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capability != learning new benchtalks with Parth Asawa on continual learning, where we discuss teaching models to learn from experience, measuring learning ability, the bet on parametric models, and more 01:06 What is continual learning? 04:10 Why capability and learning are different 06:13 Why build a benchmark? 08:07 Continual...

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vincent sunn chen

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39,598 次观看 • 8 天前 •via X (Twitter)

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Making AI learning AGI-capable: continual learning, transfer learning, lifelong learning.
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Making AI learning AGI-capable: continual learning, transfer learning, lifelong learning.

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OpenAI's Chief Scientist, Jakub Pachocki, on the continual learning wave: frontier labs are already building this into the core of the technology. The entire premise of scaling was to create systems that learn in context. Jakub says continual learning is not some separate missing piece, but “exactly what we’re working toward.”

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Continual Learning in 2026. And AGI with it? In July, Dario said he believes Continual Learning will turn out to be not as difficult as it seems. Sholto Douglas, Anthropic and former Deepmind researcher, predicted Continual Learning gets solved in a satisfying way in 2026. Sholto said that last week. Considering how capable AI models have become in 2025, it is unwise to dismiss this as fundraising or hype. It is much wiser to assume AGI is coming. That it will change the world. And that you can benefit from it... ...than it is to assume the opposite. "Not enough people are ready for AI to not be a bubble." - roon Choose wisely my friends.
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Continual Learning in 2026. And AGI with it? In July, Dario said he believes Continual Learning will turn out to be not as difficult as it seems. Sholto Douglas, Anthropic and former Deepmind researcher, predicted Continual Learning gets solved in a satisfying way in 2026. Sholto said that last week. Considering how capable AI models have become in 2025, it is unwise to dismiss this as fundraising or hype. It is much wiser to assume AGI is coming. That it will change the world. And that you can benefit from it... ...than it is to assume the opposite. "Not enough people are ready for AI to not be a bubble." - roon Choose wisely my friends.

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.Richard Sutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled. My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning. And if we have continual learning, we don't need a special training phase - the agent just learns on-the-fly - like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. I did my best to represent the view that LLMs will function as the foundation on which this experiential learning can happen. Some sparks flew. 0:00:00 – Are LLMs a dead-end? 0:13:51 – Do humans do imitation learning? 0:23:57 – The Era of Experience 0:34:25 – Current architectures generalize poorly out of distribution 0:42:17 – Surprises in the AI field 0:47:28 – Will The Bitter Lesson still apply after AGI? 0:54:35 – Succession to AI
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AGI *will* arrive in 2026. The final missing piece is Continual Learning. The following AI Leaders have stated they believe Continual Learning will be solved this year: > Shane Legg > Dario Amodei > Sholto Douglas Rumor is GDM has it but needs guradrails. Prepare yourself.
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AGI *will* arrive in 2026. The final missing piece is Continual Learning. The following AI Leaders have stated they believe Continual Learning will be solved this year: > Shane Legg > Dario Amodei > Sholto Douglas Rumor is GDM has it but needs guradrails. Prepare yourself.

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Demis confirmed DeepMind hasn’t cracked continual learning. There were rumors on X that Google DeepMind had solved it. Not true. They’re experimenting with blending AlphaZero with large foundation models to see if it can scale and generalize to the real world, not just games.

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I filmed a video version of my post 'Why I Don’t Think AGI Is Right Around The Corner' so I could show it to my YouTube audience. You can watch it too I guess. 0:00:00 - Continual Learning 0:08:06 - Computer Use 0:11:27 - Reasoning 0:12:45 - So what are my predictions?

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19:46

🌄 Beyond SWE: The Future of Long Horizon Environments A discussion with our founders about KellyBench, and the need for new environments that require agents to adapt over time and act under uncertainty. 0:00:17 What is KellyBench? 0:02:10 Openendedness, non-stationarity and continual learning 0:03:40 Analytical versus operational capabilities 0:04:13 Why are models bad at KellyBench? 0:05:39 Situational awareness in dynamic environments 0:06:37 Feature stability and real-world non-stationarity 0:07:07 The power of context 0:07:34 "The first principle is that you must not fool yourself" 0:08:20 Machiavelli, fortuna and the ability to adapt to change 0:09:23 How can models improve on evals like KellyBench? 0:10:12 Limitations of KellyBench: data availability and market odds timing 0:11:44 Implications beyond quant finance / sports betting 0:13:26 Civilisations as the ultimate time horizon 0:14:05 Would a mega prompt / better elicitation do much better on the benchmark? 0:14:52 What new types of capability is GR excited about? 0:17:48 Taste and the ability to pursue long-term goals even if they aren't immediately rewarding 0:18:56 Deep learning as an example of a method that took a long time to bear fruit 0:19:25 Optimism about the future of AI

General Reasoning

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Demis Hassabis says the next AI leap is continual learning. Not train once and freeze, but learn from real experience, constantly. AlphaGo did it in games. Now comes the messy real world. Would you let an AI learn from you? 👀
0:56

Demis Hassabis says the next AI leap is continual learning. Not train once and freeze, but learn from real experience, constantly. AlphaGo did it in games. Now comes the messy real world. Would you let an AI learn from you? 👀

VraserX e/acc

19,202 次观看 • 5 个月前

Continual learning sometimes gets discussed as if the goal is to dissolve the context/weights distinction. Let the model just keep accumulating, fine-tuning itself on the fly. Andrej Karpathy points out, though, that this isn't how humans do it. Our working memory gets wiped regularly. What we actually have is a consolidation process (sleep) that distills stuff into the brain, in a weird and lossy way. This is very different from how people sometimes talk about continual learning. It's not obvious it's something you can get for free from doing long enough RL loops.
3:21

Continual learning sometimes gets discussed as if the goal is to dissolve the context/weights distinction. Let the model just keep accumulating, fine-tuning itself on the fly. Andrej Karpathy points out, though, that this isn't how humans do it. Our working memory gets wiped regularly. What we actually have is a consolidation process (sleep) that distills stuff into the brain, in a weird and lossy way. This is very different from how people sometimes talk about continual learning. It's not obvious it's something you can get for free from doing long enough RL loops.

Dwarkesh Patel

59,763 次观看 • 1 个月前

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Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Matt Turck

64,952 次观看 • 29 天前