All language subtitles for BBC.RICL.2019.Secrets.and.Lies.2of3.How.to.Bend.the.Rules.720p.HDTV.x264.AAC.MVGroup.org.eng

If you want to understand the weird
and wonderful world around you,

then maths is your friend.

But the real trick is, that with
a little bit of mathematical

thinking, you can take those rules
and patterns, bend and twist them,

and fire them straight back at you.

Tonight, we are going to explore
just how far we can push those

barriers to achieve amazing things.

CHEERING AND APPLAUSE

Welcome to the Christmas Lectures.
I am Dr Hannah Fry

and tonight we are going
to be talking about how to give

ourselves superhuman skills.

Now, we're very good at using maths
to understand the world around us.

But if we want to bend the world
to our will, we're going

to need to be a little bit
more inventive.

We're going to need to take
this maths up a level here.

So I thought what we'd do is we
would start off tonight with

something of a competition.

So I wonder, does anyone know
how to solve one of these?

Who doesn't? Oh, actually,
quite a few of you.

OK. Who doesn't mind coming down?

Let's go with you, actually, yeah.

If you want to come down.
Round of applause, if you can,

as he comes to the stage.

What's your name? George. George.

OK. How good are you
at these, George?

Solved it a few times. OK. All
right. Let's give you a go.

We're going to give you lots of
encouragement as you go.

Can I look at it first, like...?
Yeah, you can have a little look,

yeah. Cheers. We'll be patient
with you, though. OK.

All right. Yeah.
I reckon I'm ready to go.

OK. Come on, then, George.
Come on, George!

Hang on, George. Hang on, George.

AUDIENCE MURMURS

George?

AUDIENCE: Whoo!

AUDIENCE WHOOPS

George, you've solved these more
than a few times, haven't you?

Yeah, quite a few, actually. Tell
us who you actually are, George.

I'm actually the UK champion
for solving a Rubik's Cube.

Yeah. You're the best in Britain.

Yeah. Currently reigning UK
champion. Yeah.

So tell us, OK, how do you
actually solve one of these?

OK, so I guess the first thing to
know is that, like, it's got quite

a lot of combinations. So it's not
like you just like learn one thing

and then you can solve
it straight away.

So, like, when you scramble it,
chances are that scramble's

actually never, ever been seen
before. It just has so many

combinations - it has 42 quintillion

combinations... That's a big number.
43 followed by 19 zeros.

So, you kind of need to split up
into steps and you need to learn

algorithms, like this whole
actual... A list of instructions.

Yes.

So, algorithms, when applied
to a cube, that kind of means

you're temporarily mixing
up the cube, then putting

it back, having switched
a few pieces.

So the more algorithms you know,
the more efficient you'll be.

If I combine that efficiency
with faster turning, it allows me

to be a faster solver.

Now, there's a very key point
here in what George is saying,

which is that sometimes maths
doesn't look like numbers and

equations, sometimes maths can just
look like a list of instructions.

I mean, you can solve this by
following a list of instructions.

An algorithm, as you say.

And that means, George, that if I
want to beat you at this, all I've

got to do is follow those
instructions faster than you,

right? Yeah. Well, thankfully I
have just the thing that's going to

help me, and Illius here
is the creator of this machine.

So I tell you what, hang on, let me
just scramble this one up again.

We're going to have a bit of a
competition. We're going to do you

against the machine.
OK, so here we go.

Let's scramble this one.

You're not allowed to look at this
now. All right.

So I'll tell you when you're
allowed to look.

Is it ready to go, Illius?

OK. All right. We're going to count
it down. And then you and the

machine can both look. Ready?
Three, two, one, go.

Who's going to win?
Who's going to win?

Ohhh!

George! You're letting yourself
down! I know!

That is absolutely incredible.

APPLAUSE AND CHEERING

I mean, that was amazing.
That was amazing. OK.

Let's go over here have a little
look back at what we're seeing.

I mean, this machine is doing the
same things as you. Exactly, yeah.

It's just, it's, like, seeing when
to use those algorithms way more

quickly and it does it,
like, all in one go,

so it doesn't have to pause
to look to when to use the next

algorithm or anything like that.
So we can look at a little slow-mo

replay, I think, of this.

And it is a properly mixed-up
cube, there.

Wow! Amazing.

That was absolutely incredible.

So it's not better than you.
It's just faster.

I suppose so. Yeah. It'll know
more algorithms than I do.

Yeah. Amazing. George
and the cube-solving machine,

thank you very much. Thank you.

Thank you. That was amazing.
So impressed.

That really, I think,
was a great example

of a machine following instructions.

The question is, how do you get
those instructions into the machine

in the first place?

Well, one man who knows the answer
is coder extraordinaire

Seb Lee-Delisle.

Hey, Seb!

So, Seb, tell us about the
kind of things that you code.

Well, I like to make really big
light installations

that are interactive. Oh, OK.

And we're seeing some footage
of this, here. On the side of

buildings, no less. Yeah. Yeah.

See, my ambition really is
to replace fireworks. With lasers.

With lasers. Yeah, cos, like,
fireworks are old technology.

Uh-huh. Now we've got technology
that's just as bright as fireworks,

lasers, super-bright LEDs.

But all of my technology
is controlled by computers.

OK, so talking about computers,
then, how do

you control these things?

Well, I'm going to show you
how we make something called

a particle system.

So I've got a browser open.

I'm going to do some coding
in JavaScript to make some graphics.

You're going to make an algorithm.
An algorithm, yeah. A series of

instructions for the machine.
Exactly right.

So the first thing I'm going to do
is make a single particle,

which is essentially just a shape.
And we've got to decide

what colour that shape should be.
Any ideas what colour?

AUDIENCE SHOUTS SUGGESTIONS

I think first thing I heard
was "pink".

So I'm going to actually
use magenta,

which is my favourite type of pink,

and it's a very nice bright pink.

And the next thing I've got to do
is draw a filled-in shape, so

let's hear some shapes.

AUDIENCE SHOUTS SUGGESTIONS

Circle. OK, OK!

Square. I think I heard "square".

Definitely. OK. Square.
So a square is just a rectangle.

So let's draw a filled-in rectangle.

We're going to draw it at 00,
which is the top left of the screen

in X and Y coordinates, and we're
going to make it 100 wide and

100 high, so let's look at our
square. There it is.

So this X and Y that you were
talking about there... Yeah.

..are you treating
this as though it's a graph?

Yeah. So this huge
canvas in JavaScript,

it's got pixels and I can identify
each pixel by its X and Y

co-ordinate. OK. Yeah. This is
just straight-up maths so far.

It's all maths. It's maths all the
way down, and I can change this X

value and you can see that
the rectangle moves to whatever

co-ordinate I specify for the X
position. As you increase X,

this is moving right. Yeah. Exactly.

So let's do the same thing
for the Y value.

You can see it moves down
and to the right. Ahh!

Right. So this is good.
It's the start of our particle.

But it's not moving.

It's completely static.

So in order to get it moving, I'm
going to wrap all of this stuff up

in a function, and I'm going
to call this function 60

times a seconds.

But now I can do X+=1,
which adds one to X every time.

And now, we should have the
rectangle in a brand-new place

every time. We've made some
animation. Aha!

Doesn't look much like a firework.
No.

So we get we're getting there.

So, in particle systems, usually
the particles are very small.

That's the first thing we can fix.

Let's just make it really little.
Aww!

And now, let's make some new
variables for the X velocity

and for the Y velocity.

The best part is when I give those
velocity values a random

number. Oh, OK. So you can do that
with the command "random".

If I pass in minus ten to plus ten,
then we're going to get a random

number in the X velocity
between those two numbers

and the same with the Y velocity.

And you'll see now that every time
we run it...

Goes in a different direction!
..it goes in a completely

different direction. A random
direction every time.

Right. Exactly.
So that's kind of fun.

So we've got one particle.

One particle is cute.

But really, the idea of particle
systems is that we make a whole

bunch of particles.

So now, in this next bit of code,

I'm doing essentially
the same thing,

but I'm creating an array
of particles.

And we get an effect like this. Ahh!

That looks like a sparkler!
We're getting there. Aren't we?

We're getting to the sparkler.

Now, this is an ordinary projector,
so it's not very bright.

If we want to create something
that's truly spectacular,

like a real firework,
we need to use a laser.

Ah. Wow! They're extremely bright.
Right.

They're really, really bright,
aren't they?

And so I can adjust all
the settings here.

I can change the speed,
how fast they are,

make them fade out over
time a little bit.

And, yeah, I can add some gravity
to make them fall down. Amazing!

But, of course, the stuff
that you are really well known

for, Seb, is not
just you having control

of where this is on the Y axis,
where it is on the graph...

Yeah. ..but letting the audience
control it. Exactly. I love doing

interactive things, so what I
thought I'd do is actually take

the sound from the microphone
in my laptop and apply that level

to the Y origin of the particles
and then we could make a game

out of it.
So here we've got this.

You can see the sparkler
on the left.

Now it should be that the more
noise that you make, the higher

this sparkle rises. All right.

Here we go. Three, two, one, go.

AUDIENCE CHEERS RAUCOUSLY

NOISE DROWNS SPEECH

This is it, guys!

It's getting harder.
It's getting harder.

NOISE DROWNS SEB'S SPEECH

Ohh! That was amazing!
That was such a high score.

Well done, everyone.
Amazing, amazing. Seb, thank you

for showing us your all your lasers.
Very, very welcome. Thank you.

Thank you very much. Thank you.

CHEERING AND APPLAUSE

Now, Seb's fireworks
look very realistic, there,

but I think if you want something
that looks as real as possible,

then you really can't do better
than looking at Hollywood films -

the visual effects that
they use in their movies.

Now, these are the people,
the people who create those,

they're the people who, they don't
only copy reality, but they really

know how to bend the rules
to their will

for our entertainment.

And we are very lucky, because
today, we are joined by someone who

does exactly that.
From Weta Digital in New Zealand,

please join me
in welcoming Anders Langlands.

CHEERING AND APPLAUSE

Hi. Hey, Anders!

So what films have you worked on,
Anders?

Well, Weta's got quite a long
history of going all the way back

to The Lord Of The Rings, Avatar,
Planet Of The Apes and then, most

recently, Avengers this year.
Oh, crikey.

I mean, you don't get much bigger
than Avengers. No.

How much of the animations that we
see - the CGI that we see in films -

how much of it is maths?

Well, it's all maths, really.

It's like a combination
between art and science.

And we use algorithms to simulate
how things move, to give animators

controls, to make things move
according to their desires,

and to render the pictures and make
the images through mathematical

equations, as well.

But presumably, if you want to make
things look realistic on the screen,

you have to understand how
they work in real life.

And that really is what this tower
behind us, that has just been built,

is here for.

Because I would like a volunteer
who would like to come

down and help me to dismantle
this tower.

Let's go for, yeah,
do you want to come down?

Round of applause as
they come to the stage!

CHEERING

What's your name?

APPLAUSE DROWNS RESPONSE

All right, so we have got
this bowling ball.

Now, I want you to roll it
very gently.

This is a very old and important
building and we will send you a

bill. So very gently and try
and smash down this tower.

Can I stand somewhere else? You
can stand a bit further back here

if you want to, give you a bit
of a run up, but let's count it

down. Ready? OK. Three...
ALL: Two, one.

Go!

Oh!

It's pretty good!

Yay! Ha-ha. Well done!

CHEERING

But stuff like that, it isn't just
for fun for you guys, right?

No, I mean, we have to do things
a little bit more complex than that,

but the basic equations of motion
that govern something like that,

as you've just seen,
are pretty simple.

Simple enough to solve by hand,
in a lot of cases.

And if you've done GCSE maths, then
you've probably encountered some

of that, too. Just the physics
behind what just happened?

The physics behind what just
happened. Of course, we want to

simulate much more complex things,
and even

something like this tower
falling over,

we need to use computers to do
that so we can do it fast enough

to make a moving animation.

And, in fact, talking of moving
animations, you have brought one

with you, so let's have a little
look over here.

This is our tower, right?

This is kind of the same as our
tower? Yes, pretty similar.

But this one is
a complete animation.

Yes. So we look at the forces of
gravity and the force of the ball

being thrown, analyse
the collisions, and then,

every single frame -

so, at least 24 times a second -
we update all of that and work out

where the blocks are going
to be on the next frame

to create an animation. So let's
have a little look at it

as it's animated. It's pretty good.

I mean, you've even got the
little bit I had to kick down.

Just the wrong way round.

I haven't got a digital Hannah
to kick it down, I'm afraid. OK.

So once you are at this stage,
once you have this stuff created,

you understand how the basics work,
then what do you do?

Well, then we can have a little fun
with it. So because we create the

world, we can do things like change

all the boxes into china
so they shatter when it gets hit,

rather than falling over.

We could change all the boxes into
balloons and then pump them full

of helium so they float away.

That's nice! Escaping round
the side. That's nice.

I like that a lot. It's getting
away. Or maybe if we wanted

something a bit more exciting,
we could douse them

in petrol and then set them on fire.
Oh, wow!

AUDIENCE LAUGHS

That looks very realistic.

Thanks. That's kind of the point!

How do you make that look
that realistic, though?

Well, like you said,
we have to analyse what's

going on in the real world and use
maths to try and simulate it

as closely as possible. So in the
case of fire or explosions, for

example, we look at the chemical
processes that drive that.

So, for fire, the combustion process
is turning a fuel into water

and carbon dioxide and some carbon.

And then the temperature
then decides how hot that soot gets,

and that decides what the brightness
and the colour of the flame then is.

When you're working with
buildings, say, do you take into

account the things that the
buildings are made from?

Yeah, we want to analyse all
the different material properties

that are in a building, so, like,

how bricks shatter
when they get hit.

Mortar turning into dust,
the steel bending.

We have to layer all of these things
up using either one big simulation

or lots of simulations to get
put together.

And, you know, if you're doing
a giant bath toy knocking a building

down, you're kind of outside of
the realm of real-world physics

at that point. So a lot of what our
effects artists do, a lot of their

skill comes into figuring out how to
tune the physics of the simulation

to give something that you've never
seen before that the director wants,

but also something that the audience
can believe is real.

Take reality and bend it. Yeah.
So a big part of what our team of

scientists and software engineers

at Weta do is use maths to figure
out cool tricks to make the

simulations run faster,
while still looking real.

That is amazing.
Anders, thank you. very much.

Thank you.

CHEERING

As Anders mentioned there, it
takes a huge amount of brainpower

to compute those simulations -
takes even machines days to do.

But, of course, it's not just fun
and frivolity that's on the table

when you could get machines
to do your maths for you,

because working out how to get
computers to crunch the numbers

also saves lives.

And something that has had a huge
impact from using algorithms

is organ-donor matching.

Let me explain what I am
talking about here.

So let's imagine
that you're a bit poorly

and you needed a new kidney.

Now, luckily, your friend here
has very kindly and generously

offered to donate to you
one of hers.

Almost all of us are born
with two kidneys and you can live

a very long and happy life
with just one healthy kidney.

Now, if you were both good tissue
matches or blood matches for each

other, then this would be absolutely
fine, the transplant could go ahead.

But if for some reason your blood
or tissue type didn't match,

then you would be in a position
where you had one person

who needed a kidney,

one person who is willing
to donate a kidney,

but nothing that you could do
about it.

That was until very recently.

All you could do was really wait
around on the transplant list,

hoping for another donation.

That was until quite recently,
when someone had a very bright idea.

Because while you two might be stuck
in that position, perhaps,

over here, there are another
two people

who are in a similar position.

So maybe you need a kidney.

I mean, you have one that
you are willing to donate,

but that unfortunately
doesn't match your loved one

who you're trying to help.

But what if your kidney, rather than
matching your friend here,

what if your kidney was a match
for you?

Now, that transplant could go ahead
and that would be fine.

But the question is,
why would you want to give

up your kidney to someone you've
never met before, especially

when your friend is still sick
and in need of a transplant?

But what if there was a third
pair of people?

What if we could close this loop

with another group who are over
here?

So one more person and their loved
one who is willing to give

them one, you've got a little loop
up here. Perfect. Thank you.

I'll take that down.

Now, in this situation,
if this could happen,

if this happened to be a match
for you as well,

then everyone's happy, right?

Everyone who needs a transplant
has got one and everyone has helped

make sure that their loved ones
have the operation that they need.

Now, the only problem with this is
that there are often hundreds

of people in the country
who will find themselves

in this position.

And finding these matches
is incredibly difficult.

The number of possibilities
with hundreds of people

is absolutely enormous.

And you have to make sure that
you create a closed loop every

single time so that
everybody ends up happy.

But this is the kind of thing
that computers

are absolutely perfect for.

And, in fact, this is exactly
what a team at the University

of Glasgow have done.

So here is the output
from that algorithm.

This algorithm
runs in seven seconds.

And this is the network
that it spits out.

So each one of these dots is a real
person, and each one of the lines

is, effectively, a ribbon.

And if we zoom in on this,
you can see here just how many

ribbons this algorithm is
potentially considering.

Now, clearly, there is no way that
any human would be able to weigh

all of this up within their own
minds.

But this algorithm, this isn't
just something that's theoretical.

This is something that actually does
go on to save lives because,

in fact, up there, holding
on to the green ribbon is Yuki,

who was part of a real-life chain.

So, Yuki, if you want to join us on
stage. Round of applause for Yuki.

CHEERING

Thank you for joining us, Yuki.

So how long ago
did you have your operation?

I had it three years ago.

And who was the loved one
who donated a kidney on your behalf?

It was my grandma from Japan.
From Japan.

I think we have a photograph,
actually, here, of you guys

outside Great Ormond Street.

So your grandmother's kidney
went to someone else.

Where did your kidney come from?

My kidney came from someone
in Birmingham.

And do you know how many people
there were in your chain?

I think three. So exactly
the same as we have here.

Yeah. It's a really
incredible story.

Yuki, thank you so much for joining
us and telling your story.

CHEERING

Now, this work is a monumental
breakthrough, it's something that's

been a real game-changer
for people within the UK.

And the key thing that that
algorithm can do, the thing that no

human can do on their own,

is consider this vast number of
possibilities.

And that is the superhuman power
that you open up when you hand

over your maths to a machine,
because suddenly it can use

all of its grunt to tailor things
to the individual, to work out

precisely what is right for you,

which kidney you need,

and in a much smaller,
but no less profound way,

which song or video you might like.

Now YouTube videos, Netflix,
Amazon, BBC iPlayer - all of them

are crunching through vast amounts
of information on their websites.

But how do they make sense
of the content that is uploaded?

And what kind of process
do they use to decide

what you might like to see next?
Well, OK, I'll tell you what.

Let's find out here
by making our own video.

So I wonder, can I have a volunteer,
someone who is happy to help me

commentate on
a little YouTube video?

OK. Perfect. Yeah. Let's go there.

Come on down. Round of applause as
he comes to the stage.

Now, what's your name? Anthony.

Anthony. OK, perfect. Anthony.

Right. So we're going to make...
Have you got your phone? Yeah.

OK. Perfect. Right. We're going
to make a video, Anthony.

And we want it to be seen by
as many people as possible.

Right? We want it to be picked up
by the algorithm.

So we're going to make a great
YouTube video here.

Now, thankfully, the world famous
Royal Institution demo team are on

hand to help us make our video
as exciting as possible.

So if you give me this
and I'll do your video for you.

If you take these, you can come
and stand over here and help me

commentate on this video.

So I'll take the video.

Here's Gemma. You start
off whenever you want to, Anthony.

There we go!

What's up, folks?

It's me, Anthony, again.

Thanks for subscribing.

This is my best...

..best friend
Gemma doing some science.

They have some extremely
cold liquid nitrogen.

Whoa! Look at all
these balloon dogs!

Where are they coming from?

It's the balloon-dog challenge!

Now, Gemma is going to add hot
water to the liquid nitrogen and it

will yeet everywhere!

Let's give her a countdown.

Nice and loud.

ALL: Three, two, one...

AUDIENCE WHOOPS

Now, sure to get views,
it's YouTuber Tom Scott!

And behind the cloud, appears

YouTube sensation Tom Scott.

Anthony, thank you so much. There,
and you've got your video there.

Thank you very much indeed.
Big round of applause if you can!

Do you often appear
in clouds of smoke?

No, it's the first time
I've been summoned like this.

It's wonderful. Now, I should tell
you, for any of you who don't

recognise Tom, Tom is one of
Britain's foremost YouTubers.

If there is one person that we
can ask about how to make

sure a video gets seen by as many
people as possible,

I mean, you are that person. Yeah.

I wish there was some magical way
to guarantee it but, I mean,

I've done some pretty cool stuff.

I've been in Zero G, but that video
didn't do well compared to, well,

a two-minute shot of me continuously
looking at some toasters

and talking about how long it's
going to take them to pop up.

That video did better than me
flying about in zero G.

But what did better than both of
those, to achieve, like, 25 million

views, was me attaching some
garlic bread to a helium

balloon and sending it to the edge
of space, then bringing it back

down and eating it. Now...
25 million. 25 million.

I kind of figured that one
was going to do well.

I didn't think it was going to do
THAT well. I have never, ever been

able to find anything that will get
these recommendation engines,

these algorithms, to specifically
go for a particular video.

Because it is an algorithm that's
deciding what gets promoted

on different people's YouTubes,
right? Yes. Originally,

years and years ago, it was all
down to the title and the thumbnail.

CRACKLING

Is that OK? That's crackling
a lot. I'm slightly nervous!

I mean, this is going to make
quite a YouTube video

if it explodes on us now, Tom!
I'm going to keep going.

Keep going. Originally, when I
started 2006, it was all about

the title and the thumbnail
and that was it.

So you could trick people
into clicking on something

with a lie in the thumbnail,
a lie in the title,

and that's still true now,
but it doesn't work as well,

because after that, they said
it was more about how long people

watched a video for.

So at that point, people put,
you know, a little explosion

at the end of a 20-minute video.

So you'd have to watch all the way
through. Those long introductions

that were very fashionable
for a while.

Because they kept people watching.
So now YouTube doesn't give any

advice at all on how to make
something please the algorithm.

And as a YouTube creator,
all of us just call it

"the algorithm".
This one monolithic thing.

They say that if people
watch it and people like it,

then the algorithm will also
recommend it to other people.

But they will never, ever
say what the reasons are,

because the minute they do that,
everyone will say, "Oh, yeah, well,

"I'll start doing that."

And suddenly 100,000, 200,000
people are all doing that and

no-one's watching it any more.
Yeah. There are 500 hours of video

uploaded to YouTube every minute.
Not watched - like, uploaded.

So I'm sure it's a great video
on your phone.

Statistically, it's probably not
going to go big, but it might, cos

the more times you can roll that
dice, the better your chances are.

I'm going to go create a garlic
bread video of my own now, I think.

Good luck.

Apparently it does work!

Tom Scott, thank you very much
indeed. Thank you. Thank you.

CHEERING

Now, as Tom hinted there, there
is actually something slightly

different going on here to the
fireworks

or the organ-donor algorithms
that we saw earlier.

That YouTube algorithm, it isn't
just crunching through a straight

list of instructions any more.

It's actually doing something
a little bit different.

I want to explain this to you
using a cup of tea and a robot.

Oh, OK.

Maybe not an actual robot, but
definitely the next-best thing.

Please join me
in welcoming robot Matt Parker.

CHEERING

That's a... Thank you!

That's a great outfit, Matt.
Thank you. I've made it myself.

Yeah. Sorry...

ROBOTIC VOICE: I compiled it myself.

OK. Now, robot Matt,
like all robots,

he isn't particularly bright.

Oh-oh. Offence registered.

They also take things incredibly
literally,

as we are about to discover,
because we are going to, together,

try and instruct robot Matt Parker
with how to make a cup of tea.

OK, so here we go.

What's the first thing to do if you
make a cup of tea?

You heat up the water in the
kettle.

Heat up the water in the kettle.

Perfect. Sounds sensible?

There's a kettle. OK, next step.
Next step.

You take out the teabags.
Take out the teabags.

Ha-ha! That's good. Next step?

Er, get a mug.

Get a mug.

Oh, it's a teeny, tiny one.

What should we do next?

Put the teabag in the mug.

LAUGHTER

Is that the right mug, there?

Well, let's get another.

Put the boiling water in.

LAUGHTER

He takes things very literally!

HANNAH CHUCKLES

Heat registering!

Get a normal-sized teabag and cup.

A bigger cup.

The normal teabag in the mug.

LAUGHTER

I'm going to come round there.
Hang on. One second.

Hold on a second.
I'm going to come up here.

Here we go.

What's next? What's next?

Stop pouring the water.

Put the teabag in the mug.

Pour the water into the mug.

AUDIENCE MEMBERS CHORTLE

LAUGHTER

Stop pouring the water.

Add milk and sugar to taste.

HANNAH CHUCKLES

LAUGHTER

Pour the milk from the first mug
into the mug with the tea.

OK. I think we've got one more.

Stop pouring the milk into the cup.

And the last step?

Drink it.

LAUGHTER

I made this for you.

Cheers, everyone.

Oh, that's...

LAUGHTER AND CHEERING

Thank you very much!

OK. So I think it's quite obvious
that our list of instructions there

needs we need to be quite long
and that is only for making

a cup of tea -
something very easy.

So imagine how much harder
it would be to write a list

of instructions for
a very literal computer.

OK. How would you explain to a
computer how to recognise a picture

of a dog?

What do dogs look like? Shout out.

What are the important things
about what dogs look

like that you would need?

AUDIENCE OFFERS SUGGESTIONS

Fluffy, fur, yeah.

AUDIENCE SHOUTS

Four legs.

Er, tail? Yeah. OK.

All right, all right.
So we got... We got...

We got fluffy or furry, tail,
and four legs. Great.

OK. But what about this? Uhhh...

Slight problem, there.

OK. All right. What else? What
else? What about its face?

What's important to
recognise about its face?

AUDIENCE OFFERS SUGGESTIONS

Pointy ears.

Pointy snout. OK. OK. OK.
Here we go.

All right. So "pointy snout",
someone said.

How about this, though?

Ahhh. OK.

Even if you could work out, you
know, maybe take into account

the colour, all of that stuff,

didn't you say that a dog
had to have four legs?

Because what about this guy?

Still definitely a dog.

Still definitely a dog. But it just
goes to show just how hard it is

if you are trying to write a long
list of instructions, just how hard

it is to pin down exactly
what a dog looks like.

Now, humans, we're amazing at this.

We can do this without even
thinking, but explaining how we do

it, especially to a pretty dumb
computer that takes things

very literally, ends up being quite
a lot harder than you might imagine.

But there is another way
around this to explain.

Please join me in welcoming computer
scientist extraordinaire

Anne-Marie Imafidon.

CHEERING AND APPLAUSE

There is another way to all
of this, Anne-Marie?

There is another way. We need
a different type of program

that isn't as specific. Isn't a
list of straightforward "do this,

"do this, do this". Exactly.

We need a type of program
that has an end point or a goal.

And we let the computer
or the machine figure out

its own rules to get to that
end goal. Without giving

it straightforward instructions?
Without giving

it any instructions other than
that's where we're going,

and maybe when it's got it right.
OK.

Anne-Marie,
this sounds like magic.

Oh, it is computer science
more than it's magic!

It's a lot like training an animal.

When you get a dog, you're training
it to maybe do a particular trick.

You don't say, "Move this muscle
and look that way."

You kind of just give it a treat
when it does the right thing.

And you've got a clear idea in
your mind of what it is you want

them to do. Exactly.
Whether it's sitting

or whether it's anything else.
OK. All right.

Well, on that point, then, of
training animals, what's this kind

of algorithm called, incidentally,
when it's in computer science?

So we call it a reinforcement
training algorithm.

And it's one that learns on its own,
so it's actually part of machine

learning. OK. All right.
So reinforcement learning.

Let's see how this works,
because we're going to try some

reinforcement learning
on one of you.

So who would like
to be our volunteer?

You had your hand up, I think,
first. So if you want to come down.

CHEERING

What's your name?
Emily. Emily. OK. Perfect.

Emily, if you want to come over
here. OK.

So, Emily, what we're going to
do, then, is we're going to play

a little game with you. If you just
come to the side while they bring

all these things on. What we're
going to do is we're going to show

you four pots. You can see
all the different rounds

that we've got there. These pots are
coloured and they've got sums

on them on the side, and so on.
Now, Anne-Marie and I, we've got in

our minds, we've got a very
clear idea of which pot

we want you to pick.

So, in each round, all you
do is you just touch one pot.

OK. If you are correct,
we're going to give you a treat,

we're going to give you a little
reward, and I think they might just

be in here. We're going to give
you a little reward of a humbug.

And if you're wrong, we're going
to move on to the next round. OK?

If you want to just stand in there
for me. Now, just to prove

that this really is something
that is not specific to humans,

you are going to play this game
against a competitor,

by which I mean a pigeon.

If we can welcome onto the stage

Alfie and his trainer, Lloyd.
Round of applause.

Now, I have to tell you, Emily,

Alfie has had a bit of a head start
on this. Alfie has been trained.

Look how sweet Alfie is!

Very beautiful bird.

You happy? You understand the game?
All right, let's go.

Let's go for round one.

So just touch one pot.

Incorrect.

Ah, Alfie got it right, though.
He did. He did!

Alfie, one. Human, zero.

LAUGHTER

OK. Here we go.

Round two.

Incorrect.

Ohhh! Alfie, two. Human, zero.

And here we go.

Next one.

Correct. OK!

Next one.

Ohh! Incorrect.

Alfie's got it correct again.

OK. Let's go final round.

Now. OK. The equations on this side
are getting harder and harder

and harder, almost like

there's no possible way
that you could solve them.

Ohh! Incorrect!

That is tough, that is tough!

OK. Tell us, how did you find it?
Tell us what you thought.

It was quite hard.

I just basically picked random ones.

There wasn't really a method.

What were you thinking
when you got it right?

I didn't really have a thing. I just
sort of picked one, apart from

the one where the yellow
pot fell on the floor.

Oh, did you see the yellow pot?
That was a big clue.

In fact, actually.

Which pot do you think that Alfie
was picking every time?

Shall we tell her? The yellow one?
The yellow one.

Exactly right. Exactly right.

APPLAUSE

Now, I know that was quite tough,
that was quite tough.

But, Anne-Marie, tell us, how
similar is this to the things we see

in computer science?

So this is very similar to what
we see in computer science.

But with a computer, it's able to do
this thousands, millions, billions

of times in a second,

and so can then pick up that pattern
quicker than we can as humans.

And often it can see patterns that
we humans can't see and can make

those kind of connections, as well.

And that is a really important point
because you only had a few rounds,

but if you were a computer,
you'd have had 10,000 in that time.

So there you go, have another treat
and a big round of applause.

Thank you! Thank you very much,
Emily. Thank you.

CHEERING

Now, the way that Alfie was trained
there was just using exactly

that same technique. Alfie just
had a lot more goes at it.

But as Anne-Marie said there, if an
animal can learn in this way

with very simple rewards,
never hearing full instructions,

then so can a machine. Now, that
might seem like a bit of leap.

How do computers learn, especially
when, as we found out

with that cup of tea,
machines are incredibly dumb?

Well, it is possible.

And perhaps unsurprisingly, given
that it's in this programme, it is

totally mathematical because it
turns out you CAN teach an inanimate

object to learn.

And we have been doing exactly
that for the last week.

Please welcome Matt Parker
and the Menace Machine.

Have to be honest, Matt. Menace
Machine looks quite a lot like a

whole heap of matchboxes.

That's because Menace IS
just a heap of matchboxes!

But we've taught these matchboxes
to play noughts and crosses.

OK, OK. Talk me through it.
How on Earth can matchboxes play

noughts and crosses? Well, the great
thing about noughts and crosses is

there aren't that many possible
games. There's only nine squares,

it's a nought or a cross.

And on the front of these boxes,
every single one is a different

state that the game could be in.

In fact, this is every possible
position of every possible game

that Menace could possibly face.

We've got all the first moves,

second moves, the third moves are
in blue and the final, fourth

moves are over here in pink.

And inside each box, we've put
some coloured beads.

So here's one that we haven't used
yet. And you can see there's a real

mixture. There's, like, nine
different-coloured beads

and each colour of bead corresponds
to a move Menace can make.

Because, of course,

there's only ever nine possible
places that you could play for

noughts and crosses. Exactly.

OK, so how do you actually train it,
though? Well, initially, there's

just a random collection of beads
in every single one.

So actually it's making every
possible move equally likely.

And initially, Menace is terrible.

But the reinforcement learning is
we track each game, and if Menace

wins, we give it more beads
that are the same colour

into the same boxes.

If it loses, we confiscate
those beads, which makes

it less likely to do that move
again. And to train it,

we've had it play hundreds of games.

And thank you so much, everyone
who, before the show tonight,

played against Menace.

Each time we either reward it
or we punish it.

And with each game,
it gets a little bit better.

And how good is it? Do you think
it can beat a human?

I think it will probably
not lose to a human.

All right. Well, let's give it a go.

Who would like to come
and play with Menace?

Let's get you, there, yeah. Round
of applause as she comes down.

So, what's your name?
Ali. Ali. OK, perfect.

Ali. Now, Ali, you've got the
advantage of being a sentient being,

so I think it's only fair that we
let the matchboxes go first.

OK, so Menace is going to go first.

And this is the blank box,
where there's nothing

on it because we haven't
played at all yet.

I'm going to give it a shake
and then I'm going to pull a bit

out at random.
So while Menace is the brain,

I've got to do the actual moving
around. OK.

So the first bead is green,

which is not surprising
because you look in the box -

they're all green!

There's one purple one,
because Menace

has learnt very quickly

if it goes in the middle to start
with, it's way more likely to win.

Now, there you are, Ali.
Where would you like to go?

Put a cross wherever you wish.

Oh, a corner. Bold move.

OK. I've now got to find,
in the group of second-move boxes,

this game. OK, there it is.

But if you have a look, Ali,
it's the same as this game,

but it's the mirror image.

And that's because we didn't want
to use twice as many boxes.

It's not good for the environment.

So if you don't mind me
turning the game over,

are you happy that's still the same
game? Uh-huh.

But now, it exactly matches what's
on the box. So I can give it a shake

and Menace is going to go...

Make sure it's properly random.
Yellow.

They're all yellow.
OK. So, it's learnt...

I mean, there are other moves
it could make, but it happens

to have learnt that there is
a good second move.

OK, your go.

Predictable. OK.

You're not letting it win
that easily.

That's fine. OK.

Third move. Where are we?
Where are we?

There! OK.

So you can see that one there,
that matches this state.

Here we go. Give it a shake.

OK. Oh, white. OK.

So the next move
is going to be white.

That's... Oh, that's not bad.

There you are.

You're just not letting Menace
have any luck, are you? OK.

That's fine. So now I've got
to find this in here.

Bear with me.

This is exactly as much fun
as real noughts and cross.

HANNAH CHUCKLES

Ah, OK. I think that's it.

Let's have a look. That's...

Yes. OK. Right.

Menace is going to go....

Oh, there's not many beads
left in this one.

Oh, orange. OK.

So I'm going to put an orange one
there.

Now, that move.

And now,
for the exciting conclusion...

LAUGHTER

Excellent. And now we go over...
It's a draw.

And the problem with noughts and
crosses is if everyone's playing

perfectly well,
it always ends in a draw.

So in this case, I'm going to give
Menace one extra of those beads

to say that was fine, but I'm
not going to give it as many

as if it had won. Amazing.

Well, there we go. You managed to
draw with a bunch of cardboard

boxes. Very impressive! A big round
of applause, if you can. Thank you.

The point here is that you don't
need to explain the rules

or the instructions. The machine -
in that case, the matchboxes -

learns from getting rewards

when it does the right thing and it
turns out that you can use this for

all kinds of things, so you can
teach matchboxes how to play noughts

and crosses.

And going back to that earlier
challenge of recognising dogs -

turns out you can teach machines
to do it, too.

Now, for this,
I would like a volunteer

to help me demonstrate this.

At the back there. Yeah.

With the red jumper. Round of
applause as she comes to the stage.

Thank you.

What's your name? Felicity.

OK, Felicity, right. So let's
have a little look over here.

I'm going to demonstrate to you
how a machine can learn

to recognise pictures of dogs
and not dogs.

Now, the way that this works is,
what you do is you give

the machine a bunch of pictures
of both dogs and not dogs,

and then you label them
for the machine.

So that one goes into the dog pile.
Just here.

Perfect.

Now, initially, when you give
these pictures to the machine,

it's just going to be guessing
at random, like we saw earlier

in the first stages
of that pigeon thing.

But what you can do is if you play
this over and over and over again

with tens or maybe hundreds
of thousands of different images,

and eventually, if you do this
enough times, you can end up with

something like this little app
over here.

If you want to come
and sit over here.

Now, we've got a little
algorithm running on this iPod

that has been trained in just
the way that we described.

And we are going to show
this algorithm a series of objects.

And the algorithm is going to work
out whether they are dogs

or not dogs. All right.

Is everyone ready to play
dog or not dog?

ALL: Yes!
Here we go.

Contestant number one, please.
Dog or not dog?

Hello!

DOG BARKS

Ohh! Dog!

Hooray!

Thank you, Luna!
Contestant number two!

Dog or not dog?

Not dog! Thank you very much.

Contestant number three.

AUDIENCE: Awww...

You OK, little guy?

Dog! Amazing!

Next contestant, please!

HANNAH CACKLES

Oh, this is very cute! Sit! Sit!

Not dog! Well done.

Haven't got one, mate!

Next contestant, please!

Aww! Look!

Not dog!

And our final contestant.

AUDIENCE: Aww!

Look how beautiful!

Dog!

Thank you very much,

and thank you very much for
playing the game with us.

Big round of applause for everyone
involved in dog or not dog!

Now, this idea, this kind of
algorithm, a reinforcement learning

algorithm, it can actually
have profound consequences

because the algorithm doesn't
really care what it's looking at.

If it can learn to identify
dogs, it can also learn

to identify diseases.

To explain, please welcome
an ophthalmologist

from Moorfields
Eye Hospital, Pearse Keane.

So, Pearse, tell me
what it is that you do.

So I'm a consultant ophthalmologist
at Moorfields Eye Hospital

and I specialise in the treatment
of retinal diseases.

So, in particular, I specialise
in the treatment of a disease

called macular degeneration,

and macular degeneration is
the commonest cause of blindness

in the United Kingdom.
And is it treatable?

So the thing about macular
degeneration is that, if we pick

it up early, we have some
good treatments and we can stop

people going blind from it.

The problem that we have is that it
affects so many people as they get

older that sometimes we have a
challenge to actually find

the people who've developed it
and treat them in a timely

fashion. Because there are so many
people. Because there's so many.

So we actually get 200 people who
develop the blinding forms

of macular degeneration every
single day just in the UK.

And so, for us, it seemed like
this would be a perfect example

where we could apply artificial
intelligence to try to identify

those people with the most
sight-threatening diseases at the

earliest point and save their sight.

And I've brought Elaine Manor,

a patient of mine at Moorfields
Eye Hospital.

Thank you very much
for joining us, Elaine.

For me, Elaine exemplifies
actually why this is important,

because the Elaine story is
that actually she had lost sight

in one eye from this condition,
from macular degeneration,

and then in 2012, she started
to develop it in her good eye.

And she went to her high street
optician and was told,

"You need to be urgently seen
by a retina specialist,"

someone like me, but she got an
appointment for six weeks later.

So you can imagine a situation
if you're losing sight

in your good eye and there is an
effective treatment, but you're told

that you have to wait six weeks.

What was that like at the time,
Elaine?

I was absolutely terrified.

I felt that, when I went to bed,
would I wake up to a world

of darkness?
Would I see my family again?

It was worrying. Yeah.

And this is all just because of
the sheer volume of patients?

Because of the huge number
of patients.

So what is this machine?
How does that work?

So this is an eye scanner.

It's something that scans
the retina.

It's super-high resolution,
a three-dimensional image

of the back of your eye.

But it's something that these
algorithms can get to work on.

Well, that's exactly it.

I mean, if the algorithm
can tell dog or not dog,

then it seems like it could tell
macular degeneration or not macular

degeneration, and help people...help
prevent people from going blind.

So I hope that we can have a look
at how this algorithm looks

when it tries to process an image.

Can we have a little look? You can
see here on the scan, it's called

out that there is an urgent
problem with the eye.

It's identified choroidal
neovascularisation.

This red area here is some
blood vessel growth at the back

of the eye, which is suggestive of
the condition Pearse was describing.

We've published a research article.

What we've shown is the proof
of concept that the algorithm

that we've developed is actually
as good as me or other consultants

at Moorfields or other
world-leading eye doctors

at diagnosing these diseases.

And this stuff is, I mean,
it's incredibly important to get

you to those urgent cases quicker.
Yes.

Yes. Like Elaine, I guess.

But the great thing was, although
it was stressful for Elaine

at the start, she was able
to get the treatment

that she needed in this eye in time,

and we have been able
to save the sight

that she has in her good eye.

Pearse Keane and Elaine, thank you
very much indeed for joining me.

APPLAUSE

Wow.

As I think Pearse's work
there demonstrates you can see

just how far you can go
with a machine that is capable

of categorising images.
As impressive

as all of this technology is,

it's not quite perfect.

It is important to say
that these algorithms,

they don't really understand the
world in the same way that we do.

They don't understand context,
they don't understand nuance,

and that means that it
really can make mistakes.

In particular, when you take
something and you put

it in a strange situation,
like this picture, here.

So if we take farmyard animals and
we put them in strange situations,

like being cuddled by a child,
for instance,

the algorithm labels as a dog.

This one I quite like. If you take
a farmyard animal and put it in a

tree - this is a real photograph,
by the way -

the algorithm gets a bit confused!

LAUGHTER

Surely it's an orangutan?!

And my favourite one of all
is that, if you leave them back

in the original context but instead
paint them pink, the algorithm

thinks they must be flowers,
which I think is quite sweet!

LAUGHTER

This is the consequence of the
way that these machines learn.

As we have seen a couple
of times during this show,

in the beginning they just start off
doing loads of completely random

things, which means that as
they settle in to completing

their task they can end up
with some rather

strange little quirks. Have a
look at this little video, here.

So this is a simulation

of an algorithm that was given
a body, and its objective was to

work out how to move its body.

Every time it falls over,
it fails and starts again.

And you can see it in the beginning,

it's just trying lots of things,
lots of random things.

But the consequence of that is
that it really has, like, some quite

strange...quite strange habits
that it ends up picking up

as it's sort of flailing all of
its limbs around at random.

Now, this is what happens when
you put reinforcement learning

into a body - a simulated one
in that particular case -

but there is nothing stopping you
from putting the very same ideas

into the body of a robot too.
So, to explain,

please join me in welcoming back
Anne-Marie Imafidon.

So, Anne-Marie, robots -
talk me through it.

So, embodied robots, we've got

lots of them that we're trying
to train to do all kinds

of different tasks,
and we've got an example here.

OK. So this is from Professor
Pieter Abbeel

at the Berkeley AI Research Lab.
Exactly.

So we can see this is a robot
that has never kind of known

how to move its arm before.

So it's just basically flinging
its arm around at random,

trying to get the stuff in the box?

Yes, just trying to get the peg
into the hole.

It's not doing very well.

It's not, because it hasn't had
the time to learn what an elbow

is, kind of how it needs to move
around, but also that the shape

needs to fit the hole
that it's got there.

OK. So this is after five goes.

We can see here iteration
eight as well

we've jumped to, and it's
getting very close.

Yeah, it's still not managing it.
I mean, it's basically just trying

lots and lots of things at random.

Yes. This I think, actually, it's
something that pretty much every

parent of a young child will
recognise.

This... This is Juno. Come on, then,

Juno, do you want to come
and have a little play?

Now, this is something
that you really notice,

is that, when they're very little,
they don't necessarily understand

how to control their limbs.
Let's give you a go.

You can try this, too,
Juno, here we go.

Do you want to play with this one?

Is it a similar thing going on?
Exactly.

So you learn the kind of motor
skills you might need to actually

complete this task between
12 to 18 months,

and Juno here is only six months

so it's gone in her mouth,

which the robot didn't do,
admittedly!

Different kind of reward!
Exactly.

But it's only over time, by learning
through lots and lots of

random behaviour, initially,

that we learn how to
control our limbs.

We've all been there.
We've all been Juno!

So the robots are learning
in a very similar way,

just they do have fewer
distractions.

They don't want to put things
in their mouths, and their rewards

are just a little bit different.

So if the humans take about 18
months to master this task,

how long did that robot take?

So this particular robot that we
were just looking at learned

to do that task in under an hour.

Yeah. Well, not long to go now,
Juno. You might not manage it in

an hour, but give it a few months

and you'll definitely
be doing these.

Thank you very much to Juno
and Anne-Marie! Thank you.

APPLAUSE

That is the key idea - it's all
about harnessing the power

of randomness, using trial and error
to hone in on something that works.

Babies do it, robots do it,

and as an audience now,
we're going to do it, too.

So, as a room, we're all going
to be artificial intelligence.

We're going to let you loose
like the machine-learning bots.

And for that, I'm
going to hand over

to the very wonderful WiFi Wars.

CHEERING AND APPLAUSE

OK, Steve, tell us what we're
going to do.

So what we're going to do is we'll
get you guys here to represent

random computer attempts at
navigating a maze.

What we need to do is get your
phones out

and go into your browsers.

And while you are going back
into there, I will show you the maze

if I can, please, Rob. There we are.
So what we're going to do,

we're going to put all of you in
this blue room in the bottom right

corner, but we're going to
incentivise you to go exploring.

So you will get ten points for each
square in the corridor

you get through, but, far more
importantly,

you'll get 1,000 bonus
points for each

of the coloured rooms
you manage to get to.

So the big prize in this one is get
to these three interconnected rooms

in the top left. If you do that,
I will be very impressed.

Everything we do at WiFi Wars
is competitive

so what we've done is we split
you into two halves.

So you guys on this
half of the room,

I believe, are the blue computer.
CHEERING

And you guys are the much
louder red computer!

CHEERING/BOOING

That wasn't fair. That wasn't fair.
Booing already!

OK. So I'm going to give you
90 seconds to go through that.

So, because there's so many people

and we're going to be looking
at that big map... Yeah.

..it's really difficult to work
out who you are, right?

So you are essentially each
representing a random

attempt at winning this game.
Exactly.

So if it was one or two
you could find your way through,

but we're going to make that
much more complicated. OK.

Good. Are you ready?
ALL: Yeah!

All right. Let's do a game, then.

If you send it into their phones
now, please, Rob. We'll put

90 seconds on the clock and
hopefully we're going to see...

Immediately we see you all
there appearing.

We've got a lot of blue and red
players from the two computers.

Already people are getting out - a
red person was the first one out.

My goodness me! Although
collectively the blue team...

I was going to say they're
doing better

but by the time I said it,
they're not, so the red computer

after 15 seconds have 6,000 points
and the blues 5,400.

No-one's found a coloured room yet.

And, as I've said, if you can get

to these three in
the top left corner...

Blues doing very well. Yeah, they're
all going for it. Going round

the two main channels. As you would
expect, sort of more often than not

they're picking the main
thoroughfares as they hit dead ends.

And actually we've got a few people
who are in the purple room now,

so well done. I think red was the
first so well done.

For context, if you're wondering
what's going on,

what Rob's created is a thing
that allows us - without installing

anything, any device,
browser or operating system -

to beam games onto your devices.
A red one's on the way!

Wow. All right. Yeah. Good.
I don't know if you know...

If there's a red, if you now
are in a green room, cheer.

Yes, you! Well done to you.
You were very, very good.

You've got 30 seconds left to do
something about it.

Nobody's managed to find
the orange or the red yet,

but we have got a few...

Well, we've got another red coming
for the green, but a lot more blues,

actually, so the blues
might level this up

if they can all get in there,
but with 20 seconds left

it is 70,000 points
to the red computer.

Blues, you need to do better.

You've got only 60,000
with 16 seconds left.

So many people getting stuck
in this horrible dead end

that we cruelly put in the top
right corner.

I think blues will need a miracle.
The reds are cheering.

Ten seconds left.

Five seconds left now.
88,000 for the red computer.

82,000 now for the blues,

so that is a win, I'm afraid,
for the red computer!

Wahey! Well done to you all.

"Boo!" Polite applause.

Well done. That was absolutely
fantastic.

OK. Here is the big question

on top of all of that,
because we saw in the last lecture

the highs and the lows of
uncertainty. We saw what can go

right and what can go wrong
when you rely on something

that has randomness at its core.

So, OK, if we accept that perfection
is impossible - if we acknowledge

that these things are always going
to have errors - we have to ask,

do we want to put flawed machines
in a position of power?

That, I think, is one for
the next lecture.

But, for now, who'd like
to have a rematch of this?

CHEERING