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Well, it used to be that if you wanted
to get a computer to do something new,
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you would have to program it.
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Now, programming, for those of you here
that haven't done it yourself,
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requires laying out in excruciating detail
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every single step that you want
the computer to do
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in order to achieve your goal.
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Now, if you want to do something
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that you don't know how to do yourself,
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then this is going
to be a great challenge.
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So this was the challenged faced
by this man, Arthur Samuel.
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In 1956, he wanted to get this computer
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to be able to beat him at checkers.
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How can you write a program,
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lay out in excruciating detail
how to be better than you at Checkers?
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So he came up with an idea:
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he had the computer play
against itself thousands of times
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and learn how to play checkers.
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And indeed it worked,
and in fact, by 1962,
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this computer had beaten
the Connecticut state champion.
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So Arthur Samuel was
the father of machine learning,
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and I have a great debt to him,
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because I am a machine
learning practitioner.
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I was the president of Kaggle,
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a community of over 200,000
machine learning practictioners.
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Kaggle puts up competitions
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to try and get them to solve
previously unsolved problems,
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and it's been successful
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hundreds of times.
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So from this vantage point,
I was able to find out a lot
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about what machine learning
can do in the past, can do today,
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and what it could do in the future.
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Perhaps the first big success
of machine learning commercially
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was Google.
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Google showed that it is
possible to find information
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by using a computer algorithm,
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and this algorithm is based
on machine learning.
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Since that time, there has been many
commercial successes of machine learning.
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Companies like Amazon and Netflix
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use machine learning to suggest
products that you might like to buy,
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movies that you might like to watch.
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Sometimes, it's almost creepy.
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Companies like LinkedIn and Facebook
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sometimes will tell you about
who your friends might be
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and you have no idea how it did it,
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and this is because it's using
the power of machine learning.
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These are algorithms that have
learned how to do this from data
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rather than being programmed by hand.
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This is also how IBM was successful
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in getting Watson to beat
the two world champions at Jeopardy!,
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answering incredibly subtle
and complex questions like this one.
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This is also why we are now able
to see the first self-driving cars.
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If you want to be able to tell
the difference between, say,
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a tree and a pedestrian,
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well, that's pretty important.
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We don't know how to write
those programs by hand,
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but with machine learning,
this is now possible.
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And in fact, this car has driven
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over a million miles without any accidents
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on regular roads.
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So we now know that computers can learn,
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and computers can learn to do things
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that we actually sometimes
don't know how to do ourselves,
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or maybe can do them better than us.
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One of the most amazing examples
I've seen of machine learning
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happened on a project I ran at Kaggle
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where a team run by a guy
called Geoffrey Hinton
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from the University of Toronto
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won a competition for
automatic drug discovery.
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Now, what was extraordinary here
is not just that they beat
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all of the algorithms developed by Merck
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or the international academic community,
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but nobody on the team had any background
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in chemistry or biology or life sciences,
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and they did it in two weeks.
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How did they do this?
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They used an extraordinary algorithm
called deep learning.
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So important was this that in fact
the success was covered
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in the New York Times in a front page
article a few weeks later.
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This is Geoffrey Hinton
here on the left-hand side.
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Deep learning is an algorithm
inspired by how the human brain works,
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and as a result it's an algorithm
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which has no theoretical limitations
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on what it can do.
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The more data you give it and the more
computation time you give it,
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the better it gets.
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The New York Times also
showed in this article
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another extraordinary
result of deep learning
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which I'm going to show you now.
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It shows that computers can listen
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and understand.
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(Video) Speaker: Now, the last step
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that I want to be able
to take in this process
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is to actually speak to you in Chinese.
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Now the key thing there is,
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we've been able to take
a large amount of information
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from many Chinese speakers
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and produce a text-to-speech system
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that takes Chinese text
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and converts it into Chinese language,
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and then we've taken
an hour or so of my own voice
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and we've used that to modulate
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the standard text-to-speech system
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so that it would sound like me.
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Again, the result's not perfect.
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There are in fact quite a few errors.
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(In Chinese)
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(Applause)
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There's much work to be done in this area.
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(In Chinese)
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(Applause)
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Jeremy Howard: Well, that was at
a machine learning conference in China.
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It's not often, actually,
at academic conferences
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that you do hear spontaneous applause,
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although of course sometimes
at TEDx conferences, feel free.
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Everything you saw there
was happening with deep learning.
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(Applause) Thank you.
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The transcription in English
was deep learning.
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The translation to Chinese and the text
in the top right, deep learning,
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and the construction of the voice
was deep learning as well.
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So deep learning is
this extraordinary thing.
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It's a single algorithm that
can seem to do almost anything,
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and I discovered that a year earlier,
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it had also learned to see.
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In this obscure competition from Germany
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called the German Traffic Sign Benchmark,
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deep learning had learned
to recognize traffic signs like this one.
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Not only could it
recognize the traffic signs
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better than any other algorithm,
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the leaderboard actually showed
it was better than people,
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about twice as good as people.
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So by 2011, we had the first example
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of computers that can see
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better than people.
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Since that time, a lot has happened.
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In 2012, Google announced that
they had a deep learning algorithm
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watch YouTube videos
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and crunched the data
on 16,000 computers for a month,
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and the computer independently learned
about concepts such as people and cats
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just by watching the videos.
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This is much like the way
that humans learn.
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Humans don't learn
by being told what they see,
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but by learning for themselves
what these things are.
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Also in 2012, Geoffrey Hinton,
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who we saw earlier,
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won the very popular ImageNet competition,
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looking to figure out out from
one and a half million images
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what they're pictures of.
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As of 2014, we're now down
to a six percent error rate
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in image recognition.
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This is better than people, again.
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So machines really are doing
an extraordinarily good job of this,
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and it is now being used in industry.
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For example, Google announced last year
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that they had mapped every single
location in France in two hours,
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and the way they did it is
that they fed street view images
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into a deep learning algorithm
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to recognize and read street numbers.
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Imagine how long
it would have taken before:
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dozens of people, many years.
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This is also happening in China.
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Baidu is kind of the Chinese Google,
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I guess,
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and what you see here in the top left
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is an example of a picture that I uploaded
to Baidu's deep learning system,
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and underneath you can see that the system
has understood what that picture is
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and found similar images.
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The similar images actually
have similar backgrounds,
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similar directions of the faces,
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even some with their tongue out.
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This is not clearly looking
at the text of a web page.
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All I uploaded was an image.
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So we now have computers which
really understand what they see
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and can therefore search databases
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of hundreds of millions
of images in real time.
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So what does it mean
now that computers can see?
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Well it's not just that computers can see.
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In fact, deep learning
has done more than that.
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Complex, nuanced sentences like this one
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are now understandable
with deep learning algorithms.
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As you can see here,
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this Stanford-based system
showing the red dot at the top
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has figured out that this sentence
is expressing negative sentiment.
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Deep learning now in fact
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is near human performance
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at understanding what sentences are about
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and what it is saying about those things.
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Also, deep learning has
been used to read Chinese,
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again at about native
Chinese speaker level.
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This algorithm developed
out of Switzerland
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by people, none of whom speak
or understand any Chinese.
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As I say, using deep learning
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is about the best system
in the world for this,
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even compared to native
human understanding.
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This is a system that we
put together at my company
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which shows putting
all this stuff together.
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These are pictures which
have no text attached,
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and as I'm typing in here sentences,
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in real time it's understanding
these pictures
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and figuring out what they're about
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and finding pictures that are similar
to the text that I'm writing.
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So you can see, it's actually
understanding my sentences
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and actually understanding these pictures.
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I know that you've seen
something like this on Google,
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where you can type in things
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and it will show you pictures,
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but actually what it's doing is it's
searching the webpage for the text.
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This is very different from actually
understanding the images.
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This is something that computers
have only been able to do
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for the first time in the last few months.
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So we can see now that computers
can not only see but they can also read,
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and of course we've shown that they
can understand what they hear.
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Perhaps not surprising, now, that
I'm going to tell you they can write.
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Here is some text that I generated
using a deep learning algorithm yesterday.
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And here is some text that
an algorithm out Stanford generated.
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Each of these sentences was generated
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by a deep learning algorithm
to describe each of those pictures.
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This algorithm before has never seen
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a man in a black shirt playing a guitar.
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It's seen a man before,
it's seen black before,
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it's seen a guitar before,
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but it has independently generated
this novel description of this picture.
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We're still not quite at human
performance here, but we're close.
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In tests, humans prefer
the computer-generated caption
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one out of four times.
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Now this system is now only two weeks old,
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so probably within the next year,
the computer algorithm will be
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well past human performance
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at the rate things are going.
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So computers can also write.
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So we put all this together and it leads
to very exciting opportunities.
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For example, in medicine,
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a team in Boston announced
that they had discovered
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dozens of new clinically-relevant features
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of tumors which help doctors
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make a prognosis of a cancer.
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Very similarly, in Stanford,
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a group there announced that,
looking at tissues under magnification,
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they've developed a machine
learning based system
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which in fact is better
than human pathologists
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at predicting survival rates
for cancer sufferers.
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In both of these cases, not only
were the predictions more accurate,
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but they generated new insightful science.
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In the radiology case,
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they were new clinical indicators
that humans can understand.
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In this pathology case,
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the computer system actually discovered
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that the cells around the cancer
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are as important as
the cancer cells themselves
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in making a diagnosis.
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This is the opposite of what
pathologists had been taught
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for decades.
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In each of those two cases,
they were systems developed
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by a combination of medical experts
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and machine learning experts,
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but as of last year,
we're now beyond that too.
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This is an example of
identifying cancerous areas
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of human tissue under a microscope.
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The system being shown here
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can identify those areas more accurately,
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or about as accurately,
as human pathologists,
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but was built entirely with deep learning
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using no medical expertise
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by people who have
no background in the field.
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Similarly, here, this neuron segmentation.
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We can now segment neurons
about as accurately as humans can,
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but this system was developed
with deep learning using people
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with no previous background in medicine.
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So myself, as somebody with
no previous background in medicine,
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I seem to be entirely well qualified
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to start a new medical company,
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which I did.
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I was kind of terrified of doing it,
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but the theory seemed to suggest
that it ought to be possible
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to do very useful medicine
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using just these data analytic techniques.
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And thankfully, the feedback
has been fantastic,
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not just from the media
but from the medical community,
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who have been very supportive.
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The theory is that we can take
the middle part of the medical process
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and turn that into data analysis
as much as possible,
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leaving doctors to do
what they're best at.
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I want to give you an example.
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It now takes us about 15 minutes
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to generate a new medical diagnostic test
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and I'll show you that in real time now,
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but I've compressed it
down to three minutes
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by cutting some pieces out.
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Rather than showing you
creating a medical diagnostic test,
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I'm going to show you a diagnostic test
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of car images,
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because that's something
we can all understand.
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So here we're starting with about
one and a half million car images,
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and I want to create something
that can split them into the angle
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of the photo that's being taken.
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So these images are entirely unlabeled,
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so I have to start from scratch.
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With our deep learning algorithm,
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it can automatically identify
areas of structure in these images.
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So the nice thing is that the human
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and the computer can now work together.
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So the human, as you can see here,
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is telling the computer
about areas of interest