In today's world, AI is everywhere.
- What appointments do I have today?
- Today is the Royal Institution Christmas Lectures.
- Play me a "wake me up" morning mix.
- # Another day's passing in your life... #
- It's popping up in places we could never have imagined
a few years ago.
- # I'm on my way... #
- Mike, you all set for tonight?
I love your background!
- Yeah, I'm just getting in the Christmas mood.
- Computer, will I need a coat tonight?
- Yes, take a coat.
- How aware are we of the AI in our everyday lives?
- # We're on a ride... #
- Where can we find it?
- # Ain't got no money... #
- What does it do?
- # You can't ignore it... #
- That's what we'll be asking in tonight's lectures.
APPLAUSE
Hello, I'm Mike Wooldridge,
and welcome back to the second of this year's
Christmas lectures from the Royal Institution,
an event supported by CGI.
In the last lecture, we saw how today's
artificial intelligence was inspired by the human brain.
In this lecture, we're going to investigate
how AI is shaping our lives today,
and we're going to see how games have played
an incredibly important role in advancing AI.
We're going to come back to the video that we just saw
a little bit later on in the lecture,
and we're going to discuss exactly where AI features in that video.
But first, we're going to start by exploring how AI
features in one of our most popular forms of entertainment -
video games.
Please welcome top GT driver Martin Grady,
and from Sony AI, Kaushik Subramanian.
APPLAUSE
Welcome, Martin. - Thank you.
- Take a seat...
..and make yourself comfortable.
Now, Martin, how long have you been racing?
- I've been racing for 25 years.
And I've been a pro-racing driver for 12 of those.
- OK, so what we're going to get you to do today
is we're going to get you to play against AI.
We're going to get you to play against a racing AI
that was developed by Sony AI
for the game of Gran Turismo.
By the way, who in the audience has got one of these things at home?
Anybody?
A couple of people have got one of those.
So, Martin, you get yourself going.
Kaushik, tell us, what did you want to do when you built this AI?
- Right, the first challenge what we asked ourselves is,
can we develop an AI that can race to beat the world's best
Gran Turismo racers in a competitive race?
And can the AI learn the required skills to perform at that level?
It turns out we can.
- Kaushik, where exactly is Sophy appearing in this, the AI?
- So, Martin's raced with 19 other AI cars,
and they're all controlled by Gran Turismo Sophy here.
And the way you can see that is,
if you look on the left-hand side,
you can see all the names of the player cars.
And there's a logo which looks like a heart,
and all of those logos are the Gran Turismo Sophy cars.
And all of those cars, they know what car they're driving,
they're aware of each other
and they're of course aware of Martin as well.
- OK, but they're not all trying to gang up on Martin, are they?
They're all independent?
- They're all trying to get to first place independently.
- So how did Sophy, the AI, get good enough in this race?
- This AI, Gran Turismo Sophy,
is trained using an approach called reinforcement learning,
an approach where the AI learns by trial and error.
So, at the start, it really doesn't know much about driving,
much less the rules of racing.
- OK, I think we've got the video showing here of the training.
- Right, and when you watch this,
you're going to see the cars colliding,
you're going to see the cars completely losing control,
going off the track.
And this is just within a few hours of training.
This is what it looks like.
And after a while, with reinforcement learning,
it tries many different things
and then it learns, finally, the right sequence
of actions to be able to race,
like the way Martin's looking at right now.
- LAUGHTER - Or crashing, apparently.
OK, now, we're going to come back later on in the lecture
and we're going to learn a lot more about reinforcement learning.
But, Kaushik, why is a video game like this so difficult
for reinforcement learning,
and just for machine learning generally?
- Racing has a lot of variability.
There are everchanging situations.
When you're driving at the edge of control
in those situations, that can be hard.
Now, in Gran Turismo,
players have access to nearly 500 different cars,
and when you take those cars and you put them in a race,
those cars can interact in many different ways
over multiple laps.
And in all of those situations,
the AI would need to be skilful and reliable,
and that can be challenging.
- OK. And how long did it take to train the AI that we're seeing?
- A single experiment takes about 25 PlayStations
and trains for nearly two weeks before it can race
with other human players.
- OK, so, Martin, tell us, how are you finding this?
Is it different to what you've used previously?
- Yeah, it's completely different.
In the past, you would just race a standard car,
just going on the same line over and over.
Here, you can see they're going defensive,
they're reacting to what I do.
So, if I go for a move, they're going to move to defend that.
- They can try and block you as you try to overtake?
- Yeah, they react to my reactions,
so then I have to react again.
And it just makes it so much more fun to be able to play against that
because there's endless possibilities then.
As you can see, they're going to the right-hand side.
It's just really good fun now, cos now I've got
nowhere to go, essentially. - OK!
Now, Martin, I can't help but notice you appear to be playing this game
wearing a pair of socks.
Is that a pro tip for playing Gran Turismo?
- It is a pro tip.
So take your shoes off, play in your socks.
You get a more sensitive feel on the pedal
and it allows you to just race that a little bit quicker.
- OK, listen to the experts, kids.
OK. Thank you, Martin.
Thank you, Kaushik, for coming and showing us this game.
APPLAUSE Thank you.
How did the AI get so good?
Well, we heard Kaushik mention reinforcement learning.
That's what Kaushik and his team used to train GT Sophy.
So what is reinforcement learning and how does it work?
Now, to explain, I need some help,
but my next guest is a little bit nervous.
So, when she comes on, please, let's just have silent applause.
Please keep it quiet. Don't alarm her.
So, please, let's welcome Caitlin and her dog, Freya.
FREYA BARKS
- Hello!
- Hello, Caitlin.
Hello, Freya. - Hi.
- Welcome to the Christmas lectures.
- Thank you. - So, Caitlin, how long have you been working with Freya?
- So, Freya and I have been working together for about three years.
Her mum kindly let us work together when I was training
to be a puppy school tutor, and now...
- FREYA BARKS
- ..just started some trick training together.
So she very much likes to use her voice,
so I apologise for the ears.
- OK.
So what are you going to show us today, Caitlin?
How are you going to show us how you train Freya? - Yes!
So, today, Freya is going to show us how to pickpocket someone
with a handkerchief.
What we're going to do is use positive reinforcement
to tell her that when she pulls on the handkerchief,
then she gets a reward for that.
So I use something called a marker.
So, when I tell her to get it, I'll tell her yes
and reward her for what she's done.
That's the correct thing to do.
So I'm going to tell her to get it.
Yes! Good girl! - OK.
- Very nice.
So first I want to get her interested in this.
Yes! - OK, I think she's interested.
- Nice!
So the way I do that is just make it interesting like this.
If she pulls on it... Yes!
And then give her that reward.
And then I want to add a cue to it.
So a little... Freya, get it!
Yes! Good girl.
And then reward.
So it's just following that pattern. - OK.
Now, rewards seem to be really important to Freya.
Is this a big part of how she learns?
- Absolutely. So what gets rewarded gets repeated.
So the more I reward her for the thing I want her to do,
the more it's going to happen.
But what I want to make sure of
is that she's also responding to the cue.
So, when she pulled it there, I didn't give her the reward
because I want her to respond to that to get it. - Because you hadn't given her the cue. - Exactly, yeah.
And now the last part of this is the pickpocket part,
which I will need your assistance. - OK.
- If that's OK? - Yes.
- So I'm going to give you that.
- CAITLIN LAUGHS
- And if you just pop that in your back pocket for me.
Freya, come, come!
Good girl! Sit! - OK.
- Nice. Stay. - The things I do for the Christmas lectures.
- Freya, get it!
- FREYA BARKS
- Yes! Good job!
Very nice!
- CAITLIN LAUGHS FREYA BARKS
MIKE LAUGHS
Freya, you're a lot smarter than my dog,
I have to say.
Well done.
OK, thank you so much, Caitlin. Thank you, Freya.
Remember, everybody, polite quiet applause, please.
Thank you so much. - Thank you!
- Come on, Freya! Let's go! - FREYA BARKS
- She doesn't want to leave!
So how is Freya learning?
She's given a treat every time she does a task well.
And in AI, we call that reinforcement learning.
In the last lecture, we saw how AI learns from data,
from training data.
Now, reinforcement learning is similar,
but the training data in this case comes in the form of rewards.
And reinforcement learning is really good for games
like Gran Turismo
because most games have a score and we can use scores as the reward.
When the AI scores a point, that's a reward.
And what the AI is designed to do is to learn
how to maximise its reward,
to maximise its score,
to get as many rewards as possible as quickly as possible,
just like Freya getting her treats.
And we can use neural networks to learn all that.
That's called deep reinforcement learning.
But sometimes reinforcement learning might not give us the outcomes
that we were hoping for,
and we're going to have a game to illustrate this idea.
Can I have a volunteer, please?
In the green there? Yeah, you come down.
Yes, you've been selected!
APPLAUSE
Come on down. Just stand here. What's your name? - Nivea.
- OK, well, welcome to the Christmas lectures.
Now, I'm going to give you a task.
I'm going to give you some instructions.
And I just want you to follow those instructions, OK?
Are you nimble on your feet? Are you a good runner? - Yes.
- OK, excellent. Then you're going to be perfect for this.
Now, what we've done is we've placed five bells
around the lecture theatre.
One there. One there.
One there. Very nimble on my feet, as you can see.
One there.
And finally, one there.
And what we're going to do is we're going to give you
ten seconds,
and I want to hear as many bell rings as possible.
- Mm-hm. - Got it? - Yes. - Yeah.
OK, all right, what we're going to do is
we're going to count you down from three,
and then we're going to say go, all right?
Got it?
Three, two, one.
Go! AUDIENCE: - Go!
- BELL RINGS
Go, go, go! - Go, Nivea! Run!
BELL RINGS Let's hear those bells!
- Come on! Pick up the pace!
- One, stop!
OK, how many bell rings?
Come back to the middle. We heard five, I think.
Was that about right, everybody?
Yeah? We heard five bell rings.
That was really, really good, OK?
Thank you so much for that. You can go back to your seat.
APPLAUSE AND CHEERING
Now we're going to carry out the same challenge,
but with artificial intelligence.
So please welcome the Royal Institution's
special bell-ringing robot!
Come on, bell-ringing robot.
APPLAUSE
DRILL WHIRS REPEATEDLY
Give me that. Honestly.
OK!
Now, I'm going to give you ten seconds.
I want to hear as many bell rings as possible.
So let's count down the robot.
Three, two, one, go! - Go!
- BELL RINGS
Stop!
Did anybody manage to keep track of how many that was?
OK, robot, thank you very much.
There you go.
DRILL WHIRS REPEATEDLY
APPLAUSE
So, our volunteer,
you didn't do anything wrong at all.
Because you're a human being.
I gave you some instructions
and you tried to interpret what I wanted you to do.
And what I wanted in my instructions
is to hear somebody running around the lecture theatre
pressing each of those bells.
But actually, those weren't exactly the instructions I gave you.
What I said is, I just wanted to hear
as many bell rings as possible.
And our robot took the instructions literally.
And the quickest way the robot could get a reward
was just to stand there and bang that bell.
The AI found a way to maximise its rewards
without doing what I wanted it to do.
The way we set up rewards when we use reinforcement learning
is really important
because sometimes we can set up rewards
so that the AI discovers a way to maximise its rewards
without doing what we wanted it to do.
And that's what the robot did there.
And now what we're going to see is a real example.
So let's have a look at this video on the screen.
In 2014, the AI company DeepMind
trained an AI program to play this game.
It's a 1970s video game called Breakout,
and it uses reinforcement learning.
The more it plays, the better it gets.
Now, at the beginning, most of the time, as you'll see,
it's just missing.
And if it manages to hit the ball, it's just really pure chance.
But every time it knocks a brick out,
it gets a point.
Now, after a bit more training, watch, it never misses.
It's reliably hitting that ball back every single time.
And you might think that's actually about as good as it's going to get.
It's hitting the ball every single time.
But look what happened when they trained it
just a little bit longer.
Look at the left there. Look what goes on.
It discovered completely on its own
that the way to maximise its score is to drill a hole
down the side of the wall and bounce the ball above.
So games are fun for us to play,
but they're also a great proving ground for AI
because they can provide big challenges for AI
without the possibility of hurting anybody or damaging anything.
And, earlier, we spoke to the CEO of DeepMind,
the company behind that video, Demis Hassabis.
You can see at the beginning, the AI, by the way,
is trying to focus on his image
and not doing a terribly good job of it.
I asked him about the importance of games
for artificial intelligence.
- Games and AI have always had a long history together.
Actually, if you go all the way back to Turing and Shannon,
who sort of invented the field of AI,
they all started off with things like chess programs,
and trying to figure out how could a machine,
for example, play chess well?
And we use games at DeepMind as a testing ground
for our AI ideas and algorithmic ideas
because you need a clear metric to measure them against.
And, of course, games usually have scores
that you can optimise or a win-loss condition,
You know, so you can track very clearly if you're making progress.
- So let's take a closer look at game-playing programs.
OK. And one of the easiest games to play
is noughts and crosses.
Who plays noughts and crosses?
Can you all play noughts and crosses?
OK. In the United States,
they don't call it noughts and crosses, they call it tic-tac-toe.
And because the US is so big in AI,
we have to call it tic-tac-toe in AI as well.
I need a volunteer.
Who's really good at playing tic-tac-toe?
OK, you at the back. The second from the end.
Come on down!
Hello. Come here.
What's your name? - Emmy.
- Emmy? - Yes. - OK, thanks for joining us, Emmy.
What I'd like you to do is come and stand over...
Just stand about here.
OK. Now, Emmy, we are going to get you to play tic-tac-toe
live against the Royal Institution supercomputer.
Just stand back a tiny little bit.
And this tic-tac-toe supercomputer is called BrodeRick.
What we're going to do is
we're going to get you to play against
a completely untrained BrodeRick.
BrodeRick is going to be the crosses player.
You are going to be the noughts player.
And you're just going to move your piece
just by pressing one of those buttons.
So BrodeRick's moved first.
You see where BrodeRick's moved?
And now you can play one of your pieces.
OK, moving to the middle.
Smart move.
BrodeRick moves.
OK, you need to respond.
OK, BrodeRick's going to respond again.
You get to go again.
And you've won!
APPLAUSE
Well done!
Turn and face the audience!
Take the applause.
You never know when it's going to come in life.
OK, so, well done.
You beat BrodeRick.
But BrodeRick hasn't got a clue how to play this game.
So what we're now going to do is we're going to train BrodeRick
to play tic-tac-toe,
and we're going to train it using the technique
that we've been talking about, the same technique that we saw
with Caitlynn and Freya - reinforcement learning.
So, this time, what we're going to do is tell BrodeRick
to train itself, and we're going to do that
by selecting number of players - zero.
So do you want to select number of players - zero?
And this is number of human players - zero.
BrodeRick is just going to play against itself.
Now, stay on, Emmy, because you're going to be playing
the trained model in a moment.
And what we're seeing now is BrodeRick
starting to play itself.
And it's cycling through more and more games.
And every time it wins a game, it gets a reward.
Every time it loses, it gets a punishment.
When it gets a reward,
that makes it more likely to play the moves it played again.
And we're seeing lots of games coming up on the screen,
but we're only seeing a tiny fraction of the total number
of games that BrodeRick is playing.
BrodeRick, training complete!
It's played 20,000 games of tic-tac-toe
while we've been watching it.
Well, BrodeRick's trained.
But, BrodeRick, how confident are you feeling?
- Ah! BANG!
- BrodeRick, it seems, is feeling very confident indeed.
Emmy, how confident are you feeling?
- I'm quite scared now. - You're quite scared now?
There's nothing to be scared of, Emmy.
Exactly the same procedure.
Emmy, over to you.
So we've trained BrodeRick.
BrodeRick should be better.
Let's see if that's the case.
OK.
Well spotted.
OK.
So it's a draw.
How do you feel about that, Emmy? - Not that bad.
- It's not bad at all,
because BrodeRick is basically a perfect tic-tac-toe player.
And if you have two perfect tic-tac-toe players
like BrodeRick and Emmy playing against each other,
that's what they're going to get - they're going to get a draw.
Now, BrodeRick can't guarantee to win every time it plays,
but it's never going to lose any more.
It's completely different to the untrained model
that we saw earlier on.
OK. Now, in fact, under the hood, I have to tell you,
it is not a supercomputer at all.
Dan from the demo team is going to show us the truth
about BrodeRick.
And the truth about BrodeRick is that the computer
is, in fact, this.
It's a very basic computer.
It's a £30 computer.
That's all you need to learn how to play tic-tac-toe.
So, Emmy, thank you for coming down and playing against BrodeRick.
APPLAUSE And thank you, BrodeRick.
Tic-tac-toe is a simple game.
And one of the most important reasons it's simple
is the following.
The average number of moves that you can make
at any point in tic-tac-toe is around about four.
You start with nine possible moves for the crosses player,
then the next player has eight possible moves and so on.
On average, you've got about four possible moves
available to you.
And we call that the branching factor of the game.
But even a branching factor of four
means that there are close to 20,000 different ways
that we can fill in a tic-tac-toe grid.
Let's compare that to the game of chess.
This is a much more interesting and much more difficult game.
And one of the reasons that it's much more interesting
and difficult is that the branching factor is larger.
It's around about 35.
And, remember, what that means
is that from any position on the board,
on average -
not for every possible move, but on average -
you've got about 35 possible moves available.
Now let's have a look at another game.
This is the game of Go.
It originated in ancient China,
but it's still hugely popular today in Asia.
The aim in the game of Go
is you're playing your black stones or white stones.
And what you want to try and do is to cover as much
of the board as possible with your stones
and to surround your opponent's stones on the board.
And you just take it in turns to place your stones.
So it's a very simple game.
It only really has three rules.
But the branching factor of Go makes it phenomenally hard
for human beings to play,
and it makes it very, very hard for artificial intelligence.
And that's what made it an important target for AI.
Go has a branching factor of around about 250.
Remember, that branching factor,
that's the average number of moves
that a player can make at any given time.
Let's illustrate those numbers.
If you take a look under your seat, you should find a card.
OK? So take a look at the card.
You don't need to do anything yet. Just take a look at the card.
Now, if your card has a tic-tac-toe grid on it,
stand up now.
OK, so here we are.
We've got four people in the audience
holding a tic-tac-toe grid.
That's the branching factor of tic-tac-toe.
If your card has a chess board on it,
which looks like this,
then stand up now.
That's 35 of our audience members that are standing up now.
That's the branching factor of the game of chess.
And, finally, if you have a Go board underneath your seat,
something that looks like this,
stand up now.
That's pretty much the entire audience.
That's the branching factor of the game of Go,
and compare that to the branching factor of the game of chess
and those four people that were stood up there at the beginning.
OK, everybody, you can sit down now. Thank you.
Let's think about the number of game states there are in the game of Go.
And it's this number -
ten to the power of 170.
We can hardly fit that number on the screen.
We never encounter numbers that large
in our everyday life.
They are literally astronomical.
And what we've learned today is a really important lesson about AI.
Trying to solve a problem by simply looking at all the alternatives
is called brute force.
And the AI lesson is this.
For problems like playing Go, brute force doesn't work.
It will never work.
Because the numbers are just too astronomically large.
Even if we turned the entire universe into a computer,
it wouldn't be able to play just using brute force.
So we need some way of reducing the number of alternatives to check.
And these are often called heuristics.
And we can use machine learning to help us build heuristics.
And I asked Demis Hassabis
how DeepMind trained AI to play Go.
- In the case of AlphaGo,
AlphaGo learnt for itself how to play Go,
and the strategies it would use and so on,
by playing against itself many millions of times.
And it used learning systems,
so reinforcement learning and tree search techniques,
in order to figure out for itself what the right strategies
were to be good at Go.
- In 2016, AlphaGo played against and beat Lee Sedol,
one of the world's greatest players.
And a lot of people didn't expect that.
- And so, of course, the cool thing about that is that, actually,
AlphaGo, not only did it beat the world champion in 2016,
it also came up with completely new ideas about Go -
new strategies that no human players had ever thought of,
even though Go is several thousands of years old.
- AlphaGo's achievement was a really big step forward
in the world of AI.
Because of its complexity,
we thought that Go was a barrier that AI wouldn't overcome
for another decade, or maybe even more.
And games like Go have been a really great training ground for AI,
and the knowledge that we've derived from building AI to play games
has led to major breakthroughs in science,
as we're going to see later on.
And, increasingly, AI is embedded in our lives,
everywhere that we look.
Let's go back to that video that we showed you
at the beginning of the lecture.
And this time, when you watch it, I want you to try to keep count
of how many times you think the AI appears.
- What appointments do I have today?
- Today is the Royal Institution Christmas lecture.
- Play me a "wake me up" morning mix.
- # Another day's passing in your life
# I heard you saying
# Now is our time
# I'm on the highway I'm on my way... #
- Mike, you all set for tonight?
I love your background!
- Yeah, I'm just getting in the Christmas mood.
- Computer, will I need a coat tonight?
- Yes, take a coat.
- # This time I ain't Ain't gonna stop
# We're on a ride to reach the top
# Ain't got no money But I'm full of fun
# You can't ignore it No, you won't run away... #
- I'd like to introduce you to the person who made that video,
science author and broadcaster Dr Emily Grossman.
APPLAUSE
- Hiya. - Hello.
- Hi.
- And welcome, Emily!
- Thank you.
- Was what we watched just a normal day for you?
- Yeah, pretty much.
I mean, it was a pretty typical day,
although I don't usually end up at the Christmas lectures.
- OK. Now, Emily, before you came on, I asked the audience to watch
that video and to keep track of how many times they thought
that AI appeared in it.
Shall we ask the audience?
- Yeah, let's do that. - OK, over to you.
- OK, what do you think? How many times?
- 14. - 14 times?!
Any other...? Any other suggestions?
Over here, yeah!
- Er, ten. - Ten times.
- Emily, how many times did you think
that AI appeared in that video?
- Well, actually, somewhere between the two.
It was actually 12 times that AI was in the video.
So very close, both of you.
But you've got to remember that those were only the times
that I was interacting with AI, like, directly.
So, like, when I was walking down the street,
there might've been loads of stuff around that was using AI
that I was sort of indirectly interacting with.
So it could've been actually loads more than 12.
- I think there probably was loads more than 12.
We just don't realise it. - Exactly.
- OK, were you surprised how many times
AI appears in your everyday life?
- I was, like, totally surprised.
So, before I made this video, I had no idea
how many times I might be interacting with AI
on a daily basis.
So I've got, like, a sleep app on my phone,
and I know that I use that pretty much every day,
but I had no idea that that might have AI in it.
And also things like an app for maps and even traffic lights.
I just didn't realise all of that could be using AI as well.
- OK, Emily, thanks so much for joining us.
We really appreciate it. - Thank you. Thank you.
- CHEERING AND APPLAUSE
So, from that video, it's clear that AI is embedded
in our lives already everywhere.
Something like talking to a voice-activated personal assistant
or using an automated translation app.
Now, those things can very quickly get mundane.
They seem very ordinary.
But I have to tell you,
it's incredibly frustrating for AI researchers
when they deliver something like automated translation
and everybody starts taking it for granted.
But there are some areas where AI
should never be taken for granted,
where AI is saving lives.
Let's have a look at one area that AI has the potential
to have a beneficial impact on a global scale,
where literally it will save lives and make lives better.
AI in health care.
Let's start by talking about Parkinson's disease.
And to help explain this disease,
I'd like you to welcome Dr Rutger Zietsma
and former BBC technology correspondent
Rory Cellan-Jones.
APPLAUSE
Welcome, Rutger. Welcome, Rory.
So, Rory, first, can you tell us a little bit about
what Parkinson's is and how it manifests itself?
- Well, Parkinson's disease is a degenerative brain disease.
It's basically what happens when the dopamine cells
in your brain begin to die.
And that affects an awful lot of things.
And the symptoms include a tremor, insomnia,
stiffness in movement.
Tiny writing is another symptom.
There are a whole bunch of symptoms
and it's very various.
Lots of people have one symptom, but not another.
- OK, now, you have a really amazing story
about how you came to be diagnosed. Can you tell us about that?
- Well, I first suspected something
when I was walking with my wife on holiday
and I kept dragging my right foot.
But then, a month later, I was on the television
doing a live broadcast from the island...
- We've got the broadcast here.
- Yeah, from the island of Jersey.
And as you can see, I didn't notice this at the time at all,
but my hand was shaking quite violently.
Somebody saw it, a neuroscience expert,
and wrote in to the BBC and said,
"You ought to get that sorted out
"because that could be a symptom of Parkinson's disease."
- OK, it's really incredible that you actually...
Actually, what drew your attention to it
is somebody spotting a broadcast that you were doing.
OK, Rory, you take a seat.
Rutger, tell us, how is AI being used to help
diagnose Parkinson's disease?
- Yes, I would like to introduce to you the NeuroMotor Pen,
ideally with a brief demonstration.
Rory, would you mind giving it a go?
- I will give it a go.
- All you need to do is draw an EL figure
and do it about five times.
The pen measures movements,
but there's a link between the movement in the brain
and the remote function.
So we're effectively looking at what's going on with the brain
and if a part of the brain controls fine movement,
it might perhaps be affected by, for example,
Parkinson's disease.
So it basically calculates digital biomarkers,
the symptom that the patient may be suffering from -
things like tremor, a rhythmical shake
or slowness of movement.
- So why is this better or more preferable
to a human diagnosing?
- So the human will still be involved.
The clinician is still in charge of making the diagnosis,
but this device provides you with accurate,
objective information, and that's really useful, especially
if it's very subtle and it's not very obvious, when it's not easy
to spot or interpret.
So it makes the process a little bit easier and you can make
a faster diagnosis.
- OK. And what does it mean for people like Rory who've actually
been diagnosed with the disease? What does it mean for them?
- Well, Parkinson's disease develops differently
in different individuals.
So a treatment is ideally individualised for the patient.
Currently, it's a big challenge to find out how well the person
is doing today compared to, for example, three months ago.
By taking these accurate readings, you can find out how much
of a drug they need in order to feel well.
- Now, I think we're ready to take a look at Rory's results,
and they should be on this monitor over here.
So perhaps you'd like to both join me, and Rutger,
if you could just talk us through what we're seeing here?
- OK. So the first output of the system was this was a confirmation
of Parkinson's disease.
- I've got Parkinson's disease?! - Correct. - Thank you for that.
- And looking at the symptoms, so these are Rory's symptoms.
This is interesting, because they're all sort of moderate
to high, except tremor is relatively low.
The reason is that he's using medication,
and using that medication, there's less of a tremor.
- OK. So these would be more over, the more severe the symptoms,
is that right? - That's correct. Yeah.
The other thing that's interesting here is the micrographia.
So, as Rory was drawing, you could actually see the size
of the writing deteriorates.
It became smaller as he continued to write.
OK, let's now have a look at somebody
with severe Parkinson's disease who's not on any drug.
These numbers are obviously a lot higher, simply because
there's no suppression of, for example, the tremor.
So what we're seeing here is really complex assessment
made very simple. - OK.
9.9 looks pretty dramatic there, doesn't it?
Rory, what do you think of this? What do you make of this?
- Well, this is really important because the trouble with Parkinson's
is actually measuring it, because mostly it's the doctor
telling you to walk up and down outside his office,
get up out of a chair and do various exercises.
This is beginning to develop an objective,
scientific way of measuring it.
And if you can measure it, then that's really important
because you know whether the patient's getting worse,
you know, for instance, if medication is working.
And if you're searching for a wonder drug,
you need to be able to measure what people are like before
and after much more accurately. - OK.
Thank you, Rutger, for joining us. And thank you, Rory,
for sharing your story with us. Thank you. - Thank you.
- The pen uses AI to help spot Parkinson's by comparing
against large data sets.
But AI very soon might be able to provide far more precise
and sophisticated support for people that are pregnant.
To explain how,
please welcome Professor of Computer Science, Ana Namburete.
Welcome, Ana.
OK. - Thank you.
- So, Ana, you are working to improve the monitoring
of babies during pregnancy. - Yes.
So I'm developing algorithms to provide better health care
in pregnancy. And, in particular,
I'm interested in how we can monitor the growth
of a baby's brain when it's inside the womb.
Now, the best and most accurate way of doing this is by collecting
a 3D scan, and at present the technology that we use for this
is an MRI machine. But an MRI machine,
it's very large, it's very expensive. - Very expensive.
- And it's unsuitable for many people and it's unavailable
in most settings. - Whereas this?
- Well, the advantage of using AI in health care
is that it can revolutionise the way in which we use
existing medical technology.
So in pregnancy monitoring, we have been using ultrasound
machines for over half a century.
And while we can't have a pregnant woman or scan a pregnant woman
in a lecture theatre, we can scan a phantom,
which is the 3D model of a mother's abdomen.
So can I have a volunteer, please, to help me?
- OK, we need a volunteer for this. Hard to see with all the lights.
- Is there a keen person here? Yes? - Come on.
What's your name?
- Bea. - Bea. OK, Bea, Ana's going to show you what to do.
- OK, so what we're going to do is try to find the baby
inside the mother's belly.
So what you have here is an ultrasound probe.
You're going to place this in between your thumb
and your forefinger. How does that feel?
Comfortable? - OK. - OK.
So you're going to place it on the mother's belly,
and what you'll see is the image of the foetus emerging.
Do you see that? - Yeah.
- Yeah. What we're going to try to do is to find the profile view.
And what that is, is the view where you can see the forehead,
the nose, the lips and the neck. Yeah?
So we're going to just move the probe.
You can sort of see it there, right?
So what you see is this bright kind of oval, that's the skull.
And inside it, those are the brain structures.
And it's really important to study the brain because it's the most
complex organ in our bodies,
and it grows most rapidly when we're in the womb.
OK. So I think we can take that off. - OK. Thanks so much, Bea.
OK.
So, Ana, that that was an ordinary ultrasound.
But you're using AI, right?
You're going to show us how you use AI to help you with this.
- This is a typical ultrasound image of the baby's brain inside the womb.
And what you see, that's the bright oval again, right?
So that's the skull.
And what you notice is that we're looking at a video,
but it doesn't allow us to see or measure the brain in 3D.
Right? So what AI allows us to do is to turn this fairly grainy video
into a 3D reconstruction of the brain.
And the way it does that is by taking the video,
which is essentially image slices through the brain.
And that's what you see here on the right-hand side
as this red sheet.
And what the AI has done is that it's predicted the position
and orientation of the slice of the brain that we see
in the video, but in 3D space.
And ultimately it cleverly combines them to reconstruct or produce
a single 3D image.
And from this 3D image, it allows us to measure the growth of individual
brain regions and identify babies that may be in need
of further support. - Wow.
Ana, that's absolutely incredible work.
Thank you so much for coming and telling us about it. Thank you.
So AI is already pushing back the boundaries
across medical science, but it's also revolutionising
our understanding of biology.
To help us explore this, please welcome
Professor of Molecular Biophysics from King's College, Rivka Isaacson.
So welcome, Rivka. Now, Professor of Molecular Biophysics.
What do you do exactly?
- Well, in my lab, we use physics technology to work out the shapes
of proteins, which are the molecular machines that do all kinds
of important jobs inside our bodies.
- So why are proteins so complex?
- Well, they're made as a sequence of amino acids,
so it's almost like threading beads with all different shapes
and properties onto a string.
And then that straight line has to fold up into a really
complicated three dimensional structure like this,
which is lysozyme, a protein from our tears
that kills germs that fall into our eyes.
- OK, so why is knowing the structure
like this of a protein so important?
- Well, kind of like taking a machine apart to understand
the way it works.
We can't really do that with proteins because they're so small
and they're on the nano scale and they're in our tiny, tiny cells.
So we don't have an easy way to find out their shape.
But if you know their shape, then you can work out what they do
and how they stick to each other and how they move.
It's like clockwork, kind of.
- OK, so if you know their shape, you can understand how they can
help you or hurt you. Is that right? - Absolutely.
- OK. Now while we've been talking,
it has not escaped our audience's notice that hanging above us
is a rather large, threatening, and strange contraption.
So what is it?
- So apparently this is a protein,
and it's a lot bigger, 50 million times bigger
than the ones in my lab.
- So this is a sequence of amino acids, is that right?
The linear sequence of amino acids.
- I'm sure you can tell that's exactly what it is!
- We all knew immediately that this was a sequence
of amino acids! OK.
Well, OK, we're going to fold this sequence in a moment.
But before I do that, I want you to see if you can guess
what shape it's going to be.
Who wants to have a guess on the front?
What shape do you think it's going to be?
- I think it might be a sequence of rectangles.
- A sequence of rectangles. Anybody else? You on this side?
- I think it's going to be pentagons. - A pentagon? OK.
Both really good guesses.
But now we're going to find out.
OK, so, team, let's fold our protein!
OK!
It spells Royal Institution, and...
- My initials! - ..Rivka Isaacson!
We planned that all along!
OK. So, Rivka, what causes these amino acids to fold
in the way that they do?
- Well, so, for example, if you have a positive charge
over here and a negative charge over here,
then they want to be near each other in the same way that a magnet
would attract another magnet.
And there are lots of other different properties
of the amino acids that want to be in particular places
within the three dimensional structure.
And that's what helps it find its most energetically
favourable position.
- OK. So, finding out the structure of proteins is a really important part
of your job. How did you do it before AI?
- Well, there are lots of ways to do it, but they're all very expensive
and take years and years.
- And, so, getting to grips with protein structure, protein folding,
is a really important problem.
Rivka, thank you so much for coming and telling us about
such a difficult problem. Thank you very much. - Thank you.
- So how can AI help?
Let's welcome a scientist from the team responsible for one
of the most important scientific advances made possible by AI,
DeepMind's Kathryn Tunyasuvunakool!
Kathryn, welcome to the Christmas lectures.
So tell us, what is AlphaFold?
- So AlphaFold is an AI system that we developed for predicting
the structure of proteins.
And it takes as input that amino acid sequence and then...
- Like that big thing that we saw above us. - Exactly like that big sausage of drainpipes. - Yeah.
- And then it tries to output the folded up protein.
- OK. So how did you create it?
- So AlphaFold is an example of a neural network,
and we trained it using all of the experimentally determined
structures that Rivka talked about.
So there are thousands of those that have been painstakingly
determined by scientists.
We run AlphaFold on those training proteins and we compare its
prediction against the true structure,
and then we try and make it more accurate on those examples.
- So I think you can show us how AlphaFold is actually used
by scientists like Rivka? - Absolutely.
So AlphaFold can produce a new prediction in a matter
of minutes, or you can look up a prediction in the AlphaFold
protein structure database.
And once you have a prediction, it's useful for figuring out
what the protein does, how it interacts,
and for planning experiments.
So you can see the structure of one of our predicted proteins here,
and we actually provide confidence measures to help the scientists
understand which parts of this prediction they should rely on.
- So this is a real protein we're looking at now?
- This is a real protein, yes. - That is complex. - It really is.
It's much more complex than that RI example.
And when you look at the blue regions, those are the parts
where the model is confident, and the orange regions are parts
where the model is less confident.
So that helps scientists use this prediction.
- So once scientists know this kind of structure,
that's then going to allow them to focus on the stuff
that they really need to focus on, right? - Yeah, exactly.
Structural biology is all about interpreting these things.
- OK, Kathryn, thank you so much for joining us and telling us
about AlphaFold. - Thank you.
- I asked Demis Hassabis to explain the benefits of AlphaFold
for researchers.
- The rule of thumb is it normally takes one PhD student
their entire PhD, so up to five years,
just to find the structure of one protein
using complicated systems like X-ray crystallography.
And so we managed over the last couple of years
to fold all 200 million proteins known to science.
So, out there in nature.
And so only a tiny fraction of those were known before experimentally.
And if you times the five years of a PhD by 200 million,
you get a billion years of PhD time it potentially saved.
- What's the impact been of AlphaFold on people like Rivka?
- It's been amazing, the impact, and even over the last two or three
years, over a million researchers around the world have used
AlphaFold in its structures. We think that's almost
every biologist in the world. - Thank you, Dennis.
So AI is already answering really important problems
in the world of science and medicine.
But what about creativity and the arts?
For a long time, artists assumed that they were going to be immune
from the impact of AI.
So are artists under threat from developments in AI?
Surely it can't do what a human artist does?
To help us understand, please welcome artist Eric Drass.
Welcome, Eric.
So, Eric, tell us what you do?
- Well, I'm a visual artist and I've been working with
and collaborating with AI for about five years now.
- OK. And we're going to use AI to create some pictures live
in front of this audience, is that right? - We're going to try.
- And you're going to steer the process through your laptop, right?
So, what I need, we're going to ask you for some suggestions
of pictures to create.
So put your hands up if you'd like to suggest something
that you want the AI to create. OK.
Tell us, what picture would you like to see? - Apples.
- An apple? Just an apple? OK, Eric, do we think we can do that?
- I think we can probably do that, yeah. - OK. I didn't even manage to
get back to you and you'd already come up with a picture of an apple! - It's already there!
We've already got an apple. - OK. That certainly looks like an apple.
Do you think we can make it a bit more interesting?
- Well, let's see if we can make it out of Lego, perhaps.
- I'm not sure I would have guessed that that was an apple!
But, yeah, that's what you can do with Lego.
Let's have another, let's have a look at somebody else.
We haven't been at the back here, so let's just come to the back.
OK.
And you? - A dog. - A dog.
Do you want it doing anything? Begging?
- Chasing after a ball. - A dog chasing after a ball.
Great prompt. Eric, off you go. See if you can do the same again.
See if you can do it before I manage to get back down.
- Ah-ha! - Chasing a ball, photorealistic!
OK. - There's a lot of tails there!
- Yeah, the dog does appear to have two tails! - It does!
- How many legs has it got, Eric?
- Many, many legs!
- So is it just going out on the internet,
searching for that picture and it's just pulling that in
and showing it to us? - Well, no.
Each of these images is created dynamically on the fly
by the neural network and has never existed before.
- So that image, these images never existed before?
- Not at all. This is what's known as text to image.
So to turn text into an image, you need two parts.
You need something that can understand text, and you need
something that can make pictures.
So if we look at the first part about understanding text,
part of the neural network is able to tell you how well the picture
it's trying to create matches the words that you've put in.
So in this case, this photograph, with the prompt
"a tabby cat in a basket" gives us like a 93% score.
It's pretty confident that that's what it looks like.
If we give it the same picture with a different prompt
a London bus, for example, it only says 5%.
So what we've got here is like a reinforcement learning we heard about earlier.
This is the signal that goes back into the system to tell the machine
how well it's doing when it's trying to make a picture.
Now, the second part is making the picture,
which is using diffusion training. Now, this is a bit of a head fry,
but we're going to try and explain it to you.
What you do is you take an image and you add some visual noise to it,
adding a few random pixels to it. And we're teaching the network
the relationship between these pictures.
So I know that if I add a bit more noise, it turns into this.
And a bit more and a bit more, and you keep adding noise.
- And the picture is disappearing. - And the picture disappears
until you've only got a field of noise.
And if you train that on lots and lots of images,
for example, lots and lots of cats with lots of different text,
the machine is then able to reconstruct images from the noise
by taking a bit of noise away each time.
So if I'm going to try the prompt "a tabby cat"
and I start with just pure noise,
at each step, the network tries to take away the dots
that will make it look a bit more like a tabby cat.
And you do that over and over and over again,
until the image emerges.
This model has not been trained only on cats, obviously.
It's been trained on millions and millions and millions
of images from the internet, along with their associated text.
So we've got a bus or a watercolour painting or a block of cheese.
People playing chess. And once it has all these concepts inside,
you can start blending them together.
So I can ask for a tabby cat playing chess, for example,
because it understands what chess is, it understands what cats are.
Or a tabby cat on a London bus.
- AUDIENCE: - Aw!
- Now the idea of randomising and then de-randomising,
just introducing noise until the image disappears
and then somehow being able to kind of un-randomise,
this, to be honest, is one of the weirdest
and most wonderful developments in AI over the last few years.
And there's quite a lot of mathematics
going on behind the scenes.
So even though the image that the AI creates is new,
it's worth thinking about how it knows what to create.
And it needs training data.
Remember, AI needs training data.
The more training data it has, the better it's going to be.
And so lots of artists are concerned that their images,
their creative output, is being used to train AI,
which then is used to create images like the one that we've seen.
So what is it like for you as an artist using this tool?
- What is fascinating, it's like having an infinite number
of studio assistants. I can ask them to make some ideas
for me and they come back and produce thousands of versions
and I can choose the ones that I like and manipulate them further.
So it's actually an incredible resource for creativity.
- OK. But you're the artist, you're steering it.
I mean, you're not just giving it a prompt. It's doing it, right?
- Well, to some degree, you give it a prompt
and it does something.
But it doesn't always do what you want it to. - OK.
- And it takes a lot of work to get the desired outputs.
- OK. Still images are great, but what about videos?
Now, before the lectures, we asked you to suggest some dreams
that you'd had. If you can dream it,
we think AI can produce a video of it.
And we picked out from your suggestions
some of the more interesting and original ones.
And here are a couple of the ones that we got.
"There was a cat which was eating a fish and then more fish came
"and slapped the cat with banana peels."
OK, hands up if you were responsible for this
rather remarkable... OK, this was you.
OK, I don't know what you've been eating!
"The sky was pink. I was walking and fell off a cliff.
"I landed on a huge marshmallow." Of course you did!
"With a packet of jelly beans on top."
Of course there was a packet of jelly beans on top!
"I ate the jelly beans and the marshmallow
"and carried on walking. It happened again and again."
What a remarkable life you lead!
But we picked one from Arabella. Arabella, where are you?
Arabella, you're in the middle. Right.
I'm going to have to pass the microphone.
OK, Arabella, what was your prompt?
- So I was baking, baking a mug cake,
and the mug cake started talking to me and telling me some jokes,
and, yeah.
- OK, so Eric, talk us through what you managed to do
with that prompt? - The first thing I discovered
is that AI is not very good at mug cakes!
It's blended cakes and mugs quite nicely,
but I don't think that's quite what you had in your mind.
And even if you take one of these cakes, these mug cakes,
and I ask it to tell me a joke, it's very difficult to predict
how the video model is going to work.
And as you can see, this is not quite a cake telling a joke.
Here's another one. I quite like this one.
It's got a lovely smiley face, but it decided just to rotate him
for some reason. I don't know why.
So it's early days with this technology,
and it's a bit of trial and error.
So I changed your prompt slightly and went for a cupcake telling jokes
because the model knows about cupcakes slightly better.
And I decided the cupcake wanted to start its career
in standup comedy at a comedy club called The Happy Cake.
Now, as you can see, text is one of the things
that AI is not very good at at the moment.
- It can't spell happy. - It can't spell happy.
And in the end I went for this one, the happy ca-e-ake,
just because I thought it was great.
And the character I chose was this guy, the cupcake,
which we'll see. It took quite a lot of time to get the cupcake
to tell a joke. As you can see, it grew a moustache in this one,
I don't really know why.
I was able to generate a video for you that contains the cupcake.
Everything you see has been generated by AI, all of the video,
all of the images, the voices, are AI generated.
The music at the end is generated by AI, including the singing,
and the terrible, terrible joke that he tells came from ChatGPT.
- OK, so let's see the video, Eric!
SLURPING SQUELCH
LIQUID POURING, BLENDER BUZZES
- I love telling jokes. Wow! Open Mic Night
at the Happy Cake Comedy Club! This is my chance!
- ENGINE ROARS, TYRES SQUEAL
- Why did the cupcake become a stand up comedian?
Because it had a talent for delivering punch lines
that were as sweet as its frosting!
Wow, I'm a hit!
- # Everybody loves Comedy Cake Mix! #
LAUGHTER AND APPLAUSE
Let's go back to Arabella and see what Arabella made of that.
Was that what you had in mind, Arabella? - Erm, I guess so!
- Were you surprised at how good it was or how bad it was?
- Both, really! - Both!
It kind of was a bit of both,
but it was kind of amazing, wasn't it?
- The singing was interesting!
- Arabella, thank you so much.
Arabella, Eric, you're going to have to fight over
who gets the Oscar for this one, I think!
OK. Now, remember, AI created that,
but it created it under your direction.
You just didn't give it Arabella's prompt and all of that appeared?
- If only. Not yet. We're not quite at that point.
Took me about a day or so of coercing the images out of it.
I had to put them together, add sound effects.
There's still quite a lot of human activity involved.
- And if that went to a team of human professionals, how long do you think it might have taken them?
- I imagine it would take them a week or two to generate all the assets
and build the whole thing. So it's definitely a time saver.
- OK. The technology is getting better and better all the time.
Eric, thank you so much for joining us. - Thank you.
- In this second lecture, we've looked at how AI is being used
all around us in our everyday life.
It's reshaping our health care systems and asking us to confront
what creativity means, what art means.
Does AI democratise the means of producing art?
Is art created with AI tools less emotional or engaging
than that created by humans?
Is it even art?
You have to decide that.
But, remember, AI is a tool.
It's not a mind like you or me.
In lecture three, we're going to look into the future.
We're going to consider the dreams that we have for AI,
but also the nightmares.
We're going to consider the ways in which this new technology
might develop in directions that we don't want it to develop.
And we're going to try and understand
how we can avoid those directions. Thank you.
AI is here now.
Discover the different ways it may already be revolutionising
your life in my exclusive interview.
To watch, head to bbc.co.uk/ri
and follow the links to the Open University.