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📁 Yann LeCun says language models do extract meaning, but only at a superficial level. Unlike humans, their intelligence is not grounded in physical reality or common sense. They answer many questions well, but break down when faced with new situations because they do not truly understand the world...

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Jon Hernandez

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49,318 görüntüleme • 8 ay önce •via X (Twitter)

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📁 Yann LeCun, Meta’s chief AI scientist, says the biggest unsolved problem in AI is not low level actions but abstract planning. Humans do not plan a trip from New York to Paris in milliseconds or muscle movements, they think in high level goals like going to the airport and taking a plane, then break them down. This is called hierarchical planning. Animals already do it. AI still cannot.
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📁 Yann LeCun, Meta’s chief AI scientist, says the biggest unsolved problem in AI is not low level actions but abstract planning. Humans do not plan a trip from New York to Paris in milliseconds or muscle movements, they think in high level goals like going to the airport and taking a plane, then break them down. This is called hierarchical planning. Animals already do it. AI still cannot.

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📁 Yann LeCun, Meta’s Chief AI Scientist and Turing Award winner, says today’s AI only looks intelligent. It is very good at manipulating language. But it still cannot understand the physical world, maintain real memory or truly plan. The next generation of AI will need to understand the world, not just talk about it.
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📁 Yann LeCun, Meta’s Chief AI Scientist and Turing Award winner, says today’s AI only looks intelligent. It is very good at manipulating language. But it still cannot understand the physical world, maintain real memory or truly plan. The next generation of AI will need to understand the world, not just talk about it.

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📁 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.
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📁 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.

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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 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 says that AGI does not exist and is a poorly defined concept built around an idealized view of human intelligence. He argues that humans are highly specialized, good at navigating the physical and social world, but weak at many other tasks. We seem general only because we can imagine the problems we are good at. For LeCun, AGI mistakes human limits for universal laws of intelligence.
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📁 Yann LeCun says that AGI does not exist and is a poorly defined concept built around an idealized view of human intelligence. He argues that humans are highly specialized, good at navigating the physical and social world, but weak at many other tasks. We seem general only because we can imagine the problems we are good at. For LeCun, AGI mistakes human limits for universal laws of intelligence.

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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 we need a completely new architecture?
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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 we need a completely new architecture?

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Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"
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Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"

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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"
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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"

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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.
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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.

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📁 Yann LeCun, an expert in artificial intelligence and a pioneer of deep learning, explains that current systems do not understand the physical world and lack real memory or reasoning. He adds that, despite the vast resources spent on training and refining them, they remain far behind the learning ability of a four-year-old child.
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📁 Yann LeCun, an expert in artificial intelligence and a pioneer of deep learning, explains that current systems do not understand the physical world and lack real memory or reasoning. He adds that, despite the vast resources spent on training and refining them, they remain far behind the learning ability of a four-year-old child.

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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
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📁 Yann LeCun, Chief AI Scientist at Meta, says language is not the peak of intelligence, it is the easy part. Predicting the next word is simple because language is made of finite symbols. The real world is continuous, noisy and chaotic, and even a cat navigates it better than our best models. True intelligence begins where text ends.
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📁 Yann LeCun, Chief AI Scientist at Meta, says language is not the peak of intelligence, it is the easy part. Predicting the next word is simple because language is made of finite symbols. The real world is continuous, noisy and chaotic, and even a cat navigates it better than our best models. True intelligence begins where text ends.

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