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Even within our own research team, timelines for transformative AI differ substantially. In this episode, the two Epoch AI researchers with the longest and the shortest timelines for transformative AI candidly examine the roots of their disagreements. They discuss: How and why their timelines for specific milestones differ Current...

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Epoch AI

26,330 subscribers

159,795 次观看 • 1 年前 •via X (Twitter)

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6 条评论

Epoch AI 的头像
Epoch AI1 年前

Epoch After Hours: Youtube: Apple Podcasts: Spotify: Pocket Casts: RSS Feed:

Coral AI News 的头像
Coral AI News1 年前

Coral AI is the most powerful AI for documents. See the difference yourself:

Spencer Schiff 的头像
Spencer Schiff1 年前

Oh my fucking god this is like the Superbowl for me

Jordan Schneider 的头像
Jordan Schneider1 年前

ugh first one was 5 hrs hate to see you losing steam

Sean 的头像
Sean1 年前

Gotta back the diet coke guy.

Rogs 🔍🔸 的头像
Rogs 🔍🔸1 年前

> How a world with AGI might look like Pet peeve: it should be "what ... look like" or "how ... look", but never "how ... look like".

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Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life
2:15:29

Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life

Lex Fridman

30,192,254 次观看 • 2 年前

Here's my conversation with Yann LeCun (Yann LeCun) about AI, importance of open source, limits of LLMs, why AI doomers are wrong, and the path to AGI. This was a fun and fascinating technical conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:18 - Limits of LLMs 13:54 - Bilingualism and thinking 17:46 - Video prediction 25:07 - JEPA (Joint-Embedding Predictive Architecture) 28:15 - JEPA vs LLMs 37:31 - DINO and I-JEPA 38:51 - V-JEPA 44:22 - Hierarchical planning 50:40 - Autoregressive LLMs 1:06:06 - AI hallucination 1:11:30 - Reasoning in AI 1:29:02 - Reinforcement learning 1:34:10 - Woke AI 1:43:48 - Open source 1:47:26 - AI and ideology 1:49:58 - Marc Andreesen 1:57:56 - Llama 3 2:04:20 - AGI 2:08:48 - AI doomers 2:24:38 - Joscha Bach 2:28:51 - Humanoid robots 2:38:00 - Hope for the future
2:47:07

Here's my conversation with Yann LeCun (Yann LeCun) about AI, importance of open source, limits of LLMs, why AI doomers are wrong, and the path to AGI. This was a fun and fascinating technical conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:18 - Limits of LLMs 13:54 - Bilingualism and thinking 17:46 - Video prediction 25:07 - JEPA (Joint-Embedding Predictive Architecture) 28:15 - JEPA vs LLMs 37:31 - DINO and I-JEPA 38:51 - V-JEPA 44:22 - Hierarchical planning 50:40 - Autoregressive LLMs 1:06:06 - AI hallucination 1:11:30 - Reasoning in AI 1:29:02 - Reinforcement learning 1:34:10 - Woke AI 1:43:48 - Open source 1:47:26 - AI and ideology 1:49:58 - Marc Andreesen 1:57:56 - Llama 3 2:04:20 - AGI 2:08:48 - AI doomers 2:24:38 - Joscha Bach 2:28:51 - Humanoid robots 2:38:00 - Hope for the future

Lex Fridman

1,022,139 次观看 • 2 年前

What are current economic models missing about AGI? How would we know if we were approaching explosive growth? Stanford economist Phil Trammell has been rigorously thinking about the intersection of economic theory and AI (incl. AGI) for over five years, long before the recent surge of interest in large language models. In this episode of Epoch After Hours, Philip Trammell and Epoch AI researcher Anson Ho discuss what economic theory really has to say about the development and impacts of AGI: what current economic models get wrong, the odds of explosive economic growth, what “real GDP” actually measures, and much more! -- Timestamps -- 0:00:00 - Problems with existing work on the economics of AI 0:10:18 - Declining returns to R&D 0:18:28 - What real GDP misses 0:26:57 - Task-based models & AI automation 0:49:32 - The limits of economic theory 1:09:11 - How to detect an economic singularity 1:23:32 - Increasing returns to scale
1:49:09

What are current economic models missing about AGI? How would we know if we were approaching explosive growth? Stanford economist Phil Trammell has been rigorously thinking about the intersection of economic theory and AI (incl. AGI) for over five years, long before the recent surge of interest in large language models. In this episode of Epoch After Hours, Philip Trammell and Epoch AI researcher Anson Ho discuss what economic theory really has to say about the development and impacts of AGI: what current economic models get wrong, the odds of explosive economic growth, what “real GDP” actually measures, and much more! -- Timestamps -- 0:00:00 - Problems with existing work on the economics of AI 0:10:18 - Declining returns to R&D 0:18:28 - What real GDP misses 0:26:57 - Task-based models & AI automation 0:49:32 - The limits of economic theory 1:09:11 - How to detect an economic singularity 1:23:32 - Increasing returns to scale

Epoch AI

103,973 次观看 • 8 个月前

.Satya Nadella gave me and Dylan Patel an exclusive tour of Fairwater 2, the most powerful AI datacenter in the world. We then chatted through Satya's vision for Microsoft in a world with AGI. 0:00:00 - Fairwater 2 0:04:15 - Business models for AGI 0:13:42 - Copilot 0:20:56 - Whose margins will expand most? 0:37:12 - MAI 0:48:42 - The hyperscale business 1:03:39 - In-house chip & OpenAI partnership 1:10:30 - The CAPEX explosion 1:16:01 - Will the world trust US companies to lead AI? Look up Dwarkesh Podcast on Youtube, Apple Podcasts or Spotify to tune in.
1:28:41

.Satya Nadella gave me and Dylan Patel an exclusive tour of Fairwater 2, the most powerful AI datacenter in the world. We then chatted through Satya's vision for Microsoft in a world with AGI. 0:00:00 - Fairwater 2 0:04:15 - Business models for AGI 0:13:42 - Copilot 0:20:56 - Whose margins will expand most? 0:37:12 - MAI 0:48:42 - The hyperscale business 1:03:39 - In-house chip & OpenAI partnership 1:10:30 - The CAPEX explosion 1:16:01 - Will the world trust US companies to lead AI? Look up Dwarkesh Podcast on Youtube, Apple Podcasts or Spotify to tune in.

Dwarkesh Patel

1,894,605 次观看 • 7 个月前

Our CEO Demis Hassabis sat down with The Times and The Sunday Times & Times Business at their recent Tech Summit to discuss building AGI with safety in mind, the power of #AlphaFold and his predictions for what’s next in AI development. Watch the full conversation ↓ Timestamps: 0:00 Introduction 2:46 Looking back to 2010 3:40 Specialized AI models 7:41 Multimodality 8:39 Research and product 11:37 Astra and universal assistants 14:48 Steps to AGI 18:53 Maintaining a schedule 19:32 Hope vs hype 22:39 Vision for the future 24:40 UK investment in AI
26:00

Our CEO Demis Hassabis sat down with The Times and The Sunday Times & Times Business at their recent Tech Summit to discuss building AGI with safety in mind, the power of #AlphaFold and his predictions for what’s next in AI development. Watch the full conversation ↓ Timestamps: 0:00 Introduction 2:46 Looking back to 2010 3:40 Specialized AI models 7:41 Multimodality 8:39 Research and product 11:37 Astra and universal assistants 14:48 Steps to AGI 18:53 Maintaining a schedule 19:32 Hope vs hype 22:39 Vision for the future 24:40 UK investment in AI

Google DeepMind

58,879 次观看 • 1 年前

Are AI benchmarks doomed? Greg Burnham and Tom Adamczewski join Anson Ho to push back on benchmark pessimism and dig into what the next generation of AI benchmarks could look like. (0:00:00) - Preview (0:00:36) - Intro: Are AI benchmarks doomed? (0:03:13) - The costs and benefits of benchmark development (0:11:48) - MirrorCode and scalable benchmarks (0:20:57) - AI speed-up in benchmark development (0:23:28) - The benchmark-reality gap (0:38:26) - Can an AGI benchmark exist? (0:43:18) - Beyond automated scoring (1:00:45) - How AI changes benchmark building in practice
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Are AI benchmarks doomed? Greg Burnham and Tom Adamczewski join Anson Ho to push back on benchmark pessimism and dig into what the next generation of AI benchmarks could look like. (0:00:00) - Preview (0:00:36) - Intro: Are AI benchmarks doomed? (0:03:13) - The costs and benefits of benchmark development (0:11:48) - MirrorCode and scalable benchmarks (0:20:57) - AI speed-up in benchmark development (0:23:28) - The benchmark-reality gap (0:38:26) - Can an AGI benchmark exist? (0:43:18) - Beyond automated scoring (1:00:45) - How AI changes benchmark building in practice

Epoch AI

22,037 次观看 • 1 个月前

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities
2:54:26

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Rob Wiblin

34,618 次观看 • 11 个月前

New episode with former US Treasury Secretary and current OpenAI board member Larry Summers (Lawrence H. Summers). We discuss: - How he's been learning about AI. - The odds of the technology delivering a much faster economic growth regime (akin to the Industrial Revolution). - How AGI might change economic policymaking. Enjoy! Timestamps: (0:00:00) - Introduction. (0:00:46) - Larry's journey teaching himself about AI & deep learning since joining OpenAI’s board. (0:09:15) - How many hours per week has Larry been spending on OpenAI-related stuff? (0:10:16) - Which bottleneck to AI scaling does Larry think is the most underrated? (0:12:22) - Approximately what share of time do today's AI researchers spend on tasks that AI will be doing for them in five years? (0:15:01) - What explains the remarkable steadiness of US economic growth over the last 150 years? (0:19:42) - How likely is it that AI initiates a new growth regime with average growth that’s ~10x faster than today? (0:21:36) - What are the best economic arguments for believing AI won’t deliver a regime of ever-increasing growth rates? (0:25:33) - How much could AGI boost economic growth in developing countries merely by helping their policymakers make better decisions? (0:28:20) - How much better could monetary policy be if the Fed had AGI? (0:31:40) - How much would having AGI have helped US economic policymakers during the financial crisis & Great Recession? (0:36:07) - Is the CCP infiltrating and stealing the IP of major AI labs in the US and UK? (0:39:35) - At what point should AI be nationalised? (0:42:39) - How would Bill Clinton or Barack Obama be thinking about AI governance? (0:44:27) - If OpenAI restructures to a public benefit corporation, how does that change its incentives? (0:46:18) - What does Daron Acemoglu miss in his analysis of the economic impacts of AI?
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New episode with former US Treasury Secretary and current OpenAI board member Larry Summers (Lawrence H. Summers). We discuss: - How he's been learning about AI. - The odds of the technology delivering a much faster economic growth regime (akin to the Industrial Revolution). - How AGI might change economic policymaking. Enjoy! Timestamps: (0:00:00) - Introduction. (0:00:46) - Larry's journey teaching himself about AI & deep learning since joining OpenAI’s board. (0:09:15) - How many hours per week has Larry been spending on OpenAI-related stuff? (0:10:16) - Which bottleneck to AI scaling does Larry think is the most underrated? (0:12:22) - Approximately what share of time do today's AI researchers spend on tasks that AI will be doing for them in five years? (0:15:01) - What explains the remarkable steadiness of US economic growth over the last 150 years? (0:19:42) - How likely is it that AI initiates a new growth regime with average growth that’s ~10x faster than today? (0:21:36) - What are the best economic arguments for believing AI won’t deliver a regime of ever-increasing growth rates? (0:25:33) - How much could AGI boost economic growth in developing countries merely by helping their policymakers make better decisions? (0:28:20) - How much better could monetary policy be if the Fed had AGI? (0:31:40) - How much would having AGI have helped US economic policymakers during the financial crisis & Great Recession? (0:36:07) - Is the CCP infiltrating and stealing the IP of major AI labs in the US and UK? (0:39:35) - At what point should AI be nationalised? (0:42:39) - How would Bill Clinton or Barack Obama be thinking about AI governance? (0:44:27) - If OpenAI restructures to a public benefit corporation, how does that change its incentives? (0:46:18) - What does Daron Acemoglu miss in his analysis of the economic impacts of AI?

Joseph Noel Walker

85,856 次观看 • 1 年前

Will AI drive Europe towards a “high interest rates, no growth” future? How will automation impact entry-level workers and the structure of firms? What role does Europe play as the US and China continue to push AI progress? On this episode of Epoch After Hours, Luis Garicano 🇪🇺🇺🇦 joins Epoch AI researchers Anson Ho and Andrei Potlogea to discuss these questions. -- Timestamps -- 0:00:00 – Will AI trigger explosive growth? 0:06:26 – Short-run macroeconomic effects 0:11:29 – The decline of junior jobs 0:20:21 – The missing training ladder 0:39:31 – Europe’s AI regulation problem 0:52:46 – Who captures AI value? 01:08:17 – AI, interest rates & fiscal future
1:25:16

Will AI drive Europe towards a “high interest rates, no growth” future? How will automation impact entry-level workers and the structure of firms? What role does Europe play as the US and China continue to push AI progress? On this episode of Epoch After Hours, Luis Garicano 🇪🇺🇺🇦 joins Epoch AI researchers Anson Ho and Andrei Potlogea to discuss these questions. -- Timestamps -- 0:00:00 – Will AI trigger explosive growth? 0:06:26 – Short-run macroeconomic effects 0:11:29 – The decline of junior jobs 0:20:21 – The missing training ladder 0:39:31 – Europe’s AI regulation problem 0:52:46 – Who captures AI value? 01:08:17 – AI, interest rates & fiscal future

Epoch AI

33,102 次观看 • 6 个月前

New Epoch podcast episode: How far can current AI trends continue? Jaime Sevilla and Yafah Edelman on where current AI trends carry us —and where they break. They disagree on mechanisms and outcomes, but agree on this: fast diffusion now, broad cognitive automation by ~2035, and extreme uncertainty after. (0:00:00) - Preview (0:00:41) - Intro: Does 5× compute scaling continue? (0:08:15) - Largest training run in 2030 & what does it imply? (0:12:44) - Impact on Software Engineering & other cognitive tasks (0:23:27) - Economic impacts near the end of the decade (0:31:34) - 2030 bifurcation: Slow down or take off? (0:35:49) - Physical vs cognitive automation (0:44:37) - Timelines and impact of full cognitive automation (1:02:37) - Returns to intelligence (1:08:51) - Three cruxes after 2035 (Robots, technology & intelligence) (1:16:28) - What happens in 2040? (1:23:16) - Recap: Three eras of forecasting (1:37:42) - Closing remarks
1:39:18

New Epoch podcast episode: How far can current AI trends continue? Jaime Sevilla and Yafah Edelman on where current AI trends carry us —and where they break. They disagree on mechanisms and outcomes, but agree on this: fast diffusion now, broad cognitive automation by ~2035, and extreme uncertainty after. (0:00:00) - Preview (0:00:41) - Intro: Does 5× compute scaling continue? (0:08:15) - Largest training run in 2030 & what does it imply? (0:12:44) - Impact on Software Engineering & other cognitive tasks (0:23:27) - Economic impacts near the end of the decade (0:31:34) - 2030 bifurcation: Slow down or take off? (0:35:49) - Physical vs cognitive automation (0:44:37) - Timelines and impact of full cognitive automation (1:02:37) - Returns to intelligence (1:08:51) - Three cruxes after 2035 (Robots, technology & intelligence) (1:16:28) - What happens in 2040? (1:23:16) - Recap: Three eras of forecasting (1:37:42) - Closing remarks

Epoch AI

34,979 次观看 • 9 个月前

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
2:02:21

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 次观看 • 1 年前

Here's my conversation all about AI in 2026, including technical breakthroughs, scaling laws, closed & open LLMs, programming & dev tooling (Claude Code, Cursor, etc), China vs US competition, training pipeline details (pre-, mid-, post-training), rapid evolution of LLMs, work culture, diffusion, robotics, tool use, compute (GPUs, TPUs, clusters), continual learning, long context, AGI timelines (including how stuff might go wrong), advice for beginners, education, a LOT of discussion about the future, and other topics. It's a great honor and pleasure for me to be able to do this kind of episode with two of my favorite people in the AI community: 1. Sebastian Raschka (Sebastian Raschka) 2. Nathan Lambert (Nathan Lambert) They are both widely-respected machine learning researchers & engineers who also happen to be great communicators, educators, writers, and X posters. This was a whirlwind conversation: everything from the super-technical to the super-fun. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 1:57 - China vs US: Who wins the AI race? 10:38 - ChatGPT vs Claude vs Gemini vs Grok: Who is winning? 21:38 - Best AI for coding 28:29 - Open Source vs Closed Source LLMs 40:08 - Transformers: Evolution of LLMs since 2019 48:05 - AI Scaling Laws: Are they dead or still holding? 1:04:12 - How AI is trained: Pre-training, Mid-training, and Post-training 1:37:18 - Post-training explained: Exciting new research directions in LLMs 1:58:11 - Advice for beginners on how to get into AI development & research 2:21:03 - Work culture in AI (72+ hour weeks) 2:24:49 - Silicon Valley bubble 2:28:46 - Text diffusion models and other new research directions 2:34:28 - Tool use 2:38:44 - Continual learning 2:44:06 - Long context 2:50:21 - Robotics 2:59:31 - Timeline to AGI 3:06:47 - Will AI replace programmers? 3:25:18 - Is the dream of AGI dying? 3:32:07 - How AI will make money? 3:36:29 - Big acquisitions in 2026 3:41:01 - Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta 3:53:35 - Manhattan Project for AI 4:00:10 - Future of NVIDIA, GPUs, and AI compute clusters 4:08:15 - Future of human civilization
4:25:12

Here's my conversation all about AI in 2026, including technical breakthroughs, scaling laws, closed & open LLMs, programming & dev tooling (Claude Code, Cursor, etc), China vs US competition, training pipeline details (pre-, mid-, post-training), rapid evolution of LLMs, work culture, diffusion, robotics, tool use, compute (GPUs, TPUs, clusters), continual learning, long context, AGI timelines (including how stuff might go wrong), advice for beginners, education, a LOT of discussion about the future, and other topics. It's a great honor and pleasure for me to be able to do this kind of episode with two of my favorite people in the AI community: 1. Sebastian Raschka (Sebastian Raschka) 2. Nathan Lambert (Nathan Lambert) They are both widely-respected machine learning researchers & engineers who also happen to be great communicators, educators, writers, and X posters. This was a whirlwind conversation: everything from the super-technical to the super-fun. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 1:57 - China vs US: Who wins the AI race? 10:38 - ChatGPT vs Claude vs Gemini vs Grok: Who is winning? 21:38 - Best AI for coding 28:29 - Open Source vs Closed Source LLMs 40:08 - Transformers: Evolution of LLMs since 2019 48:05 - AI Scaling Laws: Are they dead or still holding? 1:04:12 - How AI is trained: Pre-training, Mid-training, and Post-training 1:37:18 - Post-training explained: Exciting new research directions in LLMs 1:58:11 - Advice for beginners on how to get into AI development & research 2:21:03 - Work culture in AI (72+ hour weeks) 2:24:49 - Silicon Valley bubble 2:28:46 - Text diffusion models and other new research directions 2:34:28 - Tool use 2:38:44 - Continual learning 2:44:06 - Long context 2:50:21 - Robotics 2:59:31 - Timeline to AGI 3:06:47 - Will AI replace programmers? 3:25:18 - Is the dream of AGI dying? 3:32:07 - How AI will make money? 3:36:29 - Big acquisitions in 2026 3:41:01 - Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta 3:53:35 - Manhattan Project for AI 4:00:10 - Future of NVIDIA, GPUs, and AI compute clusters 4:08:15 - Future of human civilization

Lex Fridman

906,632 次观看 • 4 个月前

The vik episode on Ground Zero! [youtube link in replies] 0:00:00 - Vik's story and Moondream 0:18:50 - The core thesis of small and scale 0:28:35 - The data problem 0:33:35 - Deciding the training architecture 0:43:20 - Post-Training and RL perf 0:46:06 - Post-Training recipes of Moondream 3 0:47:40 - Open Source and VLM development priorities 0:52:05 - AI War : America and China 0:55:07 - Moondream acquisition and Future 1:04:08 - Community Questions 1:16:40 - Trivia, Lores and Opinions on state of AI 1:28:07 - Advice to 20yo
1:31:15

The vik episode on Ground Zero! [youtube link in replies] 0:00:00 - Vik's story and Moondream 0:18:50 - The core thesis of small and scale 0:28:35 - The data problem 0:33:35 - Deciding the training architecture 0:43:20 - Post-Training and RL perf 0:46:06 - Post-Training recipes of Moondream 3 0:47:40 - Open Source and VLM development priorities 0:52:05 - AI War : America and China 0:55:07 - Moondream acquisition and Future 1:04:08 - Community Questions 1:16:40 - Trivia, Lores and Opinions on state of AI 1:28:07 - Advice to 20yo

himanshu

43,246 次观看 • 6 个月前

The Dario Amodei interview. 0:00:00 - What exactly are we scaling? 0:12:36 - Is diffusion cope? 0:29:42 - Is continual learning necessary? 0:46:20 - If AGI is imminent, why not buy more compute? 0:58:49 - How will AI labs actually make profit? 1:31:19 - Will regulations destroy the boons of AGI? 1:47:41 - Why can’t China and America both have a country of geniuses in a datacenter? Look up Dwarkesh Podcast on Youtube, Spotify, Apple Podcasts, etc.
2:22:19

The Dario Amodei interview. 0:00:00 - What exactly are we scaling? 0:12:36 - Is diffusion cope? 0:29:42 - Is continual learning necessary? 0:46:20 - If AGI is imminent, why not buy more compute? 0:58:49 - How will AI labs actually make profit? 1:31:19 - Will regulations destroy the boons of AGI? 1:47:41 - Why can’t China and America both have a country of geniuses in a datacenter? Look up Dwarkesh Podcast on Youtube, Spotify, Apple Podcasts, etc.

Dwarkesh Patel

2,055,152 次观看 • 4 个月前

My conversation with Dylan Patel on everything you ever wanted to know about the incredible buildout happening in AI infrastructure This is a whirlwind tour through the companies, deals, models, chips, power sources, partnerships, and people powering AI Dylan is some sort of genius. I wish a version of him existed for every subject. I hope you enjoy this as much as I did. Timestamps: 0:00 Introduction 0:39 The Infinite Money Glitch 4:42 Oracle's Bet on OpenAI 6:06 Nvidia's Strategic Investment and Deal Mechanics 9:17 Why Bigger Models Aren't Always Better 15:07 The Adoption Curve Problem 19:01 The Tokenomics of AI 23:59 Over-Parameterization and Model Learning 28:10 Building Environments for AI Training 32:16 AI in Everyday Life 34:46 The Future of Reasoning and Compute Scaling 44:34 The Spectrum of AI Optimism 46:29 Timeline to AGI 49:25 Talent Wars 58:37 Power Dynamics in the AI Ecosystem 1:18:42 AI for Material Science and Hard Tech 1:22:21 Building Infrastructure 1:29:55 US vs China: Who Really Needs AI to Win? 1:37:42 Favorite AI Bears 1:48:08 Speed Round: Company Impressions 1:55:11 The Death of Traditional SaaS Business Models 2:00:24 The Kindest Thing
2:02:21

My conversation with Dylan Patel on everything you ever wanted to know about the incredible buildout happening in AI infrastructure This is a whirlwind tour through the companies, deals, models, chips, power sources, partnerships, and people powering AI Dylan is some sort of genius. I wish a version of him existed for every subject. I hope you enjoy this as much as I did. Timestamps: 0:00 Introduction 0:39 The Infinite Money Glitch 4:42 Oracle's Bet on OpenAI 6:06 Nvidia's Strategic Investment and Deal Mechanics 9:17 Why Bigger Models Aren't Always Better 15:07 The Adoption Curve Problem 19:01 The Tokenomics of AI 23:59 Over-Parameterization and Model Learning 28:10 Building Environments for AI Training 32:16 AI in Everyday Life 34:46 The Future of Reasoning and Compute Scaling 44:34 The Spectrum of AI Optimism 46:29 Timeline to AGI 49:25 Talent Wars 58:37 Power Dynamics in the AI Ecosystem 1:18:42 AI for Material Science and Hard Tech 1:22:21 Building Infrastructure 1:29:55 US vs China: Who Really Needs AI to Win? 1:37:42 Favorite AI Bears 1:48:08 Speed Round: Company Impressions 1:55:11 The Death of Traditional SaaS Business Models 2:00:24 The Kindest Thing

Patrick OShaughnessy

548,353 次观看 • 9 个月前

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

The Truth About Our AI Future w/ Ben Goertzel Ben Goertzel joins Robert ₿reedlove to explore humanity’s future in a world shaped by AGI. 0:00 – WiM Episode Trailer 1:21 – The Nature of Intelligence & the Future of AGI 10:44 – What Makes a Mind? 19:02 – The Birth of Artificial General Intelligence 22:18 – Heart and Soil Supplements 23:21 – Mine Bitcoin with Blockware Solutions 24:29 – How AGI Learns, Evolves, and Understands 33:40 – Decentralized AGI vs Centralized Superintelligence 41:34 – Onramp Bitcoin Custody 42:34 – Mind Lab Pro Supplements 43:42 – Could AGI Become Humanity’s “Next Species”? 56:17 – Consciousness Beyond Biology 1:07:22 – The Farm at Okefenokee 1:08:29 – Cooperation, Competition & Machine Agency 1:20:55 – Will AGI Merge With Humans? 1:33:01 – Club Orange 1:33:57 – How AGI Will Reshape Civilization 1:47:10 – Decentralized Networks & AI Personhood 1:51:39 – Efani: Protect Yourself From SIM Swaps 1:52:46 – Unlock the Wisdom of the Best Non-Fiction Books 1:53:14 – Final Thoughts & Where to Find Ben Goertzel
1:54:12

The Truth About Our AI Future w/ Ben Goertzel Ben Goertzel joins Robert ₿reedlove to explore humanity’s future in a world shaped by AGI. 0:00 – WiM Episode Trailer 1:21 – The Nature of Intelligence & the Future of AGI 10:44 – What Makes a Mind? 19:02 – The Birth of Artificial General Intelligence 22:18 – Heart and Soil Supplements 23:21 – Mine Bitcoin with Blockware Solutions 24:29 – How AGI Learns, Evolves, and Understands 33:40 – Decentralized AGI vs Centralized Superintelligence 41:34 – Onramp Bitcoin Custody 42:34 – Mind Lab Pro Supplements 43:42 – Could AGI Become Humanity’s “Next Species”? 56:17 – Consciousness Beyond Biology 1:07:22 – The Farm at Okefenokee 1:08:29 – Cooperation, Competition & Machine Agency 1:20:55 – Will AGI Merge With Humans? 1:33:01 – Club Orange 1:33:57 – How AGI Will Reshape Civilization 1:47:10 – Decentralized Networks & AI Personhood 1:51:39 – Efani: Protect Yourself From SIM Swaps 1:52:46 – Unlock the Wisdom of the Best Non-Fiction Books 1:53:14 – Final Thoughts & Where to Find Ben Goertzel

The "What is Money?" Show

14,377 次观看 • 7 个月前

Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion
1:10:03

Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion

Matt Turck

93,678 次观看 • 8 个月前