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Yann LeCun says there is no such thing as general intelligence Human intelligence is super-specialized for the physical world, and our feeling of generality is an illusion We only seem general because we can't imagine the problems we're blind to "the concept is complete BS"

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Haider.

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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 argues there is no such thing as “general intelligence” human or artificial. What we call general intelligence is just our ability to solve problems we’re wired for or can imagine. Humans are great at navigating social life and the real world but terrible at tasks like chess without training. Many animals outperform us in domains we’re blind to. So instead of aiming for "general AI," we should focus on machines mastering human-relevant domains.
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Yann LeCun argues there is no such thing as “general intelligence” human or artificial. What we call general intelligence is just our ability to solve problems we’re wired for or can imagine. Humans are great at navigating social life and the real world but terrible at tasks like chess without training. Many animals outperform us in domains we’re blind to. So instead of aiming for "general AI," we should focus on machines mastering human-relevant domains.

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📁 Yann LeCun, Chief AI Scientist at Meta, declares: "Amplifying human intelligence is the best thing we can do for humanity. It might have an effect on society similar to the appearance of the printing press in the 15th century... So maybe this is a new Renaissance we're going to see."

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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, 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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Yann LeCun says robotics could become a huge market, but the industry still doesn't know how to make humanoid robots useful The missing piece is AI that can understand the physical world and handle tasks with generality and adaptability "LLMs are useful, but for different problems"
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Yann LeCun says robotics could become a huge market, but the industry still doesn't know how to make humanoid robots useful The missing piece is AI that can understand the physical world and handle tasks with generality and adaptability "LLMs are useful, but for different problems"

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.𝔊𝔴𝔢𝔯𝔫 almost thinks there's no such thing as general intelligence. Humans and AIs just learn a large number of individual specialized tricks. In any given situation we're doing search over special cases, nothing more. What matters is just the number of individual tricks that we can search over - which is mostly determined by compute.
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.𝔊𝔴𝔢𝔯𝔫 almost thinks there's no such thing as general intelligence. Humans and AIs just learn a large number of individual specialized tricks. In any given situation we're doing search over special cases, nothing more. What matters is just the number of individual tricks that we can search over - which is mostly determined by compute.

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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 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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