字幕列表 影片播放 列印英文字幕 [MUSIC PLAYING] NICHOLAS THOMPSON: Hello, I'm Nicholas Thompson. I'm the editor in chief of "Wired." It is my honor today to get the chance to interview Geoffrey Hinton. They're a couple-- well, there are many things I love about him. But two that I'll just mention in the introduction. The first is that he persisted. He had an idea that he really believed in that everybody else said was bad. And he just kept at it. And it gives a lot of faith to everybody who has bad ideas, myself included. Then the second, as someone who spends half his life as a manager adjudicating job titles, I was looking at his job title before the introduction. And he has the most non pretentious job title in history. So please welcome Geoffrey Hinton, the engineering fellow at Google. [APPLAUSE] Welcome. GEOFFREY HINTON: Thank you. NICHOLAS THOMPSON: So nice to be here with you. All right, so let us start. 20 years ago when you write some of your early very influential papers, everybody starts to say, it's a smart idea, but we're not actually going to be able to design computers this way. Explain why you persisted, why you were so confident that you had found something important. GEOFFREY HINTON: So actually it was 40 years ago. And it seemed to me there's no other way the brain could work. It has to work by learning the strengths of connections. And if you want to make a device do something intelligent, you've got two options. You can program it, or it can learn. And we certainly weren't programmed. So we had to learn. So this had to be the right way to go. NICHOLAS THOMPSON: So explain, though-- well, let's do this. Explain what neural networks are. Most of the people here will be quite familiar. But explain the original insight and how it developed in your mind. GEOFFREY HINTON: So you have relatively simple processing elements that are very loosely models of neurons. They have connections coming in. Each connection has a weight on it. That weight can be changed to do learning. And what a neuron does is take the activities on the connections times the weights, adds them all up, and then decides whether to send an output. And if it gets a big enough sum, it sends an output. If the sum is negative, it doesn't send anything. That's about it. And all you have to do is just wire up a gazillion of those with a gazillion squared weights and just figure out how to change the weights, and it'll do anything. It's just a question of how you change the weights. NICHOLAS THOMPSON: So when did you come to understand that this was an approximate representation of how the brain works? GEOFFREY HINTON: Oh, it was always designed as that. NICHOLAS THOMPSON: Right. GEOFFREY HINTON: It was designed to be like how the brain works. NICHOLAS THOMPSON: But let me ask you this. So at some point in your career, you start to understand how the brain works. Maybe it was when you were 12. Maybe it was when you were 25. When do you make the decision that you will try to model computers after the brain? GEOFFREY HINTON: Sort of right away. That was the whole point of it. The whole idea was to have a learning device that learned like the brain like people think the brain learns by changing connection strengths. And this wasn't my idea. Turing had the same. Turing, even though he invented a lot of the basis of standard computer science, he believed that the brain was this unorganized device with random weights. And it would use reinforcement learning to change the connections. And it would learn everything, and he thought that was the best route to intelligence. NICHOLAS THOMPSON: And so you were following Turing's idea that the best way to make a machine is to model it after the human brain. This is how a human brain works. So let's make a machine like that. GEOFFREY HINTON: Yeah, it wasn't just Turing's idea. Lots of people thought that back then. NICHOLAS THOMPSON: All right, so you have this idea. Lots of people have this idea. You get a lot of credit. In the late '80s, you start to come to fame with your published work, is that correct? GEOFFREY HINTON: Yes. NICHOLAS THOMPSON: When is the darkest moment. When is the moment where other people who have been working who agreed with this idea from Turing start to back away and yet you continue to plunge ahead? GEOFFREY HINTON: There were always a bunch of people who kept believing in it, particularly in psychology. But among computer scientists, I guess in the '90s, what happened was data sets were quite small. And computers weren't that fast. And on small data sets, other methods like things called support vector machines, worked a little bit better. They didn't get confused by noise so much. And so that was very depressing because we developed back propagation in the '80s. We thought it would solve everything. And we were a bit puzzled about why it didn't solve everything. And it was just a question of scale. But we didn't really know that then. NICHOLAS THOMPSON: And so why did you think it was not working? GEOFFREY HINTON: We thought it was not working because we didn't have quite the right algorithms. We didn't have quite the right objective functions. I thought for a long time it's because we were trying to do supervised learning where you have to label data. And we should have been doing unsupervised learning, where you just learn from the data with no labels. It turned out it was mainly a question of scale. NICHOLAS THOMPSON: Oh, that's interesting. So the problem was you didn't have enough data. You thought you had the right amount of data, but you hadn't labeled it correctly. So you just misidentified the problem? GEOFFREY HINTON: I thought that using labels at all was a mistake. You would do most of your learning without making any use of labels just by trying to model the structure in the data. I actually still believe that. I think as computers get faster, for any given size data set, if you make computers fast enough, you're better off doing unsupervised learning. And once you've done the unsupervised learning, you'll be able to learn from fewer labels. NICHOLAS THOMPSON: So in the 1990s, you're continuing with your research. You're in academia. You are still publishing, but it's not coming to a claim. You aren't solving big problems. When do you start-- well, actually, was there ever a moment where you said, you know what, enough of this. I'm going to go try something else? GEOFFREY HINTON: Not really. NICHOLAS THOMPSON: Not that I'm going to go sell burgers, but I'm going to figure out a different way of doing this. You just said we're going to keep doing deep learning. GEOFFREY HINTON: Yes, something like this has to work. I mean, the connections in the brain are learning somehow. And we just have to figure it out. And probably there's a bunch of different ways of learning connection strengths. The brains using one of them. There may be other ways of doing it. But certainly, you have to have something that can learn these connection strengths.