-
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 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 practictioners.
-
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 result's 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
-
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,
-
who have been very supportive.
-
The theory is that we can take
the middle part of the medical process
-
and turn that into data analysis
as much as possible,
-
leaving doctors to do
what they're best at.
-
I want to give you an example.
-
It now takes us about 15 minutes
to generate a new medical diagnostic test
-
and I'll show you that in real time now,
-
but I've compressed it down to
three minutes by cutting some pieces out.
-
Rather than showing you
creating a medical diagnostic test,
-
I'm going to show you
a diagnostic test of car images,
-
because that's something
we can all understand.
-
So here we're starting with
about 1.5 million car images,
-
and I want to create something
that can split them into the angle
-
of the photo that's being taken.
-
So these images are entirely unlabeled,
so I have to start from scratch.
-
With our deep learning algorithm,
-
it can automatically identify
areas of structure in these images.
-
So the nice thing is that the human
and the computer can now work together.
-
So the human, as you can see here,
-
is telling the computer
about areas of interest
-
which it wants the computer then
to try and use to improve its algorithm.
-
Now, these deep learning systems actually
are in 16,000-dimensional space,
-
so you can see here the computer
rotating this through that space,
-
trying to find new areas of structure.
-
And when it does so successfully,
-
the human who is driving it can then
point out the areas that are interesting.
-
So here, the computer has
successfully found areas,
-
for example, angles.
-
So as we go through this process,
-
we're gradually telling
the computer more and more
-
about the kinds of structures
we're looking for.
-
You can imagine in a diagnostic test
-
this would be a pathologist identifying
areas of pathosis, for example,
-
or a radiologist indicating
potentially troublesome nodules.
-
And sometimes it can be
difficult for the algorithm.
-
In this case, it got kind of confused.
-
The fronts and the backs
of the cars are all mixed up.
-
So here we have to be a bit more careful,
-
manually selecting these fronts
as opposed to the backs,
-
then telling the computer
that this is a type of group
-
that we're interested in.
-
So we do that for a while,
we skip over a little bit,
-
and then we train the
machine learning algorithm
-
based on these couple of hundred things,
-
and we hope that it's gotten a lot better.
-
You can see, it's now started to fade
some of these pictures out,
-
showing us that it already is recognizing
how to understand some of these itself.
-
We can then use this concept
of similar images,
-
and using similar images, you can now see,
-
the computer at this point is able to
entirely find just the fronts of cars.
-
So at this point, the human
can tell the computer,
-
okay, yes, you've done
a good job of that.
-
Sometimes, of course, even at this point
-
it's still difficult
to separate out groups.
-
In this case, even after we let the
computer try to rotate this for a while,
-
we still find that the left sides
and the right sides pictures
-
are all mixed up together.
-
So we can again give
the computer some hints,
-
and we say, okay, try and find
a projection that separates out
-
the left sides and the right sides
as much as possible
-
using this deep learning algorithm.
-
And giving it that hint --
ah, okay, it's been successful.
-
It's managed to find a way
of thinking about these objects
-
that's separated out these together.
-
So you get the idea here.
-
This is a case not where the human
is being replaced by a computer,
-
but where they're working together.
-
What we're doing here is we're replacing
something that used to take a team
-
of five or six people about seven years
-
and replacing it with something
that takes 15 minutes
-
for one person acting alone.
-
So this process takes about
four or five iterations.
-
You can see we now have 62 percent
-
of our 1.5 million images
classified correctly.
-
And at this point, we
can start to quite quickly
-
grab whole big sections,
-
check through them to make sure
that there's no mistakes.
-
Where there are mistakes, we can
let the computer know about them.
-
And using this kind of process
for each of the different groups,
-
we are now up to
an 80 percent success rate
-
in classifying the 1.5 million images.
-
And at this point, it's just a case
-
of finding the small number
that aren't classified correctly,
-
and trying to understand why.
-
And using that approach,
-
by 15 minutes we get
to 97 percent classification rates.
-
So this kind of technique
could allow us to fix a major problem,
-
which is that there's a lack
of medical expertise in the world.
-
The World Economic Forum says
that there's between a 10x and a 20x
-
shortage of physicians
in the developing world,
-
and it would take about 300 years
-
to train enough people
to fix that problem.
-
So imagine if we can help
enhance their efficiency
-
using these deep learning approaches?
-
So I'm very excited
about the opportunities.
-
I'm also concerned about the problems.
-
The problem here is that
every area in blue on this map
-
is somewhere where services
are over 80 percent of employment.
-
What are services?
-
These are services.
-
These are also the exact things that
computers have just learned how to do.
-
So 80 percent of the world's employment
in the developed world
-
is stuff that computers
have just learned how to do.
-
What does that mean?
-
Well, it'll be fine.
They'll be replaced by other jobs.
-
For example, there will be
more jobs for data scientists.
-
Well, not really.
-
It doesn't take data scientists
very long to build these things.
-
For example, these four algorithms
were all built by the same guy.
-
So if you think, oh,
it's all happened before,
-
we've seen the results in the past
of when new things come along
-
and they get replaced by new jobs,
-
what are these new jobs going to be?
-
It's very hard for us to estimate this,
-
because human performance
grows at this gradual rate,
-
but we now have a system, deep learning,
-
that we know actually grows
in capability exponentially.
-
And we're here.
-
So currently, we see the things around us
-
and we say, "Oh, computers
are still pretty dumb." Right?
-
But in five years' time,
computers will be off this chart.
-
So we need to be starting to think
about this capability right now.
-
We have seen this once before, of course.
-
In the Industrial Revolution,
-
we saw a step change
in capability thanks to engines.
-
The thing is, though,
that after a while, things flattened out.
-
There was social disruption,
-
but once engines were used
to generate power in all the situations,
-
things really settled down.
-
The Machine Learning Revolution
-
is going to be very different
from the Industrial Revolution,
-
because the Machine Learning Revolution,
it never settles down.
-
The better computers get
at intellectual activities,
-
the more they can build better computers
to be better at intellectual capabilities,
-
so this is going to be a kind of change
-
that the world has actually
never experienced before,
-
so your previous understanding
of what's possible is different.
-
This is already impacting us.
-
In the last 25 years,
as capital productivity has increased,
-
labor productivity has been flat,
in fact even a little bit down.
-
So I want us to start
having this discussion now.
-
I know that when I often tell people
about this situation,
-
people can be quite dismissive.
-
Well, computers can't really think,
-
they don't emote,
they don't understand poetry,
-
we don't really understand how they work.
-
So what?
-
Computers right now can do the things
-
that humans spend most
of their time being paid to do,
-
so now's the time to start thinking
-
about how we're going to adjust our
social structures and economic structures
-
to be aware of this new reality.
-
Thank you.
-
(Applause)