[Large language models] require enormous amounts of data to reach a level of intelligence that is not that great in the end. And they can't really re… - Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

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[Large language models] require enormous amounts of data to reach a level of intelligence that is not that great in the end. And they can't really reason. They can't plan anything other than things they’ve been trained on. So they're not a road towards what people call “AGI.”

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About Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

Yann André LeCun (originally spelled Le Cun; born 8 July 1960) is a French-American computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta. LeCun received the 2018 Turing Award, together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning. The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning.

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Alternative Names: #258 – Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning
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My problem with sticking too close to nature is that it's like "cargo-cult" science... I don't use neural nets because they look like the brain. I use them because they are a convenient way to construct parameterized non-linear functions with good properties. But I did get inspiration from the architecture of the visual cortex to build convolutional nets.

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