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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

  • I had 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.

  • They didn't work quite well enough

  • because we didn't have enough data,

  • we didn't have enough compute power.

  • And people in AI, in computer science,

  • decided neural networks was 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 are still pursuing this technology.

  • He would show up at academic conferences

  • and being 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 gonna work? No.

  • And you did have some self-doubt?

  • I mean there were many times when I thought,

  • I'm not gonna make this work.

  • 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.

  • Computers were 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 superfast 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 the span of a few months,

  • it just takes off and it finally feel like, aha,

  • the world has finally come to my vision.

  • It's sort of a relief that people

  • finally came to their senses.

  • (laughter)

  • Next up, we have Professor Geoffrey Hinton

  • of the University of Toronto.

  • (applause)

  • Thank you.

  • (calm music)

  • For Hinton, this is obviously a really redemptive moment.

  • Now he's basically a technology celebrity.

  • And for Canada, it's the country's moment as well.

  • They have more AI researchers

  • than just about any other place on the planet

  • and the quest now is to see what these guys can do,

  • starting companies and pushing the technology forward.

  • I'm gonna set out on a journey across Canada

  • to see the best in Canadian AI technology

  • and to get a feel for how far the technology has come

  • and how far it still has to go.

  • Here is a city that gets right at the central tension

  • of modern life and the unfolding AI revolution.

  • (church bell ringing)

  • It's Montreal, a place filled with beauty

  • and old world charms that ask you to move

  • slowly through its streets and to chill for a while,

  • reflect, and think deep thoughts.

  • (calm music)

  • At the same time, it's one of the world's

  • top AI research centers.

  • Students flock here from all over the globe

  • to get deep with machine learning

  • and to take Geoff Hinton's ideas and figure out

  • how to turn them into products we all use.

  • To see just how successful they've been,

  • look no further than your pocket.

  • All this stuff started out as hardcore computer science,