I've been a driver for 30 years,
but I've never been in a car
like this one before.
This is an autonomous vehicle
and it's driving itself through Oxford.
At the turn of the century, practical driverless cars
felt like they were a very, very distant dream.
But advances in AI and computing mean that they could be just
around the corner.
And the dream of driverless cars is that they will be far safer
and more efficient than human drivers could ever be.
But some people worry that we might end up giving too much control
to artificial intelligence.
Could there be any hidden dangers, any unforeseen consequences?
And could AI even threaten our existence?
APPLAUSE
Welcome to the third and final Christmas lecture
from the Royal Institution, an event supported by CGI.
In this lecture, we'll be looking at the promise of AI
and its role in your future.
Should we be excited or should we be concerned?
Or maybe a little bit of both?
How might things go wrong and what can we do to make sure
that that doesn't happen?
And as AI becomes ever smarter, could it ever actually be conscious
and would machine consciousness be the same as ours?
Or are human beings unique?
Let's start by seeing what Demis Hassabis,
the co-founder of DeepMind, thinks about the future of AI.
- I think it's an incredibly exciting time
to be in technology and science.
I see AI as like the ultimate tool to assist experts and scientists
with their endeavours.
And what I would say for anyone starting out now in their career
is to really deep dive into how these systems work
and how they can utilise these systems to help them
in their own work.
I think it's almost limitless, the potential for utilising
these tools and in your own workflows.
But it's going to be, you know,
it's going to be changing every moment.
So I think one other thing I would say is to learn, like,
learn really fast and be adaptive to whatever's coming next.
Cos the only thing that's sure is,
there's going to be a lot of change.
- So let's go back to that driverless car journey that we showed you
earlier, and we're going to see how AI dealt with the mean streets
of my hometown, Oxford.
Please welcome, from Oxford, Professor Paul Newman.
APPLAUSE
Welcome, Paul. - Thank you.
- Now, you've been in driverless cars a million times.
That was my first time and it was a really weird experience.
We went through some quite complicated traffic in Oxford
and there was nobody behind the wheel.
So how did the car know what was going on around it?
- Well, the vehicle's equipped with various sensors.
It has cameras, it has lasers.
And you can see those cameras there
having their images processed by an AI, tracking people...
- This is our actual journey?
- This is your actual journey, yes, up through Oxford.
And you can see it's tracking cars, identifying from images,
traffic lights, where there are junctions, so it knows how to move
and what's around it.
So we have cameras that can see
and pick up different colours in images.
We have lasers that can measure the distance to things,
and we have radars that can see perhaps where lasers and cameras
may have trouble seeing through fog.
So lots of different sensors here have a robust way of knowing
where you are in the world. - OK.
So all of those sensors, they're providing a barrage of data
about your environment and what are you actually doing
with that information? What's the point of it?
- There are three key questions that the vehicles need to answer
in the vehicle.
One, where am I?
Two, what's around me?
What stuff is around me?
Three, what's going to happen with that stuff and what should I do
because of that?
How should I drive because of my predictions of what's happening?
And more recently, then, is what data should those vehicles share
so they can train other vehicles?
- OK, now, in my experience, the tough thing about driving
is other human beings.
It's other road users, whether they're in cars,
on bicycles or just pedestrians.
So how does your car decide what to do
when it encounters something unpredictable?
I think we have a video of something that happened while we were driving.
- OK, yes.
So you come up to a roundabout and a car passes and that taxi
was very close.
So what the vehicle was doing at that point, the AI, is we're making
lots of hypotheses of what could happen.
The vehicle was going to pull in, get too close.
It was going to let you go in front.
Lots of things.
- OK, what you don't get from that video
is we were going round a roundabout,
this car really drove in the middle.
And, actually, if I had been a human driver behind a wheel,
that would have been a very scary experience.
But the car just seamlessly put its brake on and you hardly,
you hardly noticed, it was really incredible.
Now, we hear in other countries, Paul, there are many more driverless
cars, for example, on the streets of the United States and so on.
And we hear about accidents, we hear about injuries and fatalities
and so on.
Is it really safer to have autonomous vehicles
on our streets?
- I think it will be.
And I think there's a really very strong reason why this technology
is going to make an extraordinary difference to transport.
So as you as a human, when you learn to drive,
you only have your own experience.
But that's going to be so different for autonomous vehicles,
that will learn to drive from every other vehicle that has driven,
cos these AIs, we can have them share every mile that was driven
by an autonomous vehicle ever.
And that is extraordinary.
And it's the exact opposite way to we as humans do it,
cos we're just ourselves, not sharing at scale.
And that is an extraordinary reason why I really believe this technology
is going to... - So, when a driverless car makes a mistake?
- That would then be shared across the whole fleet and every
vehicle can get an update in the way that we as a human set of drivers
can't get an update.
You can't get an update, an improvement, because of something
that just happened in Copenhagen.
- OK, Paul, thanks for the journey in your car
and thanks for joining us today.
- Thank you very much.
- APPLAUSE
Now, you might not think of a driverless car as a robot,
but it really is a robot and it uses many of the same techniques
that are going to be used by the robots that we're going
to encounter in our homes and our workplaces very soon.
And to see how these robots are going to work,
from Oxford Robotics Institute,
please welcome Maurice Fallon and Spot, the robot dog.
APPLAUSE
Welcome, Maurice, and welcome, Spot.
Now, earlier in the day, we let Spot loose in the lecture theatre
with the help of its operator, Mikhail, standing over there,
to map the theatre.
And on the projector you can see Spot doing that.
So, Maurice, can you tell us, how exactly is Spot mapping
the space around it?
- So, Spot is blessed with a variety of sensors on board.
It's got this laser on top, the same kind of sensor that Paul
was talking about earlier.
It's got a variety of cameras that form a ring around it so it can
see obstacles that are in the ground and it has a sensor in its legs
so it can walk. - OK.
Now, that sounds a bit like the driverless cars earlier.
Is that the same idea? - Very similar. - OK.
And so what it's doing in this is building up a map
of its environment?
- Yeah, it's using the sensors as it walks around to build up
a database of what's around it.
And we have here a view of its database.
You can see the camera feed from the onboard sensor,
the onboard camera on board the robot.
And here you can see a kind of view from above that shows the prior map
in grey and the colorised points are the points
that are being captured right now from the laser.
These straight lines indicate the kind of highway that the robot has,
where it can choose to go to. - OK.
Now, Spot's interpreted all the data, just like the driverless
cars that we saw earlier on.
And the AI has helped it to build a map of the empty lecture theatre.
Now, we think at this point, Spot can autonomously
navigate this environment.
So, earlier today, we showed Spot this ball here.
Wow, I caught it this time!
And we showed Spot what this ball looked like.
And what we're going to ask Spot to do is to navigate
to this ball completely autonomously.
So, not under anybody's control.
OK.
But we've made Spot's life a little bit more difficult.
We've introduced some obstacles.
Spot's going to have to figure out how to get
around these obstacles and also how to traverse those sleepers
on the floor there.
Now, this is, I've been waiting weeks to say this line.
Here we go.
Go, Spot, fetch!
OK.
Maurice, tell us, what's it doing?
- So it's obviously walked up the stairs and got to the end point
in its search.
And you can see here a picture, the picture from the camera
facing to the left.
The ball has been spotted.
A box is around the ball.
And we have even here a percentage which corresponds to how confident
we are that it is a ball.
- But this is really serious research.
So how might robots like Spot actually be used?
- For example, inspecting a nuclear facility,
that kind of work.
- So the idea is we'll have robots like Spot that do dangerous things
in places where it would be unsafe to send human beings.
- Exactly. - OK.
Maurice, thank you so much for coming on, and thank you, Spot!
APPLAUSE
Artificial intelligence that can navigate the world like Spot
might also be equipped with weapons to turn it into a robot soldier.
Now, we should say Spot's manufacturers have a very strict
policy against weaponising their machines in that way.
But other robotics company have no such reservations, and autonomous
fighting machines that are empowered to decide who to attack
would fundamentally change the nature of warfare.
And many AI researchers, myself included, think this is something
that we should just never do.
Whether it's acceptable to use lethal autonomous weapons
is probably the single most important and prominent issue
that's been raised by the recent success of AI.
So joining us to discuss the possibility of these machines
and the issues that they raise is political theorist,
from Queen Mary University of London, Dr Elke Schwarz.
APPLAUSE
Elke, thanks for joining us. - Thanks for having me.
- Now, I think a lot of the audience imagine that robot soldiers,
lethal autonomous weapons is either something in science fiction
or it's some time in the future.
Is that the case?
- Well, no, actually, autonomous weapon systems
have been around for many decades, at least since the '70s,
in a much more rudimentary, basic form.
But those systems that are equipped with AI, they are new,
but they are already here and we think that they're being used
in some conflicts already.
But the question really is, is there a human somewhere
overlooking that loop or overseeing that loop?
People are very secretive about full autonomous use.
It's a delicate subject, because it's not clear at all
whether that's legal or ethical. - OK.
Now, I know you have lots of concerns about the use
of AI in warfare.
Can you talk us through some of those?
- Well, we have to remember, warfare is not predictable.
Warfare has many elements of chance.
The enemy tries to deceive, to be tricky.
There's the proverbial fog of war.
We don't know what happens in warfare.
That means an AI is not likely to have enough data to be trained
reliably or to be able to be used reliably,
which means mistakes happen.
That's a big issue.
- OK, OK.
Now, of all of your concerns that you have, what's the single
biggest concern about AI in warfare?
- There are many nightmare scenarios I can think of, but one of the
things that we're concerned about are so-called flash wars.
- Flash wars?
- So a flash war would be when an AI system identifies a potential threat
and then, within seconds, pings off an action onto another AI system
which activates perhaps a weapons platform.
And then another AI reacts to that.
And all of that happens within a flash, within seconds,
so much so that no human will have oversight
over what is actually happening, why it is happening, or how to stop it.
- So that's AI just escalating until we reach a war without humans
having the opportunity to intervene. - That's right.
- Well, OK, this is on the screen in front of us.
This is Lieutenant Colonel Stanislav Petrov.
Now, I think most of you won't have heard of him, but if it weren't
for Petrov, we might not be here today.
In 1983, at the height of the Cold War, Petrov was on duty
when the Soviet Union's early warning systems indicated
a potential incoming missile strike from the United States.
And Petrov's job was to immediately report the attack.
And if he had done,
a retaliatory nuclear strike could have been launched.
But he didn't.
He intervened and decided it was a false alarm
and reported a fault in the system.
And he was right.
And an investigation later revealed that Soviet satellites
had misidentified sunlight glinting on clouds
as the engines of missiles.
Elke, in a situation like that, do you think an AI response
might have led to a catastrophe? - Absolutely.
And AI would have unfolded its programme.
It would have stayed within the context of its operation.
What Petrov did is he took a moment, he thought beyond the context,
and he took a pause and made the unobvious decision.
An AI system cannot do that. - OK.
These are big issues and issues that our audience is going
to have to grapple with in the future.
Thanks for helping us to understand them, Elke.
- Thanks very much.
- APPLAUSE
Deciding, deciding how much autonomy we want AI to have,
where the decisions that it makes will affect human beings
is a fundamental question that we need to start addressing now.
Now, some people think that AI might free us from the messy,
illogical decision-making that human beings, like all of you and me,
are prone to.
So who amongst us hasn't got irritable when we skipped lunch?
Our mental and our physical state affects our decisions all the time.
And we, all of us, have biases.
Some of those biases we're conscious and aware of,
but many are subconscious.
We're not even aware of them.
And the worst thing is that AI can actually amplify human bias.
To help us understand how this can happen,
please welcome Professor of Computer Science Sue Black.
APPLAUSE
Welcome, Sue. Thanks for joining us.
And you're going to help us to understand bias in AI.
- Yes, absolutely. Hi, Mike.
And hi, everybody.
I'd like to do an experiment which might shed some light
on the potential for bias in data sets. - OK.
Now, earlier on, we selected six people at random from the audience,
and we prepared them for the following experience.
So can those six people that we that we identified, please come down now?
APPLAUSE
Hey, just a little bit. OK, that's great.
Fantastic.
We've got six fine-looking volunteers here,
Sue, what are they going to do?
- So our volunteers are going to be our data set and our audience,
you all are going to be the AI.
We're going to train you on our volunteers and create a rule
that you can use to make decisions.
Take a good look at our volunteers.
What's the most distinctive feature that unites all of them?
The most obvious thing that you can notice about everybody here.
- OK, take a good look at them and let's just think
about it for a moment.
And I'm going to come to some of you in the audience.
I'll come to you first. - Christmas jumpers.
- Christmas jumpers. OK.
I think that's a pretty good answer, Sue.
They're all wearing Christmas jumpers.
- Yes, absolutely.
So, based on that, you've got a rule that you can use to make decisions.
And the rule is that all visitors to the Royal Institution Christmas
lectures wear Christmas jumpers.
Now, audience, as our AI, and according to that rule,
can you tell me, would this visitor...
Oh, have a look at our screen. ..would this visitor
get to come to the Christmas lectures.
- Yes or no? According to our rule? - Yes or no?
- Hands up if you think yes.
Yeah, they're wearing a Christmas jumper, right? - Great.
And how about this person?
- Ooh. - Yes or no?
- No Christmas jumper there.
- No.
And how about this visitor?
Very good. Yes.
- Thank you very much, volunteers.
You can take your seats again.
APPLAUSE
But Sue, surely people who don't wear Christmas jumpers
should nevertheless be allowed to come to the Christmas lectures.
- Yes, absolutely.
But our audience learnt from the data set
that they were given,
people that come to the Royal Institution
only wear Christmas jumpers. - OK.
So the audience, the AI audience that you have, recognised a pattern
in our data and the AI wasn't wrong.
It's just the problem with the data that it was given.
- Yes, the data we gave the AI was skewed.
It's not representative of the general population.
So this was like a simple, a toy example.
Christmas jumpers aren't really so much of a problem,
but there can be real life instances of AI causing unintended bias.
There's one example of AI software being trained to sift through
job applications and picking only men for certain roles.
And that's because the AI had been trained on successful
historical applicants in the past,
the majority of whom had all been men.
And there are also cases of facial recognition software incorrectly
identifying people with darker skin tones.
And this could be maybe because the software was trained
on people with lighter skin tones.
- Well, this is an issue that you all need to know about.
We must never assume that the AI is just right.
We all need to be prepared to speak up if we feel that a computer system
in general or an AI system is being unfair or is being biased.
Thank you, Sue, for helping us understand these risks. - Thank you.
Thanks, everyone. - Thank you.
APPLAUSE
Now, elections come around with monotonous regularity,
and every time they do, there is a concern that AI might be used
to target voters with misinformation and disinformation.
To help us make sense of the complex web of fact and fiction,
please welcome, from the University of Bangor,
Professor of Journalism Vian Bakir.
APPLAUSE
Welcome, Vian.
So, political advertising, trying to persuade somebody to vote
for this party or this party, surely that's been around
since elections were invented.
So what's new now? Why... What's the new thing?
- Well, what's new now is micro-targeting.
In the past, if you were shown a message, it was the same message
to all of you at the same time.
Now, with the help of AI, we can have a personalised message
which is shown to you or groups like you at a time
where you're perhaps most persuadable.
That's the difference. - OK.
So why is targeting important in the run-up to an election?
- Well, targeting for a political party, it allows them
to mobilise their target voters,
people that perhaps are hard to reach through conventional media.
Young people are actually very hard to reach.
But you can be targeted through social media
and with messages that really resonate with you as well,
that speak to your personal interests.
This is useful for a political party. - OK.
Now, we're going to have a little campaign now to investigate
this issue in a little bit more detail.
We are going to address, folks, the big issue, the one and only issue,
the eternal issue, the biggest issue that humanity
has ever faced.
That is... Dogs or cats?
OK, hands up if you prefer cats to dogs.
OK, quite a lot of you.
Hands up if you prefer dogs to cats.
OK, fair number. OK.
And hands up if you love them both equally or you're just unsure.
OK, we've got a few of you.
Now, you are going to be our floating voters and we are going
to target you in our campaign, our disinformation campaign,
and we're going to see if we can change your preferences.
So let's have a look at our AI-generated campaign.
- When I see a dog, my sandwich is never really safe.
Cats are better in every way.
Just say no to dogs.
- SOME LAUGHTER
OK, now I'm going to pick you.
You were unsure.
Did we manage to change your vote?
- No. - No. Not really.
Well, it was an AI-generated campaign.
It wasn't really that great.
But, nevertheless, we found your preferences by asking you.
In reality, AI will find out your preferences
from social media.
It will look at your social media feed and it will
look at your digital footprint,
what you buy online, for example.
This kind of profiling isn't unusual.
It's done constantly and it's not always malicious.
It's how you see, for example, recommendations about what movies
or TV shows to watch and what to listen to on listening apps.
But when it comes to political questions, this kind of targeting
can have very, very big consequences.
So Vian, talk us through that.
- Well, I think the biggest consequence, the biggest danger
is disinformation, basically, that's been targeted to you.
So, disinformation is deliberately deceptive information.
And if it's targeted to your fears, your hopes, your interests,
and only you see it because it's micro-targeted, how do you know
whether it's false or true, and how can society correct you?
Journalists don't know you've seen it,
cos it's micro targeted to you, so they can't fact-check it for you.
Politicians don't know you've seen it,
at least the opposition politician doesn't, and so they can't rebut it.
So there's no correction, basically, for your disinformation
that you personally have been exposed to,
and no-one can really know how much disinformation
you've been exposed to, basically, and that's a problem. - OK.
So I'm guessing, is AI in a lot of this, where does AI come into it?
- AI is all over the place here.
AI is in the data, first of all, making sense of that huge amount
of data that social media platforms have on you, for instance,
which is a huge amount.
It then learns about you from that data.
It learns about your interests, your hopes, your fears.
And then it's also used to target you with the message that resonates
with your hopes, fears and interests.
You know, it knows what to show you, whether it's a political ad
or some content at the top of your social media feed.
And then finally, it could even create the message as well.
It can hone the message or it can create deepfakes.
- OK, so as you become voters,
you need to be aware of all of these issues.
Thank you, Vian, for helping us understand them.
APPLAUSE
So...
Can we trust that the images of the people that we see online
are actually real?
Research shows that we aren't very good at recognising deepfakes.
We take things at face value, literally, and that leaves us
confused as to what's true and what's not.
So, have a look at this.
- Just say no to dogs.
- But it's not actually me at all.
HE SPEAKS MANDARIN
HE SPEAKS SPANISH
OK, in those second two clips,
that was me speaking Spanish and Mandarin, and I can assure you
I do not speak either Spanish or Mandarin.
So let's welcome the creator of fake dog-hating Mike,
founder of Immersive Fox, Alisa Patotskaya.
APPLAUSE
Welcome, Alisa.
So tell us, how did you create my evil twin up there?
- So, we use thousands of videos to train our AI at Immersive Fox,
and this way we needed only a couple of minutes of your face and voice
to clone your face, voice, mimics and gestures.
- And this is me in the training session, right?
- Yes, that's right. That's right.
So this is the video of Mike we used to train his avatar. - OK.
I had to do this for really quite a long time in order to get enough
of my clips.
So it's very cool.
What are the benefits that you see of this?
- So, the main benefit is that you don't actually need to speak
the language in order to produce video content of you talking
in this language.
And this way, we bring our customers closer to the audiences and people
across the world closer together.
- Now, the technology is very cool.
It's very, very impressive.
But lots of people are concerned.
They're concerned about politicians, for example, who've been used
to create deepfake videos.
And a lot of people are worried that they won't be able to trust
what's real and what's not.
So what's the response to that?
- So these are all valid concerns.
At Immersive Fox, we request your explicit consent
that you are happy for us to generate your AI presenter.
We also check your identity and we store your avatar very safely
at our platform so that only you can use it.
So what we do is ethical.
Of course, there could be misuses of this technology,
as any other technology.
However, we think that this technology is just too exciting
not to use it.
- OK, so there are many wonderful things that you can do about it,
but, again, our audience needs to be aware of the abuses.
OK, thanks, Alisa. - Thank you.
- APPLAUSE
We've seen, we've seen how AI can replicate a face.
And it did a pretty good job.
Not a perfect one, but a pretty good job.
Well, how about recognising faces?
That's the work of facial recognition expert
from the University of Bradford Professor Hassan Ugail.
Welcome, Hassan.
APPLAUSE
OK, Hassan, what does your software do?
- So, as you say, there are many types of facial recognition algorithms
we have embedded in our phones.
When we go through passport control, we use facial recognition.
But this particular algorithm, we have age-integrated,
which means if you, individuals,
years apart, taking photos of years apart,
we can recognise through this algorithm.
- Now, we're going to give this a try.
So on the screen behind me, we're going to see a picture
of one of the audience members as a younger child.
Now, when you all entered the lecture theatre,
we took pictures of five members of you when you were coming
into the lecture theatre.
And one of them is that young child that we saw in that picture.
And what we're going to see is whether Hassan's software
can actually identify them.
So if you see your picture on the screen in front of you,
could you all come down?
APPLAUSE
What I want you to do, folks, is stand under your picture.
So, our candidates, A through to E.
What we need to do, audience, is, we need you to decide which of A
to E we saw in that picture earlier.
Can we see the picture earlier? There we go.
OK, so have a look.
Now, we're going to ask for a vote.
If you think the young child in that picture is volunteer A,
put your hand up now.
Volunteer A, anybody?
Anybody think it was volunteer B?
Put your hand up now.
OK, there's a sprinkling of hands around the room.
Anybody think it was volunteer C?
OK, quite a lot of people think it's volunteer C.
Anybody think it's volunteer D?
OK, a few people think it's volunteer D.
And finally, is it volunteer E?
So, C, I think, was the most popular choice, fairly clearly.
Great. Now, Hassan, we're going to use your software to try to decide
which of the child, which of the children was in that picture.
Is that right? - Let's see. Yeah.
So this is the young version, the image that we've got.
And then let me just take one of those images and compare.
So the software, what it does is,
it basically compares the two images.
So if I just start comparing.
It says match unlikely.
And the similarity index we are getting is 32.6.
Now, the threshold for individuals to be the same person is 75%.
So if anything about 75%, we know it's the same individual.
So in this case, it's not the same individual.
I've actually run those analysis and this is what the computer says
on the kind of similarity.
So if you look at C, it says very strong match,
similarity index of 81.9%.
- So your judgment or the AI judgment
is that it is indeed volunteer C?
Well, time for the big reveal.
Can the volunteer whose face appeared in that young picture
please step forward now?
Yes!
APPLAUSE
OK, thank you to our volunteers, in particular for volunteer C
for allowing your young face to appear in the Christmas lectures.
Thank you. You can take your seats again, guys.
APPLAUSE
OK, Hassan, what is the AI actually doing when it's comparing
those two pictures to decide if they're the same person?
- So, first of all, what we've done is we've taken millions and millions
of facial images and trained an algorithm.
The algorithm is looking at colour, texture, various shapes of the face.
And of course, in this case, age as well.
And it's trained an algorithm to distinguish between various faces.
- And what are the applications of this?
How can it be used to benefit society?
- So, in this case, we are using this kind of software
for criminal investigation,
for example, you have millions and millions of images in a database.
You want to quickly pull out the relevant image from that.
For humans, it will be a very laborious task and computers
can do it very, very quickly.
So that's one of the things. - Finally, privacy.
Privacy is so important in the world
of big data and social media and so on.
How does privacy come into all of this?
- So, as I say, again, it's very, very important that we make sure
that the right data is used actually to train,
and also when we test, we use the right data to test it as well.
- OK, thank you so much, Hassan,
for coming on and showing us your software. - Thank you.
- APPLAUSE
Now...
AI is clearly becoming more capable
and more sophisticated by the day.
And the pace of change, the pace of improvement
is really quite remarkable.
But some people fear that this could be leading us to disaster.
Earlier, I asked Demis Hassabis about his concerns
for the future of AI.
- Yeah, I think there are two categories of risks
that I worry about.
One is bad actors, whether that's individuals or nation,
rogue states repurposing general purpose AI technology
for bad ends, not for all the good they were intended to do,
but repurposing their goals and other things to do harm.
So that's one thing I think we have to worry about.
Probably the nearer term thing.
You can think of things like misinformation and deepfakes
as part of that bucket.
And then the second thing I worry about is technical AI risk,
which is actually, as these systems become more and more sophisticated,
so not today's systems, not even the next couple of years,
but if you think about the next five to ten years, you know,
these systems will become ever more capable.
They'll become more able to do things like planning and execute
goals and more useful, of course, do actions in the world.
But then also the risks associated with those types of more agent-based
systems like we build for games,
you know, they will carry more risks as well as being more useful.
So we have to understand those systems better,
be able to analyse them and make sure
that we can keep control of them.
- OK.
For some researchers, the end goal of artificial intelligence
is what's called AGI, artificial general intelligence.
Now, there is no precise definition of what AGI is,
but broadly it means that we have an AI system
which can perform a wide range of tasks at or very close to
the level of human beings.
And we see AGI in Hollywood movies and video games and books.
And if AGI really, really succeeded, then it would be able to do anything
that a human being could do.
Now, even AGI that was much less capable of that
would be completely transformative for society.
But if AI can be as smart as us,
then of course it could be even smarter.
And then where would that leave humanity?
Could that level of machine intelligence threaten
the future of humanity?
And some people believe that's an issue we need to take seriously
now before it's too late.
To discuss this issue, please welcome Professor Stuart Russell.
APPLAUSE
Welcome, Stuart.
So we're talking about fears about the future of AI,
and in the movies and the books that I read,
the science fiction movies that I read,
the problems for humanity start when the AI becomes conscious
or self-aware or sentient.
It starts to think for itself and then decides that it's not
interested in us any more. Or maybe we're in the way.
Is that what we should be worried about? - In a word, no. - OK.
- What matters actually is competence.
Right?
It's what the machine does, or what it does to us in particular.
So if you think about this from the point of view of other species
on Earth, why are they worried about us?
It's because of what we do to them.
They're not worried about whether we're conscious or not.
They're worried about the fact that we are intelligent and we get
to decide what happens on Earth.
And so that's really the source of concern.
If we then put ourselves in that position with respect
to the machines, if we make the machines more intelligent
than us, then we have to ask a question.
"How do we humans retain power forever over entities
"that are more powerful than ourselves?"
- If we reach a future where there's sort of superintelligent AI,
can't we just switch it off, or, in the worst case,
just nuke the data centres?
Isn't that going to solve the problem?
- Since it's superintelligent, it will already have thought
about the possibility that we might try to switch it off
if it wants to pursue some objectives that we don't like.
And so it will probably have copied itself onto all the data centres
in the world.
It'll probably have infiltrated our weapons systems so they won't work,
or so that it could even use them against us.
And I think the only solution here is not to be in that conflict
in the first place,
to actually make sure that we never have AI systems
that are pursuing objectives that are not what we want
the future to be like.
Now, of course, there's a few difficulties with that, right?
You're immediately thinking,
"OK, well, humans don't all agree on what we want the future
"to be like," so you have to have some compromises
just to accommodate the wishes of everyone in the human race.
- OK. Now, actually, in my research about 20 years ago,
I discovered this issue for real.
And what I was doing was I was doing some research on AI programmes
that was supposed to come up with rules that would prevent
them from crashing.
I remember very, very vividly writing down my objective -
"Stop the trains from crashing"...
And by the way, there were no real trains involved.
There wasn't even a train track like this.
It was completely simulated.
..and pressing the button to see what the AI came out with.
And the very first thing that it came out with was,
"Stop the trains from moving." That was it.
That's literally what it came back with.
If the trains are stopped from moving,
then they're never going to do that.
But that wasn't what I wanted.
And if I'd asked a human being for rules to stop the trains
from crashing, I don't think that that's what a human being
would have come out with.
Stuart, how do we deal with issues like that?
- It's really very, very difficult to define objectives correctly
and put them into a machine.
You have to be careful what you wish for. - OK.
So is there a better way to do it? - Yeah.
The AI system has to know that it does not know
which future we prefer.
The AI system needs to ask permission before it does things.
"Is it OK if I turn the oceans into sulphuric acid?"
Definitely not. Please don't do that.
In the extreme case, the AI system will actually want us
to switch it off.
So rather than having to bomb the data centres and everything,
the AI will just say,
"Oh, if I'm doing anything that you don't like, please switch me off."
Right? It doesn't know what we don't like,
but it wants to avoid doing it.
And so it will be happy to be switched off
to make sure that doesn't happen.
- And if we designed AI that way, would it have solved my problem
with the trains? - Definitely, yes.
But I think it might say,
"Mike, you're a bit old to be playing with trains!"
- Thank you very much, Stuart. Thank you for joining us.
- Thank you.
- AI is growing in complexity and sophistication,
and it's growing in ways that we don't necessarily understand
terribly well.
And one day, maybe soon, maybe a little bit in the future,
but not too long, I think,
AI robots, AI robots like Spot are going to be around us everywhere.
We're going to encounter them at work, at school and at home.
So here's a question.
How should we treat the AI machines
that loom in our near future?
In the next segment, I need a volunteer, please.
OK, you in the white jumper there. Come on down.
Hello. Welcome to the Christmas Lectures. What's your name?
- Liv. - Liv.
OK, now, the team are going to take you backstage for a moment, Liv.
We're going to see you again in a few seconds.
They're going to kit you up with some safety gear.
OK. Now, folks, here is a toaster.
I don't like this toaster. This is a useless toaster.
This is a dumb, stupid toaster.
All it does, frankly, is make toast.
There's no AI in this toaster.
So...
Can we get our volunteer back on?
Welcome back, Liv. - Thank you. - This is a baseball bat.
Take the baseball bat.
Now, what I want you to do, when I tell you, not before,
is to whack the toaster.
Now, I don't want you to whack the brain.
That's the only brain we have in the room, I think,
so it's very important to us.
So just be careful, stand forward of the brain.
But what we're going to do is we're going to count Liv down,
and Liv, you're going to whack the toaster, got it?
You're not afraid? Yeah. I mean it, you're going to whack that toaster.
OK, folks, we're ready to count down.
Three, two, one.
Whack it!
Not bad.
But I think you can do better.
OK. Three, two, one!
Whack! Oh! OK.
Liv, I think we can safely say the toaster is toast!
Thank you very much, Liv. You can go backstage.
It's easy to hit an inanimate object, a toaster.
It's just a lump of metal and plastic. OK?
But what about something that looks like it might be alive?
Welcome back, Spot!
Now, I should be clear at this point,
Spot at this point is being remote controlled by Mikhail.
There's no autonomy. OK, Spot. Your time is up.
We saw the mess that you created outside.
It's a horrible mess. You don't want to go there.
Spot, it's time to meet your maker!
Are we ready for this?
Shall I whack Spot?
AUDIENCE: - No!
- I really want to whack Spot. AUDIENCE: - No!
- You didn't see the mess Spot made. AUDIENCE: - No!
- OK.
Hands up.
Who thinks it's OK for me to whack Spot?
Who doesn't think it's OK for me to whack Spot?
OK.
There's a few family debates going to go on
after this lecture, I think.
But here's the question for you, folks.
Spot is really just a lump of plastic and metal and glass,
just like our toaster.
So surely we should feel the same way about whacking Spot
that we felt about whacking the toaster.
To help us understand why we often feel so conflicted
when we interact with AI,
please welcome from King's College London, Dr Kate Devlin.
Kate, welcome to the lectures. Now, you saw the audience respond.
They really didn't want me to whack it.
And let's actually ask some members. OK.
Did you guys want me to whack it? Did you want me to whack it?
- No. - Why didn't you want me to whack Spot? - I just didn't...
I just didn't want you to whack him because, like....
I'm just not sure why. - OK, fair enough.
Over this side, who wants to answer? OK.
Why didn't you want me to whack Spot? - I did...
Oh, sorry. It's very expensive.
- Very expensive!
You wouldn't believe how expensive, actually.
OK, and one other answer? - Because it's, like, really important.
It, like, took a lot of hard work to make that.
So it's kind of just, like, wrong. - OK.
These are all pretty good answers, actually.
It took a lot of work to make it, and it's frankly quite expensive.
So, Kate, why do you think our audience felt so different
about whacking the...they were all up for whacking the toaster.
And Liv, we had to pull her off!
So why did they feel so different about that?
- There's definitely an element of, you know, it's expensive, yes.
But, also, Spot is a bit like an animal.
It's a zoomorphic robot, so it resembles an animal
and moves around like an animal.
- OK, so why is it odd to think about whacking a robot dog?
Mikhail's having a lot of fun out there, I can see!
- There's a number of different things at play.
So it could be that we think that maybe hitting Spot
would cause pain.
Even though we know that Spot can't feel pain,
Spot's not got any feelings.
It could also be that Spot is a little bit scary.
Acts a bit like a predator in some way.
So part of our brain is thinking,
"Don't attack Spot, because Spot could attack us back."
So there's that as well.
But also there may be an element that we don't want to be seen
as the person who has hurt Spot or damaged Spot in some way.
So there's sort of judgment as well.
A bit of social judgment coming in there, too. - OK.
What do you think that says about us as human beings?
- We make sense of the world through being social creatures.
And so when we see robots that have some semblance
of sociality, we want to engage with them in the same way.
- Kate, thank you so much for joining us and going through those issues.
And thanks again to Spot!
So for many of us, there's a compulsion to treat a robot
like Spot as if it were a living thing.
And maybe the way that we treat artificial intelligence
says less about AI and more about us as human beings.
And this takes us to one of the most hotly-contested issues in AI.
Clearly, machines are getting smarter.
We've seen that in the last few lectures.
They're getting smarter at a really remarkable rate.
But could AI at one point in the future ever be conscious?
And would conscious machines be the same as us?
Now, consciousness, beyond the fact that we all have it,
and we all know that we have it,
is really quite surprisingly tricky to pin down.
It's actually one of the biggest problems in science and philosophy.
And the big problem is this.
We know that certain electrochemical processes in the brain
give rise to consciousness.
We saw those electrochemical processes in the first lecture.
How do they do that and why? We don't know.
And, honestly, there isn't really any agreement.
So what can we say about consciousness?
Well, most experts agree that the ability to experience things
from your own personal perspective is a key ingredient.
Experiences like joy, pain, happiness, frustration.
So we're going to do an experiment to try to understand
what that idea says about AI.
And we're going to welcome some guests, some animal guests.
And our first guest, please welcome with some quiet clapping,
cos they're a little bit nervous, Dido the dog.
Hello, Dido! Hello.
Hello! Now, what would it be like to be Dido?
Walking around, smelling, sniffing things.
Can you imagine Dido's mental life?
Do you think she gets excited? Yeah?
Do you think she anticipates things?
I think anybody who owns a dog knows that they really do.
OK. Thank you, Dido.
Our next guest is Woody the rat.
Welcome, Woody.
So the same question.
Does Woody have experiences?
Can you imagine what it would be like to be Woody,
running around, smelling things, eating things?
Yeah? But I think we can also agree that the range of experiences
that Woody has is a lot less than Dido does.
Agreed? OK. Thank you, Woody.
Welcome, Jesse.
What about Jesse the tarantula?
Do you think it gets excited? Do you think it anticipates things?
It's difficult to know.
OK, who thinks that Jesse
the tarantula has a rich mental life?
OK, there's a fair number of you do.
Well, I have to tell you, I think the jury is out on this.
We don't really know, obviously, it isn't clear.
OK, and finally, what about our favourite robot dog, Spot?
Come on, Spot.
Now, after all you've learned about artificial intelligence,
do you think Spot has experiences?
No. I don't think Spot has experiences at all.
We can imagine our animals experiencing the world,
but machines, not so much.
And for that reason alone, I don't think that AI programmes
right now are conscious.
And I think part of the reason that it's impossible for machines
right now to have consciousness is that we exist in bodies.
And through our bodies we experience the world.
So maybe we need to give AI a body, like a human body,
that's equipped with sensors like a human body,
that's much richer than what we've seen with Spot.
Would that mean that we could build a conscious machine?
Now, some roboticists are trying to mimic the human sense
of touch in robots.
From the Oxford Robotics Institute, please welcome Perla Maiolino!
- Hello, Michael. Hello, everyone.
- Hello, Perla, and welcome to the Christmas Lectures.
Now, you have brought on a really quite spectacular-looking machine.
What is it?
- So, this is a robotic arm that is equipped with a soft hand
with the integrated tactile sensor.
This tactile sensor provides the sense of touch.
- Now we're going to do an experiment, aren't we? - Yeah.
- And for this, we need a volunteer.
OK, you. Come on down. Yep.
Hello. What's your name? - Trixie.
- Trixie. OK, over to Perla.
And Perla is going to show you what to do.
- Please, come close here with me. OK.
So, you should try to shake the hand.
And this is a hand shake. How do you feel it?
- It's quite strange, because it's, like, soft.
- Yeah. These actual parts are the tactile sensors,
and through them, the hand is perceiving your hand.
And I will show you, here you can see the image of the hand.
When the sensors are touched, you can see
that the colour changes and this is related to the pressure
that is applied on them.
And we can use this information to control it,
to safely interact with humans and with the environment.
- OK, Perla, thank you so much for bringing on
your incredible equipment. And Trixie, thank you so much.
Let's have a big hand for the big hand!
For all our human intellect, our incredible human intellect,
our bodies are how we navigate and interpret
and make sense of the world.
And we learn about the world
and we interpret the world through our senses,
all our senses at once.
Our brains are intrinsically connected to what we smell, we see,
we taste, and we hear, as well as to our sense of our bodies
and in the world.
Now, we're going to do an experiment to explore a little bit more
about the idea of experience and why this is so important.
Now, in front of me is a balloon, and the balloon is full of hydrogen.
And in a moment, we're going to blow it up,
because every Christmas Lecture has to have an explosion!
Now, when I light this balloon, your bodies will experience it,
and I assure you, you will experience it,
in a range of different ways.
And you're going to have to cover your ears, cos, believe me,
it's going to be loud. I'll tell you when you need to cover your ears.
I need to put on some safety equipment, because I'm the mug
that's going to blow it up.
OK.
OK. Folks, cover your ears!
You really do need to do this,
cos it is going to be really loud.
Is everybody ready?
Three, two, one!
EXPLOSIVE POP
That explosion, did you all feel it?
AUDIENCE: - Yes.
- OK. Let me ask you firstly, what physical properties
did you experience? Who felt the heat?
Yeah? OK. That's remarkable.
You all felt the heat in this lecture theatre.
And the light? Did you see that burst of light?
Yeah? And finally, the sound.
Even though you all had your ears covered,
did you hear the sound?
You could hardly fail to hear that sound.
I think they could hear it in Manchester! OK.
But that was only part of your human experience.
How did it make you feel?
Was anybody surprised by the explosion?
Put your hands up if you were surprised.
OK, I'm guessing you were surprised cos it was a lot more violent
than you expected. Is that right? Yeah. OK.
Who was really excited and anticipating the explosion
in the build-up that we did?
Yeah? OK, so we built it up.
OK, be honest now, put your hands up
if you were possibly a little bit scared by the explosion?
OK. I think you're very honest to admit that.
And that's absolutely fine.
It's perfectly normal to be scared by an explosion like that.
Now, here is my point.
We can equip robots like Spot with sensors that will be able
to detect light and sound and heat.
But those experiences you had,
surprise, anticipation, fear,
they were human.
And Spot doesn't have those.
The spectacle, the noise, the light, they all contributed it.
But it was your mental response that was a crucial part
of your human experience.
Fear, excitement, surprise, anticipation, whatever it is,
an AI programme right now doesn't have it.
Spot doesn't have it.
So, AI is doing things today that seemed pretty much impossible
20 years ago.
And you in this audience are unique.
You are unique because you are the first people
in all of human history that will reach adulthood
in a time where AI surrounds you in the world.
AI will be everywhere.
It will be embedded into pretty much everything
that you interact with in the world.
And we've seen in these lectures that there is amazing
potential there.
AI is going to be transformative in health care,
and that's going to benefit people all across the world.
It's going to be in games that are going to be so much fun to play
and give you endless hours of pleasure.
But there are also risks, the risks that we've been talking about
in this lecture, and you need to be aware
of those risks, understand what they are,
and ensure that our future doesn't realise those risks.
Now, in that future, as you go and invent that future,
there's something that you really need to remember.
AI is not a mind.
It is not like you or me.
Because human beings are unique.
You are unique.
Thank you.
APPLAUSE
AI is here now.
Discover the different ways it may already
be revolutionising your life in my exclusive interview.
To watch, head to bbc.co.uk/ri
and follow the links to the Open University.