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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... show more

Dwarkesh Patel

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2,061,652 views • 6 months ago •via X (Twitter)

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1,895,574 views • 9 months ago

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Dwarkesh Patel

261,054 views • 1 year ago

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489,541 views • 5 months ago

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747,468 views • 7 months ago

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Dwarkesh Patel

240,050 views • 1 year ago

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29,267 views • 7 days ago

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Dwarkesh Patel

113,781 views • 1 year ago

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Dwarkesh Patel

57,358 views • 9 months ago

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Mechanize

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It was an honour to host the first ever public dialogue between David Deutsch (David Deutsch) and Steven Pinker (Steven Pinker). They are longstanding admirers of each other's work. I view Steve's defence of the Enlightenment as more empirical, whereas David's is built more on first principles. But beyond their shared rational optimism, what else do they agree – or disagree – about? Topics we discuss are well captured by the timestamps below. Links to YouTube, transcript, Apple Podcasts, & Spotify in tweet below. Enjoy! Timestamps: (0:00:00) - Introduction (0:03:09) - Is AGI even possible? (0:10:17) - Scientific method will constrain runaway superintelligence (0:11:56) - Does AGI need agency to be creative? (0:28:31) - Will AGIs be sentient? (0:37:06) - AGI & AI safety (0:47:44) - P(doom) & subjective probabilities (0:53:21) - Prediction markets (1:01:43) - Universal explainers & The Language Instinct (1:04:43) - Universal explainers & heritability of behavioural traits (1:27:59) - Differential technological development (1:36:09) - What explains the Great Stagnation? (1:46:13) - Should presumed physical limits to growth make us pessimistic?

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Joseph Noel Walker

111,747 views • 2 years ago

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.Casey Handmer on how to feed the AIs. 0:00:00 – Why doesn’t China win by default 0:08:28 – Why hyperscalers choose natural gas over solar 0:18:01 – Solar's astonishing learning rates 0:27:02 – How to build 50,000 acre solar-powered data centers 0:40:24 – Environmental regulations blocking clean energy 0:44:04 – Batteries replacing the grid 0:49:14 – GDP is broken, AGI's true value must be measured in total energy use 0:58:45 – Silicon wafers in space with one mind each Available on Apple Podcasts, Spotify, YouTube, etc. Enjoy!

Dwarkesh Patel

1,408,540 views • 1 year ago

New blackboard lecture w Reiner Pope How do chips actually work – starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do. 0:00:00 – Building a multiply-accumulate from logic gates 0:16:20 – Muxes and the cost of data movement 0:25:59 – How systolic arrays work 0:39:00 – Clock cycles and pipeline registers 0:51:40 – FPGAs vs ASICs 1:03:14 – Cache vs scratchpad 1:07:16 – Why CPU cores are much bigger than GPU cores 1:11:49 – Brains vs chips 1:15:22 – A GPU is just a bunch of tiny TPUs Look up Dwarkesh Podcast on YouTube/Spotify/etc to watch. Enjoy!

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Dwarkesh Patel

936,901 views • 3 months ago

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JACOB HADES

750,939 views • 2 years ago

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

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Dwarkesh Patel

289,936 views • 1 month ago

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?

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 views • 1 year ago

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

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 views • 3 months ago