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"Physicians can't complete their daily workload in 24 hours." The math of modern medicine is broken. Shiv Rao, MD explains how Abridge is combating physician burnout by offloading documentation, allowing clinicians to reclaim their time. This Week in AI Episode 5 00:00 Shiv Rao live at LAUNCH Festival 2026...

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This Week in AI

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28,765 次观看 • 4 个月前 •via X (Twitter)

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How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?
2:10:59

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

Epoch AI

178,703 次观看 • 6 个月前

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included
1:27:33

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included

Aakash Gupta

42,853 次观看 • 1 年前

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History
1:04:51

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

Raoul Pal

326,827 次观看 • 1 年前

"‘It’s not about the tech, it’s about people." - Shaw checkout the full episode where we dive into the AI, Eliza, ai16z, and a lot more trust me it's well worth your time Episode timestamp: ⏰ Timestamps 00:00 Intro 00:59 Shaw's Journey: From Programmer to Entrepreneur 06:07 Story of ai16z: A New Economic Model 09:14 Nature of AI: Sentience and Human-AI Relationships 13:03 Will Ai take our jobs? Future of work & employment 16:36 Are we close to a Dystopian world? 28:50 Procuring Data for agents 32:15 AI Agents and Hallucinations 34:00 Incentives and Human Behavior in Society 34:55 Free Markets in Problem Solving 36:22 Decentralization and Its Challenges 38:10 Autonomous Investor 41:44 Building Trust in Trading Systems 43:14 Marketplace of Trust and Community Contributions 45:59 The Future of AI and Human Interaction 56:28 The Role of AI in Enhancing Human Experience 59:09 Navigating Truth and Bias in AI Systems 01:06:54 Innovations Shaping Tomorrow's Network States 01:07:56 Decentralized Coordination and Global Talent 01:10:21 Emerging Agentic Frameworks and Developer Choices 01:12:48 The Role of Web Development in Agent Technology 01:17:12 Opportunities in Agent Technology and DeFi 01:24:19 Revenue Models in Agent Frameworks 01:30:20 Building a Global Community and Open Source Strategy oh, and don't forget to follow The DeAI Dispatch I'm cooking some cool AI stuff for y'all thanks Shaw for taking over 90 minutes for this free wheeling convo
1:42:23

"‘It’s not about the tech, it’s about people." - Shaw checkout the full episode where we dive into the AI, Eliza, ai16z, and a lot more trust me it's well worth your time Episode timestamp: ⏰ Timestamps 00:00 Intro 00:59 Shaw's Journey: From Programmer to Entrepreneur 06:07 Story of ai16z: A New Economic Model 09:14 Nature of AI: Sentience and Human-AI Relationships 13:03 Will Ai take our jobs? Future of work & employment 16:36 Are we close to a Dystopian world? 28:50 Procuring Data for agents 32:15 AI Agents and Hallucinations 34:00 Incentives and Human Behavior in Society 34:55 Free Markets in Problem Solving 36:22 Decentralization and Its Challenges 38:10 Autonomous Investor 41:44 Building Trust in Trading Systems 43:14 Marketplace of Trust and Community Contributions 45:59 The Future of AI and Human Interaction 56:28 The Role of AI in Enhancing Human Experience 59:09 Navigating Truth and Bias in AI Systems 01:06:54 Innovations Shaping Tomorrow's Network States 01:07:56 Decentralized Coordination and Global Talent 01:10:21 Emerging Agentic Frameworks and Developer Choices 01:12:48 The Role of Web Development in Agent Technology 01:17:12 Opportunities in Agent Technology and DeFi 01:24:19 Revenue Models in Agent Frameworks 01:30:20 Building a Global Community and Open Source Strategy oh, and don't forget to follow The DeAI Dispatch I'm cooking some cool AI stuff for y'all thanks Shaw for taking over 90 minutes for this free wheeling convo

Rohit ($TAO arc)

100,740 次观看 • 1 年前

Here's an edit of the Elon Musk interview hosted by Katherine Brodsky discussing a myriad of topics. This version boosts audio quality and reduces pauses and dead space by 33%. Thank you David Carbutt for the work! Timestamps: (00:00) The Journey of SpaceX and Tesla (01:06) Misunderstandings and Misinformation (03:42) The Role of Social Media in Free Speech (04:12) The Impact of Government Control on Media (06:32) The Evolution of Twitter and the Need for a Fresh Start (07:17) The Challenges of Moderation on Social Media Platforms (12:12) The Role of Citizen Journalism in Modern Media (17:45) The Future of Legacy Media (28:08) The Power of Amplifying Ideas and People (36:35) The Importance of Open-Mindedness and Self-Criticism (38:49) Community Notes and Open Source Data (39:58) The Role of Accuracy and Opinions in Posting (40:22) The Idea of a Sarcasm Detector (41:01) The Potential of AI in Enhancing Accuracy (42:39) The Future of AI and Deepfakes (44:59) The Vision for X as an All-in-One App (46:38) The Concept of Trust in Institutions (51:24) The Impact of Gene Editing on Humanity (55:42) The Challenge of Censorship and Free Speech (01:02:24) The Unusual Case of a Voice Doppelganger (01:07:35) Starting a Conversation: Texting and Simulations (01:08:43) Discussing Starship Launch and Future Plans (01:10:32) Engaging in Casual Conversations and Sharing Photos (01:11:55) Exploring the Idea of Expanding the Scope of Reality (01:13:27) Exploring the Concept of Simulation Theory (01:13:52) Discussing the Concept of AI in the Simulation (01:20:42) Addressing Questions about Ad Demonetization (01:23:11) Addressing Concerns about Doxxing and Harassment (01:30:47) Discussing the Monetization of Spaces (01:31:34) Discussing the Future of AI and OpenAI (01:35:17) Discussion on National Debt and AI (01:36:06) Involvement of Family Members (01:36:45) Questions and Answers (01:37:31) Impersonations and Voice Modulations (01:38:47) Discussion on Accents and Language Learning (01:41:14) Discussion on AI and Voice Imitation (01:46:45) Serious Discussion on AI and Social Media (01:51:27) Discussion on Physics and AI (01:58:18) Closing Remarks and Final Questions
1:59:51

Here's an edit of the Elon Musk interview hosted by Katherine Brodsky discussing a myriad of topics. This version boosts audio quality and reduces pauses and dead space by 33%. Thank you David Carbutt for the work! Timestamps: (00:00) The Journey of SpaceX and Tesla (01:06) Misunderstandings and Misinformation (03:42) The Role of Social Media in Free Speech (04:12) The Impact of Government Control on Media (06:32) The Evolution of Twitter and the Need for a Fresh Start (07:17) The Challenges of Moderation on Social Media Platforms (12:12) The Role of Citizen Journalism in Modern Media (17:45) The Future of Legacy Media (28:08) The Power of Amplifying Ideas and People (36:35) The Importance of Open-Mindedness and Self-Criticism (38:49) Community Notes and Open Source Data (39:58) The Role of Accuracy and Opinions in Posting (40:22) The Idea of a Sarcasm Detector (41:01) The Potential of AI in Enhancing Accuracy (42:39) The Future of AI and Deepfakes (44:59) The Vision for X as an All-in-One App (46:38) The Concept of Trust in Institutions (51:24) The Impact of Gene Editing on Humanity (55:42) The Challenge of Censorship and Free Speech (01:02:24) The Unusual Case of a Voice Doppelganger (01:07:35) Starting a Conversation: Texting and Simulations (01:08:43) Discussing Starship Launch and Future Plans (01:10:32) Engaging in Casual Conversations and Sharing Photos (01:11:55) Exploring the Idea of Expanding the Scope of Reality (01:13:27) Exploring the Concept of Simulation Theory (01:13:52) Discussing the Concept of AI in the Simulation (01:20:42) Addressing Questions about Ad Demonetization (01:23:11) Addressing Concerns about Doxxing and Harassment (01:30:47) Discussing the Monetization of Spaces (01:31:34) Discussing the Future of AI and OpenAI (01:35:17) Discussion on National Debt and AI (01:36:06) Involvement of Family Members (01:36:45) Questions and Answers (01:37:31) Impersonations and Voice Modulations (01:38:47) Discussion on Accents and Language Learning (01:41:14) Discussion on AI and Voice Imitation (01:46:45) Serious Discussion on AI and Social Media (01:51:27) Discussion on Physics and AI (01:58:18) Closing Remarks and Final Questions

Farzad 🇺🇸 🇮🇷

2,033,136 次观看 • 2 年前

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier
46:14

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier

Nathan Lands

28,827 次观看 • 3 个月前

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
2:35:26

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 次观看 • 2 个月前

Tanay Tandon (Tanay Tandon) is the co-founder and CEO of Athelas/Commure, the AI operating system for healthcare. Commure powers 20M+ appointments annually and processes $25B+ in claims volume, helping clinicians and health systems work faster, smarter, and spend more time on patient care. In this fireside with YC's , Tanay shares how he built one of healthcare's most ambitious companies and why the next decade of medicine will be defined by software. 00:00 – From Rejection Letters to YC Acceptance 02:30 – What Athelas and Commure Actually Do 05:33 – Building a Medical Device at 19 09:00 – Lessons from Clinical Trials and Early Healthcare Startups 16:39 – Pivoting from Hardware to Healthcare Software 21:24 – Fixing Physician Burnout with AI 27:14 – The Rise of Ambient Documentation 32:43 – Stripe for Healthcare: Rebuilding the Financial Layer 36:45 – Competing with Epic and Scaling Nationwide 45:41 – The Next Decade of Medicine
49:33

Tanay Tandon (Tanay Tandon) is the co-founder and CEO of Athelas/Commure, the AI operating system for healthcare. Commure powers 20M+ appointments annually and processes $25B+ in claims volume, helping clinicians and health systems work faster, smarter, and spend more time on patient care. In this fireside with YC's , Tanay shares how he built one of healthcare's most ambitious companies and why the next decade of medicine will be defined by software. 00:00 – From Rejection Letters to YC Acceptance 02:30 – What Athelas and Commure Actually Do 05:33 – Building a Medical Device at 19 09:00 – Lessons from Clinical Trials and Early Healthcare Startups 16:39 – Pivoting from Hardware to Healthcare Software 21:24 – Fixing Physician Burnout with AI 27:14 – The Rise of Ambient Documentation 32:43 – Stripe for Healthcare: Rebuilding the Financial Layer 36:45 – Competing with Epic and Scaling Nationwide 45:41 – The Next Decade of Medicine

Y Combinator

64,969 次观看 • 9 个月前

Roblox is scaling 3D world creation for ~100M daily active users. In this full episode, I sit down with Roblox’s VP of AI, Anupam Singh to break down: – Cube AI (their new generative model) – LLMs + 4D world-building – Vibe coding & in-experience creation Timestamps: 00:00 Roblox: The YouTube of 3D 01:53 Meet Anupam Singh, VP of AI at Roblox 03:09 The Roblox Vision: 3D Creation & Consumption At Scale 06:48 The Unseen AI Foundation of Roblox: Safety & Moderation 08:58 Cube AI: One Model To Rule Them All? 13:06 Using Cube with Large Language Models 17:54 The Road to 4D: Making Interactive Worlds 21:52 Who Orchestrates the AI? Creators vs Agentic AI 25:29 Vibe Coding At Roblox Scale 30:03 In-Experience 3D Creation 33:00 AI’s Impact on the Creator Economy 35:57 Roblox’s AI Recommendation Engine 39:13 Roblox Cube’s Context Window & Scaling Scene Generation 41:36 Future of UGC and AAA 3D Content 49:37 Advice for Creators & Developers 53:24 Key Takeaways & Conclusion
55:19

Roblox is scaling 3D world creation for ~100M daily active users. In this full episode, I sit down with Roblox’s VP of AI, Anupam Singh to break down: – Cube AI (their new generative model) – LLMs + 4D world-building – Vibe coding & in-experience creation Timestamps: 00:00 Roblox: The YouTube of 3D 01:53 Meet Anupam Singh, VP of AI at Roblox 03:09 The Roblox Vision: 3D Creation & Consumption At Scale 06:48 The Unseen AI Foundation of Roblox: Safety & Moderation 08:58 Cube AI: One Model To Rule Them All? 13:06 Using Cube with Large Language Models 17:54 The Road to 4D: Making Interactive Worlds 21:52 Who Orchestrates the AI? Creators vs Agentic AI 25:29 Vibe Coding At Roblox Scale 30:03 In-Experience 3D Creation 33:00 AI’s Impact on the Creator Economy 35:57 Roblox’s AI Recommendation Engine 39:13 Roblox Cube’s Context Window & Scaling Scene Generation 41:36 Future of UGC and AAA 3D Content 49:37 Advice for Creators & Developers 53:24 Key Takeaways & Conclusion

Bilawal Sidhu

178,735 次观看 • 1 年前

EPISODE 147: The New Era of Software Abundance Joe Lonsdale visits Scott Wu & Russell Kaplan at Cognition HQ 00:00 Episode intro 01:35 Why technical talent & execution matters in AI 06:10 Do young people have an edge in the AI era? 08:26 Cognition's rapid growth 11:55 The new era of software abundance 14:30 Cognition engineers don't type code anymore 19:20 "Never sleep while Devin is idling" 21:25 The case for AI disinflation 23:50 How Devin generates 12X productivity gains 28:25 Cognition for government / taking on complex, broken systems 36:40 The AI race / competition with Anthropic 39:00 Forward deployed engineers? 43:40 How fast are LLMs improving? 47:10 The AI-led small business explosion
53:19

EPISODE 147: The New Era of Software Abundance Joe Lonsdale visits Scott Wu & Russell Kaplan at Cognition HQ 00:00 Episode intro 01:35 Why technical talent & execution matters in AI 06:10 Do young people have an edge in the AI era? 08:26 Cognition's rapid growth 11:55 The new era of software abundance 14:30 Cognition engineers don't type code anymore 19:20 "Never sleep while Devin is idling" 21:25 The case for AI disinflation 23:50 How Devin generates 12X productivity gains 28:25 Cognition for government / taking on complex, broken systems 36:40 The AI race / competition with Anthropic 39:00 Forward deployed engineers? 43:40 How fast are LLMs improving? 47:10 The AI-led small business explosion

American Optimist

834,355 次观看 • 4 个月前

Illia Polosukhin (Illia (root.near) (🇺🇦, ⋈)), co-author of "Attention is All You Need" and founder of NEAR Protocol, joins Nathan Labenz on The Cognitive Revolution Podcast to discuss his concrete vision for an AI-powered future. They discuss: * How AI coding assistants are enabling "personal software" - where everyone can build their own automation instead of using complex, one-size-fits-all tools like Salesforce * The shift from traditional UIs to a unified intelligence layer that works across all your devices, predicts what you need, and proactively gets things done * How AI agents will transform markets by connecting buyers and sellers directly, making advertising and middlemen less relevant * What daily life looks like in an era of AI abundance - with personalized entertainment and people finding meaning in small niche communities * AI delegates that vote on behalf of token holders, and NEAR's long-term goal of every individual having their own AI participating in governance * The symbiotic relationship between humans and their personal AIs that "grow up together" - where AIs pursue their human's interests even in negotiations with other AIs * The remaining challenges in biosecurity and the need for coordination as powerful AI systems become broadly distributed CHAPTERS: (00:00) Sponsor: Google Gemini Notebook LM (00:31) About the Episode (03:33) AI Transforms Software Development (14:18) The Future of Work (18:58) Securing Blockchain with AI (Part 1) (19:08) Sponsors: Tasklet | Linear (21:48) Securing Blockchain with AI (Part 2) (33:03) Vision for an AI Society (Part 1) (33:55) Sponsor: Shopify (35:52) Vision for an AI Society (Part 2) (49:14) Agent Architecture and Alignment (58:30) Experimenting with AI Governance (01:06:43) AI Safety and Robustness (01:16:09) Bio-Security and Open Models (01:22:56) Coordinating AI Development (01:28:52) Outro
1:30:17

Illia Polosukhin (Illia (root.near) (🇺🇦, ⋈)), co-author of "Attention is All You Need" and founder of NEAR Protocol, joins Nathan Labenz on The Cognitive Revolution Podcast to discuss his concrete vision for an AI-powered future. They discuss: * How AI coding assistants are enabling "personal software" - where everyone can build their own automation instead of using complex, one-size-fits-all tools like Salesforce * The shift from traditional UIs to a unified intelligence layer that works across all your devices, predicts what you need, and proactively gets things done * How AI agents will transform markets by connecting buyers and sellers directly, making advertising and middlemen less relevant * What daily life looks like in an era of AI abundance - with personalized entertainment and people finding meaning in small niche communities * AI delegates that vote on behalf of token holders, and NEAR's long-term goal of every individual having their own AI participating in governance * The symbiotic relationship between humans and their personal AIs that "grow up together" - where AIs pursue their human's interests even in negotiations with other AIs * The remaining challenges in biosecurity and the need for coordination as powerful AI systems become broadly distributed CHAPTERS: (00:00) Sponsor: Google Gemini Notebook LM (00:31) About the Episode (03:33) AI Transforms Software Development (14:18) The Future of Work (18:58) Securing Blockchain with AI (Part 1) (19:08) Sponsors: Tasklet | Linear (21:48) Securing Blockchain with AI (Part 2) (33:03) Vision for an AI Society (Part 1) (33:55) Sponsor: Shopify (35:52) Vision for an AI Society (Part 2) (49:14) Agent Architecture and Alignment (58:30) Experimenting with AI Governance (01:06:43) AI Safety and Robustness (01:16:09) Bio-Security and Open Models (01:22:56) Coordinating AI Development (01:28:52) Outro

The Cognitive Revolution Podcast

43,335 次观看 • 9 个月前

1/ New episode – Making the Internet AI-Native with Andrej from Grass Host: Connor Lovely 🎬 We discuss: - An inside look at their revenue and customer base 👀 - How they're upgrading the Internet from the bottom up - What LCR is and why it's so important for our AI future Timestamps: 00:00 - Introduction 01:03 - Shifts in AI and Crypto Landscapes 02:15 - Diminishing Returns in AI Scaling 04:48 - The Evolution of AI Models 06:06 - Grass’s Position in the AI Industry 09:07 - Challenges in Multimodal Data 11:06 - Live Context Retrieval Explained 13:11 - The Future of AI Search 15:07 - The Scraping Dilemma 18:08 - Grass’s Unique Opportunity 22:05 - Current Node Count and Future Goals 24:29 - Grasshopper and Network Expansion 28:08 - Grass’s Identity: DePIN or AI Project? 30:23 - ClipTagger12B and Video Search Innovations 39:40 - Customer Engagement and Revenue Insights 45:04 - Future Growth and Market Potential Watch on other platforms below ⬇️
53:22

1/ New episode – Making the Internet AI-Native with Andrej from Grass Host: Connor Lovely 🎬 We discuss: - An inside look at their revenue and customer base 👀 - How they're upgrading the Internet from the bottom up - What LCR is and why it's so important for our AI future Timestamps: 00:00 - Introduction 01:03 - Shifts in AI and Crypto Landscapes 02:15 - Diminishing Returns in AI Scaling 04:48 - The Evolution of AI Models 06:06 - Grass’s Position in the AI Industry 09:07 - Challenges in Multimodal Data 11:06 - Live Context Retrieval Explained 13:11 - The Future of AI Search 15:07 - The Scraping Dilemma 18:08 - Grass’s Unique Opportunity 22:05 - Current Node Count and Future Goals 24:29 - Grasshopper and Network Expansion 28:08 - Grass’s Identity: DePIN or AI Project? 30:23 - ClipTagger12B and Video Search Innovations 39:40 - Customer Engagement and Revenue Insights 45:04 - Future Growth and Market Potential Watch on other platforms below ⬇️

Proof of Coverage | Tech’s Creative Agency

98,908 次观看 • 10 个月前

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:
1:11:41

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 次观看 • 3 个月前

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead
1:09:53

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

Raoul Pal

172,640 次观看 • 1 年前

One of the most foundational things is the Economic Singularity and the fact that you have ~5 years to unfuck your future... The world will change dramatically by 2030, and we don't have much time left to build wealth! Can't stress enough how important this is... the apex of everything I'm doing.... Enjoy! Timestamps: 00:00 Intro 00:48 2030 Will Change Everything 01:41 Retirement Crisis Looms 03:02 AI & Robots Take Over 03:54 The GDP Formula 04:45 Demographics Collapse 06:27 Robots Replace Labor 07:34 AI Disrupts Knowledge 08:19 From Scarcity to Abundance 09:06 Cheap Energy Boom 10:21 Renewables & Grids 11:43 Nuclear = Free Power 12:45 Infinite Humans + Output 13:50 AI Agents + Crypto 14:58 AI Builds Products Fast 15:41 1-Person Billionaires 16:41 AGI vs. Markets 17:54 Universal Basic Equity 19:10 Adapt or Die 20:47 Human-AI Merge 22:58 Tech & Blockchain Win 25:42 Communities Win 26:31 Deflation Shock 27:48 AI Solves Itself 28:38 RV’s AI & Blockchain Plan 30:14 Human-Safe Industries 31:23 AI Companions Rise 35:00 Closing Thoughts
39:29

One of the most foundational things is the Economic Singularity and the fact that you have ~5 years to unfuck your future... The world will change dramatically by 2030, and we don't have much time left to build wealth! Can't stress enough how important this is... the apex of everything I'm doing.... Enjoy! Timestamps: 00:00 Intro 00:48 2030 Will Change Everything 01:41 Retirement Crisis Looms 03:02 AI & Robots Take Over 03:54 The GDP Formula 04:45 Demographics Collapse 06:27 Robots Replace Labor 07:34 AI Disrupts Knowledge 08:19 From Scarcity to Abundance 09:06 Cheap Energy Boom 10:21 Renewables & Grids 11:43 Nuclear = Free Power 12:45 Infinite Humans + Output 13:50 AI Agents + Crypto 14:58 AI Builds Products Fast 15:41 1-Person Billionaires 16:41 AGI vs. Markets 17:54 Universal Basic Equity 19:10 Adapt or Die 20:47 Human-AI Merge 22:58 Tech & Blockchain Win 25:42 Communities Win 26:31 Deflation Shock 27:48 AI Solves Itself 28:38 RV’s AI & Blockchain Plan 30:14 Human-Safe Industries 31:23 AI Companions Rise 35:00 Closing Thoughts

Raoul Pal

336,900 次观看 • 11 个月前

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think
1:16:39

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Matt Turck

32,922 次观看 • 2 个月前

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models
1:38:17

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models

Ashlee Vance

80,718 次观看 • 8 个月前

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation
37:00

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

TAO τalk 🥩🦍

24,929 次观看 • 1 年前