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Computer scientist Judea Pearl: There are mathematical limits to LLMs that cannot be crossed by scaling alone LLMs don't discover world models from raw data; they merely summarize the interpretations humans have already written down "this path is not the way to get AGI"

Haider.

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622,707 просмотров • 5 месяцев назад •via X (Twitter)

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Yann LeCun argues that large language models (LLMs) cannot reach human-level or superintelligence just by scaling. He says the current LLM paradigm is hitting its limits. Many researchers are now exploring “agentic systems,” but building them on top of LLMs alone is flawed. LLMs can't plan actions well because they don’t truly understand or predict consequences. To get intelligent behavior, we need something fundamentally different.

Yann LeCun argues that large language models (LLMs) cannot reach human-level or superintelligence just by scaling. He says the current LLM paradigm is hitting its limits. Many researchers are now exploring “agentic systems,” but building them on top of LLMs alone is flawed. LLMs can't plan actions well because they don’t truly understand or predict consequences. To get intelligent behavior, we need something fundamentally different.

Wes Roth

71,803 просмотров • 4 месяцев назад

"we will not get to human-level AI by just scaling up LLMs... it's not going to happen" Meta AI chief scientist, Yann LeCun: scaling up LLMs alone will not lead to human-level AI ...what we’ll have instead are systems with huge memory and retrieval abilities, but still not able to invent solutions to new problems

"we will not get to human-level AI by just scaling up LLMs... it's not going to happen" Meta AI chief scientist, Yann LeCun: scaling up LLMs alone will not lead to human-level AI ...what we’ll have instead are systems with huge memory and retrieval abilities, but still not able to invent solutions to new problems

Haider.

123,610 просмотров • 1 год назад

Professor Judea Pearl — the pioneer who invented causal reasoning in AI — says scaling won't save us. "Mathematical limitations that are not crossable by scaling up." The brutal truth: LLMs aren’t learning how the world works. They are learning how we describe the world. This resonates with most biologists: Drug discovery is hitting the same wall. We have mountains of genomic data, but most AI models just find patterns in published papers — not in the raw biology itself. They're learning what scientists think causes disease, not what actually does. Pearl's causal revolution? That's how we move from "this gene correlates with cancer" to "this gene causes cancer" — and finally design drugs that work. Until then, we're building very expensive parrots.

Professor Judea Pearl — the pioneer who invented causal reasoning in AI — says scaling won't save us. "Mathematical limitations that are not crossable by scaling up." The brutal truth: LLMs aren’t learning how the world works. They are learning how we describe the world. This resonates with most biologists: Drug discovery is hitting the same wall. We have mountains of genomic data, but most AI models just find patterns in published papers — not in the raw biology itself. They're learning what scientists think causes disease, not what actually does. Pearl's causal revolution? That's how we move from "this gene correlates with cancer" to "this gene causes cancer" — and finally design drugs that work. Until then, we're building very expensive parrots.

Bo Wang

802,496 просмотров • 4 месяцев назад

4. AGI timeline is 2027-2034 Yann LeCun's exact words: "3-5 years to get world models working. Then scaling until human-level AI... within a decade or so." But it won't come from scaling LLMs.

4. AGI timeline is 2027-2034 Yann LeCun's exact words: "3-5 years to get world models working. Then scaling until human-level AI... within a decade or so." But it won't come from scaling LLMs.

Karl Mehta

96,079 просмотров • 9 месяцев назад

📁 Yann LeCun says that scaling models will not get us to human intelligence. He explains that the industry remains obsessed with making LLMs bigger, but that this path is fundamentally broken. It does not matter how many parameters we add or how many clusters we build, because the models only imitate language patterns. Human intelligence does not emerge from size, it emerges from understanding the world.

📁 Yann LeCun says that scaling models will not get us to human intelligence. He explains that the industry remains obsessed with making LLMs bigger, but that this path is fundamentally broken. It does not matter how many parameters we add or how many clusters we build, because the models only imitate language patterns. Human intelligence does not emerge from size, it emerges from understanding the world.

Jon Hernandez

156,331 просмотров • 6 месяцев назад

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

53,303 просмотров • 8 месяцев назад

My new podcast. Links below. First episode: Are LLMs creative? 0:25 LLM outputs are implicit in the weights 2:10 All programs are inherently limited 3:51 There is an AGI program 7:12 LLMs are not ‘just math’ 8:05 LLMs are more than just the training data 9:07 Can LLMs be as creative as DNA? 12:12 Q&A

My new podcast. Links below. First episode: Are LLMs creative? 0:25 LLM outputs are implicit in the weights 2:10 All programs are inherently limited 3:51 There is an AGI program 7:12 LLMs are not ‘just math’ 8:05 LLMs are more than just the training data 9:07 Can LLMs be as creative as DNA? 12:12 Q&A

Nivi

20,382 просмотров • 2 лет назад

Richard Sutton says that the current dominance of LLMs is a "momentary fixation" the real breakthroughs will come from scaling computation, not building AI systems based on how humans think they work. LLMs will not be the leading edge of AI for more than another decade, perhaps even half a decade.

Richard Sutton says that the current dominance of LLMs is a "momentary fixation" the real breakthroughs will come from scaling computation, not building AI systems based on how humans think they work. LLMs will not be the leading edge of AI for more than another decade, perhaps even half a decade.

Haider.

137,573 просмотров • 1 год назад

Francois Chollet: OpenAI has set back the progress towards AGI by 5-10 years because frontier research is no longer being published and LLMs are an offramp on the path to AGI

Francois Chollet: OpenAI has set back the progress towards AGI by 5-10 years because frontier research is no longer being published and LLMs are an offramp on the path to AGI

Tsarathustra

889,059 просмотров • 2 лет назад

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"

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.

50,251 просмотров • 8 месяцев назад

Computer Scientist, Stuart Russell, predicts 4 future predictions: 1) scaling up LLMs won’t lead to AGI 2) big AI labs already realize this and are exploring new methods, with AI likely to surpass humans within a decade 3) governments probably won’t act on AI safety until a major incident 4) best case: a disaster (like Chernobyl) will force action, worst case: we lose control permanently

Computer Scientist, Stuart Russell, predicts 4 future predictions: 1) scaling up LLMs won’t lead to AGI 2) big AI labs already realize this and are exploring new methods, with AI likely to surpass humans within a decade 3) governments probably won’t act on AI safety until a major incident 4) best case: a disaster (like Chernobyl) will force action, worst case: we lose control permanently

Haider.

85,146 просмотров • 1 год назад

Yann LeCun says you cannot build a reliable agentic system without a world model LLMs don't have world models. They can't predict the consequences of their actions before taking them "they just act, and whatever happens next is someone else's problem" Without that, it's not intelligence

Yann LeCun says you cannot build a reliable agentic system without a world model LLMs don't have world models. They can't predict the consequences of their actions before taking them "they just act, and whatever happens next is someone else's problem" Without that, it's not intelligence

Haider.

331,167 просмотров • 1 месяц назад

Geoffrey Hinton says AI systems can keep improving when they are not limited by human data Like AlphaZero can create endless training data through self-play, and math offers a similar path by letting models generate and test their own conjectures "LLMs could eventually work the same way"

Geoffrey Hinton says AI systems can keep improving when they are not limited by human data Like AlphaZero can create endless training data through self-play, and math offers a similar path by letting models generate and test their own conjectures "LLMs could eventually work the same way"

Haider.

81,165 просмотров • 1 месяц назад

David Deutsch says all computation from humans to LLMs shares the same fundamental power: the universal Turing machine The difference is only in programs and outputs. But AGI is different; we don't even understand it in principle. "so the true sign would be a theory, not a machine"

David Deutsch says all computation from humans to LLMs shares the same fundamental power: the universal Turing machine The difference is only in programs and outputs. But AGI is different; we don't even understand it in principle. "so the true sign would be a theory, not a machine"

Haider.

11,720 просмотров • 9 месяцев назад

A key argument for short timelines is that LLMs are basically baby AGIs who only need some small unhobblings in order to match humans. But why is this argument any different than saying AlphaZero is baby AGI? After all, it was superhuman in some domains, genuinely creative (remember move 37?), great at exploration, and could even generalize to new games. Why won't we look back in 10 years at LLMs as another AlphaZero-like step in the decades-long path to AGI, but not the thing itself? I asked AI researchers Sholto Douglas and Trenton Bricken: "You do need to crack general conceptual understand of the world. [With LLMs] you get the initial reward signal on tasks in the real world in harder to specify domains. All of a sudden you get access to a reward signal from the real world and you can start climbing it. AlphaZero didn't have the first rung to pull on."

A key argument for short timelines is that LLMs are basically baby AGIs who only need some small unhobblings in order to match humans. But why is this argument any different than saying AlphaZero is baby AGI? After all, it was superhuman in some domains, genuinely creative (remember move 37?), great at exploration, and could even generalize to new games. Why won't we look back in 10 years at LLMs as another AlphaZero-like step in the decades-long path to AGI, but not the thing itself? I asked AI researchers Sholto Douglas and Trenton Bricken: "You do need to crack general conceptual understand of the world. [With LLMs] you get the initial reward signal on tasks in the real world in harder to specify domains. All of a sudden you get access to a reward signal from the real world and you can start climbing it. AlphaZero didn't have the first rung to pull on."

Dwarkesh Patel

46,667 просмотров • 1 год назад

This guy literally breaks down the best way to use LLMs

This guy literally breaks down the best way to use LLMs

Eric Wang

156,833 просмотров • 5 месяцев назад

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 просмотров • 4 месяцев назад

DeepMind Principal Scientist Murray Shanahan calls LLMs "exotic mind-like entities" -- because we don't yet have the words for what they really are.

DeepMind Principal Scientist Murray Shanahan calls LLMs "exotic mind-like entities" -- because we don't yet have the words for what they really are.

vitrupo

83,873 просмотров • 1 год назад

The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.

The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.

Milk Road AI

130,417 просмотров • 2 месяцев назад

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

53,427 просмотров • 6 месяцев назад