(playful music) This is Geoff Hinton. Because of a back condition,
he hasn't been able to sit down for more than 12 years. I hate standing, I would
much rather sit down, but if I sit down I have
a disc that comes out. So.
Okay. Well, at least now standing
desks are fashionable and-- Yeah, but I was ahead. (laughs) I was standing when they
weren't fashionable. Since he can't sit in a car or on a bus, Hinton walks everywhere. (playful music) The walk says a lot about
Hinton and his resolve. For nearly 40 years,
Hinton has been trying to get computers to learn like people do. A quest almost everyone thought was crazy, or at least hopeless. Right up until the moment
it revolutionized the field. Google thinks this is the
future of the company. Amazon thinks this is the
future of the company. Apple thinks it's future of the company. My own department thinks this
stuff's probably nonsense and we shouldn't be doing any more of it. (laughs) So, I talked everybody into
it except my own department. (playful music) You obviously grew up in the UK, and you had this very prestigious family full of famous
mathematicians and economists and, I was curious what
that was like for you. Yeah, there was a lot of pressure. I think by the time I was about seven, I realized I was gonna have to get a Ph.D. (laughing) Did you rebel against that? Or you went along with it? I dropped out every so often. I became a carpenter for a while. Geoff Hinton pretty
early on became obsessed with this idea of figuring
out how the mind works. He started off getting into physiology, the anatomy of how the brain works, then he got into psychology,
and then finally, he settled on more of a
computer science approach to modeling the brain, and got
into artificial intelligence. My feeling is, if you want to understand a really complicated device like a brain, you should build one. I mean, you can look at cars, and you could think you
could understand cars. When you try to build a
car, you suddenly discover then there's this stuff that
has to go under the hood, otherwise it doesn't work. Yeah. (laughs) As Geoff was starting to
think about these ideas, he got inspired by some AI
researchers across the pond. Specifically, this guy: Frank Rosenblatt. Rosenblatt, in the the late 1950s, developed what he called a perceptron, and it was a neural
network, a computing system that would mimic the brain. The basic idea is a collection of small units, called neurons. These are little computing units, but they're actually modeled on the way that the human brain
does it's computation. They take their incoming data
like we do from our senses, and they actually learn, so the neural net can learn to make decisions over time. Rosenblatts's hope was that you could feed a neural network a bunch of data, like pictures of men and women, and it would eventually
learn how to tell them apart. Just like humans do. There was just one problem:
it didn't work very well. Rosenblatt, his neural
network was the single layer of neurons, and it was
limited in what it could do. Extremely limited. And a colleague of his
wrote a book in the late 60s that showed these limitations. And, it kind of put the
whole area of research into a deep freeze for a good 10 years. No one wanted to work in this area. They were sure it would never work. Well, almost no one. It was just obvious to me that everything was about ready to go. The brain's a big neural network, and so, it has to be that
stuff like this can work, because it works in our brains. There's just never any doubt about that. And what do you think
that it was inside of you that kept you wanting to pursue this when everyone else was giving up? Just, that you thought it was
the right direction to go? No, that everyone else was wrong. Okay. (laughs) (upbeat music) Hinton decides he's got an idea of how these neural nets might work, and he's going to pursue
it no matter what. For a little while, he's bouncing around research institutions in the US. He kind of gets fed up that most of them were funded by the Defense Department, and he starts looking for
somewhere else he can go. I didn't want to take
Defense Department money. I sort of didn't like
the idea that this stuff was going to be used for purposes that I didn't think were good. He suddenly hears that
Canada might be interested in funding artificial intelligence. And that was very attractive, that I could go off to
this civilized town, and just get on with it. So I came to the University of Toronto. And then in the mid-80s, we discovered how to make more complicated neural nets so they could solve those problems that the simple ones couldn't solve. He and his collaborators developed a multi-layered neural
network, a deep neural network. And this started to work in a lot of ways. Using a neural network, a guy named Dean Pomerleau built a
self-driving car in the late 80s. And it drove on public roads. Yann LeCun, in the 90s, built a system that could recognize handwritten digits, and this ended up being used commercially. But again, they hit a ceiling. (upbeat music) It didn't work quite well enough, because we didn't have enough data, we didn't have enough compute power. And people in AI and computer science, decided that neural networks were wishful thinking, basically. So, it was a big disappointment. Through the 90s, into the 2000s, Geoff was one of only a
handful of people on the planet who were still pursuing this technology. He would show up at academic conferences and be banished to the back rooms, he was treated as, really like a pariah. Was there like a time when you thought this just wasn't going to work? And you had some self-doubt? I mean there were many
times when I thought, "I'm not going to make this work." (laughs) But Geoff was consumed by
this and couldn't stop. He just kept pursuing the idea that computers could learn. Until about 2006, when
the world catches up to Hinton's ideas. (upbeat music) Computers are now a lot faster. And now, it's behaving like I thought it would behave in the mid-80s. It's solving everything. The arrival of super-fast chips, and the massive amounts of
data produced on the internet gave Hinton's algorithms a magical boost. Suddenly, computers could
identify what was in an image. Then, they could recognize speech and translate from one
language to another. By 2012, words like neural
nets and machine learning were popping up on the front page of the New York Times. You have to go all these years, and then all of a sudden, in
a the span of a few months, it just takes off. Did it finally feel like aha, the world has finally come to my vision? It was sort of a relief that people finally came to their senses. (laughs) (gentle music) For Hinton, this was
clearly a redemptive moment after decades of toil. And for Canada, it meant
something even bigger. Hinton and his students
put the country on the map as an AI superpower, something no one, and no computer, could ever have predicted.
