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,