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Then we got very excited because now there was this very simple local-learning rule. On paper it looked just great. I mean, you could take this great… - Geoffrey Hinton
" "Then we got very excited because now there was this very simple local-learning rule. On paper it looked just great. I mean, you could take this great big network, and you could train up all the weights to do just the right thing, just with a simple local learning rule. It felt like we'd solved the problem . That must be how the brain works. I guess if it hadn't been for computer simulations, I'd still believe that, but the problem was the noise. It was just a very very slow learning rule. It got swamped by the noise because in the learning rule you take the difference between two noisy variables--two sampled correlations, both of which have sampling noise. The noise in the difference is terrible. I still think that's the nicest piece of theory I'll ever do. It worked out like a question in an exam where you put it all together and a beautiful answer pops out.
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About Geoffrey Hinton
Geoffrey Everest Hinton (born 6 December 1947) is an English-Canadian cognitive psychologist and computer scientist best known for his work on artificial neural networks. Since 2013, he divides his time working for Google (Google Brain) and the University of Toronto.
Biography information from Wikiquote
Also Known As
Alternative Names:
Geoffrey Everest Hinton
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Geoff Hinton
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Geoffrey E. Hinton
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G. E. Hinton
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Additional quotes by Geoffrey Hinton
If you or I learn something and want to transfer that knowledge to someone else, we can’t just send them a copy. But I can have 10,000 neural networks, each having their own experiences, and any of them can share what they learn instantly. That’s a huge difference. It’s as if there were 10,000 of us, and as soon as one person learns something, all of us know it. It’s a completely different form of intelligence. A new and better form of intelligence.
I first of all explained to him why it wouldn't work, based on an argument in Rosenblatt's book, which showed that essentially it was an algorithm that couldn't break symmetry... The next argument I gave him was that it would get stuck in local minima... We programmed a backpropagation net, and we tried to get this fast relearning. It didn't give fast relearning, so I made one of these crazy inferences that people make--which was, that backpropagation is not very interesting... [One year of trying and failing to scale up Boltzmann machines later] "Well, maybe, why don't I just program up that old idea of Rumelhart's, and see how well that works on some of the problems we've been trying?"... We had all the arguments: It's assuming that neurons can send real numbers to each other; of course they can only send bits to each other ; you have to have stochastic binary neurons; these real-valued neurons are totally unrealistic. It's ridiculous." So they just refused to work on it, not even to write a program, so I had to do it myself.
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I'm much more interested in how the brain does it. I'm only interested in applications just to prove that this is interesting stuff to keep the funding flowing. To do an application really well, you have to put your whole heart into it; you need to spend a year immersing yourself in what the application' s all about. I guess I've never really been prepared to do that.
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