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  • It used to be that if you wanted to get a computer to do something new,

  • you would have to program it.

  • Now, programming, for those of you here that haven't done it yourself,

  • requires laying out in excruciating detail

  • every single step that you want the computer to achieve, to do

  • in order to achieve your goal.

  • Now, if you want to do something that you don't know how to do yourself,

  • then this is going to be a great challenge.

  • So this was the challenge faced by this man, Arthur Samuel.

  • In 1956, he wanted to get this computer

  • to be able to beat him at checkers.

  • How can you write a program,

  • lay out in excruciating detail, how to be better than you at checkers?

  • So he came up with an idea:

  • he had the computer play against itself thousands of times

  • and learn how to play checkers.

  • And indeed it worked, and in fact, by 1962,

  • this computer had beaten the Connecticut state champion.

  • So Arthur Samuel was the father of machine learning,

  • and I have a great debt to him,

  • because I am a machine learning practitioner.

  • I was the president of Kaggle,

  • a community of over 200,000 machine learning practitioners.

  • Kaggle puts up competitions

  • to try and get them to solve previously unsolved problems,

  • and it's been successful hundreds of times.

  • So from this vantage point, I was able to find out

  • a lot about what machine learning can do in the past, can do today,

  • and what it could do in the future.

  • Perhaps the first big success of machine learning commercially was Google.

  • Google showed that it is possible to find information

  • by using a computer algorithm,

  • and this algorithm is based on machine learning.

  • Since that time, there have been many commercial successes of machine learning.

  • Companies like Amazon and Netflix

  • use machine learning to suggest products that you might like to buy,

  • movies that you might like to watch.

  • Sometimes, it's almost creepy.

  • Companies like LinkedIn and Facebook

  • sometimes will tell you about who your friends might be

  • and you have no idea how it did it,

  • and this is because it's using the power of machine learning.

  • These are algorithms that have learned how to do this from data

  • rather than being programmed by hand.

  • This is also how IBM was successful

  • in getting Watson to beat the two world champions at "Jeopardy,"

  • answering incredibly subtle and complex questions like this one.

  • ["The ancient 'Lion of Nimrud' went missing from this city's national museum in 2003 (along with a lot of other stuff)"]

  • This is also why we are now able to see the first self-driving cars.

  • If you want to be able to tell the difference between, say,

  • a tree and a pedestrian, well, that's pretty important.

  • We don't know how to write those programs by hand,

  • but with machine learning, this is now possible.

  • And in fact, this car has driven over a million miles

  • without any accidents on regular roads.

  • So we now know that computers can learn,

  • and computers can learn to do things

  • that we actually sometimes don't know how to do ourselves,

  • or maybe can do them better than us.

  • One of the most amazing examples I've seen of machine learning

  • happened on a project that I ran at Kaggle

  • where a team run by a guy called Geoffrey Hinton

  • from the University of Toronto

  • won a competition for automatic drug discovery.

  • Now, what was extraordinary here is not just that they beat

  • all of the algorithms developed by Merck or the international academic community,

  • but nobody on the team had any background in chemistry or biology or life sciences,

  • and they did it in two weeks.

  • How did they do this?

  • They used an extraordinary algorithm called deep learning.

  • So important was this that in fact the success was covered

  • in The New York Times in a front page article a few weeks later.

  • This is Geoffrey Hinton here on the left-hand side.

  • Deep learning is an algorithm inspired by how the human brain works,

  • and as a result it's an algorithm

  • which has no theoretical limitations on what it can do.

  • The more data you give it and the more computation time you give it,

  • the better it gets.

  • The New York Times also showed in this article

  • another extraordinary result of deep learning

  • which I'm going to show you now.

  • It shows that computers can listen and understand.

  • (Video) Richard Rashid: Now, the last step

  • that I want to be able to take in this process

  • is to actually speak to you in Chinese.

  • Now the key thing there is,

  • we've been able to take a large amount of information from many Chinese speakers

  • and produce a text-to-speech system

  • that takes Chinese text and converts it into Chinese language,

  • and then we've taken an hour or so of my own voice

  • and we've used that to modulate

  • the standard text-to-speech system so that it would sound like me.

  • Again, the results are not perfect.

  • There are in fact quite a few errors.

  • (In Chinese)

  • (Applause)

  • There's much work to be done in this area.

  • (In Chinese)

  • (Applause)

  • Jeremy Howard: Well, that was at a machine learning conference in China.

  • It's not often, actually, at academic conferences

  • that you do hear spontaneous applause,

  • although of course sometimes at TEDx conferences, feel free.

  • Everything you saw there was happening with deep learning.

  • (Applause) Thank you.

  • The transcription in English was deep learning.

  • The translation to Chinese and the text in the top right, deep learning,

  • and the construction of the voice was deep learning as well.

  • So deep learning is this extraordinary thing.

  • It's a single algorithm that can seem to do almost anything,

  • and I discovered that a year earlier, it had also learned to see.

  • In this obscure competition from Germany

  • called the German Traffic Sign Recognition Benchmark,

  • deep learning had learned to recognize traffic signs like this one.

  • Not only could it recognize the traffic signs

  • better than any other algorithm,

  • the leaderboard actually showed it was better than people,

  • about twice as good as people.

  • So by 2011, we had the first example

  • of computers that can see better than people.

  • Since that time, a lot has happened.

  • In 2012, Google announced that they had a deep learning algorithm

  • to watch YouTube videos

  • and crunched the data on 16,000 computers for a month,

  • and the computer independently learned about concepts such as people and cats

  • just by watching the videos.

  • This is much like the way that humans learn.

  • Humans don't learn by being told what they see,

  • but by learning for themselves what these things are.

  • Also in 2012, Geoffrey Hinton, who we saw earlier,

  • won the very popular ImageNet competition,

  • looking to try to figure out from one and a half million images

  • what they're pictures of.

  • As of 2014, we're now down to a six percent error rate

  • in image recognition.

  • This is better than people, again.

  • So machines really are doing an extraordinarily good job of this,

  • and it is now being used in industry.

  • For example, Google announced last year

  • that they had mapped every single location in France in two hours,

  • and the way they did it was that they fed street view images

  • into a deep learning algorithm to recognize and read street numbers.

  • Imagine how long it would have taken before:

  • dozens of people, many years.

  • This is also happening in China.

  • Baidu is kind of the Chinese Google, I guess,

  • and what you see here in the top left

  • is an example of a picture that I uploaded to Baidu's deep learning system,

  • and underneath you can see that the system has understood what that picture is

  • and found similar images.

  • The similar images actually have similar backgrounds,

  • similar directions of the faces,

  • even some with their tongue out.

  • This is not clearly looking at the text of a web page.

  • All I uploaded was an image.

  • So we now have computers which really understand what they see

  • and can therefore search databases

  • of hundreds of millions of images in real time.

  • So what does it mean now that computers can see?

  • Well, it's not just that computers can see.

  • In fact, deep learning has done more than that.

  • Complex, nuanced sentences like this one

  • are now understandable with deep learning algorithms.

  • As you can see here,

  • this Stanford-based system showing the red dot at the top

  • has figured out that this sentence is expressing negative sentiment.

  • Deep learning now in fact is near human performance

  • at understanding what sentences are about and what it is saying about those things.

  • Also, deep learning has been used to read Chinese,

  • again at about native Chinese speaker level.

  • This algorithm developed out of Switzerland

  • by people, none of whom speak or understand any Chinese.

  • As I say, using deep learning

  • is about the best system in the world for this,

  • even compared to native human understanding.

  • This is a system that we put together at my company

  • which shows putting all this stuff together.

  • These are pictures which have no text attached,

  • and as I'm typing in here sentences,

  • in real time it's understanding these pictures

  • and figuring out what they're about

  • and finding pictures that are similar to the text that I'm writing.

  • So you can see, it's actually understanding my sentences

  • and actually understanding these pictures.

  • I know that you've seen something like this on Google,

  • where you can type in things and it will show you pictures,

  • but actually what it's doing is it's searching the webpage for the text.

  • This is very different from actually understanding the images.

  • This is something that computers have only been able to do

  • for the first time in the last few months.

  • So we can see now that computers can not only see but they can also read,

  • and, of course, we've shown that they can understand what they hear.

  • Perhaps not surprising now that I'm going to tell you they can write.

  • Here is some text that I generated using a deep learning algorithm yesterday.

  • And here is some text that an algorithm out of Stanford generated.

  • Each of these sentences was generated

  • by a deep learning algorithm to describe each of those pictures.

  • This algorithm before has never seen a man in a black shirt playing a guitar.

  • It's seen a man before, it's seen black before,

  • it's seen a guitar before,

  • but it has independently generated this novel description of this picture.

  • We're still not quite at human performance here, but we're close.

  • In tests, humans prefer the computer-generated caption

  • one out of four times.

  • Now this system is now only two weeks old,

  • so probably within the next year,

  • the computer algorithm will be well past human performance

  • at the rate things are going.

  • So computers can also write.

  • So we put all this together and it leads to very exciting opportunities.

  • For example, in medicine,

  • a team in Boston announced that they had discovered

  • dozens of new clinically relevant features

  • of tumors which help doctors make a prognosis of a cancer.

  • Very similarly, in Stanford,

  • a group there announced that, looking at tissues under magnification,

  • they've developed a machine learning-based system

  • which in fact is better than human pathologists

  • at predicting survival rates for cancer sufferers.

  • In both of these cases, not only were the predictions more accurate,

  • but they generated new insightful science.

  • In the radiology case,

  • they were new clinical indicators that humans can understand.

  • In this pathology case,

  • the computer system actually discovered that the cells around the cancer

  • are as important as the cancer cells themselves

  • in making a diagnosis.

  • This is the opposite of what pathologists had been taught for decades.

  • In each of those two cases, they were systems developed

  • by a combination of medical experts and machine learning experts,

  • but as of last year, we're now beyond that too.

  • This is an example of identifying cancerous areas

  • of human tissue under a microscope.

  • The system being shown here can identify those areas more accurately,

  • or about as accurately, as human pathologists,

  • but was built entirely with deep learning using no medical expertise

  • by people who have no background in the field.

  • Similarly, here, this neuron segmentation.

  • We can now segment neurons about as accurately as humans can,

  • but this system was developed with deep learning

  • using people with no previous background in medicine.

  • So myself, as somebody with no previous background in medicine,

  • I seem to be entirely well qualified to start a new medical company,

  • which I did.

  • I was kind of terrified of doing it,

  • but the theory seemed to suggest that it ought to be possible

  • to do very useful medicine using just these data analytic techniques.

  • And thankfully, the feedback has been fantastic,

  • not just from the media but from the medical community,