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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 görüntüleme • 8 gün önce •via X (Twitter)

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Making AI learning AGI-capable: continual learning, transfer learning, lifelong learning.
38:08

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

Ben Goertzel

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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.”
0:52

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.”

Jacob Effron

15,065 görüntüleme • 2 ay önce

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.
0:26

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.

Dan McAteer

69,496 görüntüleme • 6 ay önce

Richard Sutton argues that AI must move beyond human-generated static data into the “Era of Experience,” where agents learn through continuous interaction with the world. This will require building upon RL with better algorithms capable of continual learning and meta-learning.
9:06

Richard Sutton argues that AI must move beyond human-generated static data into the “Era of Experience,” where agents learn through continuous interaction with the world. This will require building upon RL with better algorithms capable of continual learning and meta-learning.

The Humanoid Hub

31,193 görüntüleme • 1 yıl önce

.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
1:07:08

.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

Dwarkesh Patel

3,079,604 görüntüleme • 9 ay önce

Security, scalability, and interoperability are the core foundations of Oasis technology Dawn Song explains how cyber-attacks are becoming more sophisticated — using machine learning models themselves — and why we need to build resilient AI machine learning systems
1:30

Security, scalability, and interoperability are the core foundations of Oasis technology Dawn Song explains how cyber-attacks are becoming more sophisticated — using machine learning models themselves — and why we need to build resilient AI machine learning systems

Oasis Labs

24,279 görüntüleme • 3 yıl önce

Demis on the breakthroughs needed for AGI. It is not many. Predict Google DeepMind has Continual Learning before 2026 is finished, and likely other labs too.
0:21

Demis on the breakthroughs needed for AGI. It is not many. Predict Google DeepMind has Continual Learning before 2026 is finished, and likely other labs too.

Dan McAteer

37,271 görüntüleme • 5 ay önce

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.
0:21

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.

Dan McAteer

27,228 görüntüleme • 5 ay önce

Deep learning works extraordinarily well. And we still largely don't know why. A new paper from Jamie Simon, Daniel Kunin, and 12 co-authors argues that a scientific theory of deep learning is emerging, and coins a name for the emerging field: learning mechanics. We sat down with Jamie and Dan on Generally Intelligent to talk about what a physics of deep learning would actually look like, why now, and what's left to figure out. 3:05 Learning mechanics as the physics to mechanistic interpretability's biology 4:13 Why deep learning needs a theory 7:07 Why deep learning is uniquely hard to engineer 12:11 How a week in the woods became a paper 25:59 The barrier to theory isn't opacity, but complexity 36:26 Deep learning's first gas law 47:22 Why more particles makes the problem easier 56:22 The discretization hypothesis 1:01:50 The strongest signal that a compact theory exists 1:05:07 The Platonic Representation Hypothesis 1:15:41 Why learning mechanics and mech interp need each other 1:25:29 Theory as safety infrastructure
1:33:22

Deep learning works extraordinarily well. And we still largely don't know why. A new paper from Jamie Simon, Daniel Kunin, and 12 co-authors argues that a scientific theory of deep learning is emerging, and coins a name for the emerging field: learning mechanics. We sat down with Jamie and Dan on Generally Intelligent to talk about what a physics of deep learning would actually look like, why now, and what's left to figure out. 3:05 Learning mechanics as the physics to mechanistic interpretability's biology 4:13 Why deep learning needs a theory 7:07 Why deep learning is uniquely hard to engineer 12:11 How a week in the woods became a paper 25:59 The barrier to theory isn't opacity, but complexity 36:26 Deep learning's first gas law 47:22 Why more particles makes the problem easier 56:22 The discretization hypothesis 1:01:50 The strongest signal that a compact theory exists 1:05:07 The Platonic Representation Hypothesis 1:15:41 Why learning mechanics and mech interp need each other 1:25:29 Theory as safety infrastructure

Imbue

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Our new podcast on evals, with Max Niederman, Ege Erdil, and Stephen Yang. 0:00:00 – What's an eval, and how's it different from an RL environment? 0:19:33 – Why are models bad at building an emulator when the task is fully verifiable? 0:42:00 – How does training on bad data teach models to write terrible code? 1:04:00 – Why is continual learning still so bad? 2:25:24 – Why haven't software engineers been replaced when coding is basically solved? Listen to the Mechanize Podcast on YouTube, Spotify, etc. Enjoy!
2:42:03

Our new podcast on evals, with Max Niederman, Ege Erdil, and Stephen Yang. 0:00:00 – What's an eval, and how's it different from an RL environment? 0:19:33 – Why are models bad at building an emulator when the task is fully verifiable? 0:42:00 – How does training on bad data teach models to write terrible code? 1:04:00 – Why is continual learning still so bad? 2:25:24 – Why haven't software engineers been replaced when coding is basically solved? Listen to the Mechanize Podcast on YouTube, Spotify, etc. Enjoy!

Mechanize

37,807 görüntüleme • 16 gün önce

Demis Hassabis says scaling still delivers strong returns, but the early exponential doublings have slowed Most areas are ahead of where the field expected them to be But the big gap is continual learning "today's systems stop learning after training. once deployed, they don't adapt"
1:19

Demis Hassabis says scaling still delivers strong returns, but the early exponential doublings have slowed Most areas are ahead of where the field expected them to be But the big gap is continual learning "today's systems stop learning after training. once deployed, they don't adapt"

Haider.

17,885 görüntüleme • 2 ay önce

What is learning? Paul A. Kirschner and I discuss why learning is inextricably linked with memory.
3:37

What is learning? Paul A. Kirschner and I discuss why learning is inextricably linked with memory.

Carl Hendrick

18,476 görüntüleme • 1 yıl önce

On the latest episode of our podcast, research lead Irina and host Hannah Fry discuss the exciting potential of AI tutors – like our LearnLM Learning Coach on YouTube – to personalize learning and support teachers. 🧠 Watch the full episode now ↓ Timestamps: 00:06 Intro 01:37 Breaking into education 04:08 AI tutor 08:12 The future 10:38 Irina's journey 13:53 Reasoning and teaching 15:05 The optimal strategy for teaching 17:54 Good pedagogy 21:15 GenAI for Education 23:50 AI for history 27:55 Arizona State University 29:43 Self disclosure and anthropomorphism 34:45 Cheating and student engagement 37:00 Dependency on AI 39:42 Research results 41:25 Reflections from Hannah
42:53

On the latest episode of our podcast, research lead Irina and host Hannah Fry discuss the exciting potential of AI tutors – like our LearnLM Learning Coach on YouTube – to personalize learning and support teachers. 🧠 Watch the full episode now ↓ Timestamps: 00:06 Intro 01:37 Breaking into education 04:08 AI tutor 08:12 The future 10:38 Irina's journey 13:53 Reasoning and teaching 15:05 The optimal strategy for teaching 17:54 Good pedagogy 21:15 GenAI for Education 23:50 AI for history 27:55 Arizona State University 29:43 Self disclosure and anthropomorphism 34:45 Cheating and student engagement 37:00 Dependency on AI 39:42 Research results 41:25 Reflections from Hannah

Google DeepMind

127,249 görüntüleme • 1 yıl önce

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.
1:27

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.

Yuchen Jin

75,633 görüntüleme • 5 ay önce

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?
17:13

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?

Dwarkesh Patel

113,781 görüntüleme • 11 ay önce

On agent factories, memory, continual learning & building on the frontier while growing a family In conversation with Sam Whitmore Sam Whitmore, eng at Cursor this is a special one! dive in: 00:25 Sam’s background 01:38 Family building while in frontier tech 11:50 Agent factories at cursor 15:46 Continual learning, agent self-awareness, memory, and user goal alignment 23:51 Building a thoughtful product culture at Cursor 27:00 Thinking about defensibility in 2026 30:01 Selling outside of the tech/X bubble 33:49 Kids & AI use 35:12 On reading fiction 38:32 On pregnancy, hormones, and building 42:58 On agent predictions for the next 6 months 46:35 Hiring at Cursor
47:58

On agent factories, memory, continual learning & building on the frontier while growing a family In conversation with Sam Whitmore Sam Whitmore, eng at Cursor this is a special one! dive in: 00:25 Sam’s background 01:38 Family building while in frontier tech 11:50 Agent factories at cursor 15:46 Continual learning, agent self-awareness, memory, and user goal alignment 23:51 Building a thoughtful product culture at Cursor 27:00 Thinking about defensibility in 2026 30:01 Selling outside of the tech/X bubble 33:49 Kids & AI use 35:12 On reading fiction 38:32 On pregnancy, hormones, and building 42:58 On agent predictions for the next 6 months 46:35 Hiring at Cursor

ashe

59,539 görüntüleme • 2 ay önce

🌄 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
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

15,585 görüntüleme • 2 ay önce

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 görüntüleme • 5 ay önce

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 görüntüleme • 1 ay önce

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
49:06

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 görüntüleme • 29 gün önce