Geoffrey Hinton Quotes
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I think the way we're doing computer vision is just wrong.
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All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.
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I had a stormy graduate career, where every week we would have a shouting match. I kept doing deals where I would say, 'Okay, let me do neural nets for another six months, and I will prove to you they work.' At the end of the six months, I would say, 'Yeah, but I am almost there. Give me another six months.'
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I get very excited when we discover a way of making neural networks better - and when that's closely related to how the brain works.
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A deep-learning system doesn't have any explanatory power.
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I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain. That is the goal I have been pursuing. We are making progress, though we still have lots to learn about how the brain actually works.
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My view is we should be doing everything we can to come up with ways of exploiting the current technology effectively.
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The pooling operation used in convolutional neural networks is a big mistake, and the fact that it works so well is a disaster.
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My father was an entomologist who believed in continental drift. In the early '50s, that was regarded as nonsense. It was in the mid-'50s that it came back. Someone had thought of it 30 or 40 years earlier named Alfred Wegener, and he never got to see it come back.
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Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology: You're trying to make computers that can perceive and act and adapt like animals.
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I am scared that if you make the technology work better, you help the NSA misuse it more. I'd be more worried about that than about autonomous killer robots.
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Most people in AI, particularly the younger ones, now believe that if you want a system that has a lot of knowledge in, like an amount of knowledge that would take millions of bits to quantify, the only way to get a good system with all that knowledge in it is to make it learn it. You are not going to be able to put it in by hand.
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We want to take AI and CIFAR to wonderful new places, where no person, no student, no program has gone before.
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Making everything more efficient should make everybody happier.
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My main interest is in trying to find radically different kinds of neural nets.
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The brain has about ten thousand parameters for every second of experience. We do not really have much experience about how systems like that work or how to make them be so good at finding structure in data.
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Any new technology, if it's used by evil people, bad things can happen. But that's more a question of the politics of the technology.
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In A.I., the holy grail was how do you generate internal representations.
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I am betting on Google's team to be the epicenter of future breakthroughs.
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The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?
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We now think of internal representation as great big vectors, and we do not think of logic as the paradigm for how to get things to work. We just think you can have these great big neural nets that learn, and so, instead of programming, you are just going to get them to learn everything.
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Machines can do things cheaper and better. We're very used to that in banking, for example. ATM machines are better than tellers if you want a simple transaction. They're faster, they're less trouble, they're more reliable, so they put tellers out of work.
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Take any old classification problem where you have a lot of data, and it's going to be solved by deep learning. There's going to be thousands of applications of deep learning.
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Deep learning is already working in Google search and in image search; it allows you to image-search a term like 'hug.' It's used to getting you Smart Replies to your Gmail. It's in speech and vision. It will soon be used in machine translation, I believe.