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Francois Chollet says LLMs aren't enough for human-like continual learning They store skills as vector programs learned via gradient descent — not efficient, not adaptive True AGI needs to learn from experience and generalize fast "LLMs can be part of AGI, but not the solution"

Haider.

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50,251 次观看 • 10 个月前 •via X (Twitter)

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Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."

Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."

Wes Roth

38,833 次观看 • 7 个月前

this was the most important bit of the ilya interview imo. humans are not AGI, and LLMs won’t become like humans with more scaling. humans lack a huge amount of knowledge and have insanely good “continual learning” powered by extremely good reward functions (except for addiction) that guide them. and so it’s more about the learning rather than knowledge. the term AGI was coined as a response to the narrow AI which was only capable of intelligence in a specific domain (chess, game, code, etc) but General intelligence is able to learn different topics because that’s what “learning” is about. pre-training was thought to be the solution to reaching AGI because you train AI on so many different topics. but they have “overshot” that target. amazing perspective.

this was the most important bit of the ilya interview imo. humans are not AGI, and LLMs won’t become like humans with more scaling. humans lack a huge amount of knowledge and have insanely good “continual learning” powered by extremely good reward functions (except for addiction) that guide them. and so it’s more about the learning rather than knowledge. the term AGI was coined as a response to the narrow AI which was only capable of intelligence in a specific domain (chess, game, code, etc) but General intelligence is able to learn different topics because that’s what “learning” is about. pre-training was thought to be the solution to reaching AGI because you train AI on so many different topics. but they have “overshot” that target. amazing perspective.

ℏεsam

53,444 次观看 • 9 个月前

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.

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 次观看 • 8 个月前

Geoffrey Hinton says LLMs work like the human brain, not like normal computer software They aren't built on explainable lines of code, but rather on trillions of connection strengths that learn from data "it's largely a mystery" We don't know what the individual neurons are doing

Geoffrey Hinton says LLMs work like the human brain, not like normal computer software They aren't built on explainable lines of code, but rather on trillions of connection strengths that learn from data "it's largely a mystery" We don't know what the individual neurons are doing

Haider.

35,431 次观看 • 7 个月前

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

Chris

15,578 次观看 • 4 个月前

.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

.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

3,085,311 次观看 • 11 个月前

Google DeepMind's Logan Kilpatrick says AGI will be a product experience. Not a model. His bet: whoever nails memory + context around decent model at a product level wins. Users will suddenly feel like they're talking to AGI. Not from capability breakthrough, but experience breakthrough. At some point, the AGI race is actually a UX problem.

Google DeepMind's Logan Kilpatrick says AGI will be a product experience. Not a model. His bet: whoever nails memory + context around decent model at a product level wins. Users will suddenly feel like they're talking to AGI. Not from capability breakthrough, but experience breakthrough. At some point, the AGI race is actually a UX problem.

vitrupo

177,073 次观看 • 1 年前

Google DeepMind say true AGI will “reason, adapt, and learn continuously,” and they estimate it’s about 5-10 years away. Meta’s chief scientist Yann LeCun likewise views continual learning as a key pillar of human level AI and is optimistic about achieving it by around 2030 Currently, large language models only improve via offline updates (fine-tuning or reinforcement learning from human feedback) rather than meta learning or modular networks that update themselves gradually. Thankfully All major research teams (OpenAI, DeepMind, Anthropic, Meta, etc.) are actively working on these directions, aiming for models that continuously learn from mistakes as they interact a capability they believe will emerge as we approach true AGI later this decade 2029

Google DeepMind say true AGI will “reason, adapt, and learn continuously,” and they estimate it’s about 5-10 years away. Meta’s chief scientist Yann LeCun likewise views continual learning as a key pillar of human level AI and is optimistic about achieving it by around 2030 Currently, large language models only improve via offline updates (fine-tuning or reinforcement learning from human feedback) rather than meta learning or modular networks that update themselves gradually. Thankfully All major research teams (OpenAI, DeepMind, Anthropic, Meta, etc.) are actively working on these directions, aiming for models that continuously learn from mistakes as they interact a capability they believe will emerge as we approach true AGI later this decade 2029

Chris

284,206 次观看 • 11 个月前

Journalist Karen Hao says AGI is not scientifically grounded; it's a quasi-religious movement in Silicon Valley Since we still don't agree on what human intelligence is, companies like OpenAI can shape the definition of AI to suit their needs "the belief that AGI is coming soon is faith, not evidence"

Journalist Karen Hao says AGI is not scientifically grounded; it's a quasi-religious movement in Silicon Valley Since we still don't agree on what human intelligence is, companies like OpenAI can shape the definition of AI to suit their needs "the belief that AGI is coming soon is faith, not evidence"

Haider.

412,020 次观看 • 7 个月前

THE SMARTEST MAN IN AI EXPOSED THE ENTIRE AGI NARRATIVE AS A LIE. GOOGLE DEEPMIND CEO DEMIS HASSABIS SAYS TODAY'S AI STILL CAN'T DO THE ONE THING THAT WOULD PROVE TRUE GENERAL INTELLIGENCE: CREATE A TRULY ORIGINAL IDEA. HIS TEST? TRAIN AN AI ONLY ON HUMAN KNOWLEDGE FROM 1901. THEN ASK IT TO DISCOVER EINSTEIN'S THEORY OF RELATIVITY ON ITS OWN. NO INTERNET. NO MEMORIZED ANSWER. NO HUMAN SOLUTION TO COPY. JUST PURE REASONING. NOT A SINGLE AI TODAY CAN DO IT. AGI MAY BE CLOSER THAN EVER... BUT HASSABIS SAYS WE'RE NOT THERE YET 🤯

THE SMARTEST MAN IN AI EXPOSED THE ENTIRE AGI NARRATIVE AS A LIE. GOOGLE DEEPMIND CEO DEMIS HASSABIS SAYS TODAY'S AI STILL CAN'T DO THE ONE THING THAT WOULD PROVE TRUE GENERAL INTELLIGENCE: CREATE A TRULY ORIGINAL IDEA. HIS TEST? TRAIN AN AI ONLY ON HUMAN KNOWLEDGE FROM 1901. THEN ASK IT TO DISCOVER EINSTEIN'S THEORY OF RELATIVITY ON ITS OWN. NO INTERNET. NO MEMORIZED ANSWER. NO HUMAN SOLUTION TO COPY. JUST PURE REASONING. NOT A SINGLE AI TODAY CAN DO IT. AGI MAY BE CLOSER THAN EVER... BUT HASSABIS SAYS WE'RE NOT THERE YET 🤯

Vivek Sen

58,397 次观看 • 16 天前

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Haider.

347,799 次观看 • 3 个月前

The people of Haiti may be people of African descent but for heaven's sake, they are members of the human race like us and they deserve as much, not less - William Ruto

The people of Haiti may be people of African descent but for heaven's sake, they are members of the human race like us and they deserve as much, not less - William Ruto

Kenyans.co.ke

107,246 次观看 • 11 个月前

Why haven’t LLMs done new science yet? One reason from Periodic Labs cofounder Ekin Dogus Cubuk: science is iterative. LLMs haven’t been able to iterate experimentally yet. “ LLMs can be very smart, but if they're not iterating on science, they won't discover science. To be honest, humans won't either. You put a human in a room without any chance to iterate on something, they won't discover anything important. So we feel like the important thing to teach these LLMs is the method of scientific inquiry.”

Why haven’t LLMs done new science yet? One reason from Periodic Labs cofounder Ekin Dogus Cubuk: science is iterative. LLMs haven’t been able to iterate experimentally yet. “ LLMs can be very smart, but if they're not iterating on science, they won't discover science. To be honest, humans won't either. You put a human in a room without any chance to iterate on something, they won't discover anything important. So we feel like the important thing to teach these LLMs is the method of scientific inquiry.”

a16z

53,361 次观看 • 11 个月前

“The thing that happened with AGI and pretraining is that in some sense they overshot the target. You will realize that a human being is not an AGI. Because a human being lacks a huge amount of knowledge. Instead, we rely on continual learning. If I produce a super intelligent 15 -year -old, they don't know very much at all. A great student, very eager. [You can say,] ‘You go and be a programmer. You go and be a doctor. Go and learn.’ So you could imagine that the deployment itself will involve some kind of a learning trial and error period. It's a process as opposed to, you drop the finished thing.” Ilya Sutskever

“The thing that happened with AGI and pretraining is that in some sense they overshot the target. You will realize that a human being is not an AGI. Because a human being lacks a huge amount of knowledge. Instead, we rely on continual learning. If I produce a super intelligent 15 -year -old, they don't know very much at all. A great student, very eager. [You can say,] ‘You go and be a programmer. You go and be a doctor. Go and learn.’ So you could imagine that the deployment itself will involve some kind of a learning trial and error period. It's a process as opposed to, you drop the finished thing.” Ilya Sutskever

Dwarkesh Patel

661,619 次观看 • 9 个月前

Culture was possibly the key breakthrough in human history. Once we could share ideas, learning became much more efficient, and complex technologies became possible. LLMs haven't yet built their own culture or organisations. But it seems plausible they will. On the podcast last year, Andrej Karpathy speculated about what it will take for LLMs to build their own culture, and why they're not there yet.

Culture was possibly the key breakthrough in human history. Once we could share ideas, learning became much more efficient, and complex technologies became possible. LLMs haven't yet built their own culture or organisations. But it seems plausible they will. On the podcast last year, Andrej Karpathy speculated about what it will take for LLMs to build their own culture, and why they're not there yet.

Dwarkesh Patel

27,463 次观看 • 3 个月前

Without World Models, There Is No AGI. Google Just Proved It. If AGI ever happens, it will not come from bigger chatbots alone. From the very start of this interview, one thing is crystal clear: without world models, we will never reach AGI. And right now, Google is leading with its world simulator Genie 3. Here is the core of what Demis Hassabis explains in this conversation: • World models are the missing core of AGI Hassabis says his deepest long term focus has always been world models and simulations. Not just language. Not just prediction. Actual internal simulations of reality. • LLMs are impressive, but incomplete Language models understand more about the world than expected because human language encodes a lot of reality. Still, language is only a shadow of the real thing. • What text can never fully teach Reality includes things text struggles to express: •3D space and spatial dynamics •Physical causality and mechanics •Sensorimotor experience like movement, force, smell, or balance • Experience beats description To close the gap, AI must learn from interaction and experience, not just static text. That is how you build an internal world simulator. • Why Genie 3 matters With Google DeepMind pushing systems like Genie 3, AI starts to model reality itself, not just talk about it. • Robots and real world assistants depend on this True robotics, smart glasses, and universal assistants require AI that understands the physical world you live in, not just your screen. Bottom line: AGI will not emerge from better text prediction. It will emerge from systems that can simulate, predict, and understand reality itself. Right now, Google is clearly ahead on that path. Curious what you think. Are world models the real AGI unlock, or just another stepping stone?

Without World Models, There Is No AGI. Google Just Proved It. If AGI ever happens, it will not come from bigger chatbots alone. From the very start of this interview, one thing is crystal clear: without world models, we will never reach AGI. And right now, Google is leading with its world simulator Genie 3. Here is the core of what Demis Hassabis explains in this conversation: • World models are the missing core of AGI Hassabis says his deepest long term focus has always been world models and simulations. Not just language. Not just prediction. Actual internal simulations of reality. • LLMs are impressive, but incomplete Language models understand more about the world than expected because human language encodes a lot of reality. Still, language is only a shadow of the real thing. • What text can never fully teach Reality includes things text struggles to express: •3D space and spatial dynamics •Physical causality and mechanics •Sensorimotor experience like movement, force, smell, or balance • Experience beats description To close the gap, AI must learn from interaction and experience, not just static text. That is how you build an internal world simulator. • Why Genie 3 matters With Google DeepMind pushing systems like Genie 3, AI starts to model reality itself, not just talk about it. • Robots and real world assistants depend on this True robotics, smart glasses, and universal assistants require AI that understands the physical world you live in, not just your screen. Bottom line: AGI will not emerge from better text prediction. It will emerge from systems that can simulate, predict, and understand reality itself. Right now, Google is clearly ahead on that path. Curious what you think. Are world models the real AGI unlock, or just another stepping stone?

VraserX e/acc

23,784 次观看 • 8 个月前

David Chalmers says we don't know what makes a system conscious, not in brains, not in AIs The hard problem applies to both. If both compute, why couldn't both be conscious? LLMs can look very human, but they're trained in ways humans never are "they imitate us, but we don't know what's missing, or what consciousness needs"

David Chalmers says we don't know what makes a system conscious, not in brains, not in AIs The hard problem applies to both. If both compute, why couldn't both be conscious? LLMs can look very human, but they're trained in ways humans never are "they imitate us, but we don't know what's missing, or what consciousness needs"

Haider.

26,951 次观看 • 7 个月前