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We got quite a few applications, and one of these applications I couldn't decide if the guy was a total flake or not... He wrote a spiel about the ma… - Geoffrey Hinton
" "We got quite a few applications, and one of these applications I couldn't decide if the guy was a total flake or not... He wrote a spiel about the machine code of the brain and how it was stochastic, and so the brain had this stochastic machine code. It looked like rubbish to me, but the guy obviously had some decent publications and was in a serious place, so I didn't know what to make of him... David Marr said, "Oh yes, I've met him." I said, "So what did you think of him?" David Marr said, 'Well, he was a bit weird, but he was definitely, smart." So I thought, OK, so we'll invite him. That guy was Terry Sejnowski, of course... the book was one of the first books to come out about neural networks for a long time. It was the beginning of the end of the drought... both Dave Rumelhart and Terry said that from their point of view, just getting all these people interested and in the same room was a real legitimizing breakthrough.
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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
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.
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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