just got a new player?and it's fully open-sourced. Aria, a multimodal LLM developed by Tokyo-based Rhymes AI, is capable of processing text, code, images, and video all within a single architecture.What should catch your attention, though, isn't just its versatility, but its efficiency. It?s not a huge model like its multimodal counterparts, which means it is more energy?and hardware?friendly.Rhymes AI achieved this by employing a Mixture-of-Experts (MoE) framework. This architecture is similar to having a team of specialized mini experts, each trained to excel in specific areas or tasks.When a new input is given to the model, only the relevant experts (or a subset) are activated instead of using the entire model. This way, running just a specific section of the model means it will be lighter than running a complete know-it-all entity that tries to process everything.
Overall, Aria is a solid competitor that seems promising due to its architecture, openness and ability to scale. If you still want to try or train the model, it?s available for free at Hugging Face. Remember you need at least 80GB of VRAM, a powerful GPU or three RTX 4090's working together. It?s still new, so no quantized versions (less precise but more efficient) are available.Despite these hardware constraints, new developments like this in the open source space are a significant step to achieving the dream of having a fully open ChatGPT competitor that people can run at home and improve based on their specific needs. Let's see where they go next.
00:00:00 Model introduction00:00:37 Benchmark performance00:01:35 Surpassing GPT-400:02:27 Reward modeling00:03:37 Dataset innovation00:04:34 Performance results00:05:37 Style control00:06:33 Practical testing00:07:38 Reasoning challenge00:09:18 Prompt engineering00:10:37 Counting ability00:11:52 Future implications