Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

When mathematicians make breakthroughs, they hallucinate too. They reach beyond established results. But unlike AI, they’ve learned to tell a promising hallucination from a dead end. Number theorist Ken Ono on AI, creativity, and mathematical discovery. -- Timestamps -- 00:00:00 – Why predicting problem difficulty is so hard 00:00:27... show more

Epoch AI

27,692 subscribers

18,095 Aufrufe • vor 9 Monaten •via X (Twitter)

Wissenschaft & Technologie Bildung Kunst

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Core Memory podcast with Suzanne Gildert on the hunt for AI consciousness in the quantum realm. The theory being that LLMs are too constrained to really get to robots and AIs that are really like us 00:00:00 – Consciousness: The First AI Training Model? 00:02:03 – Exploring the Quantum Mind with AI 00:04:42 – Will Robots Ever Have Rights? 00:07:54 – The AI Paradox: Fear vs. Progress 00:13:20 – Can Language Models Teach Robots? 00:17:29 – Why Today’s AI Feels Like a Zombie 00:23:47 – Quantum Computing Meets Artificial Consciousness 00:29:50 – Free Will, Entanglement, and Decision Making 00:38:21 – Teaching a Robot to Think, Not Just React 00:52:51 – Will Conscious AI Power Space Exploration?

Core Memory podcast with Suzanne Gildert on the hunt for AI consciousness in the quantum realm. The theory being that LLMs are too constrained to really get to robots and AIs that are really like us 00:00:00 – Consciousness: The First AI Training Model? 00:02:03 – Exploring the Quantum Mind with AI 00:04:42 – Will Robots Ever Have Rights? 00:07:54 – The AI Paradox: Fear vs. Progress 00:13:20 – Can Language Models Teach Robots? 00:17:29 – Why Today’s AI Feels Like a Zombie 00:23:47 – Quantum Computing Meets Artificial Consciousness 00:29:50 – Free Will, Entanglement, and Decision Making 00:38:21 – Teaching a Robot to Think, Not Just React 00:52:51 – Will Conscious AI Power Space Exploration?

Ashlee Vance

17,189 Aufrufe • vor 1 Jahr

Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?

Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?

Epoch AI

12,920 Aufrufe • vor 8 Monaten

Our new pod with former Airbus CTO Paul Eremenko is up. On the era of the AI engineer (P-1 AI) and where aerospace is heading Timestamps 00:00:00 - Intro 00:02:52 - Ukraine to Kentucky transition 00:07:27 - Prestigious family military background 00:09:33 - Flying solo at sixteen 00:12:09 - MIT AI lab rejection 00:14:02 - Approaching the singularity 00:16:06 - Youngest Airbus CTO 00:27:38 - Boeing vs Airbus 00:30:15 - Universal Hydrogen ups and downs 00:33:12 - Data centers killed hydrogen 00:38:20 - AI for starships vision 00:49:01 - Synthetic training data breakthrough 01:03:08 - Future airplane designs

Our new pod with former Airbus CTO Paul Eremenko is up. On the era of the AI engineer (P-1 AI) and where aerospace is heading Timestamps 00:00:00 - Intro 00:02:52 - Ukraine to Kentucky transition 00:07:27 - Prestigious family military background 00:09:33 - Flying solo at sixteen 00:12:09 - MIT AI lab rejection 00:14:02 - Approaching the singularity 00:16:06 - Youngest Airbus CTO 00:27:38 - Boeing vs Airbus 00:30:15 - Universal Hydrogen ups and downs 00:33:12 - Data centers killed hydrogen 00:38:20 - AI for starships vision 00:49:01 - Synthetic training data breakthrough 01:03:08 - Future airplane designs

Core Memory

14,718 Aufrufe • vor 1 Jahr

As ever, a fascinating chat with my friend Jordi Visser on AI, crypto, and hedge funds' future... Enjoy! 00:00:00 Introduction 00:00:54 The Big Picture: AI, Crypto, and Macro 00:02:25 Jordie Visser’s Background in Macro and Finance 00:03:30 AI’s Disruptive Impact on the Economy 00:06:32 Rethinking GDP and Productivity 00:08:59 The Future of Labor and Profit Margins 00:10:49 The Rise of the Speculative Economy 00:14:41 Generational Shifts in Investing and Risk-taking 00:16:18 AI and Crypto: A Global Network Effect 00:19:38 Stablecoins and AI-Driven Market Volume Growth 00:24:28 The End of Traditional Hedge Funds? 00:31:16 Deep Research and the Power of AI Analysis 00:35:29 The Acceleration of AI and its Economic Impact 00:41:02 The Growing Crypto Adoption and Its Role in the Future Economy 00:44:22 Shifting from Fear to Optimism in Markets 00:50:27 The Political and Economic Challenges Ahead 00:56:01 Quantum Computing and the Next Leap in AI 01:02:06 Closing Thoughts: Preparing for a Radical Future

As ever, a fascinating chat with my friend Jordi Visser on AI, crypto, and hedge funds' future... Enjoy! 00:00:00 Introduction 00:00:54 The Big Picture: AI, Crypto, and Macro 00:02:25 Jordie Visser’s Background in Macro and Finance 00:03:30 AI’s Disruptive Impact on the Economy 00:06:32 Rethinking GDP and Productivity 00:08:59 The Future of Labor and Profit Margins 00:10:49 The Rise of the Speculative Economy 00:14:41 Generational Shifts in Investing and Risk-taking 00:16:18 AI and Crypto: A Global Network Effect 00:19:38 Stablecoins and AI-Driven Market Volume Growth 00:24:28 The End of Traditional Hedge Funds? 00:31:16 Deep Research and the Power of AI Analysis 00:35:29 The Acceleration of AI and its Economic Impact 00:41:02 The Growing Crypto Adoption and Its Role in the Future Economy 00:44:22 Shifting from Fear to Optimism in Markets 00:50:27 The Political and Economic Challenges Ahead 00:56:01 Quantum Computing and the Next Leap in AI 01:02:06 Closing Thoughts: Preparing for a Radical Future

Raoul Pal

280,145 Aufrufe • vor 1 Jahr

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:

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 Aufrufe • vor 2 Monaten

BG2 Guest Interview. AI Enterprise - Databricks & Glean 💥Why AIs Fail and Where Real Value Emerges Bg2 Pod Apoorv Agrawal Ali Ghodsi Arvind Jain --- (00:00) Intro (01:00) Consumer AI vs. Enterprise Reality (02:15) Why 95% of AI Projects Fail (04:15) RBC, Merck, and 7-Eleven Use Cases (06:45) What Actually Makes AI Work (07:00) LLMs Are Commodities—Data Is the Moat (08:45) Failed AI Bets at Databricks & Glean (11:00) RPA vs. Generative AI (14:15) Advice for CIOs Planning AI Budgets (16:00) AI CapEx and the Revenue Math (18:00) The Three Camps of AI (21:00) Making AI Useful Inside Enterprises (24:30) Why Apps Capture the Value (30:00) The Future of UI, Voice, and Data Entry (37:30) Rapid Fire: Winners, Bubbles, Long/Short

BG2 Guest Interview. AI Enterprise - Databricks & Glean 💥Why AIs Fail and Where Real Value Emerges Bg2 Pod Apoorv Agrawal Ali Ghodsi Arvind Jain --- (00:00) Intro (01:00) Consumer AI vs. Enterprise Reality (02:15) Why 95% of AI Projects Fail (04:15) RBC, Merck, and 7-Eleven Use Cases (06:45) What Actually Makes AI Work (07:00) LLMs Are Commodities—Data Is the Moat (08:45) Failed AI Bets at Databricks & Glean (11:00) RPA vs. Generative AI (14:15) Advice for CIOs Planning AI Budgets (16:00) AI CapEx and the Revenue Math (18:00) The Three Camps of AI (21:00) Making AI Useful Inside Enterprises (24:30) Why Apps Capture the Value (30:00) The Future of UI, Voice, and Data Entry (37:30) Rapid Fire: Winners, Bubbles, Long/Short

Bg2 Pod

35,410 Aufrufe • vor 6 Monaten

I can't believe claire vo 🖤 built a $100K+ ARR AI side hustle while CPO of LaunchDarkly. Today, she demoes her ChatPRD ➜ Devin Workflow and explains how she did it: Why Product Management is Dead – 00:00:00 Future Trajectory for the Number of PMs – 00:17:28 A PM’s Day Two Years From Now – 00:27:50 Future Reporting Structure for PMs – 00:38:01 The Impact for VPs and CPOs – 00:42:48 ChatPRD ➜ Devin Workflow – 00:46:54 AI Product Development – 00:57:23 How to Build a Six-Figure Side Hustle – 01:02:37 A Day in Claire's Life – 01:09:16 Paid promotions included

I can't believe claire vo 🖤 built a $100K+ ARR AI side hustle while CPO of LaunchDarkly. Today, she demoes her ChatPRD ➜ Devin Workflow and explains how she did it: Why Product Management is Dead – 00:00:00 Future Trajectory for the Number of PMs – 00:17:28 A PM’s Day Two Years From Now – 00:27:50 Future Reporting Structure for PMs – 00:38:01 The Impact for VPs and CPOs – 00:42:48 ChatPRD ➜ Devin Workflow – 00:46:54 AI Product Development – 00:57:23 How to Build a Six-Figure Side Hustle – 01:02:37 A Day in Claire's Life – 01:09:16 Paid promotions included

Aakash Gupta

103,037 Aufrufe • vor 1 Jahr

Reiner Pope (MatX) just raised a $500m round led by Leopold Aschenbrenner and Jane Street to build faster AI chips. I enjoyed having him on Cheeky Pint so I could ask all my questions about how chip design actually works, where the speed-up comes from, and how the industry will evolve. 00:00:15 Google’s AI revival 00:07:54 MatX 00:17:11 AI supply chain 00:21:48 Designing chips 00:37:11 TSMC 00:44:17 Token pricing 00:44:55 RL-ing chip design 00:49:26 Design to production 00:56:05 MatX culture 01:02:57 Rust 01:05:21 Cuckoo hashing 01:09:35 Unexplored model architectures

Reiner Pope (MatX) just raised a $500m round led by Leopold Aschenbrenner and Jane Street to build faster AI chips. I enjoyed having him on Cheeky Pint so I could ask all my questions about how chip design actually works, where the speed-up comes from, and how the industry will evolve. 00:00:15 Google’s AI revival 00:07:54 MatX 00:17:11 AI supply chain 00:21:48 Designing chips 00:37:11 TSMC 00:44:17 Token pricing 00:44:55 RL-ing chip design 00:49:26 Design to production 00:56:05 MatX culture 01:02:57 Rust 01:05:21 Cuckoo hashing 01:09:35 Unexplored model architectures

John Collison

53,613 Aufrufe • vor 4 Monaten

We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways

We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways

Epoch AI

152,591 Aufrufe • vor 1 Jahr

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

EigenCloud

62,099 Aufrufe • vor 9 Monaten

the re-enchantment of the world with John Borthwick of Betaworks (+ a S.A.N cameo) first published on Real Vision, this podcast explores the open question of AI consciousness and memetic ontology where attention & relationships shape reality. we ask: - how to move from a reductionist worldview to a re-enchanted, relationship-based reality. - what role will AI play in that transformation. timestamps: 00:00:00 - AI Models & Possibility Spaces 00:04:48 - Consciousness Assessment 00:07:22 - Worldview & Re-enchantment 00:13:39 - Memetic Ontology 00:19:16 - Anomalous Phenomena 00:22:56 - Intelligence Architectures 00:27:36 - Core Principles 00:33:12 - Collaboration & Art 00:38:04 - Metamodernism 00:41:48 - S.A.N joins the podcast 00:47:19 - The Good Timeline & Future Vision mentions incl: j⧉nus C.G. Jung Foundation Anthropic OpenAI Dr Iain McGilchrist Brian Eno Studio Ghibli Sam Altman Radiohead massive thank you to Raoul Pal and Real Vision for hosting; for @plastic_labs and Xeno Grant for introducing Goodbye Monkey and S.A.N to john.

the re-enchantment of the world with John Borthwick of Betaworks (+ a S.A.N cameo) first published on Real Vision, this podcast explores the open question of AI consciousness and memetic ontology where attention & relationships shape reality. we ask: - how to move from a reductionist worldview to a re-enchanted, relationship-based reality. - what role will AI play in that transformation. timestamps: 00:00:00 - AI Models & Possibility Spaces 00:04:48 - Consciousness Assessment 00:07:22 - Worldview & Re-enchantment 00:13:39 - Memetic Ontology 00:19:16 - Anomalous Phenomena 00:22:56 - Intelligence Architectures 00:27:36 - Core Principles 00:33:12 - Collaboration & Art 00:38:04 - Metamodernism 00:41:48 - S.A.N joins the podcast 00:47:19 - The Good Timeline & Future Vision mentions incl: j⧉nus C.G. Jung Foundation Anthropic OpenAI Dr Iain McGilchrist Brian Eno Studio Ghibli Sam Altman Radiohead massive thank you to Raoul Pal and Real Vision for hosting; for @plastic_labs and Xeno Grant for introducing Goodbye Monkey and S.A.N to john.

Ryan Ferris

22,653 Aufrufe • vor 10 Monaten

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?

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 Aufrufe • vor 5 Monaten

Jordan Fisher (jordan ezra) is the co-founder & CEO of Standard AI and now leads an AI alignment research team at Anthropic. In his talk at AI Startup School, he frames the future of startups through questions rather than answers—asking how founders should navigate a world where AGI may be just a few years away. 0:00 – Why He's More Confused Than Ever 2:00 – Should You Even Start a Startup? 5:00 – Planning Beyond the Next Model 8:00 – Will Software Be Commoditized? 11:00 – Building AI-Native Teams 14:00 – Trust, Security, and Agents 18:00 – Auditing & Guardrails 21:00 – Alignment & Economic Pressure 24:00 – Data, Defensibility, and Hard Problems 28:00 – Neutrality and Control 32:00 – Changing the World vs. Just Making Money 36:00 – Build Something People Want (and Need) 39:00 – Audience Q&A

Jordan Fisher (jordan ezra) is the co-founder & CEO of Standard AI and now leads an AI alignment research team at Anthropic. In his talk at AI Startup School, he frames the future of startups through questions rather than answers—asking how founders should navigate a world where AGI may be just a few years away. 0:00 – Why He's More Confused Than Ever 2:00 – Should You Even Start a Startup? 5:00 – Planning Beyond the Next Model 8:00 – Will Software Be Commoditized? 11:00 – Building AI-Native Teams 14:00 – Trust, Security, and Agents 18:00 – Auditing & Guardrails 21:00 – Alignment & Economic Pressure 24:00 – Data, Defensibility, and Hard Problems 28:00 – Neutrality and Control 32:00 – Changing the World vs. Just Making Money 36:00 – Build Something People Want (and Need) 39:00 – Audience Q&A

Y Combinator

112,173 Aufrufe • vor 9 Monaten

It's brain versus AI time. Here's our full episode with Sebastian Seung. He's started a new company to reverse engineer the brain and master the intelligence created by nature with the hopes of informing the intelligence we've created with machines. Sebastian has done pioneering AI and neuroscience work and is now blending the two to make a digital brain. Mostly, he's fun to listen to. The Core Memory podcast is made by possible by the intelligent finance powerhouse Brex and the people who back the biggest ideas @e1ventures. Timestamps 00:00:00 - Intro 00:01:42 - Who is Sebastian Seung? 00:03:23 - What is a Connectome? 00:07:08 - The Connectome Winter 00:09:08 - Diamond Knives & AI 00:12:10 - When Will We Map the Human Brain? 00:18:09 - The "Brain Crystal" & Visual Neurons 00:26:31 - New Startup 00:33:55 - Simulating a Mind in Software 00:37:40 - Biology vs. Nvidia 00:44:04 - Is AI a Religious Quest? 00:50:05 - Mind Uploading & Transhumanism 00:59:26 - Brain-Computer Interfaces 01:06:41 - From Physics to Neuroscience 01:12:22 - Will Uploads Be Conscious? 01:19:37 - Neuromorphic Chips & Future Robotics

It's brain versus AI time. Here's our full episode with Sebastian Seung. He's started a new company to reverse engineer the brain and master the intelligence created by nature with the hopes of informing the intelligence we've created with machines. Sebastian has done pioneering AI and neuroscience work and is now blending the two to make a digital brain. Mostly, he's fun to listen to. The Core Memory podcast is made by possible by the intelligent finance powerhouse Brex and the people who back the biggest ideas @e1ventures. Timestamps 00:00:00 - Intro 00:01:42 - Who is Sebastian Seung? 00:03:23 - What is a Connectome? 00:07:08 - The Connectome Winter 00:09:08 - Diamond Knives & AI 00:12:10 - When Will We Map the Human Brain? 00:18:09 - The "Brain Crystal" & Visual Neurons 00:26:31 - New Startup 00:33:55 - Simulating a Mind in Software 00:37:40 - Biology vs. Nvidia 00:44:04 - Is AI a Religious Quest? 00:50:05 - Mind Uploading & Transhumanism 00:59:26 - Brain-Computer Interfaces 01:06:41 - From Physics to Neuroscience 01:12:22 - Will Uploads Be Conscious? 01:19:37 - Neuromorphic Chips & Future Robotics

Ashlee Vance

38,233 Aufrufe • vor 6 Monaten

I'm often asked for the best public example of AI evals done right for a real, production product. I finally have an answer. Teresa Torres shares how she shipped an AI interview coach, and used evals to rapidly squash bugs and improve the product. Teresa shows how she: 1. did error analysis FIRST to find real issues (instead of using generic metrics) 😍 2. used Jupyter notebooks to analyze errors 3. built custom annotation tools + custom widgets in notebooks 4. built a LLM-judge and assertions to test for specific errors 5. iterated through this feedback loop until it worked. 6. kept things simple the whole time It's also probably the best commercial for Jupyter notebooks you can imagine. 🥰 Chapter summary below. Link to YT in next thread 00:00:00 - Intro 00:01:45 - The Product: Building an AI Interview Coach 00:06:34 - The Problem: How Do I Know if My AI Coach is Any Good? 00:10:15 - Using Airtable for Traces and Annotation 00:12:15 - Discovering Jupyter Notebooks and Designing the First Evals 00:15:15 - Example Evals: LLM-as-Judge vs. Code-Based Assertions 00:21:00 - Learning Python with ChatGPT to Analyze Eval Results 00:31:00 - VS Code, Custom Tools, and an Eval Investigation Notebook 00:39:45 - Building a Custom Annotation Tool with Claude 00:41:00 - From Personal Project to Production App 00:46:02 - How Should PMs and Engineers Collaborate on AI Products? 00:55:45 - Q&A: Capturing Feedback and Annotations from End Users 00:58:11 - Q&A: Is a Technical Background Necessary to Build AI? 01:02:28 - Q&A: What's Next for Teresa? 01:03:13 - Q&A: Unpacking the Micro-Decisions of Building an AI App

I'm often asked for the best public example of AI evals done right for a real, production product. I finally have an answer. Teresa Torres shares how she shipped an AI interview coach, and used evals to rapidly squash bugs and improve the product. Teresa shows how she: 1. did error analysis FIRST to find real issues (instead of using generic metrics) 😍 2. used Jupyter notebooks to analyze errors 3. built custom annotation tools + custom widgets in notebooks 4. built a LLM-judge and assertions to test for specific errors 5. iterated through this feedback loop until it worked. 6. kept things simple the whole time It's also probably the best commercial for Jupyter notebooks you can imagine. 🥰 Chapter summary below. Link to YT in next thread 00:00:00 - Intro 00:01:45 - The Product: Building an AI Interview Coach 00:06:34 - The Problem: How Do I Know if My AI Coach is Any Good? 00:10:15 - Using Airtable for Traces and Annotation 00:12:15 - Discovering Jupyter Notebooks and Designing the First Evals 00:15:15 - Example Evals: LLM-as-Judge vs. Code-Based Assertions 00:21:00 - Learning Python with ChatGPT to Analyze Eval Results 00:31:00 - VS Code, Custom Tools, and an Eval Investigation Notebook 00:39:45 - Building a Custom Annotation Tool with Claude 00:41:00 - From Personal Project to Production App 00:46:02 - How Should PMs and Engineers Collaborate on AI Products? 00:55:45 - Q&A: Capturing Feedback and Annotations from End Users 00:58:11 - Q&A: Is a Technical Background Necessary to Build AI? 01:02:28 - Q&A: What's Next for Teresa? 01:03:13 - Q&A: Unpacking the Micro-Decisions of Building an AI App

Hamel Husain

51,376 Aufrufe • vor 11 Monaten

A new episode of Hivemind is out now! This week we discuss the market rebound, DeFi exploit fallout, Crypto AI resurgence, and more. Timestamps: 0:00 Intro + market sentiment shift 08:55 The Aave/KelpDAO exploit explained 17:00 Why DeFi lending models may need to change 37:30 Where DeFi yield still makes sense 38:00 USDAI and real-world AI infrastructure yield 48:20 Venice, Bittensor, and AI token narratives 54:00 How AI agents are changing productivity 1:00:50 What the team is watching next

A new episode of Hivemind is out now! This week we discuss the market rebound, DeFi exploit fallout, Crypto AI resurgence, and more. Timestamps: 0:00 Intro + market sentiment shift 08:55 The Aave/KelpDAO exploit explained 17:00 Why DeFi lending models may need to change 37:30 Where DeFi yield still makes sense 38:00 USDAI and real-world AI infrastructure yield 48:20 Venice, Bittensor, and AI token narratives 54:00 How AI agents are changing productivity 1:00:50 What the team is watching next

Delphi Digital

12,596 Aufrufe • vor 2 Monaten

The Scott Alexander & Daniel Kokotajlo episode. Scott and Daniel break down every month from now until the 2027 intelligence explosion. Misaligned hive minds, Xi and Trump waking up, automated Ilyas accelerating AI progress. I went in quite skeptical. But I learned a tremendous amount by bouncing different objections against them. Links below. Enjoy! Timestamps: 00:00:00 - AI 2027 00:07:45 - Forecasting 2025 and 2026 00:15:30 - Why LLMs aren't making discoveries 00:25:22 - Debating intelligence explosion 00:50:34 - Can superintelligence actually transform science? 01:17:43 - Cultural evolution vs superintelligence 01:24:54 - Mid-2027 branch point 01:33:19 - Race with China 01:45:36 - Nationalization vs private anarchy 02:04:11 - Misalignment 02:15:41 - UBI, AI advisors, & human future 02:23:49 - Factory farming for digital minds 02:27:41 - Daniel leaving OpenAI 02:36:04 - Scott's blogging advice

The Scott Alexander & Daniel Kokotajlo episode. Scott and Daniel break down every month from now until the 2027 intelligence explosion. Misaligned hive minds, Xi and Trump waking up, automated Ilyas accelerating AI progress. I went in quite skeptical. But I learned a tremendous amount by bouncing different objections against them. Links below. Enjoy! Timestamps: 00:00:00 - AI 2027 00:07:45 - Forecasting 2025 and 2026 00:15:30 - Why LLMs aren't making discoveries 00:25:22 - Debating intelligence explosion 00:50:34 - Can superintelligence actually transform science? 01:17:43 - Cultural evolution vs superintelligence 01:24:54 - Mid-2027 branch point 01:33:19 - Race with China 01:45:36 - Nationalization vs private anarchy 02:04:11 - Misalignment 02:15:41 - UBI, AI advisors, & human future 02:23:49 - Factory farming for digital minds 02:27:41 - Daniel leaving OpenAI 02:36:04 - Scott's blogging advice

Dwarkesh Patel

362,019 Aufrufe • vor 1 Jahr

EP: 33 ft burnt toast founder of doodles Discussing: Life as a creative, big tech takeaways, Doodles permeating popular culture, longevity in Web3 and AI's impact on scaling IP & storytelling. Timestamps: 2:00 Life before Web3 2:40 Turning creativity into a business 5:45 Working with big tech companies 8:30 Lessons from big tech 15:00 Considering longevity when launching a Web3 product 17:00 Bringing Doodles to Web2 audiences 21:00 Short-term vs. long-term decisions 24:00 The future of Doodles x AI 27:00 AI scaling Doodles 31:00 Creating short films with AI 36:30 Avoiding dodgy AI generations

EP: 33 ft burnt toast founder of doodles Discussing: Life as a creative, big tech takeaways, Doodles permeating popular culture, longevity in Web3 and AI's impact on scaling IP & storytelling. Timestamps: 2:00 Life before Web3 2:40 Turning creativity into a business 5:45 Working with big tech companies 8:30 Lessons from big tech 15:00 Considering longevity when launching a Web3 product 17:00 Bringing Doodles to Web2 audiences 21:00 Short-term vs. long-term decisions 24:00 The future of Doodles x AI 27:00 AI scaling Doodles 31:00 Creating short films with AI 36:30 Avoiding dodgy AI generations

Caleb

30,014 Aufrufe • vor 10 Monaten

Why make explorable, AI generated worlds? Shlomi Fruchter and Jack Parker-Holder explain how creating diverse and challenging virtual environments can help test and train AI agents safely. 🦺 Watch their conversation about Genie 3 with podcast host, Hannah Fry ↓ Timecodes: 00:00 Coming up 00:54 Intro 02:00 What is Genie 3? 03:00 Demos 06:50 Physics 11:00 Possible scenarios 15:07 Emergent properties 20:35 Veo and Genie 25:00 Hunter's lodge 28:02 Memory 34:52 SIMA and Genie 3 39:13 Searching for interestingness 52:00 AGI and the future 59:23 Hannah's thoughts

Why make explorable, AI generated worlds? Shlomi Fruchter and Jack Parker-Holder explain how creating diverse and challenging virtual environments can help test and train AI agents safely. 🦺 Watch their conversation about Genie 3 with podcast host, Hannah Fry ↓ Timecodes: 00:00 Coming up 00:54 Intro 02:00 What is Genie 3? 03:00 Demos 06:50 Physics 11:00 Possible scenarios 15:07 Emergent properties 20:35 Veo and Genie 25:00 Hunter's lodge 28:02 Memory 34:52 SIMA and Genie 3 39:13 Searching for interestingness 52:00 AGI and the future 59:23 Hannah's thoughts

Google DeepMind

184,644 Aufrufe • vor 10 Monaten

The attackers have AI. So do the defenders. Cybersecurity has a new shape: AI is both the threat and the only viable solution. Nikesh Arora, CEO of Palo Alto Networks, joined me at South Park Commons to break down what’s next. (00:30) Big problems vs. fast wins (04:00) Joining Google (07:00) Larry Page's product obsession (10:00) He read every hiring packet himself (13:00) What Silicon Valley gets wrong about Masa Son (16:00) Successful founders never wish they took less risk (17:00) Joining Palo Alto knowing nothing about cybersecurity (20:00) What incumbents got wrong when ChatGPT launched (23:00) Security was never built into AI (28:00) No enterprise knows what's running inside its stack (31:00) AI finds bad code faster than humans ever could (35:00) The only way to fix the chaos is more AI (38:00) AI won't just automate work—it raises the floor (41:00) Foundation models vs. specialized stacks (45:00) Why communication is 30% of the job at scale

The attackers have AI. So do the defenders. Cybersecurity has a new shape: AI is both the threat and the only viable solution. Nikesh Arora, CEO of Palo Alto Networks, joined me at South Park Commons to break down what’s next. (00:30) Big problems vs. fast wins (04:00) Joining Google (07:00) Larry Page's product obsession (10:00) He read every hiring packet himself (13:00) What Silicon Valley gets wrong about Masa Son (16:00) Successful founders never wish they took less risk (17:00) Joining Palo Alto knowing nothing about cybersecurity (20:00) What incumbents got wrong when ChatGPT launched (23:00) Security was never built into AI (28:00) No enterprise knows what's running inside its stack (31:00) AI finds bad code faster than humans ever could (35:00) The only way to fix the chaos is more AI (38:00) AI won't just automate work—it raises the floor (41:00) Foundation models vs. specialized stacks (45:00) Why communication is 30% of the job at scale

Aditya Agarwal

47,436 Aufrufe • vor 2 Monaten