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Every reasonable ML technique has some sort of mathematical guarantee. For example, neural nets have a finite VC dimension, hence they are consistent… - Yann LeCun
" "Every reasonable ML technique has some sort of mathematical guarantee. For example, neural nets have a finite VC dimension, hence they are consistent and have generalization bounds... every single bound is terrible and useless in practice. As long as your method minimizes some sort of objective function and has a finite capacity (or is properly regularized), you are on solid theoretical grounds.
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About Yann LeCun
Yann André LeCun (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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LeCun
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YL Cun
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Yann L. Cun
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Y. LeCun
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Y LeCun
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Yann André LeCun
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Yann André Le Cun
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Y. Le Cun
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Yann A. LeCun
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Yann Andre LeCun
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Yann Andre Le Cun
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Additional quotes by Yann LeCun
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.
[Can a commercial entity] produce Wikipedia? No. Wikipedia is crowdsourced because it works. So it's going to be the same for AI systems, they're going to have to be trained, or at least fine-tuned, with the help of everyone around the world. And people will only do this if they can contribute to a widely-available open platform.
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