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  • (leaves rustling)

  • As fall breaks out in Canada,

  • I'm reminded of all the beauty, innocence and gun-free fun

  • available from our neighbors to the North.

  • (majestic music)

  • There's the majesty of Toronto,

  • vast hockey rinks,

  • spectacular batches of poutine,

  • and gallons of maple syrup that you can chug openly

  • and guilt-free for this maple syrup is pure and nourishing.

  • The changing of the seasons also happens to be

  • the perfect time to encounter one of Canada's

  • most prized creatures,

  • the artificial intelligence nerd.

  • (resolute music)

  • Not too long ago, these beings were rare

  • and hidden away in university dungeons.

  • But today they flourish.

  • They primp with instinctual grace.

  • They wave their hands impressively

  • to assert their intellectual dominance.

  • They carb-load like overpaid professional athletes.

  • And this makes some sense because they're among

  • the best paid professionals in the world.

  • Together these creatures did something truly remarkable.

  • Without anyone paying much notice,

  • they gave birth to an AI revolution.

  • They turned Canada, yes, Canada,

  • into one of the great AI superpowers.

  • This is the story of how all this came to be.

  • It's the story of one nation's quest

  • to teach computers to think like humans.

  • It's the story of what this science experiment will mean

  • for all our lives and for the future of the human species.

  • So if you're a human, or something trying to imitate one,

  • you'll wanna pay attention.

  • Ever since people first came up with the idea of computers,

  • they've dreamed of imbuing them

  • with artificial intelligence.

  • I am a smart fellow

  • as I have a very fine brain.

  • That's the most remarkable thing I've ever seen.

  • AI is just a computer that is able to mimic

  • or simulate human thought or human behavior.

  • Within that there's a subset called machine learning

  • that it's now the underpinning

  • of what is most exciting about AI.

  • By allowing computers to learn

  • how to solve problems on their own,

  • machine learning has made a series of breakthroughs

  • that once seemed nearly impossible.

  • It's the reason computers can understand your voice,

  • spot a friend's face in a photo, and steer a car.

  • And it's the reason people are actively talking

  • about the arrival of human-like AI.

  • And whether that would be a good thing

  • or a horrific end of days thing.

  • Many people made this moment possible,

  • but one figure towers above the rest.

  • I've come to the University of Toronto

  • to see the man they call the godfather

  • of Modern Artificial Intelligence.

  • Geoff Hinton.

  • (calm music)

  • Because of a back condition, Geoff Hinton hasn't been able

  • to sit down for more than 12 years.

  • I hate standing.

  • I much rather sit down, but if I sit down

  • I have a disc that comes out.

  • Well at least now standing desks are fashionable.

  • Yeah, but I was ahead.

  • (laughter)

  • I was standing when they weren't fashionable.

  • Since he can't sit in a car or on a bus,

  • Hinton walks everywhere.

  • 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 this is the future of the company,

  • my own department thinks it's just probably nonsense

  • and we shouldn't be doing any more of it.

  • (laughter)

  • So I talked everybody into it except my own department.

  • You obviously grew up in the UK

  • and you had this very prestigious family

  • full of famous mathematicians and economist,

  • and I was curious what it 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 PhD.

  • Did you rebel against that or you--

  • 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 in to artificial intelligence.

  • My feeling is if you wanna understand

  • a really complicated device, like a brain,

  • you should build one.

  • I mean you could look at cars and you could think

  • you could understand cars.

  • When you try and build a car you suddenly discover

  • this is stuff that has to go under the hood,

  • otherwise it doesn't work. Yeah.

  • 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 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 its computation.

  • They take incoming data like we do from our senses

  • and they actually learn so the neural net can learn

  • to make decisions over time.

  • Rosenblatt'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 a single layer of neurons

  • and it was limiting what it could do, extremely limited.

  • And a colleague of his wrote a book in the late '60s

  • that show these limitations.

  • And it kinda 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 it was the right way to go.

  • The brain's a big neural network

  • and so it has to be that stuff like this can work

  • 'cause it works in our brains.

  • There's just never any doubt about that.

  • What do you think 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?

  • I know that everyone else was wrong.

  • Okay.

  • Hinton decides he's got an idea

  • of how these neural nets might work,

  • and he's gonna pursue it no matter what.

  • For a little while, he's bouncing around

  • research institutions in the US.

  • He kinda gets fed up that most of them are funded

  • by the defense departments and he starts looking

  • for somewhere else he can go.

  • I didn't wanna take defense department money.

  • I sort of didn't like the idea

  • that this stuff was gonna 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