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  • Chris Anderson: Help us understand what machine learning is,

  • because that seems to be the key driver

  • around artificial intelligence.

  • How does machine learning work?

  • Sebastian Thrun: So, artificial intelligence and machine learning

  • is about 60 years old

  • and has not had a great day in its past until recently.

  • And the reason is that today,

  • we have reached a scale of computing and datasets

  • that was necessary to make machines smart.

  • So here's how it works:

  • If you program a computer today, say, your phone,

  • then you hire software engineers

  • that write a very, very long kitchen recipe,

  • like, "If the water is too hot, turn down the temperature.

  • If it's too cold, turn up the temperature."

  • The recipes are not just 10 lines long.

  • They are millions of lines long.

  • A modern cell phone has 12 million lines of code.

  • A browser has five million lines of code.

  • And each bug in this recipe can cause your computer to crash.

  • That's why a software engineer makes so much money.

  • The new thing now is that computers can find their own rules.

  • So instead of an expert deciphering, step by step,

  • a rule for every contingency,

  • what you do now is you give the computer examples

  • and have it infer its own rules.

  • A really good example is AlphaGo, which recently was won by Google.

  • Normally, in game playing, you would really write down all the rules,

  • but in AlphaGo's case,

  • the system looked over a million games

  • and was able to infer its own rules

  • and then beat the world's residing Go champion.

  • That is exciting, because it relieves the software engineer

  • of the need of being super smart,

  • and pushes the burden towards the data.

  • As I said, the inflection point where this has become really possible --

  • very embarrassing, my thesis was about machine learning.

  • It was completely insignificant, don't read it,

  • because it was 20 years ago

  • and back then, the computers were as big as a cockroach brain.

  • Now they are powerful enough to really emulate

  • kind of specialized human thinking.

  • And then the computers take advantage of the fact

  • that they can look at much more data than people can.

  • So I'd say AlphaGo looked at more than a million games.

  • No human expert can ever study a million games.

  • Google has looked at over a hundred billion web pages.

  • No person can ever study a hundred billion web pages.

  • So as a result, the computer can find rules

  • that even people can't find.

  • CA: So instead of looking ahead to, "If he does that, I will do that,"

  • it's more saying, "Here is what looks like a winning pattern,

  • here is what looks like a winning pattern."

  • ST: Yeah. I mean, think about how you raise children.

  • You don't spend the first 18 years giving kids a rule for every contingency

  • and set them free and they have this big program.

  • They stumble, fall, get up, they get slapped or spanked,

  • and they have a positive experience, a good grade in school,

  • and they figure it out on their own.

  • That's happening with computers now,

  • which makes computer programming so much easier all of a sudden.

  • Now we don't have to think anymore. We just give them lots of data.

  • CA: And so, this has been key to the spectacular improvement

  • in power of self-driving cars.

  • I think you gave me an example.

  • Can you explain what's happening here?

  • ST: This is a drive of a self-driving car

  • that we happened to have at Udacity

  • and recently made into a spin-off called Voyage.

  • We have used this thing called deep learning

  • to train a car to drive itself,

  • and this is driving from Mountain View, California,

  • to San Francisco

  • on El Camino Real on a rainy day,

  • with bicyclists and pedestrians and 133 traffic lights.

  • And the novel thing here is,

  • many, many moons ago, I started the Google self-driving car team.

  • And back in the day, I hired the world's best software engineers

  • to find the world's best rules.

  • This is just trained.

  • We drive this road 20 times,

  • we put all this data into the computer brain,

  • and after a few hours of processing,

  • it comes up with behavior that often surpasses human agility.

  • So it's become really easy to program it.

  • This is 100 percent autonomous, about 33 miles, an hour and a half.

  • CA: So, explain it -- on the big part of this program on the left,

  • you're seeing basically what the computer sees as trucks and cars

  • and those dots overtaking it and so forth.

  • ST: On the right side, you see the camera image, which is the main input here,

  • and it's used to find lanes, other cars, traffic lights.

  • The vehicle has a radar to do distance estimation.

  • This is very commonly used in these kind of systems.

  • On the left side you see a laser diagram,

  • where you see obstacles like trees and so on depicted by the laser.

  • But almost all the interesting work is centering on the camera image now.

  • We're really shifting over from precision sensors like radars and lasers

  • into very cheap, commoditized sensors.

  • A camera costs less than eight dollars.

  • CA: And that green dot on the left thing, what is that?

  • Is that anything meaningful?

  • ST: This is a look-ahead point for your adaptive cruise control,

  • so it helps us understand how to regulate velocity

  • based on how far the cars in front of you are.

  • CA: And so, you've also got an example, I think,

  • of how the actual learning part takes place.

  • Maybe we can see that. Talk about this.

  • ST: This is an example where we posed a challenge to Udacity students

  • to take what we call a self-driving car Nanodegree.

  • We gave them this dataset

  • and said "Hey, can you guys figure out how to steer this car?"

  • And if you look at the images,

  • it's, even for humans, quite impossible to get the steering right.

  • And we ran a competition and said, "It's a deep learning competition,

  • AI competition,"

  • and we gave the students 48 hours.

  • So if you are a software house like Google or Facebook,

  • something like this costs you at least six months of work.

  • So we figured 48 hours is great.

  • And within 48 hours, we got about 100 submissions from students,

  • and the top four got it perfectly right.

  • It drives better than I could drive on this imagery,

  • using deep learning.

  • And again, it's the same methodology.

  • It's this magical thing.

  • When you give enough data to a computer now,

  • and give enough time to comprehend the data,

  • it finds its own rules.

  • CA: And so that has led to the development of powerful applications

  • in all sorts of areas.

  • You were talking to me the other day about cancer.

  • Can I show this video?

  • ST: Yeah, absolutely, please. CA: This is cool.

  • ST: This is kind of an insight into what's happening

  • in a completely different domain.

  • This is augmenting, or competing --

  • it's in the eye of the beholder --

  • with people who are being paid 400,000 dollars a year,

  • dermatologists,

  • highly trained specialists.

  • It takes more than a decade of training to be a good dermatologist.

  • What you see here is the machine learning version of it.

  • It's called a neural network.

  • "Neural networks" is the technical term for these machine learning algorithms.

  • They've been around since the 1980s.

  • This one was invented in 1988 by a Facebook Fellow called Yann LeCun,

  • and it propagates data stages

  • through what you could think of as the human brain.

  • It's not quite the same thing, but it emulates the same thing.

  • It goes stage after stage.

  • In the very first stage, it takes the visual input and extracts edges

  • and rods and dots.

  • And the next one becomes more complicated edges

  • and shapes like little half-moons.

  • And eventually, it's able to build really complicated concepts.

  • Andrew Ng has been able to show

  • that it's able to find cat faces and dog faces

  • in vast amounts of images.

  • What my student team at Stanford has shown is that

  • if you train it on 129,000 images of skin conditions,

  • including melanoma and carcinomas,

  • you can do as good a job

  • as the best human dermatologists.

  • And to convince ourselves that this is the case,

  • we captured an independent dataset that we presented to our network

  • and to 25 board-certified Stanford-level dermatologists,

  • and compared those.

  • And in most cases,

  • they were either on par or above the performance classification accuracy

  • of human dermatologists.

  • CA: You were telling me an anecdote.

  • I think about this image right here.

  • What happened here?

  • ST: This was last Thursday. That's a moving piece.

  • What we've shown before and we published in "Nature" earlier this year

  • was this idea that we show dermatologists images

  • and our computer program images,

  • and count how often they're right.

  • But all these images are past images.

  • They've all been biopsied to make sure we had the correct classification.

  • This one wasn't.

  • This one was actually done at Stanford by one of our collaborators.

  • The story goes that our collaborator,

  • who is a world-famous dermatologist, one of the three best, apparently,

  • looked at this mole and said, "This is not skin cancer."

  • And then he had a second moment, where he said,

  • "Well, let me just check with the app."

  • So he took out his iPhone and ran our piece of software,

  • our "pocket dermatologist," so to speak,

  • and the iPhone said: cancer.

  • It said melanoma.

  • And then he was confused.

  • And he decided, "OK, maybe I trust the iPhone a little bit more than myself,"

  • and he sent it out to the lab to get it biopsied.

  • And it came up as an aggressive melanoma.

  • So I think this might be the first time that we actually found,

  • in the practice of using deep learning,

  • an actual person whose melanoma would have gone unclassified,

  • had it not been for deep learning.

  • CA: I mean, that's incredible.

  • It feels like there'd be an instant demand for an app like this right now,

  • that you might freak out a lot of people.

  • Are you thinking of doing this, making an app that allows self-checking?

  • ST: So my in-box is flooded about cancer apps,

  • with heartbreaking stories of people.

  • I mean, some people have had 10, 15, 20 melanomas removed,

  • and are scared that one might be overlooked, like this one,

  • and also, about, I don't know,

  • flying cars and speaker inquiries these days, I guess.

  • My take is, we need more testing.

  • I want to be very careful.

  • It's very easy to give a flashy result and impress a TED audience.

  • It's much harder to put something out that's ethical.

  • And if people were to use the app

  • and choose not to consult the assistance of a doctor

  • because we get it wrong,

  • I would feel really bad about it.

  • So we're currently doing clinical tests,

  • and if these clinical tests commence and our data holds up,

  • we might be able at some point to take this kind of technology

  • and take it out of the Stanford clinic

  • and bring it to the entire world,

  • places where Stanford doctors never, ever set foot.

  • CA: And do I hear this right,

  • that it seemed like what you were saying,

  • because you are working with this army of Udacity students,

  • that in a way, you're applying a different form of machine learning

  • than might take place in a company,

  • which is you're combining machine learning with a form of crowd wisdom.

  • Are you saying that sometimes you think that could actually outperform

  • what a company can do, even a vast company?

  • ST: I believe there's now instances that blow my mind,

  • and I'm still trying to understand.

  • What Chris is referring to is these competitions that we run.

  • We turn them around in 48 hours,

  • and we've been able to build a self-driving car

  • that can drive from Mountain View to San Francisco on surface streets.

  • It's not quite on par with Google after seven years of Google work,

  • but it's getting there.

  • And it took us only two engineers and three months to do this.

  • And the reason is, we have an army of students

  • who participate in competitions.

  • We're not the only ones who use crowdsourcing.

  • Uber and Didi use crowdsource for driving.

  • Airbnb uses crowdsourcing for hotels.

  • There's now many examples where people do bug-finding crowdsourcing

  • or protein folding, of all things, in crowdsourcing.

  • But we've been able to build this car in three months,

  • so I am actually rethinking

  • how we