What AI is -- and isn't | Sebastian Thrun and Chris Anderson

260,110 views ・ 2017-12-21

TED


Please double-click on the English subtitles below to play the video.

00:12
Chris Anderson: Help us understand what machine learning is,
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because that seems to be the key driver
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of so much of the excitement and also of the concern
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around artificial intelligence.
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How does machine learning work?
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Sebastian Thrun: So, artificial intelligence and machine learning
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is about 60 years old
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and has not had a great day in its past until recently.
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And the reason is that today,
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we have reached a scale of computing and datasets
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that was necessary to make machines smart.
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So here's how it works.
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If you program a computer today, say, your phone,
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then you hire software engineers
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that write a very, very long kitchen recipe,
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like, "If the water is too hot, turn down the temperature.
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If it's too cold, turn up the temperature."
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The recipes are not just 10 lines long.
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They are millions of lines long.
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A modern cell phone has 12 million lines of code.
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A browser has five million lines of code.
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And each bug in this recipe can cause your computer to crash.
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That's why a software engineer makes so much money.
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The new thing now is that computers can find their own rules.
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So instead of an expert deciphering, step by step,
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a rule for every contingency,
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what you do now is you give the computer examples
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and have it infer its own rules.
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A really good example is AlphaGo, which recently was won by Google.
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Normally, in game playing, you would really write down all the rules,
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but in AlphaGo's case,
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the system looked over a million games
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and was able to infer its own rules
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and then beat the world's residing Go champion.
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That is exciting, because it relieves the software engineer
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of the need of being super smart,
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and pushes the burden towards the data.
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As I said, the inflection point where this has become really possible --
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very embarrassing, my thesis was about machine learning.
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It was completely insignificant, don't read it,
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because it was 20 years ago
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and back then, the computers were as big as a cockroach brain.
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Now they are powerful enough to really emulate
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kind of specialized human thinking.
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And then the computers take advantage of the fact
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that they can look at much more data than people can.
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So I'd say AlphaGo looked at more than a million games.
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No human expert can ever study a million games.
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Google has looked at over a hundred billion web pages.
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No person can ever study a hundred billion web pages.
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So as a result, the computer can find rules
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that even people can't find.
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CA: So instead of looking ahead to, "If he does that, I will do that,"
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it's more saying, "Here is what looks like a winning pattern,
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here is what looks like a winning pattern."
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ST: Yeah. I mean, think about how you raise children.
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You don't spend the first 18 years giving kids a rule for every contingency
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and set them free and they have this big program.
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They stumble, fall, get up, they get slapped or spanked,
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and they have a positive experience, a good grade in school,
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and they figure it out on their own.
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That's happening with computers now,
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which makes computer programming so much easier all of a sudden.
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Now we don't have to think anymore. We just give them lots of data.
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CA: And so, this has been key to the spectacular improvement
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in power of self-driving cars.
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I think you gave me an example.
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Can you explain what's happening here?
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ST: This is a drive of a self-driving car
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that we happened to have at Udacity
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and recently made into a spin-off called Voyage.
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We have used this thing called deep learning
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to train a car to drive itself,
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and this is driving from Mountain View, California,
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to San Francisco
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on El Camino Real on a rainy day,
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with bicyclists and pedestrians and 133 traffic lights.
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And the novel thing here is,
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many, many moons ago, I started the Google self-driving car team.
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And back in the day, I hired the world's best software engineers
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to find the world's best rules.
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This is just trained.
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We drive this road 20 times,
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we put all this data into the computer brain,
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and after a few hours of processing,
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it comes up with behavior that often surpasses human agility.
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So it's become really easy to program it.
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This is 100 percent autonomous, about 33 miles, an hour and a half.
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CA: So, explain it -- on the big part of this program on the left,
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you're seeing basically what the computer sees as trucks and cars
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and those dots overtaking it and so forth.
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ST: On the right side, you see the camera image, which is the main input here,
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and it's used to find lanes, other cars, traffic lights.
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The vehicle has a radar to do distance estimation.
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This is very commonly used in these kind of systems.
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On the left side you see a laser diagram,
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where you see obstacles like trees and so on depicted by the laser.
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But almost all the interesting work is centering on the camera image now.
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We're really shifting over from precision sensors like radars and lasers
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into very cheap, commoditized sensors.
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A camera costs less than eight dollars.
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CA: And that green dot on the left thing, what is that?
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Is that anything meaningful?
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ST: This is a look-ahead point for your adaptive cruise control,
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so it helps us understand how to regulate velocity
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based on how far the cars in front of you are.
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CA: And so, you've also got an example, I think,
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of how the actual learning part takes place.
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Maybe we can see that. Talk about this.
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ST: This is an example where we posed a challenge to Udacity students
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to take what we call a self-driving car Nanodegree.
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We gave them this dataset
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and said "Hey, can you guys figure out how to steer this car?"
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And if you look at the images,
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it's, even for humans, quite impossible to get the steering right.
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And we ran a competition and said, "It's a deep learning competition,
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AI competition,"
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and we gave the students 48 hours.
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So if you are a software house like Google or Facebook,
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something like this costs you at least six months of work.
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So we figured 48 hours is great.
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And within 48 hours, we got about 100 submissions from students,
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and the top four got it perfectly right.
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It drives better than I could drive on this imagery,
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using deep learning.
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And again, it's the same methodology.
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It's this magical thing.
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When you give enough data to a computer now,
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and give enough time to comprehend the data,
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it finds its own rules.
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CA: And so that has led to the development of powerful applications
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in all sorts of areas.
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You were talking to me the other day about cancer.
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Can I show this video?
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ST: Yeah, absolutely, please. CA: This is cool.
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ST: This is kind of an insight into what's happening
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in a completely different domain.
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This is augmenting, or competing --
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it's in the eye of the beholder --
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with people who are being paid 400,000 dollars a year,
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dermatologists,
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highly trained specialists.
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It takes more than a decade of training to be a good dermatologist.
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What you see here is the machine learning version of it.
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It's called a neural network.
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"Neural networks" is the technical term for these machine learning algorithms.
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They've been around since the 1980s.
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This one was invented in 1988 by a Facebook Fellow called Yann LeCun,
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and it propagates data stages
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through what you could think of as the human brain.
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It's not quite the same thing, but it emulates the same thing.
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It goes stage after stage.
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In the very first stage, it takes the visual input and extracts edges
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and rods and dots.
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And the next one becomes more complicated edges
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and shapes like little half-moons.
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And eventually, it's able to build really complicated concepts.
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Andrew Ng has been able to show
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that it's able to find cat faces and dog faces
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in vast amounts of images.
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What my student team at Stanford has shown is that
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if you train it on 129,000 images of skin conditions,
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including melanoma and carcinomas,
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you can do as good a job
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as the best human dermatologists.
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And to convince ourselves that this is the case,
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we captured an independent dataset that we presented to our network
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and to 25 board-certified Stanford-level dermatologists,
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and compared those.
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And in most cases,
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they were either on par or above the performance classification accuracy
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of human dermatologists.
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CA: You were telling me an anecdote.
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I think about this image right here.
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What happened here?
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ST: This was last Thursday. That's a moving piece.
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What we've shown before and we published in "Nature" earlier this year
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was this idea that we show dermatologists images
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and our computer program images,
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and count how often they're right.
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But all these images are past images.
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They've all been biopsied to make sure we had the correct classification.
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This one wasn't.
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This one was actually done at Stanford by one of our collaborators.
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The story goes that our collaborator,
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who is a world-famous dermatologist, one of the three best, apparently,
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looked at this mole and said, "This is not skin cancer."
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And then he had a second moment, where he said,
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"Well, let me just check with the app."
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So he took out his iPhone and ran our piece of software,
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our "pocket dermatologist," so to speak,
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and the iPhone said: cancer.
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It said melanoma.
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And then he was confused.
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And he decided, "OK, maybe I trust the iPhone a little bit more than myself,"
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and he sent it out to the lab to get it biopsied.
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And it came up as an aggressive melanoma.
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So I think this might be the first time that we actually found,
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in the practice of using deep learning,
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an actual person whose melanoma would have gone unclassified,
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had it not been for deep learning.
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CA: I mean, that's incredible.
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(Applause)
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It feels like there'd be an instant demand for an app like this right now,
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that you might freak out a lot of people.
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Are you thinking of doing this, making an app that allows self-checking?
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ST: So my in-box is flooded about cancer apps,
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with heartbreaking stories of people.
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I mean, some people have had 10, 15, 20 melanomas removed,
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and are scared that one might be overlooked, like this one,
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and also, about, I don't know,
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flying cars and speaker inquiries these days, I guess.
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My take is, we need more testing.
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I want to be very careful.
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It's very easy to give a flashy result and impress a TED audience.
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It's much harder to put something out that's ethical.
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And if people were to use the app
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and choose not to consult the assistance of a doctor
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because we get it wrong,
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I would feel really bad about it.
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So we're currently doing clinical tests,
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and if these clinical tests commence and our data holds up,
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we might be able at some point to take this kind of technology
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and take it out of the Stanford clinic
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and bring it to the entire world,
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places where Stanford doctors never, ever set foot.
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CA: And do I hear this right,
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that it seemed like what you were saying,
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because you are working with this army of Udacity students,
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that in a way, you're applying a different form of machine learning
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than might take place in a company,
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which is you're combining machine learning with a form of crowd wisdom.
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Are you saying that sometimes you think that could actually outperform
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what a company can do, even a vast company?
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ST: I believe there's now instances that blow my mind,
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and I'm still trying to understand.
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What Chris is referring to is these competitions that we run.
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We turn them around in 48 hours,
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and we've been able to build a self-driving car
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that can drive from Mountain View to San Francisco on surface streets.
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It's not quite on par with Google after seven years of Google work,
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but it's getting there.
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And it took us only two engineers and three months to do this.
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And the reason is, we have an army of students
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who participate in competitions.
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We're not the only ones who use crowdsourcing.
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Uber and Didi use crowdsource for driving.
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Airbnb uses crowdsourcing for hotels.
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There's now many examples where people do bug-finding crowdsourcing
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or protein folding, of all things, in crowdsourcing.
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But we've been able to build this car in three months,
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so I am actually rethinking
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how we organize corporations.
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We have a staff of 9,000 people who are never hired,
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that I never fire.
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They show up to work and I don't even know.
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Then they submit to me maybe 9,000 answers.
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I'm not obliged to use any of those.
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I end up -- I pay only the winners,
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so I'm actually very cheapskate here, which is maybe not the best thing to do.
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But they consider it part of their education, too, which is nice.
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But these students have been able to produce amazing deep learning results.
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So yeah, the synthesis of great people and great machine learning is amazing.
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CA: I mean, Gary Kasparov said on the first day [of TED2017]
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that the winners of chess, surprisingly, turned out to be two amateur chess players
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with three mediocre-ish, mediocre-to-good, computer programs,
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that could outperform one grand master with one great chess player,
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like it was all part of the process.
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And it almost seems like you're talking about a much richer version
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of that same idea.
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ST: Yeah, I mean, as you followed the fantastic panels yesterday morning,
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two sessions about AI,
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robotic overlords and the human response,
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many, many great things were said.
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But one of the concerns is that we sometimes confuse
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what's actually been done with AI with this kind of overlord threat,
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where your AI develops consciousness, right?
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The last thing I want is for my AI to have consciousness.
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I don't want to come into my kitchen
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and have the refrigerator fall in love with the dishwasher
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and tell me, because I wasn't nice enough,
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my food is now warm.
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I wouldn't buy these products, and I don't want them.
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But the truth is, for me,
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AI has always been an augmentation of people.
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It's been an augmentation of us,
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to make us stronger.
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And I think Kasparov was exactly correct.
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It's been the combination of human smarts and machine smarts
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that make us stronger.
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The theme of machines making us stronger is as old as machines are.
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The agricultural revolution took place because it made steam engines
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and farming equipment that couldn't farm by itself,
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that never replaced us; it made us stronger.
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And I believe this new wave of AI will make us much, much stronger
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as a human race.
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CA: We'll come on to that a bit more,
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but just to continue with the scary part of this for some people,
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like, what feels like it gets scary for people is when you have
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a computer that can, one, rewrite its own code,
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so, it can create multiple copies of itself,
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try a bunch of different code versions,
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possibly even at random,
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and then check them out and see if a goal is achieved and improved.
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So, say the goal is to do better on an intelligence test.
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You know, a computer that's moderately good at that,
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you could try a million versions of that.
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You might find one that was better,
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and then, you know, repeat.
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And so the concern is that you get some sort of runaway effect
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where everything is fine on Thursday evening,
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and you come back into the lab on Friday morning,
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and because of the speed of computers and so forth,
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things have gone crazy, and suddenly --
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ST: I would say this is a possibility,
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but it's a very remote possibility.
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So let me just translate what I heard you say.
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In the AlphaGo case, we had exactly this thing:
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the computer would play the game against itself
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and then learn new rules.
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And what machine learning is is a rewriting of the rules.
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It's the rewriting of code.
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But I think there was absolutely no concern
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that AlphaGo would take over the world.
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It can't even play chess.
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CA: No, no, no, but now, these are all very single-domain things.
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But it's possible to imagine.
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I mean, we just saw a computer that seemed nearly capable
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of passing a university entrance test,
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that can kind of -- it can't read and understand in the sense that we can,
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but it can certainly absorb all the text
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and maybe see increased patterns of meaning.
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Isn't there a chance that, as this broadens out,
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there could be a different kind of runaway effect?
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ST: That's where I draw the line, honestly.
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And the chance exists -- I don't want to downplay it --
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but I think it's remote, and it's not the thing that's on my mind these days,
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because I think the big revolution is something else.
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Everything successful in AI to the present date
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has been extremely specialized,
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and it's been thriving on a single idea,
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which is massive amounts of data.
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The reason AlphaGo works so well is because of massive numbers of Go plays,
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and AlphaGo can't drive a car or fly a plane.
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The Google self-driving car or the Udacity self-driving car
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thrives on massive amounts of data, and it can't do anything else.
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It can't even control a motorcycle.
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It's a very specific, domain-specific function,
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and the same is true for our cancer app.
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There has been almost no progress on this thing called "general AI,"
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where you go to an AI and say, "Hey, invent for me special relativity
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or string theory."
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It's totally in the infancy.
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The reason I want to emphasize this,
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I see the concerns, and I want to acknowledge them.
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But if I were to think about one thing,
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I would ask myself the question, "What if we can take anything repetitive
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and make ourselves 100 times as efficient?"
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16:51
It so turns out, 300 years ago, we all worked in agriculture
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and did farming and did repetitive things.
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Today, 75 percent of us work in offices
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17:00
and do repetitive things.
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17:02
We've become spreadsheet monkeys.
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And not just low-end labor.
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We've become dermatologists doing repetitive things,
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lawyers doing repetitive things.
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17:11
I think we are at the brink of being able to take an AI,
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look over our shoulders,
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and they make us maybe 10 or 50 times as effective in these repetitive things.
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That's what is on my mind.
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CA: That sounds super exciting.
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The process of getting there seems a little terrifying to some people,
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because once a computer can do this repetitive thing
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much better than the dermatologist
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or than the driver, especially, is the thing that's talked about
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so much now,
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suddenly millions of jobs go,
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and, you know, the country's in revolution
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before we ever get to the more glorious aspects of what's possible.
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ST: Yeah, and that's an issue, and it's a big issue,
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and it was pointed out yesterday morning by several guest speakers.
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Now, prior to me showing up onstage,
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I confessed I'm a positive, optimistic person,
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so let me give you an optimistic pitch,
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which is, think of yourself back 300 years ago.
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Europe just survived 140 years of continuous war,
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none of you could read or write,
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18:14
there were no jobs that you hold today,
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18:17
like investment banker or software engineer or TV anchor.
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18:21
We would all be in the fields and farming.
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18:24
Now here comes little Sebastian with a little steam engine in his pocket,
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18:27
saying, "Hey guys, look at this.
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It's going to make you 100 times as strong, so you can do something else."
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18:32
And then back in the day, there was no real stage,
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18:35
but Chris and I hang out with the cows in the stable,
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18:38
and he says, "I'm really concerned about it,
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18:40
because I milk my cow every day, and what if the machine does this for me?"
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The reason why I mention this is,
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we're always good in acknowledging past progress and the benefit of it,
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like our iPhones or our planes or electricity or medical supply.
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18:53
We all love to live to 80, which was impossible 300 years ago.
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18:57
But we kind of don't apply the same rules to the future.
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19:02
So if I look at my own job as a CEO,
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I would say 90 percent of my work is repetitive,
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19:09
I don't enjoy it,
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19:10
I spend about four hours per day on stupid, repetitive email.
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19:14
And I'm burning to have something that helps me get rid of this.
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19:18
Why?
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19:19
Because I believe all of us are insanely creative;
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19:22
I think the TED community more than anybody else.
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19:25
But even blue-collar workers; I think you can go to your hotel maid
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19:29
and have a drink with him or her,
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and an hour later, you find a creative idea.
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What this will empower is to turn this creativity into action.
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19:39
Like, what if you could build Google in a day?
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19:43
What if you could sit over beer and invent the next Snapchat,
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19:46
whatever it is,
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and tomorrow morning it's up and running?
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And that is not science fiction.
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19:51
What's going to happen is,
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we are already in history.
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We've unleashed this amazing creativity
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by de-slaving us from farming
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and later, of course, from factory work
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and have invented so many things.
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It's going to be even better, in my opinion.
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And there's going to be great side effects.
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One of the side effects will be
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that things like food and medical supply and education and shelter
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20:17
and transportation
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will all become much more affordable to all of us,
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not just the rich people.
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CA: Hmm.
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So when Martin Ford argued, you know, that this time it's different
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because the intelligence that we've used in the past
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to find new ways to be
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20:33
will be matched at the same pace
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20:35
by computers taking over those things,
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20:38
what I hear you saying is that, not completely,
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20:41
because of human creativity.
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Do you think that that's fundamentally different from the kind of creativity
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20:48
that computers can do?
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ST: So, that's my firm belief as an AI person --
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20:55
that I haven't seen any real progress on creativity
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and out-of-the-box thinking.
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21:01
What I see right now -- and this is really important for people to realize,
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21:05
because the word "artificial intelligence" is so threatening,
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21:07
and then we have Steve Spielberg tossing a movie in,
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21:10
where all of a sudden the computer is our overlord,
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21:12
but it's really a technology.
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21:14
It's a technology that helps us do repetitive things.
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21:17
And the progress has been entirely on the repetitive end.
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21:20
It's been in legal document discovery.
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21:22
It's been contract drafting.
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21:24
It's been screening X-rays of your chest.
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21:28
And these things are so specialized,
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21:30
I don't see the big threat of humanity.
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21:32
In fact, we as people --
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21:34
I mean, let's face it: we've become superhuman.
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21:36
We've made us superhuman.
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21:38
We can swim across the Atlantic in 11 hours.
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21:41
We can take a device out of our pocket
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21:43
and shout all the way to Australia,
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21:45
and in real time, have that person shouting back to us.
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21:48
That's physically not possible. We're breaking the rules of physics.
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21:51
When this is said and done, we're going to remember everything
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21:54
we've ever said and seen,
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you'll remember every person,
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21:57
which is good for me in my early stages of Alzheimer's.
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22:00
Sorry, what was I saying? I forgot.
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22:02
CA: (Laughs)
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ST: We will probably have an IQ of 1,000 or more.
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22:06
There will be no more spelling classes for our kids,
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22:10
because there's no spelling issue anymore.
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22:12
There's no math issue anymore.
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22:14
And I think what really will happen is that we can be super creative.
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22:17
And we are. We are creative.
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22:19
That's our secret weapon.
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22:21
CA: So the jobs that are getting lost,
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22:23
in a way, even though it's going to be painful,
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22:25
humans are capable of more than those jobs.
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22:27
This is the dream.
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The dream is that humans can rise to just a new level of empowerment
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22:33
and discovery.
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22:35
That's the dream.
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22:36
ST: And think about this:
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if you look at the history of humanity,
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22:40
that might be whatever -- 60-100,000 years old, give or take --
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3328
22:43
almost everything that you cherish in terms of invention,
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22:47
of technology, of things we've built,
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22:49
has been invented in the last 150 years.
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22:53
If you toss in the book and the wheel, it's a little bit older.
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22:56
Or the axe.
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22:58
But your phone, your sneakers,
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23:00
these chairs, modern manufacturing, penicillin --
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23:04
the things we cherish.
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Now, that to me means
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23:09
the next 150 years will find more things.
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23:12
In fact, the pace of invention has gone up, not gone down, in my opinion.
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I believe only one percent of interesting things have been invented yet. Right?
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23:22
We haven't cured cancer.
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We don't have flying cars -- yet. Hopefully, I'll change this.
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23:27
That used to be an example people laughed about. (Laughs)
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23:31
It's funny, isn't it? Working secretly on flying cars.
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23:34
We don't live twice as long yet. OK?
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23:36
We don't have this magic implant in our brain
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23:39
that gives us the information we want.
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23:41
And you might be appalled by it,
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23:42
but I promise you, once you have it, you'll love it.
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I hope you will.
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23:46
It's a bit scary, I know.
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23:48
There are so many things we haven't invented yet
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that I think we'll invent.
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23:52
We have no gravity shields.
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23:53
We can't beam ourselves from one location to another.
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23:56
That sounds ridiculous,
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23:57
but about 200 years ago,
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23:58
experts were of the opinion that flight wouldn't exist,
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24:01
even 120 years ago,
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24:02
and if you moved faster than you could run,
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24:05
you would instantly die.
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24:06
So who says we are correct today that you can't beam a person
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24:10
from here to Mars?
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24:12
CA: Sebastian, thank you so much
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for your incredibly inspiring vision and your brilliance.
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24:16
Thank you, Sebastian Thrun.
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That was fantastic. (Applause)
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About this website

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