- A hundred years ago most lifts were driven
by trained operators. The technology was there to replace them but people just didn't feel comfortable with the idea of automation. Level one, please. And then lift designers made some small but groundbreaking changes. - [Elevator voice] First
floor, Christmas Lectures. - Add that to a stop button
and some relaxing music and suddenly trust in
automated lift soared. And here we are today. But what about today? Should we trust the
machines that surround us or are we right to be cautious? (upbeat intro music) (crowd cheering) Welcome to the Christmas Lectures, Am Dr. Hannah Fry. And tonight we're gonna ask whether we should trust the maths. Just how far should we be going with our mathematical skills? And let's demonstrate those
mathematical skills first off because we are joined by Scott
Hamlin and your BMX bike, right? - Hello. - And a rather carefully
placed ramp just here. - Very carefully indeed. Yeah. To make sure it's
absolutely amazing. - And you've been in since
eight o'clock this morning, calculating exactly where
this ramp should be, shape of the ramp, everything. Cause there's quite a short
space that you got in here. - Yes, absolutely. We need to make sure
that we can get enough velocity to give us lift off the ramp. And I need to use my
personal calculations, okay? instincts to better perform my stunts and stop before we crash into to the wall. (laughing) - And when you say stunts go, what are you gonna do? - Well, It depends. It might be a back flip,
if people wanna see one? - Do you want to see a back flip? - [Crowd] Yes. - Yes, they want a back flip. All right Scott, are you
ready to give us a go? - Are you guys ready? - [Crowd] Yes! - Alright, here we go. - Okay, go on then. We'll give you a countdown
Scott, when you were in place. Here we go. And then I'm going to get
really far out of the way. (crowd laughing) Okay. Whenever you're happy. - Right? We ready? - [Dr. Hannah] Yeah. Happy Scott? - I didn't hear the kids. (crowd laughing) - [Dr. Hannah] You guys ready for this? - [Crowd] Yes! - All right, We're gonna
give you a countdown, Scott. - [Scott] Go for it. - [Crowd] Five! Four! Three! Two! One! - [Dr. Hannah] Go! (crowd cheering) - Am alive. (crowd clapping) - Well done Scott. That was some tight
calculations going on there. - Yeah, it certainly was. I'm glad it paid off. Thanks to you guys for making the noise. - Well, no, thank you to you. Scott Hamlin, thank you very much. (crowd cheering) Now, okay. We all know the math is
amazing in this kind of stuff. It's out there in physics and engineering. It's doing a brilliant job, just as long as you do
your sums correctly. But I want to tell you a little story about a bridge that I think demonstrates, that's quite a lot easier said than done. Because this here, this is the, this is the Millennium Bridge in London. And it had this grand
opening in the year 2000, but you might also know
this bridge by its nickname. This is known as the Wobbly Bridge. Because there something that happened very soon after it opened. Now all bridges, including this one, they're built to move left
and right just a little bit. It's no biggie, but there was something about this particular bridge, that the designers just hadn't thought of. They missed off something quite important in their equations. Let me explain what happened here. Well, one of these, little metronome. This thing here, there's ticks and tocks and time. If I wanted to write a set of
equations for this metronome, be quite simple. Some very straight forward physics. And if I wanted to write some equations for a number of metronomes, I'd just do the same thing
over and over again, right? It's not like these things can
communicate with one another. I can treat each one as though they're completely individual. But, now let's see what happens, when they are on a bridge, with just a little bit of movement. Because something rather
intriguing happens. I don't want it to fall too
far. Let's go from there. Okay. Let's see what
happens when these things, are on a bridge, that can move itself, left and right. Just a little bit. Okay. So now, these things can move left and right. Something a little bit unusual happens, because every time that
a metronome is ticking or tocking in one direction, that left and right movement, means that whole bridge, will just be knocked ever
so slightly left and right. What that means is that
now these metronomes can effectively start
listening to each other. They're all now connected by the bridge which means that very, very slowly, you just see it moving very
slightly there left and right. And very slowly, they start to synchronize
with one another. Like a creepy metronome army, huh? Now this is something
that my equations just wouldn't have taken into account. But, humans like metronomes, actually have to be handled
quite a lot of care. And so for that, am gonna put you, in very capable hands, of my
good friend and mathematician, Matt Parker. - Oh, hey, so, thanks Hannah. I'm over here in the library
at Lawrence institution, where instead of very small metronomes, we have a massive Wobbly Bridge simulator. We've borrowed this from
the University of Cambridge. They use it to test things
like, well, full-size bridges. It's a very firm structure
with a tray attached to it. Which is able to move a
little bit side to side. We've got two treadmills. I'm joined by Dylan here. Who's gonna walk on one of
these treadmills with me. So if we turn these on, in theory we'll start
walking slowly to start with. There we go. And then, should go up to four. Let's try a speed of four. So now we are trying to
walk on a bridge which, is not staying still at all. And it feels a bit like being on a boat maybe where it's moving around and you're trying to
compensate for that movement. And it means we have perfectly synced up our walking and that's
causing it to move, I'll say a concerning amount. Yeah. You're keeping a brave face but it's terrifying up here. All our movements is causing
it to shift backwards and forwards. Hannah, you can imagine, what
would happen if you had loads of people doing this, on
a much bigger structure. - Just imagine indeed. Turns out, this is
precisely what happened. Because the people who
designed the Millennium Bridge hadn't taken into account the fact that, people can affect each other with the way that they're walking. So those very small
movements left to right suddenly became a very big deal. And it meant that on the opening day, you had hundreds of people walking over an 18 million Pound bridge. The beginning of a new Millennium and every single one of them, was hanging on for dear life. Look at that. That's a, British ingenuity
at its finest. Just there. But there is an important
point in all of this, with wobbly bridges and BMX bikes. Maths can do an amazing job, but only if your equations
actually match up to the world that you're describing. Just as long as you've
got the right equations for bikes and bridges you can be certain of what's gonna happen next. But there are some things, that are quite hard to write equations for in the first place. So, okay. Let's imagine, that it's
long into the future and you're trying to
write an algorithm that can help a doctor, work out what's wrong with their patients. Now a doctor's job is quite different to that of an engineer or a physicist. Because if someone just
comes in with a headache, a doctor can't just take measurements of what's wrong and end
up with an exact answer. Cause that headache it could mean a whole host
of different things, right? Somehow, the doctor has to
use a whole bunch of clues, to build a picture of what
might be wrong with you. Now, this is the difference
between calculating the answer and just making your best possible guess. And no one had any idea
how to do that in equation, until the 17 hundreds, when the Reverend Thomas Bayes, thought of a very clever game. Now we're gonna play a
version of this game, just with a little bit more fire. So who would like to come
down and volunteer for this? Let's go, let's go for you. Just there. Round of applause. You
can come to the stage. (crowd clapping) Okay. What's your name? - Emma. - Emma? - Yeah.
- Okay Emma. Right. What we're gonna do is we're gonna play a version of this game and it involves, this red hat. If you don't mind just
popping that on your head. Now, just so that we can
get your view of things. Oh, its a bit... hold on one second. Let me tighten this up. There we go. Now, just so we can get
your view of things. I've also got a version of this red hats over
here for this camera. So we'll be able to see what you see. Now the other thing that
we need for this game, is a whole host of balloons
which are just coming on, just coming on behind you. If you want to turn around,
Emma, just have a little look, just have a little look at these balloons. What color are these balloons are? We are gonna stand over here actually. Just stand over here. What color were the balloons? - Red. - Red okay. So if we look through this camera now, we will be able to see that
they do indeed all look red. And yet, to... just wanna step over here sorry, and yet, to everyone in this audience, we can see that in fact, what you're looking, at are 99 orange balloons and one yellow one. Now we all know where
the yellow balloon is. No one's allowed to give it away, but your job, Emma, is to try and pop that yellow balloon. And if you manage it, we're all gonna, we're all
gonna explode in excitement. And if you fail, we're going to respond with
disappointed silence. Okay? So here we go. You get to pick a balloon
at random and pop one. Which ones you wanna pop? You stand here, that will do. - [Emma] Erm. - Up? Up? - Down. - This one. - Yep.
- [Dr. Hannah] Perfect. - That one? That one there? - Yep.
- Ready? - [Dr. Hannah] Here we
go. (balloon popping) - That is not the yellow balloon. That's okay though. I mean, it's pretty hard in the beginning. What was, I mean, you know, how can we possibly
guess it the first time? So what we're gonna do as an audience we're gonna help her here. So we are gonna tell you, when on my cue, we're gonna tell you, if based on the last
balloon that you poped, whether you should go higher
or lower, left or, right. Right. So if you think
it's higher, say higher. If you think it's lower, say lower. If you think it's neither
higher, nor lower, than I want exactly 50%
of you to say higher, 50% of you say lower. And I'll let you work
out between yourselves, which one is which. Okay. So based on her last balloon pop, should she go higher or lower? - [Crowd] Lower! - And should she go left or right. - [Crowd] Left! - Okay. Where do you wanna pop. - Erm, lower. That one. - That one? Ready? (balloon popping) - Oh, okay. We'll give her another go. Do you want to go, based
on the balloon pop, should she go higher or lower? - [Crowd] Lower! - And should she go left or right. - (crowd shouting) - Oh, Interesting. Okay. Which way is gonna go? - Down. Down. That one. - Ready? (balloons popping) (crowd cheering) - You got there. Thank you so much, Emma. You got there amazingly quickly. Okay. Do you want to take this? Tell me about that. How, what was your stress
in the first place? - Just pick one. - Just pick one at random?
- Yeah. - You're just getting it running. And did our clues help you
to hone in on the answer? - Yeah. - You, you knew probably where
the balloon was by the end. - Yeah. - Were you getting more
and more confident? - Yeah. - Yes. Perfect. All right, Emma, thank you so much. You're amazing. (crowd clapping) What Emma was doing there, she
was demonstrating something that's called Bayesian thinking. And actually it's something
that all of us do instinctively but it wasn't until Bayes, thought of a version of that game, that the world realized
that you can actually write down that way of
thinking into an equation. I'm really not exaggerating
when I tell you, that that Bayes Theorem is one of the most important
equations of all time. Because suddenly, it
doesn't matter if you're not completely sure of the answer, you can still get a really
good sense of the right answer even from incomplete
pieces of information. And that is something
that is incredibly useful. Let me show you. So, okay. Let's imagine, that you are making a driverless car. Now, hasn't got a driver in it. So you need to make sure
that you know where you are. And, okay. You could use GPS to do
that, but GPS isn't perfect. So sometimes your GPS will, will get your position out
by about a meter or so. And if you're a human,
that's fine, no big deal. You can, you can work out where you are. But if you're a driverless car, the difference of a few meters, can mean the difference
between driving on the pavement and driving into oncoming
traffic, which isn't ideal. So driverless cars, they
also have cameras on board. Now cameras are pretty good at letting you know where you are, but again, they're not perfect, because skies look a lot like water, and, you know Lori tarpaulin looks a lot
like a cloudy in the sky. The point about driverless cars, is that you don't just have one thing, that gives you exactly where you are. You have lots of different things, that you use as clues to
indicate where you are. And that is something that
is especially important, when you're driving at 200 miles an hour. So this thing here, this is the world's
fastest autonomous car. Its built for racing, and it's got all kinds
of different sensors to help it work out where it is. So it's got, a little cameras here, it's got another kind of
camera called LIDAR over here. It's got a radar over here at the back. All of these are the
clues for the car and GPS, within this, the computer
that's just inside here. Now this thing, is basically
a Bayesian machine. So that computer that is
the size of just a lunchbox is churning through, trillions of calculations every second, to make sure that this
car knows where it is and finishes the race
as quickly as possible, all while avoiding other competitors. I think that's the thing about how these modern inventions work. That's how they deal with uncertainty. They don't just have one sensor. They don't just have two senses. They have a whole host of sensors that they used to layer up
and give them information. Something that's true of driverless cars. But it's also true, of
this little guy here, who I believe, it's gonna follow me into the studio. Here we are. Come on in. Come on. Come on. (drone whizzing) Hey, a round of applause
for our little drone. (crowd clapping) (drone whizzing) - Oh, that was a lovely landing. Right? I want you to join me
in welcoming to the stage, Duncan and the Skyports drone. (crowd clapping) This is quite some drone, Duncan. - This is.
- This is quite a big one. This is one of our delivery drones. So we can do, medical samples
or E-commerce deliveries with this one. - [Dr. Hannah] So what kind
of things is useful then? - We do blood samples. We can do them between hospitals
and medical facilities. We can do packages. We were flying them in Finland recently. Basically anything you can fit in that box up to about five kilograms we can fly it. - So how does this thing avoid crashing? - It's got a number of systems on it. It's got 4G, just like mobile phone. It's got a wifi network of its own. And it's also got, If all else fails, a satellite
communications network. - What's this thing over here,
what is this little bit here? That's a, that's the fail safe. So if everything goes
wrong, that's a parachute. And that would deploy, say for example, if one of the rotors stops or one of the motors doesn't work, be a big alarm that goes off, that will deploy and it'll
come down to earth very safely. - So it needs all of
those different systems running in parallel? - It needs more running in parallel. Hopefully you only ever use one of them but the rest we call redundancy. it is there just in case
something goes wrong. - So what's the of future
drones like this then? - So this is becoming
more and more prevalent. We're flying in Africa. We're doing snakebite antivenom. So very urgent staff, often
very bad road networks. We're flying in the
West coast of Scotland, doing some medical samples again. Ultimately it will come into cities, much more complex environments, got lots more people, lots more buildings, lots more things to keep of the way off. But you know, the technology's,
there is good enough now that you can fly pretty
much any environment. - Talking of people, can I ever get a personal passenger drone? - You can. In fact, you can already. - So this is your company,
Personal Passenger Drones, I think? - Yes. So this is a
company called Volocopter. And these are live. They're going through certification now. Within two years everybody here will be
able to get in one of these and fly around. - [Dr. Hannah] Within two years? - [Duncan] Within two years. - [Dr. Hannah] Goodness me. Will our skies be full
of them in the future, then do you think? - [Duncan] The airspace is
vast, cities and are very dense. It's hard to put more
infrastructure into cities. These can fly in our, in
our under utilized air. - [Dr. Hannah] Amazing. Duncan there. A view of
the future there I think. Big round of applause if you can. (crowd clapping) Now, Duncan was talking a
lot there by having backups on backups on backups, just to make sure that if
there's ever a problem, you know that the drone won't crash. And that is something actually, that Matt Parker has
been thinking about too. - Yes. And I've brought a
comedy oversize slice of cheese. - A slice of cheese? - Slice of cheese. - Okay, all right. - Because when a lot
of people are thinking about things like drones and
trying to avoid disasters, they find it's useful to think
of it in terms of cheese. - Okay. - And so, things can go wrong with drones. You can have, one of the
motors might break, a battery might run out of charge. And when that happens you don't
want it to crash into people and cause a disaster. So you imagine these
mistakes, these errors, coming at your system and you put in barriers like, so like a slice of cheese, to stop them from making it. Bear with me, making it through and becoming a disaster. So this could be for
example, like the GPS system. - Okay. - So it's tracking where it
is, everything that goes wrong, it shouldn't be a disaster.
- Why is there holes though? - Well, well spotted. So GPS, as you know is not perfect. Like you were saying, you could be on the sidewalk
according to the GPS. And so it might be an accurate, it might giving you the wrong data. No one layer, to try and stop
disasters will be perfect. - So this is trying to block
disasters from happening. Mostly it works, but just
occasionally, it's gonna fail. - Occasionally a mistake will slip through and GPS won't be enough. - Okay. - But if you see over here,
we've got more cheese. So if you'd like to take a
seat here, I'll get the cheese. - Okay. Go on. - It's fine. - Are you sure? - You trust the maths. Here we go. - And why should I
trust you though, Matt-- - Wise, very wise. ( crowd laughing) So, this is another slice of cheese. And this one is the parachute. So if the big drone fails and the GPS is wrong, the
parachute will deploy. - Okay. - And so with two layers together-- - They both... This one
has hole in it as well. - Well, that's true. But what we hope is the
holes in this layer, don't line up with the
holes in the other layers. And actually we can get another layer. This one's just called rules, which doesn't sound very exciting, but we all need rules. So when we brought the drone in here, we weren't allowed to
fly it above a crowd. And that's one of the
rules for using a drone. And sure-- - So even if, an accident happens, even if the parachute
fails and the GPS fails, at least people won't get hurt. - In theory, if you're following
the rules, it'll be fine. Although of course, sometimes
people break the rules and you hope the other
layers will help you out. And so, I'm just gonna grab a camera so I can show you a point of view. Thank you. - Can I just, you know what Matt? I mean, it's not that I don't trust you. - No, it's literally
that you don't trust me. - No, its actually
that, I don't trust you. - Okay. So if you have
a look from Hannah's point of view underneath, you can see up through some holes, but then almost straight away it's blocked by a
different slice of cheese. And the point of view of the top here you got again there are some
holes that go partway down, but then they stop. So what we're gonna do now, is rain some errors down on you. We've got a whole bucket of errors. And in theory, if we drip them down, while they will make it through some of the slices of cheese,
that will be stopped by the more errors, more errors. Right? And so, some of them are being
stopped by the first layer. Some of them are getting
through the first layer but they're stopped by the next layer. And so, in theory--
- Okay, I get this, I get it. I get it. I get it because yeah, overall, overall it's fine. Right? Like all of these
different layers are blocking, blocking it from happening. - Yeah. And so even though any
one individual layer, you're like, Oh, look at
all those holes in it, If you sit down and you look
through the whole lot at once you're like, ah, it's amazing. From here, I can't see any hole, which goes to the entire way through. - That is good. But then, Matt, what happens
if the holes do go the entire way through? - Now, that's a good point
because every-- (crowd laughing) You make a valid point. - I think we need more errors. - Or bring it in an interesting way. - More errors, more errors. (crowd cheering) - I'm trying to make a serious point here. (Dr.Hannah Laughing) Occasionally your cheese
holes will line up and a few mistakes normally, will make it through
and become a disaster. - And there's nothing
you can do about that. - Nothing you can do about that. - Disasters, Matt, are inevitable. - Yeap.I'm coming to terms
with that as we speak. - Matt Parker. A round of applause. Thank you very much. (crowd clapping) So this is the really big
downside of uncertainty. You have to accept that
perfection is impossible. Mistakes are effectively inevitable. And I think that that raises a very big and important question. If we know for sure that our algorithms are
never gonna be perfect, do we want to put them in
charge of making decisions? Especially in situations where
people's lives are at stake, like in the courtroom. Now, someone who's
thought about this a lot, is Professor Katie Atkinson. (crowd clapping) Katie, - Hello. - The courtroom, it doesn't feel like a
natural place where you would find mathematics. - Well, perhaps not. But actually AI and law
researchers are working on building models of legal reasoning, using mathematical models
that are then turned into software programs, that can help judges and lawyers. - Why do they need them? Then why can't the judges
just do it all themselves? - Well, the point of using
these mathematical models is that we can get consistent
efficient decisions. And then we know that any kind of unconscious biases
have been stripped out. - So, judges makes mistakes, they have unconscious biases, and the idea is that using algorithms can help
to minimize that. Right? - Yeah. That's absolutely right. So we're hoping this can help. - So I understand that you
brought a little friend along to help us understand
what's going on here. - Yeah, that's right. This is Pepper, the robot. - Pepper, the robot. Hello Pepper. - All rise for the courts that will decide the case of Popov V Hayashi. - So this links us to a legal case that was in The United States, and involved a baseball
and people catching and dropping a baseball. - [Dr. Hannah] So someone hit
a baseball into the crowds and then two people fought over the baseball at the last minute, this baseball was very
valuable, wasn't it? - [Katie] Indeed it was
worth $450,000 in the end. - Oh, cranky. So I can understand why two
people were particularly upset. - Yeah. - Okay. So what we have
to do is first of all, work out what the facts of the case are. And then we have to work
out what the arguments are for the two different sides
within the legal case. - And that's what Pepper
is gonna help us with? - That's right. So the facts of the case are, that Mr.Popov, stopped the
forward motion of the ball, once it was hit into the crowd. He tried to get it under control but then he was thrown to the ground by this mob who were also
trying to secure the baseball. - Which sounds a bit unfair
really, if you are, you know, you caught it and then
it's not your fault. - That's right. So that's why he felt
that he should have been given the opportunity
to complete the catch. And then Mr.Hayashi, found the loose ball on the floor and he picked it up and claimed it as his. - Okay. Well, I mean, he was the one who ended
up with it at the end. That kinda seems like
he's got a fair claim too. - That's right. And in particular, he
wasn't part of this mob that threw Mr. Popov to the ground. So he did no wrong at any of this. So he shouldn't be punished and that's really the facts
and the arguments of the case. - So you can take all of
those facts and arguments of the case, put them into equations, compare them to cases that
were like this in the past. And then ultimately, can the
algorithm, give us a verdict? - Yeah, that's right. - Okay. Pepper. What's the verdict in this case, then? - The decision of this court
is that the ball should be sold at auction and the proceeds
split evenly between Mr.Popov, and Mr.Hayashi. - Okay. That's very clear. I mean, we were having lots of fun here with a humanoid robot, but it's not the intention to actually have humanoid
robots in courtrooms, Is it? - That's absolutely right. We're aiming writing
these mathematical models that we're turning into AI tools, that will be on the computer
and helping judges and lawyers, Who are they certainly using
these for decision support. - It's all the stuff inside
Pepper, not Pepper herself. - That's right. I can't imagine seeing Pepper
in a court anytime soon. - If she's sending you off to jail, looking at you like that, you know. - [Katie] No, certainly not. - Just the example, sounds like a very positive
thing you could get through, I imagine like a big backlog of, of cases with something like on your side. But algorithms in the
courtroom, they're not sort of, they haven't been universally
positive, have they? - Yeah, that's right. So there is a big issue of trust as well as whether the actual
technology works in itself. - Because if you're putting
all of history into a series of equations, I mean,
history wasn't exactly fair. Was it? - That's right. And we want to make
these decisions as fair as we possibly can and get AI technologies
to help us do this. - I quite agree, Katie, thank
you very much for joining us. - You're welcome. (crowd clapping) - I think Katie made a
really important point in all of that, because
if all you're doing, is just chucking in everything
that happened in the past, to your equations, then you're gonna
perpetuate all of society's biggest imbalances going forward. And a machine is only
ever as good as the data that it's trained on. Let me show you what
I'm talking about here, because I'd like you to
welcome back to the stage, Matt Parker, with a very
special shoe detecting machine. (crowd clapping) (speaker drowned out by crowd) - All right. I bring a range of shirts. So, this is my shoe detecting device. - Okay.
- So you can have a go. - Ooh. - I call it "shoe do you think you are". - Okay. Got it. Very nice. - And if you point it at something, it can tell you if it's a shoe or not. - Oh, okay. Let me go over
here. Lets try your face. - Okay. That's not a shoe. Not a shoe. - Correct. All right, let's
try, lets try your shoes. - Okay. Here. - Oh, hang on. It says they are not shoes. - Oh, the device is fine. We just haven't trained it yet. - Oh. - We have to put in some training data. You were here for lecture too, right? (laughing) And so, we've got to teach
it what a shoe looks like and then it'll be amazing. - Okay. All right then. So let's get a group of people
to help me train this shoe. Let's get some people from over here. Let's get that little corner up there. And up here if you guys
all want to come down and help me train this machine. Round of applause as
they come to the stage. (crowd clapping) Okay. So here's what we're gonna do. We're gonna give you 20 seconds and I would just like you
to draw a picture of a shoe that can scan into the machine. Here we go. (techno music) Two, one, stop. Okay. All right. Let's, let's turn those around.
Hold them up to the camera. We'll have a little look. Let's scan these. Okay. So we've got some
Lacey numbers, some nice... A top shot of a shoe there. This is great. Lots of laces, that you
have some trainers there. This is all perfect. Okay, great. Perfect. It's all... I think it's all trained, Matt. So let's give it a go, let's,
let's try your shoes there. Okay. Perfect. Spotted your shoes. Amazing. It's great. - Is it detecting shoes? - Yeah, yeah, yeah. Look. Yeah, perfect, Amazing. That's great. - Aw, that's amazing. So can I just pop through
and get a closer look? That's incredible. (crowd laughing) - Okay, yes. - And it's not failed yet on any of these near identical shoes? - No, that's... I mean, you didn't all
draw very similar shoes. - Give us a go.
- Let's try this one. I don't know. So no, it doesn't, it doesn't
detect your shoes at all. Or you need a much diverse
range of training and... I'm very disappointed in all of you. (crowd laughing) - I mean, guys, you did just
basically draw your own shoe. So let's try again. Let's try again. Turn your paper around. Try again. And this time, try and
think of as wide arrange of shoes as possible. Off you go. (techno music) Three, two, one stop. Okay, here we go. Let's get this in scanning mode. We're ready to go to.
Don't turn it around. Hold it up to the camera.
- This is not a shoe. - That's, it looks
pretty much like a shoe. This is pretty good. This is great. We've got some high
heels, lots of high heels. We've got flip flops. We've got boots. We've got, you know, some wellies there. This is lovely ballerina shoe. That's great. Okay. Perfect. All right, Matt. I think it's good now. I think it's got to be good now. It's got to be good now. Surely
this works on all shoes now. - Okay. Lets have a closer look at it. Excuse me. (crowd chattering) Shoe? - Shoe? Not a shoe. - I mean-- - Shoe? - That is, that is debatable
whether that's a shoe. - Its definitely footwear.
- Okay. Let's give it a go. - With the right data it could
have detected that as a shoe. - This is true with the right data. But I think the point
here, is that even when, you go draw as diverse
range of shoes as possible it's actually just really
hard to think of everything. - Just unbelievable. (crowd laughing) - Thank you very much, Matt. And a round of applause for my volunteers. Thank you very much. (crowd clapping) Now there is a really
important point in all of this because if you don't think through all of the possible situations that your your machine needs to include, it can end up having very
big real life consequences. To tell us a little bit more, please join me in welcoming
from the University of York, an expert in image
recognition for surveillance, Dr. Kofi Appiah. (crowd clapping) Now, Kofi, you've done a lot of work in
facial recognition before, right?
- That's right. - Tell me how just facial detection work. - So for face detection, all that we need is to be able to pick out the elements of the face, which is the eye. The key features the eye, the nose, the mouth. And to be able to pick up these features, there's a big contrast that
we can find between the eye and the eyelashes, the
eyelashes, and the skin itself. When it comes to the mouth, the lips you have an edge there. So this is the big contracts
that we are able to pick. And as far as we are able
to find eyes, nose, mouth, we got a face. - So is it looking, is it,
so if I come over here then to this one here, so is it
looking for areas of light and darkness. They're like,
on my nose, for instance, - Yes. - It's kind of darker on either side and then lighter down the middle. - That's correct. - And then if it gets a
strip of like darker pixels and nice fixes, it
knows it's found a nose? - That's right. So it's looking for these key features. This is what a face normally will have inside the key features. - The eyes, That's perfect. - And that's what we train our systems to do to recognize. - That's perfect. Just one thing though, Kofi, that I'm noticing here, it's
getting my face okay, but. - Right. So it's not able to pick my face and it's relating to the
data that you just mentioned. This system has not been
trained with enough data. What the contrasting
features that you've got between your eyebrows and
the skin texture is different from mine and it's not picking it out. So in this case, I'm going to have this. Its picking up, as a face
because it can pick some of the salient features
that I was talking about. It's going to put a
bounding box around it. Whereas in my case no. It don't pick it. - But this, I mean, this stuff is actually you know, it's a really big deal. It's not just in cameras. I mean, we're now seeing facial detection and all kinds of things. Passport queues for instance. - That right. Yes. Yeah. So obviously in this case,
if you've got a face like me, unfortunately it's going
to be a long delay for you. We do using it in law enforcement as well. And if, I example, if it's not
able to pick the right face, you're going to be prosecuted for something that you've not done. We using this facial recognition to be able to unlock phones. It's like a password now. So if it's not working right look at the harm that it can do. - Is it, is it improving? Are the algorithms getting better? - Yes, It's getting better and better. And, what they are trying to
do is to make it known by us, by training, with diverse
non-biased datasets. So that as you can see can pick some of the
features, it's pick mine. You can also-- - And works a whole
different range of faces. - Is it try to fiX that... So it's improving over time
and we're getting there. - Obviously the issues of bias and fairness are incredibly
important when it comes to facial recognition, but there is another
concern that people have when it comes to this technology. Which is that, some people don't like the idea
that they can be identified in a crowd based on their facial features. Some people don't like the
idea of losing their anonymity. So some people have been experimenting with different ways to try and trick facial recognition cameras. And we are lucky to be joined from Glow-Up by makeup
artists, Tiffany Hunt and Eva. (crowd clapping) (crowd clapping drowns out speaker) Okay now Eva, you have had, as we can see, Tiffany has been doing your
makeup for a little while backstage. It looks very unreco. Is this the kind of thing that would work on a, to fool facial detection? - Oh, Yes. So this kind of system, because it's looking for that contrasting between the eye, eye lashes, the nose. But the way the face been painted now it's breaking all the symmetry. Like it can't find it at, the nose. - So Tiffany, talk us
through what you were, what you were going for. I mean, it's quite, it's quite dramatic. - (laughing) Just a bit. Just something like natural. So basically what I was
thinking was something that was a monochromatic illusion. So I really want to break up
the specific facial features such as like this sort of area,
the central area of the face which is sort of the area
that gets picked up the most, also like sort of changing
the eye shape the lashes and just really going for something with light colors that are so different to your usual skin tone and
just changing a face totally, really. - Do you like it? - Yeah. (crowd laughing) - Friday night out. (laughing) But the proof is in the pudding. Let's have a look. So Tiffany we're picking you up. You step to one side, it's
not getting you at all. (laughing) Not getting you to all. I think a big round of applause there for the dazzling make-up. (crowd clapping) Very impressive. To Eva, Tiffany and Kofi. Thank you very much everyone. (crowd clapping) Okay, so, we've got some ways now
that we know we can avoid being detected by facial
recognition cameras, might look a little bit crazy if you're wandering around
with that all the time. But when it comes to
protecting our privacy there are some people who are worried that even this won't be enough. There are some people who are worried that with the help of mathematical algorithms we are building and processing vast profiles on each and every one of us, often without our knowledge, now, to explain this,
please welcome to the stage computer scientists, extraordinaire, Dr Anne-Marie Imafidon. (crowd clapping) Anne-Marie, do you think that we're
seeing the death of privacy? - In some ways we are,
in other ways we aren't. I know if you go back far enough, we used to communicate
with fire and smoke signals or by yelling things long distances. - Not that long ago really. - Yeah. Basically, but now with
the algorithms that we have and the time that we spend online, really in-depth profiles are
being built up on all of us continuously. - And it's those profiles
that are connecting pieces of information together, right? That's kind of the big thing. - Exactly. So where those things
might not have been private before being able to connect
them together to build up that picture of someone, was harder whereas now it's very easy. And we use things like social media where we're putting that information out. - Yeah, voluntarily. - Exactly. So the profiles
are pretty complex. - Okay. So to give us a sense of
these kind of profiles, I wanted to get, let's say three volunteers here, Cause Anne-Marie got a
little demonstration for us. Okay. Perfect. All right. Okay. Perfect. Well, we'll go for you. You get to come down. Let's go for, let's go for
you there in the jumper and you there in the stripy jumper. (crowd clapping) Lets bring them down the stage. (crowd clapping) What's your name. Pardon. Natasha. Okay. Perfect. Natasha,
if you want to go there. What was is name? - Kieren. - Kieren. Okay, perfect. Dear, do you want to go there Kieren? And what is your name? - Caitlin. - Caitlin. Perfectly Caitlin. You jump around there. Okay. Perfect. Anne-Marie, is gonna talk us
through a little demonstration. - Yes. So each of you have got
a website that we've loaded up for you on your laptop there. And I want to make sure
you've accepted cookies and I'd like you to get clicking and browsing on these sites
and doing some cool things. And as you've accepted cookies, I'm gonna give each of you a cookie. (crowd chattering) They are rather large cookies. Thank you very much. So as you're browsing and
you're clicking around you have this cookie in your sites, and in your laptop. And there's different data and information that you're putting in. So I can see you here, you're about to watch some YouTube videos. I can see which channel that you're on. So I'm gonna pop a little bit
of a chocolate chip there. That's some data. I can see the artist that you've got as well. So I'm gonna pop a little bit more data on your chocolate chip cookie. I can see you're shopping here. Brilliant curtains. So that's a good bit of data. (crowd laughing) She's got a new house and she likes blue and white curtains. So two more bits of data that
we've got there Fantastic. I think you've added them
to your basket as well. So we're gonna add that into your cookie. Just that when you come back, you don't need to browse blue
and white curtains again. And just over here, we're
looking at your address. So that's a bit of data and I can see you're getting
directions to school. So that's even more data that
we've got popped in there. - This is a lot of data now, Anne-Marie. - A lot of data just from
browsing and entering information that is going into these cookies. They're stored on their laptops. Now, if you stop browsing
now, you'll see that we've got quite a lot of data on
these cookies and these cookies don't just stay within your laptop. Many of you might have
accepted cookies that have got third party at sites as well. So here's our third-party site-- - What does that mean then? - And you actually get that
data as well from their cookies. So you know that it's blue-white curtains that she's looking for. - So, if you except that about cookies, that means another person
can just buy that data and add it to a whole host of
other data that they've got from all loads of other websites and build this incredibly
detailed profile of you. - That's exactly what's happening, I mean do we realize that's
what we're really doing when we're clicking around? - Probably not. - Okay. But the thing is, that it's not just the information
that you're clicking on that builds, that helps to
build this profile of you, it's also things that
you're not clicking on to. And so for this, let me welcome
to the stage, Mark Kerstein. (crowd clapping) Mark, you build websites, don't you? - Yes, that's right. - And, you've in fact
built a website for us. - Yes, so-- - So let's have a look at this, talk us through what you built. - So this is just a webpage that I've created that
lists a bunch of animals. - Okay. Perfect. All right so what I'd like you to do, if it's okay, it's just have a little look through this and, just you know, have a little flick through and stop on... and have a little read of whichever one whichever one you think ends up being particularly interesting. How could, if you stopped
to one if you picked one. - Yeah. - Okay. Perfect. All right. Now Mark, which one did she pick? - That will be the dog. Is that correct? Yes.
- And how did you know that? - Yes. So it's not just
about the information you're actively putting in but it's enough to simply
scroll through something, to find out what someone's thinking of. So in this case, this webpage, as you scroll through you're seeing the different
animals that are being looked at in real time being
transmitted to the server. - So Anne-Marie, websites aren't just tracking
what you're clicking on. They're tracking on
what you're pausing on. - Yeah. How fast your mouse is moving, the kind of device that you're using, to access the website. They can even tell the difference
between a click and a tap, but often there's even more. So things like your IP address, which might give a clue as to where you are
physically browsing from. - Just imagine how much information, you could get on someone, if you're a professional
company that had been doing it for years, Anne-Marie. - Exactly. And that's
why it's so valuable. There's so many insights
that we can pick up from lots of different websites. There's so many of us that
are using these platforms. And so these cookies are
pretty valuable over time to lots of different people. - Incredibly valuable, indeed. Thank you. (crowd clapping) And thank you Anne-Marie. Now we don't wanna get stuck. We're not saying that it's necessarily bad but I think it is
important to realize just exactly how much we are giving away. Because I think, if someone can work out
what kind of person you are, they can use that
information to target you with very precisely tailored messages. Now that might be adverse to
persuade you to buy something but it might also be linked to political messages
to persuade you to vote in a certain way. And that is something that's
been in the news a lot recently but I think it is important to remember, this is kind of how our
whole online world is designed to work. But what makes people
particularly unhappy, is when that targeting
happens with fake news. But even there, there is maths
hiding behind the scenes. Because with algorithms on your side, it is easier to create realistic fake stuff now than it's ever been before. I want to show you, just how easy it can be
to create fake stuff. I want to see, if you as
an audience are capable of spotting some fake classical music. We're gonna play a little game, that I like to call, "Is the
Bach worse than the Byte?". Okay. I was pretty proud of that. Thanks. What they gonna do? They're gonna play you two
pieces of classical music. One of them was composed
by the very great Vivaldi, and the other one was composed by a mathematical algorithm
in the style of the Vivaldi. What I want you to do, is to see if you can guess which one is which, okay? So two pieces of music, your
job, spot the real Vivaldi. Okay, here we go. Piece of music number one. (playing classical music) That was lovely. (crowd clapping) Okay. That was piece of music number one. Here is piece of music number two. (playing classical music) Ah, lovely. (crowd clapping) okay. But one of those, one of those was a fake. Okay. So you've got to spot which one. If you think that the real
Vivaldi was the first song, give me a cheer. (crowd cheering) If you think the second
song was the real Vivaldi give me a cheer. (crowd cheering) I mean that's basically 50, 50 guys. I'm just guessing at random. I see. And the real answer was, which one it was? - Number one. - Number one was the real Vivaldi. Well done if you go that right. (crowd clapping) Now, okay. That fake Vivaldi, it was
impressive enough to kind of full half a room full of people
as to which one was real. But the algorithm itself was
actually incredibly simple. All you do, is you take a catalog of all
of the songs that Vivaldi has ever written, and then you give the algorithm a chord and then it will tell you,
with what probability, the next chord that was likely to come up
in Vivaldi's original music. So what do you do? Give it a chord, it gives you a chord back
based on probability. You give it a chord. It gives you a chord back
based on probability. You chain those chords
together one row the other, until you end up with something
that is entirely original. And, that's, I think is the,
is the real giveaway here about the fact that this is the fake. Those very simple chord transitions that go on in the background. And there are other bits though, right? There, I mean, there was
one bit at the end there particularly which,
which looked like it was it was quite difficult. - I'm afraid there are passages later on, which are impossible to play. - Impossible to play. Impossible how? - At the speed that the
artificial intelligence has asked us to play, you physically can't
reach the extreme ends of the instrument quick enough. - And I think that that's
an important point. That Vivaldi, he had knowledge
of how to play instruments. He had knowledge of human
hands and human bodies, but the algorithm, doesn't. It just kind of shoves
loads of stuff in together. And, but I should tell you that it turns out that you can use this very same idea
for lots of other things. You can use it for music, but you can also use it for lyrics. And because it's Christmas,
what I decided to do, I started to feed in, some
classic Christmas carols and I decided to train
my very own algorithm, to create, to generate a whole new Carol. And so for this, please
welcome to the stage, Rob Levy, and join us for a rendition, of a mathematical Christmas Carol. Round of applause for Rob Levy. (crowd clapping) Feel free to sing along. (christmas carol playing) ♪ It's Christmas time, there's no ear ♪ ♪ May ye shepherds quake at turkeys ♪ ♪ In a pear tree thy candles shine ♪ ♪ Out in a winter wonderland ♪ ♪ Now the angels singing in
our way ♪ ♪ Fa la la la ♪ ♪ La la la la la ♪ ♪ La la la la la la ♪ ♪ La la la la la la ♪ ♪ We'll have snow on Christmas Day ♪ ♪ Good girl, Santa must be ♪ ♪ Ding dong ♪ ♪ Ding dong ♪ ♪ Ding dong ♪ ♪ Ding dong ♪ (crowd clapping) - Rob Levy and the Aolian
String Quartet everybody. Thank you very much. (crowd clapping) Here is the thing, about using math in
this very creative way. It's very impressive, but
it's also not really human. And there is something a
little bit uncomfortable about a world where fakes can fool. Especially when you're
not just mimicking music, but people. To explain let's welcome, Dr. Alex Adams, (crowd clapping) Now Alex, you work in an
area called deepfakes, right? - That's right so-- - What are they? - So deep, fake algorithms
are a special kind of machine learning
algorithm that can take say one person's face or their
heads or their entire body or their voice, and turn it
into another person's head. That's what it is.
- So to make people look like they've done stuff in
videos that they've never done. - Absolutely, absolutely. - Okay. I mean, that's quite, that's
quite something, right? You can manipulate someone. - Yeah, its quite, it's quite scary. You can essentially puppet people. - So how does it work? - So, you imagine if I have a video of you and a video of me. So we take those two videos and we split them up into their
sort of individual frames, so their images that make up that video. And we'll show the computer all of those different
images of me and you. And what the algorithms
will start to learn is, they'll start to learn things like, what's the structure
of our different faces and what are the different kinds of expressions that we make. But critically they learn
how to distinguish the two. So one of them will know, okay, I can make red hair and a
particular kind of skin tone. And another one will say I can make Brown hair in a
particular kind of skin tone. But they'll separate our auto expressions. So I'll be able to say, okay, I want to take you smiling
and put that smile on to me. - So, you're splitting up
what your face looks like, and what your face is doing. - That's exactly right. - And then you can put what
your face is doing on my face. - Whilst keeping all of your
other features the same. - Okay. - Okay. All right. So, so we've got a couple of photos here. So here's a picture of you then, you're doing a little, a little snarl.
- A little snarl. - I like that lot. A little bit of an eyebrow raise as well. Okay. So you can make me do this face. - Yeah. That's exactly right.
- How do you do it? - So if we just flip that over, so what we can think of, is
we can think of my expression as having some kind of abstract
mathematical representation which I'm sort of thinking
of as these puzzle pieces. And what I can do is I
can say, well, piece 32 and say 37 over there correspond
to me making this snarl. And what I can do, is I
can just take those pieces. Let's imagine I've got them over here and I can just slot them
in over here into your, into your face. - So you're shuffling my
face around, essentially? - Yeah, I'm basically just saying turn on the bits of your face, that will make you make that snarl. - Okay, great. So once you've completed that jigsaw, if you can flip it over, what am gonna do is that... Oh, there we go. That's a
little snarly face., isn't it? - Exactly. Will now get,
kind of with the snarl. - Are there, are there concerns about
this kind of technology? - So like my day job is I'm
a data scientist at faculty and that I work on using artificial intelligence techniques to sort of detect these kinds
of video manipulations. Obviously there's a lots of implications for the fact that you
can just pop at people. I could record a video of
myself saying something and just transform it into
some celebrity, for example, and this has implications
for say, privacy, but also for say democracy
and political disinformation. So there are lots of, sort
of concerns about that which is why it's important
to be able to detect this kind of content online. There are also many great
applications though, like dubbing, special
effects and things like that. - Do you have any top tips
for spotting deepfakes? - Yes. So I think my top tip for sort of spotting
deepfakes is always just, if you're watching a video of something and you sort of suspect that it might be a deepfake, just ask the question. Do you really believe
that this person would do or say these things that
the video is portraying? So that's like my number one suggestion. My second suggestion would be
sort of do some fact checking. Sort of try to see if
you can find that video on some trusted, trusted news outlets. Particularly if you found
it posted on social media or something that's sort
of harder to verify. And from a technical perspective, I think, it's useful to look
for things like objects in the background of the video. So if, as I move you notice
objects in the background of the video are moving with me, that's a pretty strong indication that the video has been manipulated. - That I think it's an
excellent tips that will only serve as well in the future. And Alex, thank you very much indeed. - Thank you. (crowd clapping) - Now, we really wanted to show you how this deepfake stuff works. So what we've done, is we've been working with
Alex's team and we've created, a deepfake of someone in the audience. We just get to break boundaries here at The Christmas lectures. So to give this moment, the real sense of
occasion that it deserves, I would like to introduce
you, to a brand new talk show. Because tonight, for one night only, please welcome your host, It's Matt Parker, and,
"This is your face." (game show music) (crowd clapping) - Thank you. - Welcome to, "This is your face." And can you please welcome
to the stage tonight's face, It's Kaia. (game show music) (crowd clapping) Come on down Kaia. Just take a seat. - Thank you. (crowd clapping) - So Kaia, it's great to meet the person behind the face and to get to know you, my first question is, do
you have a favorite food? - Yes. I like pizza and pasta. - Pizza and pasta. That's pretty uncontroversial. - Pizza and pasta? No, we're not having that. Let's have another go. I really like vegetables. Just plain, old vegetables. specially Brussels sprouts, actually. That's, that's number one. And often maybe with a
side of extra vegetables. - Well, Kaia, that is your face. My next question. Do you have a favorite type of animal? - Yeah, I love cats. - You love cats? They're pretty adorable, aren't they? - Yeah. - Have you got like a
least favorite creature? - Probably insects. - You don't like insects. - No. - All right, interesting. Interesting. - Hmm. Let's see, shall we? - I just love ants. I think they're amazing. I love the idea of them
like crawling all over me, in my hair, up my nose, in my ears. Just like a massive
swarm of ants everywhere. - Kaia, you better talk to your face. Now my last question,
you've got a younger sister, haven't you?
- Yes. - Are they in tonight? - Yeah. She's right there. - Right over there? Excellent. And you get pocket money? - Yeah. - Okay. My final question, just
hypothetically, your pocket money, - Hmm - You wouldn't mind giving all of that to, let's say your sister, for
the next roughly five years? - No. - No? Hmm. - That's your sister, right? - Yeah.
- Don't worry. I've got you. - I've decided to give my
sister all of my pocket money for the next five, 10, 20 years. (crowd laughing) She deserves it. You know. She's just, she's better
than me. She can have it. And she have all of my desserts too. I don't need those. And she can always have
my phone, and my room. Which is where she can
keep all of my clothes, because they are now hers. (crowd laughing) - Wow. That is your face. So, thank you very much for coming along. - No problem. - No, not you, your face. - Thanks. It's been wonderful to be here and I completely stand
by all of my answers. (game show music playing) (crowd clapping) - There is a point, to all of this. Because, whether you are
talking about deepfakes or driverless cars or facial recognition, under the surface, all of these things
that have such potential to change our world. There ultimately mathematical creations. And math, isn't just
about bridges and bicycles and buildings. Behind the scenes, it's mathematical leavers
that are powering the changes to our society. It's got the potential
to change everything. What know, how we talk to each other, even how our whole
democracy is structured. Now don't get me wrong. I don't think this is about being afraid of the advancement of machines but I do think that we need to be honest with ourselves about the
awesome power of mathematics. And I think we need to be careful of the very real limitations, of something that will never be perfect. But you know, I think if we
can be aware of the pitfalls if we can work our way
around the challenges, I am optimistic, about a future where humans and machines can work together. Exploiting each other's strengths and acknowledging each other's weaknesses. A partnership that has the real potential to be a force for good. And so to finish, let's
combine human and machine in the performance of collaboration. Please welcome the Chineke Orchestra. (crowd clapping) Your job, is to guess
which parts are human and which were the mathematical algorithm. Enjoy. (classical music playing)
