Geoffrey Hinton Quotes

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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.

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.

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.

The paradigm for intelligence was logical reasoning, and the idea of what an internal representation would look like was it would be some kind of symbolic structure. That has completely changed with these big neural nets.

In the brain, you have connections between the neurons called synapses, and they can change. All your knowledge is stored in those synapses.

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.

In science, you can say things that seem crazy, but in the long run, they can turn out to be right. We can get really good evidence, and in the end, the community will come around.

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.

I feel slightly embarrassed by being called 'the godfather.'

I have a Reagan-like ability to believe in my own data.

In a sensibly organised society, if you improve productivity, there is room for everybody to benefit.

Now that neural nets work, industry and government have started calling neural nets AI. And the people in AI who spent all their life mocking neural nets and saying they'd never do anything are now happy to call them AI and try and get some of the money.

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.'

The brain sure as hell doesn't work by somebody programming in rules.

I am betting on Google's team to be the epicenter of future breakthroughs.

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.

We want to take AI and CIFAR to wonderful new places, where no person, no student, no program has gone before.

I think the way we're doing computer vision is just wrong.

My view is we should be doing everything we can to come up with ways of exploiting the current technology effectively.

The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?

Everybody right now, they look at the current technology, and they think, 'OK, that's what artificial neural nets are.' And they don't realize how arbitrary it is. We just made it up! And there's no reason why we shouldn't make up something else.

My main interest is in trying to find radically different kinds of neural nets.

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