I have a much better understanding of Sutton’s perspective now. I wanted to reflect on it a bit.(00:00:00) - The steelman(00:02:42) - TLDR of my current thoughts(00:03:22) - Imitation learning is continuous with and complementary to RL(00:08:26) - Continual learning(00:10:31) - Concluding thoughts Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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11:39
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11:39
Richard Sutton – Father of RL thinks LLMs are a dead end
Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end.After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need some new architecture to enable continual learning.And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals.This new paradigm will render our current approach with LLMs obsolete.In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew.A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment.Enjoy!Watch on YouTube; listen on Apple Podcasts or Spotify.Sponsors* Labelbox makes it possible to train AI agents in hyperrealistic RL environments. With an experienced team of applied researchers and a massive network of subject-matter experts, Labelbox ensures your training reflects important, real-world nuance. Turn your demo projects into working systems at labelbox.com/dwarkesh* Gemini Deep Research is designed for thorough exploration of hard topics. For this episode, it helped me trace reinforcement learning from early policy gradients up to current-day methods, combining clear explanations with curated examples. Try it out yourself at gemini.google.com* Hudson River Trading doesn’t silo their teams. Instead, HRT researchers openly trade ideas and share strategy code in a mono-repo. This means you’re able to learn at incredible speed and your contributions have impact across the entire firm. Find open roles at hudsonrivertrading.com/dwarkeshTimestamps(00:00:00) – Are LLMs a dead end?(00:13:04) – Do humans do imitation learning?(00:23:10) – The Era of Experience(00:33:39) – Current architectures generalize poorly out of distribution(00:41:29) – Surprises in the AI field(00:46:41) – Will The Bitter Lesson still apply post AGI?(00:53:48) – Succession to AIs Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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1:06:22
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1:06:22
Fully autonomous robots are much closer than you think – Sergey Levine
Sergey Levine, one of the world’s top robotics researchers and co-founder of Physical Intelligence, thinks we’re on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030.If Sergey’s right, the world 5 years from now will be an insanely different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion.Watch on YouTube; listen on Apple Podcasts or Spotify.Sponsors* Labelbox provides high-quality robotics training data across a wide range of platforms and tasks. From simple object handling to complex workflows, Labelbox can get you the data you need to scale your robotics research. Learn more at labelbox.com/dwarkesh* Hudson River Trading uses cutting-edge ML and terabytes of historical market data to predict future prices. I got to try my hand at this fascinating prediction problem with help from one of HRT’s senior researchers. If you’re curious about how it all works, go to hudson-trading.com/dwarkesh* Gemini 2.5 Flash Image (aka nano banana) isn’t just for generating fun images — it’s also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google’s AI Studio: ai.studio/bananaTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps(00:00:00) – Timeline to widely deployed autonomous robots(00:17:25) – Why robotics will scale faster than self-driving cars(00:27:28) – How vision-language-action models work(00:45:37) – Changes needed for brainlike efficiency in robots(00:57:59) – Learning from simulation(01:09:18) – How much will robots speed up AI buildouts?(01:18:01) – If hardware’s the bottleneck, does China win by default? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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1:28:28
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1:28:28
How Hitler almost starved Britain – Sarah Paine
In this lecture, military historian Sarah Paine explains how Britain used sea control, peripheral campaigns, and alliances to defeat Nazi Germany during WWII. She then applies this framework to today, arguing that Russia and China are similarly constrained by their geography, making them vulnerable in any conflict with maritime powers (like the U.S. and its allies).Watch on YouTube; listen on Apple Podcasts or Spotify.Sponsors* Labelbox partners with researchers to scope, generate, and deliver the exact data frontier models need, no matter the domain. Whether that’s multi-turn audio, SOTA robotics data, advanced STEM problem sets, or even novel RL environments, Labelbox delivers high-quality data, fast. Learn more at labelbox.com/dwarkesh* Warp is the best interface I’ve found for coding with agents. It makes building custom tools easy: Warp’s UI helps you understand agent behavior and its in-line text editor is great for making tweaks. You can try Warp for free, or, for a limited time, use code DWARKESH to get Warp’s Pro Plan for only $5. Go to warp.dev/dwarkeshTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps00:00:00 – How WW1 shaped WW200:15:10 – Hitler and Churchill’s battle to command the Atlantic00:30:10 – Peripheral theaters leading up to Normandy00:37:13 – The Eastern front00:48:04 – Russia’s & China’s geographic prisons01:00:28 – Hitler’s blunders & America’s industrial might01:15:03 – Bismarck’s limited wars vs Hitler’s total war Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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1:35:17
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1:35:17
Evolution designed us to die fast; we can change that — Jacob Kimmel
Jacob Kimmel thinks he can find the transcription factors to reverse aging. We do a deep dive on why this might be plausible and why evolution hasn’t optimized for longevity. We also talk about why drug discovery has been getting exponentially harder, and what a new platform for biological understanding to speed up progress would look like. As a bonus, we get into the nitty gritty of gene delivery and Jacob’s controversial takes on CAR-T cells. For full disclosure, I am an angel investor in NewLimit. This did not impact my decision to interview Jacob, nor the questions I asked him.Watch on YouTube; listen on Apple Podcasts or Spotify.SPONSORS* Hudson River Trading uses deep learning to tackle one of the world's most complex systems: global capital allocation. They have a massive in-house GPU cluster, and they’re constantly adding new racks of B200s to ensure their researchers are never constrained by compute. Explore opportunities at hudsonrivertrading.com/dwarkesh\* Google’s Gemini CLI turns ideas into working applications FAST, no coding required. It built a complete podcast post-production tool in 10 minutes, including fully functional backend logic, and the entire build used less than 10% of Gemini’s session context. Check it out on Github now!* To sponsor a future episode, visit dwarkesh.com/advertise.TIMESTAMPS(00:00:00) – Three reasons evolution didn’t optimize for longevity(00:12:07) – Why didn't humans evolve their own antibiotics?(00:25:26) – De-aging cells via epigenetic reprogramming(00:44:43) – Viral vectors and other delivery mechanisms(01:06:22) – Synthetic transcription factors(01:09:31) – Can virtual cells break Eroom’s Law?(01:31:32) – Economic models for pharma Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Portugal