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Yann LeCun says language isn’t intelligence. Predicting text doesn’t mean understanding reality. The real world is messy, physical, and causal and today’s LLMs barely touch that. The next leap is Physical AI: world models, cause and effect, real planning. Do you think LLMs can evolve into this, or do... show more

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76,154 Aufrufe • vor 5 Monaten •via X (Twitter)

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Yann LeCun says the real world is far more complex than the world of language LLMs can accumulate knowledge, but they fail with high-dimensional, continuous, noisy sensory data "the next revolution is physical AI" Systems that can truly plan, reason, and understand the physical environment

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Yann LeCun (Yann LeCun ) explains why LLMs are so limited in terms of real-world intelligence. Says the biggest LLM is trained on about 30 trillion words, which is roughly 10 to the power 14 bytes of text. That sounds huge, but a 4 year old who has been awake about 16,000 hours has also taken in about 10 to the power 14 bytes through the eyes alone. So a small child has already seen as much raw data as the largest LLM has read. But the child’s data is visual, continuous, noisy, and tied to actions: gravity, objects falling, hands grabbing, people moving, cause and effect. From this, the child builds an internal “world model” and intuitive physics, and can learn new tasks like loading a dishwasher from a handful of demonstrations. LLMs only see disconnected text and are trained just to predict the next token. So they get very good at symbol patterns, exams, and code, but they lack grounded physical understanding, real common sense, and efficient learning from a few messy real-world experiences. --- From 'Pioneer Works' YT channel (link in comment)

Yann LeCun (Yann LeCun ) explains why LLMs are so limited in terms of real-world intelligence. Says the biggest LLM is trained on about 30 trillion words, which is roughly 10 to the power 14 bytes of text. That sounds huge, but a 4 year old who has been awake about 16,000 hours has also taken in about 10 to the power 14 bytes through the eyes alone. So a small child has already seen as much raw data as the largest LLM has read. But the child’s data is visual, continuous, noisy, and tied to actions: gravity, objects falling, hands grabbing, people moving, cause and effect. From this, the child builds an internal “world model” and intuitive physics, and can learn new tasks like loading a dishwasher from a handful of demonstrations. LLMs only see disconnected text and are trained just to predict the next token. So they get very good at symbol patterns, exams, and code, but they lack grounded physical understanding, real common sense, and efficient learning from a few messy real-world experiences. --- From 'Pioneer Works' YT channel (link in comment)

Rohan Paul

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Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)

Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)

Rohan Paul

104,460 Aufrufe • vor 6 Monaten

📁 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 Aufrufe • vor 6 Monaten

Ilya on LLMs understanding the world: "predicting the next token well, means that you understand the underlying reality that let to the creation of that token" Seem like the opposite view of Yann.

Ilya on LLMs understanding the world: "predicting the next token well, means that you understand the underlying reality that let to the creation of that token" Seem like the opposite view of Yann.

Lior Alexander

1,222,086 Aufrufe • vor 2 Jahren

Yann LeCun says LLMs are intrinsically unsafe because they cannot be made fully reliable They can still hallucinate or take agentic actions without predicting the consequences properly "coding works because you can verify the output, but real-world tasks are harder to verify"

Yann LeCun says LLMs are intrinsically unsafe because they cannot be made fully reliable They can still hallucinate or take agentic actions without predicting the consequences properly "coding works because you can verify the output, but real-world tasks are harder to verify"

Haider.

33,279 Aufrufe • vor 1 Monat

Yann LeCun just exposed AI’s fundamental flaw. We’re celebrating systems that can’t do what insects do effortlessly. LeCun: “The biggest difficulty is not to get fooled into thinking that a computer system is intelligent simply because it can manipulate language.” Language feels like intelligence because we experience it as the highest form of human thought. So when a machine produces fluent, articulate, convincing text, the instinct is to conclude it understands. It doesn’t. LeCun: “It turns out the real world is much, much more complicated.” Language is actually the easy part. A sequence of discrete symbols with a finite number of possibilities. Predicting the next word is a tractable mathematical problem. Impressive at scale. Not understanding. Pattern matching in symbol space. The real world is something else entirely. A high-dimensional, continuous, noisy signal that changes every millisecond in ways no text corpus can capture. Physical reality doesn’t come in tokens. LeCun: “Which your house cat is perfectly able to deal with. But not computers yet.” This is the Moravec paradox. The things that feel hard to humans: writing essays, solving equations, passing bar exams. Computationally straightforward. The things that feel trivially easy: walking across a room, catching a falling object, folding a shirt. Extraordinarily difficult for machines. Your house cat navigates a complex three-dimensional physical environment in real time. Predicts trajectories. Adjusts to surprises. Understands cause and effect through direct interaction with the world. The most powerful AI systems ever built cannot do what your cat does before breakfast. That’s not a minor gap. That’s the entire frontier. Language is the easy problem that looks hard to humans. The physical world is the hard problem that looks easy because evolution solved it billions of years ago. We’re pouring hundreds of billions into making language models marginally better at the simple problem. The actual intelligence problem remains unsolved. LeCun has spent fifteen years on this. Not making chatbots more fluent. Giving machines the ability to understand, predict, and interact with physical reality the way animals do instinctively. The benchmark that matters isn’t passing a bar exam. It’s folding a shirt. Loading a dishwasher. Navigating an unfamiliar room without a map. We built systems that can write your dissertation before we built systems that can tie your shoes. That’s where AI actually is. Everything else is autocomplete at scale.

Yann LeCun just exposed AI’s fundamental flaw. We’re celebrating systems that can’t do what insects do effortlessly. LeCun: “The biggest difficulty is not to get fooled into thinking that a computer system is intelligent simply because it can manipulate language.” Language feels like intelligence because we experience it as the highest form of human thought. So when a machine produces fluent, articulate, convincing text, the instinct is to conclude it understands. It doesn’t. LeCun: “It turns out the real world is much, much more complicated.” Language is actually the easy part. A sequence of discrete symbols with a finite number of possibilities. Predicting the next word is a tractable mathematical problem. Impressive at scale. Not understanding. Pattern matching in symbol space. The real world is something else entirely. A high-dimensional, continuous, noisy signal that changes every millisecond in ways no text corpus can capture. Physical reality doesn’t come in tokens. LeCun: “Which your house cat is perfectly able to deal with. But not computers yet.” This is the Moravec paradox. The things that feel hard to humans: writing essays, solving equations, passing bar exams. Computationally straightforward. The things that feel trivially easy: walking across a room, catching a falling object, folding a shirt. Extraordinarily difficult for machines. Your house cat navigates a complex three-dimensional physical environment in real time. Predicts trajectories. Adjusts to surprises. Understands cause and effect through direct interaction with the world. The most powerful AI systems ever built cannot do what your cat does before breakfast. That’s not a minor gap. That’s the entire frontier. Language is the easy problem that looks hard to humans. The physical world is the hard problem that looks easy because evolution solved it billions of years ago. We’re pouring hundreds of billions into making language models marginally better at the simple problem. The actual intelligence problem remains unsolved. LeCun has spent fifteen years on this. Not making chatbots more fluent. Giving machines the ability to understand, predict, and interact with physical reality the way animals do instinctively. The benchmark that matters isn’t passing a bar exam. It’s folding a shirt. Loading a dishwasher. Navigating an unfamiliar room without a map. We built systems that can write your dissertation before we built systems that can tie your shoes. That’s where AI actually is. Everything else is autocomplete at scale.

Dustin

284,134 Aufrufe • vor 4 Monaten