Many of the papers that make it passed the review process are [good but boring] papers that bring an improvement to a well-established technique... T… - Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

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Many of the papers that make it passed the review process are [good but boring] papers that bring an improvement to a well-established technique... Truly innovative papers rarely make it, largely because reviewers are unlikely to understand the point or foresee the potential of it.

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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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[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.”

Don't get fooled by people who claim to have a solution to Artificial General Intelligence, who claim to have AI systems that work "just like the human brain", or who claim to have figured out how the brain works (well, except if it's Geoff Hinton making the claim). Ask them what error rate they get on MNIST or ImageNet.

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