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[DEVICE CHIMES]
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JULIETTE LOVE: Hi, Alpha.
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ALPHA: Hello.
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LOVE: Can you
help me write code?
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ALPHA: I was trained
to answer questions,
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but I'm able to learn.
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LOVE: That's very
open-minded of you.
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ALPHA: Thank you.
I'm glad you're happy with me.
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What's this guy doing?
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ALPHA: That's a developer.
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What do you think
he's working on?
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ALPHA:
That's a tough question.
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He might be working
on a new feature,
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a bug fix or something else.
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It's quite possible.
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ALPHA: Yes.
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LOVE: Do you see my backpack?
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ALPHA:
That's a badminton racket.
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It's a squash racket,
but that's pretty close.
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ALPHA:
That's a badminton racket.
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No, but you're not
the first person
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to make that mistake.
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[UPBEAT MUSIC PLAYING]
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NEWSREADER 1:
AI, the technology
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that has been advancing
at breakneck speed.
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NEWSREADER 2: Artificial
intelligence is all the rage.
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NEWSREADER 3: Some are now
raising alarm about...
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NEWSREADER 4:
It is definitely concerning.
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NEWSREADER 5:
This is an AI arms race.
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NEWSREADER 6: We don't know
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how this is all
going to shake out,
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but it's clear
something is happening.
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DEMIS HASSABIS:
I'm kind of restless.
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Trying to build AGI
is the most exciting journey,
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in my opinion, that humans
have ever embarked on.
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If you're really going
to take that seriously,
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there isn't a lot of time.
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Life's very short.
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My whole life goal is to solve
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artificial
general intelligence.
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And on the way,
use AI as the ultimate tool
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to solve all the world's
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most complex
scientific problems.
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I think that's bigger
than the Internet.
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I think that's bigger
than mobile.
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I think it's more like
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the advent
of electricity or fire.
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ANNOUNCER: World leaders
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and artificial
intelligence experts
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are gathering
for the first ever
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global AI safety summit,
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set to look at the risks
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of the fast growing technology
and also...
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HASSABIS: I think
this is a hugely
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critical moment
for all humanity.
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It feels like
we're on the cusp
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of some incredible things
happening.
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NEWSREADER:
Let me take you through
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some of the reactions
in today's papers.
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HASSABIS: AGI is pretty close,
I think.
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There's clearly huge interest
in what it is capable of,
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where it's taking us.
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HASSABIS: This is the moment
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I've been living
my whole life for.
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[MID-TEMPO
ELECTRONIC MUSIC PLAYS]
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I've always been fascinated
by the mind.
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So I set my heart
on studying neuroscience
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because I wanted
to get inspiration
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from the brain for AI.
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ELEANOR MAGUIRE:
I remember asking Demis,
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"What's the end game?"
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You know?
So you're going to come here
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and you're going
to study neuroscience
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and you're going to maybe
get a Ph.D. if you work hard.
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And he said,
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"You know, I want
to be able to solve AI.
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"I want to be able
to solve intelligence."
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HASSABIS: The human brain
is the only existent proof
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we have, perhaps
in the entire universe,
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that general intelligence
is possible at all.
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And I thought
someone in this building
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should be interested
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in general intelligence
like I am.
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And then Shane's name
popped up.
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HOST: Our next speaker today
is Shane Legg.
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He's from New Zealand,
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where he trained in math
and classical ballet.
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Are machines actually
becoming more intelligent?
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Some people say yes,
some people say no.
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It's not really clear.
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We know they're getting
a lot faster
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at doing computations.
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But are we actually
going forwards
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in terms
of general intelligence?
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HASSABIS: We were both
obsessed with AGI,
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artificial
general intelligence.
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So today I'm going
to be talking about
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different approaches
to building AGI.
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With my colleague
Demis Hassabis,
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we're looking at ways
to bring in ideas
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from theoretical neuroscience.
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I felt like we were
the keepers of a secret
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that no one else knew.
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Shane and I knew
no one in academia
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would be supportive
of what we were doing.
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AI was almost
an embarrassing word
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to use in academic circles,
right?
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If you said
you were working on AI,
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then you clearly weren't
a serious scientist.
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So I convinced Shane
the right way to do it
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would be to start a company.
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SHANE LEGG: Okay,
we're going to try to do
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artificial
general intelligence.
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It may not even be possible.
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We're not quite sure
how we're going to do it,
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but we have some ideas
or, kind of, approaches.
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Huge amounts of money,
huge amounts of risk,
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lots and lots of compute.
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And if we pull this off,
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it'll be the biggest thing
ever, right?
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That is a very hard thing
for a typical investor
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to put their money on.
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It's almost like
buying a lottery ticket.
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I'm going to be speaking about
the system of neuroscience
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and how it might be used
to help us build AGI.
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HASSABIS:
Finding initial funding
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for this was very hard.
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We're going to solve
all of intelligence.
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You can imagine
some of the looks I got
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when we were
pitching that around.
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So I'm a V.C.
and I look at about
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700 to 1,000 projects a year.
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And I fund
literally 1% of those.
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About eight projects a year.
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So that means 99% of the time,
you're in "No" mode.
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"Wait a minute.
I'm telling you,
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"this is the most important
thing of all time.
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"I'm giving you
all this build-up
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"about how... explain
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"how it connects
with the brain,
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"why the time's right now,
and then you're asking me,
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"'But what's your, how are you
going to make money?
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"'What's your product?'"
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It's like,
so prosaic a question.
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You know?
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"Have you not been listening
to what I've been saying?"
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LEGG: We needed investors
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who aren't necessarily
going to invest
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because they think
it's the best
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investment decision.
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They're probably
going to invest
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because they just think
it's really cool.
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NEWSREADER:
He's the Silicon Valley
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version of the man
behind the curtain
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inThe Wizard of Oz.
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He had a lot to do
with giving you
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PayPal, Facebook,
YouTube and Yelp.
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LEGG: If everyone says "X,"
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Peter Thiel suspects
that the opposite of X
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is quite possibly true.
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HASSABIS: So Peter Thiel
was our first big investor.
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But he insisted that
we come to Silicon Valley
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because that
was the only place we could...
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There would be the talent,
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and we could build
that kind of company.
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But I was pretty adamant
we should be in London
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because I think
London's an amazing city.
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Plus, I knew there were
really amazing people
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trained at Cambridge
and Oxford and UCL.
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In Silicon Valley,
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everybody's founding
a company every year,
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and then if it doesn't work,
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you chuck it
and you start something new.
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That is not conducive
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to a long-term
research challenge.
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So we were totally
an outlier for him.
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Hi, everyone.
Welcome to DeepMind.
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So, what is our mission?
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We summarize it as...
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DeepMind's mission is to build
the world's first
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general learning machine.
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So we always stress the word
"general" and "learning" here
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are the key things.
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LEGG: Our mission
was to build an AGI,
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an artificial
general intelligence.
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And so that means that we need
a system which is general.
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It doesn't learn to do
one specific thing.
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That's a really key part
of human intelligence.
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We can learn to do
many, many things.
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It's going to, of course,
be a lot of hard work.
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But one of the things
that keeps me up at night
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is to not waste this
opportunity to, you know,
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to really make
a difference here,
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and have a big impact
on the world.
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LEGG: The first people
that came
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and joined DeepMind
really believed in the dream.
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But this was, I think,
one of the first times
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they found a place
full of other dreamers.
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You know, we collected
this Manhattan Project,
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if you like,
together to solve AI.
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HELEN KING:
In the first two years,
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we were in total stealth mode.
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And so we couldn't
say to anyone
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what were we doing
or where we worked.
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It was all quite vague.
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BEN COPPIN: It had
no public presence at all.
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You couldn't
look at a website.
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The office
was at a secret location.
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When we would interview people
in those early days,
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they would show up
very nervously.
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[LAUGHS]
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I had at least one candidate
who said,
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"I just messaged my wife
to tell her exactly
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"where I'm going just in case
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"this turns out to be some
kind of horrible scam
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"and I'm going
to get kidnapped."
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Well, my favorite new person
who's an investor,
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who I've been working
for a year, is Elon Musk.
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So for those of you
who don't know,
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this is what he looks like.
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And he hadn't really thought
much about AI
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until we chatted.
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His mission is to die on Mars
or something.
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- But not on impact.
- [LAUGHTER]
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So...
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We made some big decisions
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about how we were going
to approach building AI.
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This is a reinforcement
learning setup.
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This is the kind of setup
that we think about
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when we say we're building,
you know, an AI agent.
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It's basically the agent,
which is the AI,
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and then there's
the environment
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that it's interacting with.
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We decided that games,
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as long as
you're very disciplined
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about how you use them,
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are the perfect
training ground
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for AI development.
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LEGG: We wanted
to try to create one algorithm
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that could to be
trained up to play
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several dozen
different Atari games.
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So just like a human,
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you have to use the same brain
to play all the games.
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DAVID SILVER:
You can think of it
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that you provide the agent
with the cartridge.
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And you say,
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"Okay, imagine you're born
into that world
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"with that cartridge,
and you just get to interact
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"with the pixels
and see the score.
253
00:09:40,362 --> 00:09:41,668
"What can you do?"
254
00:09:43,974 --> 00:09:47,369
So what you're going to do is
take your Q function. Q-K...
255
00:09:47,412 --> 00:09:49,197
HASSABIS: Q-learning
is one of the oldest methods
256
00:09:49,240 --> 00:09:50,894
for reinforcement learning.
257
00:09:50,938 --> 00:09:53,680
And what we did was combine
reinforcement learning
258
00:09:53,723 --> 00:09:56,770
with deep learning
in one system.
259
00:09:56,813 --> 00:09:59,337
No one had ever combined
those two things together
260
00:09:59,381 --> 00:10:01,426
at scale to do
anything impressive,
261
00:10:01,470 --> 00:10:03,515
and we needed
to prove out this thesis.
262
00:10:03,559 --> 00:10:06,780
LEGG: We tried doingPong
as the first game.
263
00:10:06,823 --> 00:10:08,129
It seemed like the simplest.
264
00:10:08,172 --> 00:10:10,218
It hasn't been told
265
00:10:10,261 --> 00:10:11,828
anything about
what it's controlling
266
00:10:11,872 --> 00:10:12,873
or what it's supposed to do.
267
00:10:12,916 --> 00:10:14,657
All it knows
is that score is good
268
00:10:14,701 --> 00:10:18,052
and it has to learn
what its controls do,
269
00:10:18,095 --> 00:10:20,620
and build everything...
first principles.
270
00:10:21,229 --> 00:10:22,752
[GAME BEEPING]
271
00:10:29,193 --> 00:10:30,412
It wasn't really working.
272
00:10:32,588 --> 00:10:34,068
HASSABIS: I was just
saying to Shane,
273
00:10:34,111 --> 00:10:37,071
"Maybe we're just wrong,
and we can't even doPong."
274
00:10:37,114 --> 00:10:38,812
LEGG: It was a bit
nerve-racking,
275
00:10:38,855 --> 00:10:40,422
thinking how far we had to go
276
00:10:40,465 --> 00:10:42,337
if we were going
to really build
277
00:10:42,380 --> 00:10:44,426
a generally
intelligent system.
278
00:10:44,469 --> 00:10:45,732
HASSABIS: And it felt like
it was time
279
00:10:45,775 --> 00:10:47,255
to give up and move on.
280
00:10:48,169 --> 00:10:49,387
And then suddenly...
281
00:10:49,431 --> 00:10:51,346
[STIRRING MUSIC PLAYS]
282
00:10:51,389 --> 00:10:53,609
We got our first point.
283
00:10:53,653 --> 00:10:56,612
And then it was like,
"Is this random?"
284
00:10:56,656 --> 00:10:59,180
"No, no, it's really
getting a point now."
285
00:10:59,223 --> 00:11:00,703
It was really exciting
that this thing
286
00:11:00,747 --> 00:11:02,226
that previously
couldn't even figure out
287
00:11:02,270 --> 00:11:03,532
how to move a paddle
288
00:11:03,575 --> 00:11:05,926
had suddenly been able
to totally get it right.
289
00:11:05,969 --> 00:11:07,144
HASSABIS: Then it was getting
a few points.
290
00:11:07,188 --> 00:11:08,624
And then it won
its first game.
291
00:11:08,668 --> 00:11:10,974
And then three months later,
no human could beat it.
292
00:11:11,018 --> 00:11:14,238
You hadn't told it the rules,
how to get the score, nothing.
293
00:11:14,282 --> 00:11:16,110
And you just tell it
to maximize the score,
294
00:11:16,153 --> 00:11:17,372
and it goes away and does it.
295
00:11:17,415 --> 00:11:18,678
This is the first time
296
00:11:18,721 --> 00:11:20,549
anyone had done
this end-to-end learning.
297
00:11:20,592 --> 00:11:24,292
"Okay, so we have this working
in quite a general way.
298
00:11:24,335 --> 00:11:25,772
"Now let's try another game."
299
00:11:25,815 --> 00:11:27,512
HASSABIS: So then
we triedBreakout.
300
00:11:27,556 --> 00:11:29,123
At the beginning,
after 100 games,
301
00:11:29,166 --> 00:11:30,777
the agent is not very good.
302
00:11:30,820 --> 00:11:32,474
It's missing the ball
most of the time,
303
00:11:32,517 --> 00:11:34,389
but it's starting to get
the hang of the idea
304
00:11:34,432 --> 00:11:35,999
that the bat should go
towards the ball.
305
00:11:36,043 --> 00:11:37,566
Now, after 300 games,
306
00:11:37,609 --> 00:11:40,656
it's about as good as
any human can play this.
307
00:11:40,700 --> 00:11:42,049
We thought,
"Well, that's pretty cool,"
308
00:11:42,092 --> 00:11:44,312
but we left the system playing
for another 200 games,
309
00:11:44,355 --> 00:11:46,053
and it did this amazing thing.
310
00:11:46,096 --> 00:11:47,358
It found the optimal strategy
311
00:11:47,402 --> 00:11:49,404
was to dig a tunnel
around the side
312
00:11:49,447 --> 00:11:51,667
and put the ball
around the back of the wall.
313
00:11:51,711 --> 00:11:53,234
KORAY KAVUKCUOGLU:
Finally, the agent
314
00:11:53,277 --> 00:11:54,365
is actually achieving
315
00:11:54,409 --> 00:11:55,627
what you thought
it would achieve.
316
00:11:55,671 --> 00:11:57,325
That is a great feeling.
Right?
317
00:11:57,368 --> 00:11:59,283
Like, I mean,
when we do research,
318
00:11:59,327 --> 00:12:00,676
that is the best
we can hope for.
319
00:12:00,720 --> 00:12:03,200
We started generalizing
to 50 games,
320
00:12:03,244 --> 00:12:05,463
and we basically
created a recipe.
321
00:12:05,507 --> 00:12:06,813
We could just take a game
322
00:12:06,856 --> 00:12:08,379
that we have
never seen before.
323
00:12:08,423 --> 00:12:09,903
We would run
the algorithm on that,
324
00:12:09,946 --> 00:12:13,036
and DQN could train itself
from scratch,
325
00:12:13,080 --> 00:12:14,429
achieving human level
326
00:12:14,472 --> 00:12:15,996
or sometimes better
than human level.
327
00:12:16,039 --> 00:12:18,433
LEGG: We didn't build it
to play any of them.
328
00:12:18,476 --> 00:12:20,522
We could just give it
a bunch of games
329
00:12:20,565 --> 00:12:22,654
and would figure it out
for itself.
330
00:12:22,698 --> 00:12:25,179
And there was something
quite magical in that.
331
00:12:25,222 --> 00:12:26,528
MURRAY SHANAHAN:
Suddenly you had something
332
00:12:26,571 --> 00:12:27,921
that would respond and learn
333
00:12:27,964 --> 00:12:30,358
whatever situation
it was parachuted into.
334
00:12:30,401 --> 00:12:33,013
And that was like a huge,
huge breakthrough.
335
00:12:33,056 --> 00:12:35,276
It was in many respects
336
00:12:35,319 --> 00:12:36,625
the first example
337
00:12:36,668 --> 00:12:39,062
of any kind of thing
you could call
338
00:12:39,106 --> 00:12:40,542
a general intelligence.
339
00:12:42,109 --> 00:12:43,893
HASSABIS: Although we were
a well-funded startup,
340
00:12:43,937 --> 00:12:47,375
holding us back
was not enough compute power.
341
00:12:47,418 --> 00:12:49,203
I realized that
this would accelerate
342
00:12:49,246 --> 00:12:51,466
our time scale
to AGI massively.
343
00:12:51,509 --> 00:12:53,163
I used to see Demis
quite frequently.
344
00:12:53,207 --> 00:12:55,035
We'd have lunch, and he did...
345
00:12:56,210 --> 00:12:58,865
say to me that
he had two companies
346
00:12:58,908 --> 00:13:02,564
that were involved
in buying DeepMind.
347
00:13:02,607 --> 00:13:04,609
And he didn't know
which one to go with.
348
00:13:04,653 --> 00:13:08,526
The issue was,
would any commercial company
349
00:13:08,570 --> 00:13:12,661
appreciate the real importance
of the research?
350
00:13:12,704 --> 00:13:15,838
And give the research time
to come to fruition
351
00:13:15,882 --> 00:13:17,797
and not be breathing down
their necks,
352
00:13:17,840 --> 00:13:21,539
saying, "We want some kind of
commercial benefit from this."
353
00:13:23,411 --> 00:13:24,586
[MACHINERY HUMMING]
354
00:13:27,284 --> 00:13:32,463
Google has bought DeepMind
for a reported £400,000,000,
355
00:13:32,507 --> 00:13:34,726
making the artificial
intelligence firm
356
00:13:34,770 --> 00:13:37,947
its largest
European acquisition so far.
357
00:13:37,991 --> 00:13:39,644
The company was founded
358
00:13:39,688 --> 00:13:43,344
by 37-year-old entrepreneur
Demis Hassabis.
359
00:13:43,387 --> 00:13:45,563
After the acquisition,
I started mentoring
360
00:13:45,607 --> 00:13:47,261
and spending time with Demis,
361
00:13:47,304 --> 00:13:48,915
and just listening to him.
362
00:13:48,958 --> 00:13:52,396
And this is a person
who fundamentally
363
00:13:52,440 --> 00:13:55,573
is a scientist
and a natural scientist.
364
00:13:55,617 --> 00:13:58,489
He wants science to solve
every problem in the world,
365
00:13:58,533 --> 00:14:00,622
and he believes it can do so.
366
00:14:00,665 --> 00:14:03,625
That's not a normal person
you find in a tech company.
367
00:14:05,496 --> 00:14:07,455
HASSABIS: We were able
to not only join Google
368
00:14:07,498 --> 00:14:10,240
but run independently
in London,
369
00:14:10,284 --> 00:14:11,328
build our culture,
370
00:14:11,372 --> 00:14:13,461
which was optimized
for breakthroughs
371
00:14:13,504 --> 00:14:15,419
and not deal with products,
372
00:14:15,463 --> 00:14:17,726
do pure research.
373
00:14:17,769 --> 00:14:19,293
Our investors
didn't want to sell,
374
00:14:19,336 --> 00:14:20,685
but we decided
375
00:14:20,729 --> 00:14:22,731
that this was the best thing
for the mission.
376
00:14:22,774 --> 00:14:24,646
In many senses,
we were underselling
377
00:14:24,689 --> 00:14:26,169
in terms of value
before it more matured,
378
00:14:26,213 --> 00:14:28,128
and you could have sold it
for a lot more money.
379
00:14:28,171 --> 00:14:32,828
And the reason is because
there's no time to waste.
380
00:14:32,872 --> 00:14:35,178
There's so many things
that got to be cracked
381
00:14:35,222 --> 00:14:37,659
while the brain
is still in gear.
382
00:14:37,702 --> 00:14:39,008
You know, I'm still alive.
383
00:14:39,052 --> 00:14:40,880
There's all these things
that gotta be done.
384
00:14:40,923 --> 00:14:42,925
So you haven't got--
I mean, how many...
385
00:14:42,969 --> 00:14:44,492
How many billions
would you trade for
386
00:14:44,535 --> 00:14:45,797
another five years of life,
you know,
387
00:14:45,841 --> 00:14:48,365
to do what you set out to do?
388
00:14:48,409 --> 00:14:49,758
Okay, all of a sudden,
389
00:14:49,801 --> 00:14:52,717
we've got this massive scale
compute available to us.
390
00:14:52,761 --> 00:14:53,936
What can we do with that?
391
00:14:56,591 --> 00:14:59,942
HASSABIS: Go is the pinnacle
of board games.
392
00:14:59,986 --> 00:15:04,642
It is the most complex game
ever devised by man.
393
00:15:04,686 --> 00:15:06,688
There are more possible
board configurations
394
00:15:06,731 --> 00:15:09,821
in the game of Go than there
are atoms in the universe.
395
00:15:09,865 --> 00:15:13,390
SILVER: Go is the holy grail
of artificial intelligence.
396
00:15:13,434 --> 00:15:14,522
For many years,
397
00:15:14,565 --> 00:15:16,002
people have looked
at this game
398
00:15:16,045 --> 00:15:17,917
and they've thought,
"Wow, this is just too hard."
399
00:15:17,960 --> 00:15:20,180
Everything we've ever
tried in AI,
400
00:15:20,223 --> 00:15:22,704
it just falls over when
you try the game of Go.
401
00:15:22,747 --> 00:15:23,966
And so that's why
it feels like
402
00:15:24,010 --> 00:15:26,099
a real litmus test
of progress.
403
00:15:26,142 --> 00:15:28,579
We had just bought DeepMind.
404
00:15:28,623 --> 00:15:30,712
They were working
on reinforcement learning
405
00:15:30,755 --> 00:15:32,975
and they were the world's
experts in games.
406
00:15:33,019 --> 00:15:34,846
And so when
they introduced the idea
407
00:15:34,890 --> 00:15:37,197
that they could beat
the top level Go players
408
00:15:37,240 --> 00:15:40,113
in a game that was thought
to be incomputable,
409
00:15:40,156 --> 00:15:42,724
I thought, "Well,
that's pretty interesting."
410
00:15:42,767 --> 00:15:46,467
Our ultimate next step
is to play the legendary
411
00:15:46,510 --> 00:15:49,209
Lee Sedol
in just over two weeks.
412
00:15:50,514 --> 00:15:52,038
NEWSREADER 1:
A match like no other
413
00:15:52,081 --> 00:15:54,257
is about to get underway
in South Korea.
414
00:15:54,301 --> 00:15:57,826
NEWSREADER 2: Lee Sedol
is getting ready to rumble.
415
00:15:57,869 --> 00:15:59,262
HASSABIS:
Lee Sedol is probably
416
00:15:59,306 --> 00:16:01,699
one of the greatest players
of the last decade.
417
00:16:01,743 --> 00:16:04,224
I describe him
as the Roger Federer of Go.
418
00:16:05,573 --> 00:16:06,922
ERIC SCHMIDT: He showed up,
419
00:16:06,966 --> 00:16:09,838
and all of a sudden
we have a thousand Koreans
420
00:16:09,881 --> 00:16:13,059
who represent
all of Korean society,
421
00:16:13,102 --> 00:16:14,190
the top Go players.
422
00:16:15,583 --> 00:16:17,802
And then we have Demis.
423
00:16:17,846 --> 00:16:19,848
And the great
engineering team.
424
00:16:20,588 --> 00:16:22,285
He's very famous
425
00:16:22,329 --> 00:16:25,506
for very creative
fighting play.
426
00:16:25,549 --> 00:16:28,770
So this could be
difficult for us.
427
00:16:28,813 --> 00:16:31,991
SCHMIDT: I figured Lee Sedol
is going to beat these guys,
428
00:16:32,034 --> 00:16:34,297
but they'll make
a good showing.
429
00:16:34,341 --> 00:16:35,733
Good for a startup.
430
00:16:38,040 --> 00:16:39,563
I went over
to the technical group
431
00:16:39,607 --> 00:16:40,912
and they said,
432
00:16:40,956 --> 00:16:42,305
"Let me show you
how our algorithm works."
433
00:16:43,654 --> 00:16:45,091
RESEARCHER: If you step
through the actual game,
434
00:16:45,134 --> 00:16:47,789
we can see, kind of,
how AlphaGo thinks.
435
00:16:47,832 --> 00:16:50,226
HASSABIS: The way we start off
on training AlphaGo
436
00:16:50,270 --> 00:16:52,968
is by showing it 100,000 games
437
00:16:53,012 --> 00:16:54,491
that strong amateurs
have played.
438
00:16:54,535 --> 00:16:55,710
And we first initially
439
00:16:55,753 --> 00:16:58,887
get AlphaGo to mimic
the human player,
440
00:16:58,930 --> 00:17:00,802
and then through
reinforcement learning,
441
00:17:00,845 --> 00:17:02,369
it plays against
different versions of itself
442
00:17:02,412 --> 00:17:05,720
many millions of times
and learns from its errors.
443
00:17:05,763 --> 00:17:07,591
Hmm, this is interesting.
444
00:17:07,635 --> 00:17:08,679
ANNOUNCER 1: All right, folks,
445
00:17:08,723 --> 00:17:10,594
you're going to see
history made.
446
00:17:10,638 --> 00:17:11,856
[ANNOUNCER 2 SPEAKING KOREAN]
447
00:17:12,770 --> 00:17:14,772
SCHMIDT: So the game starts.
448
00:17:14,816 --> 00:17:15,991
ANNOUNCER 1:
He's really concentrating.
449
00:17:16,035 --> 00:17:17,645
ANNOUNCER 3:
If you really look at the...
450
00:17:19,473 --> 00:17:20,865
[ANNOUNCERS EXCLAIM]
451
00:17:20,909 --> 00:17:25,348
That's a very surprising move.
452
00:17:25,392 --> 00:17:27,916
ANNOUNCER 3: I think we're
seeing an original move here.
453
00:17:34,401 --> 00:17:35,793
Yeah, that's an exciting move.
454
00:17:36,185 --> 00:17:37,230
I like...
455
00:17:37,273 --> 00:17:38,579
SILVER:
Professional commentators
456
00:17:38,622 --> 00:17:40,102
almost unanimously said
457
00:17:40,146 --> 00:17:43,323
that not a single human player
would have chosen move 37.
458
00:17:43,366 --> 00:17:45,803
So I actually had a poke
around in AlphaGo
459
00:17:45,847 --> 00:17:47,414
to see what AlphaGo thought.
460
00:17:47,457 --> 00:17:50,330
And AlphaGo actually agreed
with that assessment.
461
00:17:50,373 --> 00:17:53,811
AlphaGo said there was a one
in 10,000 probability
462
00:17:53,855 --> 00:17:57,772
that move 37 would have been
played by a human player.
463
00:17:57,815 --> 00:18:00,775
[SEDOL SPEAKING IN KOREAN]
464
00:18:08,826 --> 00:18:10,176
SILVER: The game of Go
has been studied
465
00:18:10,219 --> 00:18:11,568
for thousands of years.
466
00:18:11,612 --> 00:18:15,137
And AlphaGo discovered
something completely new.
467
00:18:16,878 --> 00:18:19,707
ANNOUNCER: He resigned.
Lee Sedol has just resigned.
468
00:18:19,750 --> 00:18:21,187
He's beaten.
469
00:18:21,230 --> 00:18:22,666
[ELECTRONIC MUSIC PLAYING]
470
00:18:22,710 --> 00:18:24,668
NEWSREADER 1: The battle
between man versus machine,
471
00:18:24,712 --> 00:18:26,235
a computer just came out
the victor.
472
00:18:26,279 --> 00:18:28,237
NEWSREADER 2: Google
put its DeepMind team
473
00:18:28,281 --> 00:18:29,673
to the test against
474
00:18:29,717 --> 00:18:32,023
one of the brightest minds
in the world and won.
475
00:18:32,067 --> 00:18:33,721
SCHMIDT:
That's when we realized
476
00:18:33,764 --> 00:18:35,244
the DeepMind people knew
what they were doing
477
00:18:35,288 --> 00:18:37,551
and to pay attention
to reinforcement learning
478
00:18:37,594 --> 00:18:38,900
as they have invented it.
479
00:18:40,075 --> 00:18:41,816
Based on that experience,
480
00:18:41,859 --> 00:18:44,819
AlphaGo got better
and better and better.
481
00:18:44,862 --> 00:18:45,950
And they had a little chart
482
00:18:45,994 --> 00:18:47,517
of how much better
they were getting.
483
00:18:47,561 --> 00:18:49,302
And I said,
"When does this stop?"
484
00:18:50,085 --> 00:18:50,999
And Demis said,
485
00:18:51,042 --> 00:18:52,696
"When we beat the Chinese guy,
486
00:18:52,740 --> 00:18:55,743
"the top-rated player
in the world."
487
00:18:56,961 --> 00:18:59,225
ANNOUNCER 1:
Ke Jie versus AlphaGo.
488
00:19:03,620 --> 00:19:04,795
ANNOUNCER 2:
And I think we will see
489
00:19:04,839 --> 00:19:06,145
AlphaGo pushing through there.
490
00:19:06,188 --> 00:19:08,190
ANNOUNCER 1:
AlphaGo is ahead quite a bit.
491
00:19:08,234 --> 00:19:11,454
SCHMIDT: About halfway
through the first game,
492
00:19:11,498 --> 00:19:14,675
the best player in the world
was not doing so well.
493
00:19:14,718 --> 00:19:17,808
ANNOUNCER 1:
What can black do here?
494
00:19:19,114 --> 00:19:21,247
ANNOUNCER 2: Looks difficult.
495
00:19:21,290 --> 00:19:23,336
SCHMIDT:
And at a critical moment...
496
00:19:32,997 --> 00:19:35,913
the Chinese government
ordered the feed cut off.
497
00:19:38,307 --> 00:19:41,745
It was at that moment
we were telling the world
498
00:19:41,789 --> 00:19:44,966
that something new
had arrived on earth.
499
00:19:47,621 --> 00:19:48,970
In the 1950s
500
00:19:49,013 --> 00:19:51,929
when Russia'sSputnik
satellite was launched,
501
00:19:53,279 --> 00:19:55,194
it changed
the course of history.
502
00:19:55,237 --> 00:19:57,544
TV HOST: It is a challenge
that America must meet
503
00:19:57,587 --> 00:19:59,633
to survive in the Space Age.
504
00:19:59,676 --> 00:20:02,375
SCHMIDT: This has been
called theSputnik moment.
505
00:20:02,418 --> 00:20:06,335
The Sputnikmoment created
a massive reaction in the US
506
00:20:06,379 --> 00:20:10,034
in terms of funding
for science and engineering,
507
00:20:10,078 --> 00:20:12,254
and particularly
of space technology.
508
00:20:12,298 --> 00:20:15,823
For China,
AlphaGo was the wakeup call,
509
00:20:15,866 --> 00:20:17,128
the Sputnikmoment.
510
00:20:17,172 --> 00:20:19,870
It launched an AI space race.
511
00:20:21,220 --> 00:20:23,047
HASSABIS: We had this
huge idea that worked,
512
00:20:23,091 --> 00:20:26,355
and now the whole world knows.
513
00:20:26,399 --> 00:20:28,879
It's always easier
to land on the moon
514
00:20:28,923 --> 00:20:30,838
if someone's already
landed there.
515
00:20:32,056 --> 00:20:34,450
It is going to matter
who builds AI,
516
00:20:34,494 --> 00:20:36,626
and how it gets built.
517
00:20:36,670 --> 00:20:38,324
I always feel that pressure.
518
00:20:42,066 --> 00:20:43,764
SILVER: There's been
a big chain of events
519
00:20:43,807 --> 00:20:46,680
that followed on from all
of the excitement of AlphaGo.
520
00:20:46,723 --> 00:20:48,072
When we played
against Lee Sedol,
521
00:20:48,116 --> 00:20:49,248
we actually had a system
522
00:20:49,291 --> 00:20:50,684
that had been trained
on human data,
523
00:20:50,727 --> 00:20:52,251
on all of the millions
of games
524
00:20:52,294 --> 00:20:55,036
that have been played
by human experts.
525
00:20:55,079 --> 00:20:56,994
We eventually found
a new algorithm,
526
00:20:57,038 --> 00:20:59,170
a much more elegant approach
to the whole system,
527
00:20:59,214 --> 00:21:01,172
which actually stripped out
all of the human knowledge
528
00:21:01,216 --> 00:21:03,697
and just started
completely from scratch.
529
00:21:03,740 --> 00:21:06,700
And that became a project
which we called AlphaZero.
530
00:21:06,743 --> 00:21:09,529
Zero, meaning having zero
human knowledge in the loop.
531
00:21:11,879 --> 00:21:13,054
Instead of learning
from human data,
532
00:21:13,097 --> 00:21:15,796
it learned from its own games.
533
00:21:15,839 --> 00:21:17,841
So it actually
became its own teacher.
534
00:21:21,280 --> 00:21:23,499
HASSABIS:
AlphaZero is an experiment
535
00:21:23,543 --> 00:21:26,720
in how little knowledge
can we put into these systems
536
00:21:26,763 --> 00:21:28,243
and how quickly
and how efficiently
537
00:21:28,287 --> 00:21:29,723
can they learn?
538
00:21:29,766 --> 00:21:32,552
But the other thing is AlphaZero
doesn't have any rules.
539
00:21:32,595 --> 00:21:33,553
It learns through experience.
540
00:21:36,120 --> 00:21:38,862
The next stage
was to make it more general,
541
00:21:38,906 --> 00:21:40,995
so that it could play
any two-player game.
542
00:21:41,038 --> 00:21:42,344
Things like chess,
543
00:21:42,388 --> 00:21:44,085
and in fact,
any kind of two-player
544
00:21:44,128 --> 00:21:45,391
perfect information game.
545
00:21:45,434 --> 00:21:46,653
It's going really well.
546
00:21:46,696 --> 00:21:47,828
It's going
really, really well.
547
00:21:47,871 --> 00:21:50,091
- Oh, wow.
- It's going down, like fast.
548
00:21:50,134 --> 00:21:53,050
HASSABIS: AlphaGo used
to take a few months to train,
549
00:21:53,094 --> 00:21:55,662
but AlphaZero could start
in the morning
550
00:21:55,705 --> 00:21:57,794
playing completely randomly
551
00:21:57,838 --> 00:22:01,015
and then by tea
be at superhuman level.
552
00:22:01,058 --> 00:22:03,365
And by dinner it will be
the strongest chess entity
553
00:22:03,409 --> 00:22:04,758
there's ever been.
554
00:22:04,801 --> 00:22:06,629
- Amazing, it's amazing.
- Yeah.
555
00:22:06,673 --> 00:22:09,371
It's discovered its own
attacking style, you know,
556
00:22:09,415 --> 00:22:11,417
to take on the current
level of defense.
557
00:22:11,460 --> 00:22:12,766
I mean, I never
in my wildest dreams...
558
00:22:12,809 --> 00:22:14,942
I agree. Actually, I was not
expecting that either.
559
00:22:14,985 --> 00:22:16,422
And it's fun for me.
560
00:22:16,465 --> 00:22:18,598
I mean, it's inspired me
to get back into chess again,
561
00:22:18,641 --> 00:22:20,164
because it's cool to see
562
00:22:20,208 --> 00:22:22,384
that there's even more depth
than we thought in chess.
563
00:22:24,473 --> 00:22:25,431
[HORN BLOWS]
564
00:22:31,611 --> 00:22:34,440
HASSABIS: I actually got
into AI through games.
565
00:22:35,658 --> 00:22:37,660
Initially, it was board games.
566
00:22:37,704 --> 00:22:40,054
I was thinking,
"How is my brain doing this?"
567
00:22:40,097 --> 00:22:41,969
Like, what is it doing?
568
00:22:43,362 --> 00:22:47,148
I was very aware of that
from a very young age.
569
00:22:47,191 --> 00:22:50,064
So I've always been thinking
about thinking.
570
00:22:50,107 --> 00:22:52,762
NEWSREADER: The British
and American chess champions
571
00:22:52,806 --> 00:22:55,025
meet to begin
a series of matches.
572
00:22:55,069 --> 00:22:56,592
Playing alongside them
are the cream
573
00:22:56,636 --> 00:22:59,073
of Britain and America's
youngest players.
574
00:22:59,116 --> 00:23:01,467
NEWSREADER 2: Demis Hassabis
is representing Britain.
575
00:23:06,210 --> 00:23:07,864
COSTAS HASSABIS:
When Demis was four,
576
00:23:07,908 --> 00:23:11,259
he first showed
an aptitude for chess.
577
00:23:12,739 --> 00:23:14,044
By the time he was six,
578
00:23:14,088 --> 00:23:18,048
he became London
under-eight champion.
579
00:23:18,092 --> 00:23:19,485
HASSABIS: My parents
were very interesting
580
00:23:19,528 --> 00:23:20,834
and unusual, actually.
581
00:23:20,877 --> 00:23:23,750
I'd probably describe them
as quite bohemian.
582
00:23:23,793 --> 00:23:25,491
My father
was a singer-songwriter
583
00:23:25,534 --> 00:23:26,709
when he was younger,
584
00:23:26,753 --> 00:23:28,363
and Bob Dylan was his hero.
585
00:23:32,846 --> 00:23:34,500
[HORN HONKS]
586
00:23:34,543 --> 00:23:36,110
[ANGELA HASSABIS SPEAKING]
587
00:23:38,068 --> 00:23:39,287
Yeah, yeah.
588
00:23:41,637 --> 00:23:44,031
HOST: What is it
that you like about this game?
589
00:23:45,075 --> 00:23:47,121
It's just a good
thinking game.
590
00:23:49,253 --> 00:23:51,038
HASSABIS: At the time,
I was the second-highest rated
591
00:23:51,081 --> 00:23:52,692
chess player in the world
for my age.
592
00:23:52,735 --> 00:23:54,345
But although I was on track
593
00:23:54,389 --> 00:23:55,956
to be a professional
chess player,
594
00:23:55,999 --> 00:23:57,523
I thought that was what
I was going to do.
595
00:23:57,566 --> 00:23:59,220
No matter how much
I loved the game,
596
00:23:59,263 --> 00:24:01,222
it was incredibly stressful.
597
00:24:01,265 --> 00:24:03,354
Definitely was not fun
and games for me.
598
00:24:03,398 --> 00:24:05,226
My parents used to, you know,
599
00:24:05,269 --> 00:24:06,923
get very upset
when I lost the game
600
00:24:06,967 --> 00:24:10,405
and angry
if I forgot something.
601
00:24:10,449 --> 00:24:12,407
And because it was quite high
stakes for them, you know,
602
00:24:12,451 --> 00:24:14,061
it cost a lot of money
to go to these tournaments.
603
00:24:14,104 --> 00:24:15,715
And my parents
didn't have much money.
604
00:24:18,413 --> 00:24:19,719
My parents thought, you know,
605
00:24:19,762 --> 00:24:22,069
"If you interested
in being a chess professional,
606
00:24:22,112 --> 00:24:25,376
"this is really important.
It's like your exams."
607
00:24:27,291 --> 00:24:30,077
I remember
I was about 12-years-old
608
00:24:30,120 --> 00:24:32,079
and I was at this
international chess tournament
609
00:24:32,122 --> 00:24:34,255
in Liechtenstein
up in the mountains.
610
00:24:36,562 --> 00:24:39,303
[BELL TOLLING]
611
00:24:43,307 --> 00:24:45,571
And we were in this
huge church hall
612
00:24:47,181 --> 00:24:48,399
with, you know,
613
00:24:48,443 --> 00:24:50,184
hundreds of international
chess players.
614
00:24:52,403 --> 00:24:56,016
And I was playing
the ex-Danish champion.
615
00:24:56,059 --> 00:24:58,801
He must have been
in his 30s, probably.
616
00:25:00,411 --> 00:25:02,979
In those days,
there was a long time limit.
617
00:25:03,023 --> 00:25:05,068
The games could
literally last all day.
618
00:25:05,591 --> 00:25:08,289
[YAWNS]
619
00:25:08,332 --> 00:25:10,813
- [TIMER TICKING]
- We were into our tenth hour.
620
00:25:10,857 --> 00:25:12,380
[TIMER TICKS FRANTICALLY]
621
00:25:17,690 --> 00:25:20,214
[MOUSE GASPS]
622
00:25:20,257 --> 00:25:23,086
And we were in this
incredibly unusual ending.
623
00:25:23,130 --> 00:25:24,566
I think it should be a draw.
624
00:25:26,307 --> 00:25:28,657
But he kept on trying
to win for hours.
625
00:25:32,182 --> 00:25:33,401
[HORSE NEIGHS]
626
00:25:35,359 --> 00:25:38,319
Finally, he tried
one last cheap trick.
627
00:25:42,541 --> 00:25:44,455
All I had to do
was give away my queen.
628
00:25:44,499 --> 00:25:45,674
Then it would be stalemate.
629
00:25:47,023 --> 00:25:48,634
But I was so tired,
630
00:25:48,677 --> 00:25:50,157
I thought it was inevitable
I was going to be checkmated.
631
00:25:52,289 --> 00:25:53,508
And so I resigned.
632
00:25:57,294 --> 00:25:59,558
He jumped up.
Just started laughing.
633
00:25:59,601 --> 00:26:00,559
[LAUGHING]
634
00:26:01,777 --> 00:26:02,909
And he went,
635
00:26:02,952 --> 00:26:04,171
"Why have you resigned?
It's a draw."
636
00:26:04,214 --> 00:26:05,346
And he immediately,
with a flourish,
637
00:26:05,389 --> 00:26:06,652
sort of showed me
the drawing move.
638
00:26:09,045 --> 00:26:12,396
I felt so sick to my stomach.
639
00:26:12,440 --> 00:26:14,137
It made me think of
the rest of that tournament.
640
00:26:14,181 --> 00:26:16,662
Like, are we wasting
our minds?
641
00:26:16,705 --> 00:26:19,708
Is this the best use
of all this brain power?
642
00:26:19,752 --> 00:26:22,363
Everybody's, collectively,
in that building?
643
00:26:22,406 --> 00:26:24,060
If you could somehow plug in
644
00:26:24,104 --> 00:26:27,542
those 300 brains
into a system,
645
00:26:27,586 --> 00:26:29,022
you might be able
to solve cancer
646
00:26:29,065 --> 00:26:30,501
with that level
of brain power.
647
00:26:31,633 --> 00:26:33,504
This intuitive feeling
came over me
648
00:26:33,548 --> 00:26:35,071
that although I love chess,
649
00:26:35,115 --> 00:26:38,335
this is not the right thing
to spend my whole life on.
650
00:26:51,479 --> 00:26:53,089
LEGG: Demis and myself,
651
00:26:53,133 --> 00:26:56,092
our plan was always
to fill DeepMind
652
00:26:56,136 --> 00:26:57,224
with some of the most
653
00:26:57,267 --> 00:26:59,226
brilliant scientists
in the world.
654
00:26:59,269 --> 00:27:01,054
So we had the human brains
655
00:27:01,097 --> 00:27:04,927
necessary to create
an AGI system.
656
00:27:04,971 --> 00:27:09,279
By definition, the "G"
in AGI is about generality.
657
00:27:09,323 --> 00:27:13,109
What I imagine is being able
to talk to an agent,
658
00:27:13,153 --> 00:27:15,198
the agent can talk back,
659
00:27:15,242 --> 00:27:18,898
and the agent is able to solve
novel problems
660
00:27:18,941 --> 00:27:20,595
that it hasn't seen before.
661
00:27:20,639 --> 00:27:22,728
That's a really key part
of human intelligence,
662
00:27:22,771 --> 00:27:24,468
and it's that
cognitive breadth
663
00:27:24,512 --> 00:27:27,733
and flexibility
that's incredible.
664
00:27:27,776 --> 00:27:29,473
The only natural
general intelligence
665
00:27:29,517 --> 00:27:30,910
we know of as humans,
666
00:27:30,953 --> 00:27:33,434
we obviously learn a lot
from our environment.
667
00:27:33,477 --> 00:27:35,871
So we think that
simulated environments
668
00:27:35,915 --> 00:27:38,874
are one of the ways
to create an AGI.
669
00:27:40,528 --> 00:27:42,356
SIMON CARTER:
The very early humans
670
00:27:42,399 --> 00:27:44,314
were having to solve
logic problems.
671
00:27:44,358 --> 00:27:46,752
They were having to solve
navigation, memory,
672
00:27:46,795 --> 00:27:48,971
and we evolved
in that environment.
673
00:27:50,364 --> 00:27:52,496
If we can create
a virtual recreation
674
00:27:52,540 --> 00:27:54,629
of that kind of environment,
675
00:27:54,673 --> 00:27:56,326
that's the perfect
testing ground
676
00:27:56,370 --> 00:27:57,458
and training ground
677
00:27:57,501 --> 00:27:59,329
for everything
we do at DeepMind.
678
00:28:04,247 --> 00:28:05,727
GUY SIMMONS:
What they were doing here
679
00:28:05,771 --> 00:28:09,252
was creating environments
for childlike beings,
680
00:28:09,296 --> 00:28:11,690
the agents to exist
within and play.
681
00:28:12,429 --> 00:28:13,648
That just sounded like
682
00:28:13,692 --> 00:28:16,782
the most interesting thing
in all the world.
683
00:28:16,825 --> 00:28:19,132
SHANAHAN: A child
learns by tearing things up
684
00:28:19,175 --> 00:28:20,655
and then throwing food around
685
00:28:20,699 --> 00:28:23,353
and getting a response
from mommy or daddy.
686
00:28:23,397 --> 00:28:25,616
This seems like an important
idea to incorporate
687
00:28:25,660 --> 00:28:27,880
in the way you train an agent.
688
00:28:27,923 --> 00:28:30,970
RESEARCHER 1: The humanoid
is supposed to stand up.
689
00:28:31,013 --> 00:28:32,972
As his center
of gravity rises,
690
00:28:33,015 --> 00:28:34,408
it gets more points.
691
00:28:37,454 --> 00:28:38,804
You have a reward
692
00:28:38,847 --> 00:28:40,849
and the agent
learns from the reward,
693
00:28:40,893 --> 00:28:43,373
like, you do something well,
you get a positive reward.
694
00:28:43,417 --> 00:28:47,508
You do something bad,
you get a negative reward.
695
00:28:47,551 --> 00:28:49,379
RESEARCHER 2: [EXCLAIMS]
It looks like it's standing.
696
00:28:50,859 --> 00:28:52,165
It's still a bit drunk.
697
00:28:52,208 --> 00:28:53,644
RESEARCHER 1:
It likes to walk backwards.
698
00:28:53,688 --> 00:28:55,342
RESEARCHER 2: [CHUCKLES] Yeah.
699
00:28:55,385 --> 00:28:57,518
The whole algorithm
is trying to optimize
700
00:28:57,561 --> 00:28:59,694
for receiving as much rewards
as possible,
701
00:28:59,738 --> 00:29:02,305
and it's found that
walking backwards,
702
00:29:02,349 --> 00:29:05,352
it's good enough
to get very good scores.
703
00:29:07,746 --> 00:29:09,573
RAIA HADSELL:
When we learn to navigate,
704
00:29:09,617 --> 00:29:11,271
when we learn to get around
in our world,
705
00:29:11,314 --> 00:29:13,490
we don't start with maps.
706
00:29:13,534 --> 00:29:16,319
We just start
with our own exploration,
707
00:29:16,363 --> 00:29:18,017
adventuring off
across the park,
708
00:29:18,060 --> 00:29:21,890
without our parents
by our side,
709
00:29:21,934 --> 00:29:24,327
or finding our way home
from school when we're young.
710
00:29:24,371 --> 00:29:25,546
[FAST ELECTRONIC
MUSIC PLAYING]
711
00:29:26,939 --> 00:29:28,723
HADSELL: A few of us
came up with this idea
712
00:29:28,767 --> 00:29:31,987
that if we had an environment
where a simulated robot
713
00:29:32,031 --> 00:29:33,728
just had to run forward,
714
00:29:33,772 --> 00:29:36,296
we could put all sorts of
obstacles in its way
715
00:29:36,339 --> 00:29:38,211
and see if it could manage
to navigate
716
00:29:38,254 --> 00:29:40,474
different types of terrain.
717
00:29:40,517 --> 00:29:43,042
The idea would be like
a parkour challenge.
718
00:29:46,393 --> 00:29:48,743
It's not graceful,
719
00:29:48,787 --> 00:29:51,833
but was never trained to hold
a glass whilst it was running
720
00:29:51,877 --> 00:29:53,052
and not spill water.
721
00:29:54,183 --> 00:29:55,706
You set this objective
that says,
722
00:29:55,750 --> 00:29:58,231
"Just move forward,
forward velocity,
723
00:29:58,274 --> 00:30:00,581
"and you'll get
a reward for that."
724
00:30:00,624 --> 00:30:02,670
And the learning algorithm
figures out
725
00:30:02,713 --> 00:30:05,412
how to move
this complex set of joints.
726
00:30:06,152 --> 00:30:07,370
That's the power of
727
00:30:07,414 --> 00:30:10,069
reward-based
reinforcement learning.
728
00:30:10,112 --> 00:30:12,767
SILVER: Our goal
is to try and build agents
729
00:30:12,811 --> 00:30:15,770
which, we drop them in,
they know nothing,
730
00:30:15,814 --> 00:30:18,381
they get to play around in
whatever problem you give them
731
00:30:18,425 --> 00:30:22,342
and eventually figure out how
to solve it for themselves.
732
00:30:22,385 --> 00:30:24,823
Now we want something
which can do that
733
00:30:24,866 --> 00:30:27,390
in as many different types
of problems as possible.
734
00:30:29,262 --> 00:30:32,918
A human needs diverse skills
to interact with the world.
735
00:30:32,961 --> 00:30:35,050
How to deal
with complex images,
736
00:30:35,094 --> 00:30:37,879
how to manipulate
thousands of things at once,
737
00:30:37,923 --> 00:30:40,447
how to deal
with missing information.
738
00:30:40,490 --> 00:30:42,014
We think all of these things
together
739
00:30:42,057 --> 00:30:45,278
are represented
by this game calledStarCraft.
740
00:30:45,321 --> 00:30:47,280
All it's being trained
to do is,
741
00:30:47,323 --> 00:30:50,457
given this situation,
this screen,
742
00:30:50,500 --> 00:30:51,850
what would a human do?
743
00:30:51,893 --> 00:30:55,244
We took inspiration from
large language models
744
00:30:55,288 --> 00:30:57,638
where you simply train
a model
745
00:30:57,681 --> 00:30:59,553
to predict the next word,
746
00:31:03,731 --> 00:31:05,472
which is exactly the same as
747
00:31:05,515 --> 00:31:07,735
predict the next
StarCraft move.
748
00:31:07,778 --> 00:31:08,954
SILVER: Unlike chess or Go,
749
00:31:08,997 --> 00:31:11,217
where players take turns
to make moves,
750
00:31:11,260 --> 00:31:14,133
inStarCraft there's a
continuous flow of decisions.
751
00:31:15,134 --> 00:31:16,483
On top of that,
752
00:31:16,526 --> 00:31:18,659
you can't even see
what the opponent is doing.
753
00:31:18,702 --> 00:31:20,879
There is no longer
a clear definition
754
00:31:20,922 --> 00:31:22,315
of what it means
to play the best way.
755
00:31:22,358 --> 00:31:23,925
It depends on
what your opponent does.
756
00:31:23,969 --> 00:31:25,492
HADSELL: This is the way
that we'll get to
757
00:31:25,535 --> 00:31:27,494
a much more fluid,
758
00:31:27,537 --> 00:31:31,672
more natural, faster,
more reactive agent.
759
00:31:31,715 --> 00:31:33,021
ORIOL VINYALS:
This is a huge challenge
760
00:31:33,065 --> 00:31:35,415
and let's see how far
we can push.
761
00:31:35,458 --> 00:31:36,459
TIM LILLICRAP: Oh!
762
00:31:36,503 --> 00:31:38,200
Holy monkey!
763
00:31:38,244 --> 00:31:40,376
I'm a pretty
low-level amateur.
764
00:31:40,420 --> 00:31:42,857
I'm okay, but I'm
a pretty low-level amateur.
765
00:31:42,901 --> 00:31:45,991
These agents have
a long ways to go.
766
00:31:46,034 --> 00:31:48,515
HASSABIS: We couldn't
beat someone of Tim's level.
767
00:31:48,558 --> 00:31:50,430
You know, that was
a little bit alarming.
768
00:31:50,473 --> 00:31:51,997
LILLICRAP:
At that point, it felt like
769
00:31:52,040 --> 00:31:53,520
it was going to be, like,
a really big long challenge,
770
00:31:53,563 --> 00:31:55,000
maybe a couple of years.
771
00:31:58,177 --> 00:32:01,832
VINYALS: Dani is the best
DeepMind StarCraft 2 player.
772
00:32:01,876 --> 00:32:05,097
I've been playing the agent
every day for a few weeks now.
773
00:32:07,186 --> 00:32:08,883
I could feel that the agent
774
00:32:08,927 --> 00:32:11,103
was getting better
really fast.
775
00:32:11,146 --> 00:32:13,366
[CHEERING, LAUGHTER]
776
00:32:13,409 --> 00:32:15,020
Wow, we beat Danny.
That, for me,
777
00:32:15,063 --> 00:32:16,935
was already
like a huge achievement.
778
00:32:18,284 --> 00:32:19,372
HASSABIS: The next step is
779
00:32:19,415 --> 00:32:21,722
we're going to book in
a pro to play.
780
00:32:23,071 --> 00:32:24,203
[KEYBOARD TAPPING]
781
00:32:27,032 --> 00:32:28,250
[GROANS]
782
00:32:28,294 --> 00:32:30,513
[CHEERING, WHOOPING]
783
00:32:32,994 --> 00:32:35,562
[CHEERING, WHOOPING]
784
00:32:35,605 --> 00:32:37,868
- [LAUGHS]
- [PEOPLE CLAPPING]
785
00:32:42,003 --> 00:32:44,049
It feels a bit unfair.
All you guys against me.
786
00:32:44,092 --> 00:32:45,572
[ALL LAUGH]
787
00:32:45,615 --> 00:32:46,965
HASSABIS: We're way
ahead of what I thought
788
00:32:47,008 --> 00:32:49,402
we would do, given where
we were two months ago.
789
00:32:49,445 --> 00:32:50,794
Just trying to digest it all,
actually.
790
00:32:50,838 --> 00:32:52,753
But it's very, very cool.
791
00:32:52,796 --> 00:32:54,146
SILVER: Now we're in
a position where
792
00:32:54,189 --> 00:32:56,061
we can finally share
the work that we've done
793
00:32:56,104 --> 00:32:57,192
with the public.
794
00:32:57,236 --> 00:32:58,585
This is a big step.
795
00:32:58,628 --> 00:33:00,674
We are really putting
ourselves on the line here.
796
00:33:00,717 --> 00:33:02,589
- Take it away. Cheers.
- Thank you.
797
00:33:02,632 --> 00:33:04,460
We're going to be live
from London.
798
00:33:04,504 --> 00:33:05,722
It's happening.
799
00:33:08,638 --> 00:33:10,597
ANNOUNCER 1:
Welcome to London.
800
00:33:10,640 --> 00:33:13,252
We are going to have
a live exhibition match,
801
00:33:13,295 --> 00:33:15,341
MaNa against AlphaStar.
802
00:33:15,384 --> 00:33:17,169
[CHEERING, APPLAUSE]
803
00:33:18,344 --> 00:33:19,998
At this point now,
804
00:33:20,041 --> 00:33:23,827
AlphaStar, 10 and 0
against professional gamers.
805
00:33:23,871 --> 00:33:25,873
Any thoughts
before we get into this game?
806
00:33:25,916 --> 00:33:27,483
VINYALS: I just want to see
a good game, yeah.
807
00:33:27,527 --> 00:33:28,963
I want to see a good game.
808
00:33:29,007 --> 00:33:30,486
SILVER: Absolutely,
good game. We're all excited.
809
00:33:30,530 --> 00:33:33,011
ANNOUNCER: All right. Let's
see what MaNa can pull off.
810
00:33:34,969 --> 00:33:36,405
ANNOUNCER 2:
AlphaStar is definitely
811
00:33:36,449 --> 00:33:38,364
dominating the pace
of this game.
812
00:33:38,407 --> 00:33:41,106
[SPORADIC CHEERING]
813
00:33:41,149 --> 00:33:44,152
ANNOUNCER 1: Wow. AlphaStar
is playing so smartly.
814
00:33:44,196 --> 00:33:46,807
[LAUGHTER]
815
00:33:46,850 --> 00:33:48,461
This really looks like
I'm watching
816
00:33:48,504 --> 00:33:49,940
a professional human gamer
817
00:33:49,984 --> 00:33:51,290
from the AlphaStar
point of view.
818
00:33:53,031 --> 00:33:54,858
[KEYBOARD TAPPING]
819
00:33:57,252 --> 00:34:01,952
HASSABIS: I hadn't really seen
a pro playStarCraft up close,
820
00:34:01,996 --> 00:34:03,563
and the 800 clicks per minute.
821
00:34:03,606 --> 00:34:06,000
I don't understand how anyone
can even click 800 times,
822
00:34:06,044 --> 00:34:09,482
let alone doing
800 useful clicks.
823
00:34:09,525 --> 00:34:11,092
ANNOUNCER 1:
Oh, another good hit.
824
00:34:11,136 --> 00:34:13,094
- [ALL GROAN]
- AlphaStar is just
825
00:34:13,138 --> 00:34:14,617
completely relentless.
826
00:34:14,661 --> 00:34:16,141
SILVER: We need to be careful
827
00:34:16,184 --> 00:34:19,361
because many of us grew up
as gamers and are gamers.
828
00:34:19,405 --> 00:34:21,363
And so to us,
it's very natural
829
00:34:21,407 --> 00:34:23,800
to view games
as what they are,
830
00:34:23,844 --> 00:34:26,977
which is pure vehicles
for fun,
831
00:34:27,021 --> 00:34:29,719
and not to see
that more militaristic side
832
00:34:29,763 --> 00:34:32,853
that the public might see
if they looked at this.
833
00:34:32,896 --> 00:34:37,553
You can't look at gunpowder
and only make a firecracker.
834
00:34:37,597 --> 00:34:41,209
All technologies inherently
point into certain directions.
835
00:34:43,124 --> 00:34:44,691
MARGARET LEVI:
I'm very worried about
836
00:34:44,734 --> 00:34:46,606
the certain ways in which AI
837
00:34:46,649 --> 00:34:49,652
will be used
for military purposes.
838
00:34:51,306 --> 00:34:55,049
And that makes it even clearer
how important it is
839
00:34:55,093 --> 00:34:58,357
for our societies
to be in control
840
00:34:58,400 --> 00:35:01,055
of these new technologies.
841
00:35:01,099 --> 00:35:05,190
The potential for abuse
from AI will be significant.
842
00:35:05,233 --> 00:35:08,758
Wars that occur faster
than humans can comprehend
843
00:35:08,802 --> 00:35:11,065
and more powerful
surveillance.
844
00:35:12,458 --> 00:35:15,765
How do you keep power forever
845
00:35:15,809 --> 00:35:19,421
over something that's
much more powerful than you?
846
00:35:19,465 --> 00:35:21,380
[STEPHEN HAWKING SPEAKING]
847
00:35:43,053 --> 00:35:45,752
Technologies can be used
to do terrible things.
848
00:35:47,493 --> 00:35:50,452
And technology can be used
to do wonderful things
849
00:35:50,496 --> 00:35:52,150
and solve
all kinds of problems.
850
00:35:53,586 --> 00:35:54,978
When DeepMind
was acquired by Google...
851
00:35:55,022 --> 00:35:56,589
- Yeah.
- ...you got Google to promise
852
00:35:56,632 --> 00:35:58,025
that technology you developed
won't be used by the military
853
00:35:58,068 --> 00:35:59,418
- for surveillance.
- Right.
854
00:35:59,461 --> 00:36:00,593
- Yes.
- Tell us about that.
855
00:36:00,636 --> 00:36:03,161
I think technology
is neutral in itself,
856
00:36:03,204 --> 00:36:05,598
um, but how, you know,
we as a society
857
00:36:05,641 --> 00:36:07,382
or humans and companies
and other things,
858
00:36:07,426 --> 00:36:09,515
other entities and governments
decide to use it
859
00:36:09,558 --> 00:36:12,561
is what determines whether
things become good or bad.
860
00:36:12,605 --> 00:36:16,261
You know, I personally think
having autonomous weaponry
861
00:36:16,304 --> 00:36:17,479
is just a very bad idea.
862
00:36:19,177 --> 00:36:21,266
ANNOUNCER 1:
AlphaStar is playing
863
00:36:21,309 --> 00:36:24,094
an extremely intelligent game
right now.
864
00:36:24,138 --> 00:36:27,359
CUKIER: There is an element to
what's being created
865
00:36:27,402 --> 00:36:28,882
at DeepMind in London
866
00:36:28,925 --> 00:36:34,148
that does seem like
the Manhattan Project.
867
00:36:34,192 --> 00:36:37,586
There's a relationship between
Robert Oppenheimer
868
00:36:37,630 --> 00:36:39,675
and Demis Hassabis
869
00:36:39,719 --> 00:36:44,202
in which they're unleashing
a new force upon humanity.
870
00:36:44,245 --> 00:36:46,204
ANNOUNCER 1:
MaNa is fighting back, though.
871
00:36:46,247 --> 00:36:48,162
Oh, man!
872
00:36:48,206 --> 00:36:50,208
HASSABIS:
I think that Oppenheimer
873
00:36:50,251 --> 00:36:52,471
and some of the other leaders
of that project got caught up
874
00:36:52,514 --> 00:36:54,908
in the excitement
of building the technology
875
00:36:54,951 --> 00:36:56,170
and seeing if it was possible.
876
00:36:56,214 --> 00:36:58,520
ANNOUNCER 1:
Where is AlphaStar?
877
00:36:58,564 --> 00:36:59,782
Where is AlphaStar?
878
00:36:59,826 --> 00:37:01,958
I don't see AlphaStar's units
anywhere.
879
00:37:02,002 --> 00:37:03,525
HASSABIS: They did not think
carefully enough
880
00:37:03,569 --> 00:37:07,312
about the morals of what
they were doing early enough.
881
00:37:07,355 --> 00:37:08,965
What we should do
as scientists
882
00:37:09,009 --> 00:37:11,011
with powerful new technologies
883
00:37:11,054 --> 00:37:13,883
is try and understand it in
controlled conditions first.
884
00:37:14,928 --> 00:37:16,799
ANNOUNCER 1: And that is that.
885
00:37:16,843 --> 00:37:19,411
MaNa has defeated AlphaStar.
886
00:37:29,551 --> 00:37:31,336
I mean, my honest feeling is
that I think it is
887
00:37:31,379 --> 00:37:33,207
a fair representation
of where we are.
888
00:37:33,251 --> 00:37:35,949
And I think that part feels...
feels okay.
889
00:37:35,992 --> 00:37:37,429
- I'm very happy for you.
- I'm happy.
890
00:37:37,472 --> 00:37:38,865
So well... well done.
891
00:37:38,908 --> 00:37:40,867
My view is that the approach
to building technology
892
00:37:40,910 --> 00:37:43,348
which is embodied by
move fast and break things,
893
00:37:43,391 --> 00:37:46,220
is exactly what
we should not be doing,
894
00:37:46,264 --> 00:37:47,961
because you can't afford
to break things
895
00:37:48,004 --> 00:37:49,049
and then fix them afterwards.
896
00:37:49,092 --> 00:37:50,398
- Cheers.
- Thank you so much.
897
00:37:50,442 --> 00:37:52,008
Yeah, get... get some rest.
You did really well.
898
00:37:52,052 --> 00:37:53,923
- Cheers, yeah?
- Thank you for having us.
899
00:38:01,627 --> 00:38:03,281
[ELECTRONIC MUSIC PLAYING]
900
00:38:04,238 --> 00:38:05,500
HASSABIS: When I was eight,
901
00:38:05,544 --> 00:38:06,849
I bought my first computer
902
00:38:06,893 --> 00:38:09,548
with the winnings
from a chess tournament.
903
00:38:09,591 --> 00:38:11,158
I sort of had this intuition
904
00:38:11,201 --> 00:38:13,726
that computers
are this magical device
905
00:38:13,769 --> 00:38:15,902
that can extend
the power of the mind.
906
00:38:15,945 --> 00:38:17,382
I had a couple
of school friends,
907
00:38:17,425 --> 00:38:19,166
and we used to have
a hacking club,
908
00:38:19,209 --> 00:38:21,908
writing code, making games.
909
00:38:26,260 --> 00:38:27,827
And then over
the summer holidays,
910
00:38:27,870 --> 00:38:29,219
I'd spend the whole day
911
00:38:29,263 --> 00:38:31,526
flicking through
games magazines.
912
00:38:31,570 --> 00:38:33,441
And one day I noticed
there was a competition
913
00:38:33,485 --> 00:38:35,878
to write an original version
of Space Invaders.
914
00:38:35,922 --> 00:38:39,621
And the winner won a job
at Bullfrog.
915
00:38:39,665 --> 00:38:42,320
Bullfrog at the time was the
best game development house
916
00:38:42,363 --> 00:38:43,756
in all of Europe.
917
00:38:43,799 --> 00:38:45,279
You know, I really wanted
to work at this place
918
00:38:45,323 --> 00:38:48,587
and see how they build games.
919
00:38:48,630 --> 00:38:50,415
NEWSCASTER: Bullfrog,
based here in Guildford,
920
00:38:50,458 --> 00:38:52,286
began with a big idea.
921
00:38:52,330 --> 00:38:54,680
That idea turned into the game
Populous,
922
00:38:54,723 --> 00:38:56,551
which became
a global bestseller.
923
00:38:56,595 --> 00:38:59,859
In the '90s, there was
no recruitment agencies.
924
00:38:59,902 --> 00:39:02,122
You couldn't go out and say,
you know,
925
00:39:02,165 --> 00:39:04,951
"Come and work
in the games industry."
926
00:39:04,994 --> 00:39:08,171
It was still not even
considered an industry.
927
00:39:08,215 --> 00:39:11,218
So we came up with the idea
to have a competition
928
00:39:11,261 --> 00:39:13,655
and we got
a lot of applicants.
929
00:39:14,700 --> 00:39:17,616
And one of those was Demis's.
930
00:39:17,659 --> 00:39:20,706
I can still remember clearly
931
00:39:20,749 --> 00:39:23,970
the day that Demis came in.
932
00:39:24,013 --> 00:39:27,147
He walked in the door,
he looked about 12.
933
00:39:28,670 --> 00:39:30,019
I thought, "Oh, my God,
934
00:39:30,063 --> 00:39:31,586
"what the hell are we going
to do with this guy?"
935
00:39:31,630 --> 00:39:32,979
I applied to Cambridge.
936
00:39:33,022 --> 00:39:35,373
I got in but they said
I was way too young.
937
00:39:35,416 --> 00:39:37,853
So...
So I needed to take a year off
938
00:39:37,897 --> 00:39:39,899
so I'd be at least 17
before I got there.
939
00:39:39,942 --> 00:39:42,771
And that's when I decided
to spend that entire gap year
940
00:39:42,815 --> 00:39:44,469
working at Bullfrog.
941
00:39:44,512 --> 00:39:46,166
They couldn't even
legally employ me,
942
00:39:46,209 --> 00:39:48,298
so I ended up being paid
in brown paper envelopes.
943
00:39:48,342 --> 00:39:49,343
[CHUCKLES]
944
00:39:50,823 --> 00:39:54,304
I got a feeling of being
really at the cutting edge
945
00:39:54,348 --> 00:39:58,047
and how much fun that was
to invent things every day.
946
00:39:58,091 --> 00:40:00,572
And then you know,
a few months later,
947
00:40:00,615 --> 00:40:03,662
maybe everyone... a million
people will be playing it.
948
00:40:03,705 --> 00:40:06,665
MOLYNEUX: In those days
computer games had to evolve.
949
00:40:06,708 --> 00:40:08,536
There had to be new genres
950
00:40:08,580 --> 00:40:11,757
which were more
than just shooting things.
951
00:40:11,800 --> 00:40:14,063
Wouldn't it be amazing
to have a game
952
00:40:14,107 --> 00:40:18,807
where you design and build
your own theme park?
953
00:40:18,851 --> 00:40:21,027
[GAME CHARACTERS SCREAMING]
954
00:40:22,594 --> 00:40:25,945
Demis and I started to talk
aboutTheme Park.
955
00:40:25,988 --> 00:40:28,904
It allows the player
to build a world
956
00:40:28,948 --> 00:40:31,864
and see the consequences
of your choices
957
00:40:31,907 --> 00:40:34,127
that you've made
in that world.
958
00:40:34,170 --> 00:40:36,085
HASSABIS: A human player
set out the layout
959
00:40:36,129 --> 00:40:38,566
of the theme park and designed
the roller coaster
960
00:40:38,610 --> 00:40:41,351
and set the prices
in the chip shop.
961
00:40:41,395 --> 00:40:43,615
What I was working on was
the behaviors of the people.
962
00:40:43,658 --> 00:40:45,138
They were autonomous
963
00:40:45,181 --> 00:40:47,314
and that was the AI
in this case.
964
00:40:47,357 --> 00:40:48,881
So what I was trying to do
was mimic
965
00:40:48,924 --> 00:40:51,013
interesting human behavior
966
00:40:51,057 --> 00:40:52,319
so that the simulation
would be
967
00:40:52,362 --> 00:40:54,582
more interesting
to interact with.
968
00:40:54,626 --> 00:40:56,541
MOLYNEUX: Demis worked
on ridiculous things,
969
00:40:56,584 --> 00:40:59,413
like you could place down
these shops
970
00:40:59,457 --> 00:41:03,591
and if you put a shop too near
a very dangerous ride,
971
00:41:03,635 --> 00:41:05,375
then people on the ride
would throw up
972
00:41:05,419 --> 00:41:08,030
because they'd just eaten.
973
00:41:08,074 --> 00:41:09,641
And then that would make
other people throw up
974
00:41:09,684 --> 00:41:12,121
when they saw the throwing-up
on the floor,
975
00:41:12,165 --> 00:41:14,559
so you then had to have
lots of sweepers
976
00:41:14,602 --> 00:41:17,823
to quickly sweep it up
before the people saw it.
977
00:41:17,866 --> 00:41:19,520
That's the cool thing
about it.
978
00:41:19,564 --> 00:41:22,784
You as the player tinker with
it and then it reacts to you.
979
00:41:22,828 --> 00:41:25,874
MOLYNEUX: All those nuanced
simulation things he did
980
00:41:25,918 --> 00:41:28,094
and that was an invention
981
00:41:28,137 --> 00:41:31,227
which never really
existed before.
982
00:41:31,271 --> 00:41:34,230
It was
unbelievably successful.
983
00:41:34,274 --> 00:41:35,710
DAVID GARDNER:
Theme Park actually turned out
984
00:41:35,754 --> 00:41:37,190
to be a top ten title
985
00:41:37,233 --> 00:41:39,932
and that was the first time
we were starting to see
986
00:41:39,975 --> 00:41:43,022
how AI could make
a difference.
987
00:41:43,065 --> 00:41:44,806
[BRASS BAND PLAYING]
988
00:41:46,155 --> 00:41:47,592
CARTER: We were doing
some Christmas shopping
989
00:41:47,635 --> 00:41:51,247
and were waiting for the taxi
to take us home.
990
00:41:51,291 --> 00:41:54,947
I have this very clear memory
of Demis talking about AI
991
00:41:54,990 --> 00:41:56,209
in a very different way,
992
00:41:56,252 --> 00:41:58,428
in a way that we didn't
commonly talk about.
993
00:41:58,472 --> 00:42:02,345
This idea of AI being useful
for other things
994
00:42:02,389 --> 00:42:04,086
other than entertainment.
995
00:42:04,130 --> 00:42:07,437
So being useful for, um,
helping the world
996
00:42:07,481 --> 00:42:10,310
and the potential of AI
to change the world.
997
00:42:10,353 --> 00:42:13,226
I just said to Demis,
"What is it you want to do?"
998
00:42:13,269 --> 00:42:14,532
And he said to me,
999
00:42:14,575 --> 00:42:16,795
"I want to be the person
that solves AI."
1000
00:42:22,670 --> 00:42:25,760
HASSABIS:
Peter offered me £1 million
1001
00:42:25,804 --> 00:42:27,675
to not go to university.
1002
00:42:30,199 --> 00:42:32,593
But I had a plan
from the beginning.
1003
00:42:32,637 --> 00:42:35,814
And my plan was always
to go to Cambridge.
1004
00:42:35,857 --> 00:42:36,902
I think a lot of
my schoolfriends
1005
00:42:36,945 --> 00:42:38,033
thought I was mad.
1006
00:42:38,077 --> 00:42:39,252
Why would you not...
1007
00:42:39,295 --> 00:42:40,688
I mean, £1 million,
that's a lot of money.
1008
00:42:40,732 --> 00:42:43,517
In the '90s,
that is a lot of money, right?
1009
00:42:43,561 --> 00:42:46,346
For a...
For a poor 17-year-old kid.
1010
00:42:46,389 --> 00:42:50,219
He's like this little seed
that's going to burst through,
1011
00:42:50,263 --> 00:42:53,658
and he's not going to be able
to do that at Bullfrog.
1012
00:42:56,443 --> 00:42:59,098
I had to drop him off
at the train station
1013
00:42:59,141 --> 00:43:02,580
and I can still see
that picture
1014
00:43:02,623 --> 00:43:07,019
of this little elven character
disappear down that tunnel.
1015
00:43:07,062 --> 00:43:09,804
That was an incredibly
sad moment.
1016
00:43:13,242 --> 00:43:14,635
HASSABIS:
I had this romantic ideal
1017
00:43:14,679 --> 00:43:16,942
of what Cambridge
would be like,
1018
00:43:16,985 --> 00:43:18,639
1,000 years of history,
1019
00:43:18,683 --> 00:43:21,033
walking the same streets
that Turing,
1020
00:43:21,076 --> 00:43:23,601
Newton and Crick had walked.
1021
00:43:23,644 --> 00:43:26,647
I wanted to explore
the edge of the universe.
1022
00:43:26,691 --> 00:43:27,735
[CHURCH BELLS TOLLING]
1023
00:43:29,084 --> 00:43:30,346
When I got to Cambridge,
1024
00:43:30,390 --> 00:43:32,653
I'd basically been working
my whole life.
1025
00:43:33,741 --> 00:43:35,090
Every single summer,
1026
00:43:35,134 --> 00:43:37,136
I was either playing chess
professionally,
1027
00:43:37,179 --> 00:43:39,704
or I was working,
doing an internship.
1028
00:43:39,747 --> 00:43:43,708
So I was, like, "Right,
I am gonna have fun now
1029
00:43:43,751 --> 00:43:46,711
"and explore what it means
to be a normal teenager."
1030
00:43:47,973 --> 00:43:50,192
[PEOPLE CHEERING, LAUGHING]
1031
00:43:50,236 --> 00:43:52,238
Come on! Go, boy, go!
1032
00:43:52,281 --> 00:43:54,022
TIM STEVENS: It was work hard
and play hard.
1033
00:43:54,066 --> 00:43:55,807
[ALL SINGING]
1034
00:43:55,850 --> 00:43:57,025
I first met Demis
1035
00:43:57,069 --> 00:43:59,201
because we both attended
Queens' College.
1036
00:44:00,115 --> 00:44:01,203
Our group of friends,
1037
00:44:01,247 --> 00:44:03,205
we'd often drink beer
in the bar,
1038
00:44:03,249 --> 00:44:04,946
play table football.
1039
00:44:04,990 --> 00:44:07,340
HASSABIS: In the bar,
I used to play speed chess,
1040
00:44:07,383 --> 00:44:09,255
pieces flying off the board,
1041
00:44:09,298 --> 00:44:11,083
you know, the whole game
in one minute.
1042
00:44:11,126 --> 00:44:12,301
Demis sat down opposite me.
1043
00:44:12,345 --> 00:44:13,563
And I looked at him
and I thought,
1044
00:44:13,607 --> 00:44:15,217
"I remember you
from when we were kids."
1045
00:44:15,261 --> 00:44:17,176
HASSABIS: I had actually been
in the same chess tournament
1046
00:44:17,219 --> 00:44:18,786
as Dave in Ipswich,
1047
00:44:18,830 --> 00:44:20,440
where I used to go and try
and raid his local chess club
1048
00:44:20,483 --> 00:44:22,703
to win a bit of prize money.
1049
00:44:22,747 --> 00:44:24,618
COPPIN: We were studying
computer science.
1050
00:44:24,662 --> 00:44:26,794
Some people,
who at the age of 17
1051
00:44:26,838 --> 00:44:28,404
would have come in and made
sure to tell everybody
1052
00:44:28,448 --> 00:44:29,492
everything about themselves.
1053
00:44:29,536 --> 00:44:30,972
"Hey, I worked at Bullfrog
1054
00:44:31,016 --> 00:44:33,018
"and built the world's
most successful video game."
1055
00:44:33,061 --> 00:44:34,715
But he wasn't like that
at all.
1056
00:44:34,759 --> 00:44:36,412
SILVER: At Cambridge,
Demis and myself
1057
00:44:36,456 --> 00:44:38,414
both had an interest
in computational neuroscience
1058
00:44:38,458 --> 00:44:40,242
and trying to understand
how computers and brains
1059
00:44:40,286 --> 00:44:42,636
intertwined
and linked together.
1060
00:44:42,680 --> 00:44:44,290
JOHN DAUGMAN:
Both David and Demis
1061
00:44:44,333 --> 00:44:46,422
came to me for supervisions.
1062
00:44:46,466 --> 00:44:49,774
It happens just by coincidence
that the year 1997,
1063
00:44:49,817 --> 00:44:51,645
their third and final year
at Cambridge,
1064
00:44:51,689 --> 00:44:55,301
was also the year when
the first chess grandmaster
1065
00:44:55,344 --> 00:44:56,781
was beaten by
a computer program.
1066
00:44:56,824 --> 00:44:58,260
[CAMERA SHUTTERS CLICKING]
1067
00:44:58,304 --> 00:45:00,088
NEWSCASTER: Round one today
of a chess match
1068
00:45:00,132 --> 00:45:03,701
between the ranking
world champion Garry Kasparov
1069
00:45:03,744 --> 00:45:06,007
and an opponent named
Deep Blue
1070
00:45:06,051 --> 00:45:10,490
to test to see if the human
brain can outwit a machine.
1071
00:45:10,533 --> 00:45:11,621
HASSABIS: I remember the drama
1072
00:45:11,665 --> 00:45:13,798
of Kasparov
losing the last match.
1073
00:45:13,841 --> 00:45:15,234
NEWSCASTER 2: Whoa!
1074
00:45:15,277 --> 00:45:17,192
Kasparov has resigned!
1075
00:45:17,236 --> 00:45:19,586
When Deep Blue
beat Garry Kasparov,
1076
00:45:19,629 --> 00:45:21,457
that was a real
watershed event.
1077
00:45:21,501 --> 00:45:23,155
HASSABIS:
My main memory of it was
1078
00:45:23,198 --> 00:45:25,331
I wasn't that impressed
with Deep Blue.
1079
00:45:25,374 --> 00:45:27,246
I was more impressed
with Kasparov's mind.
1080
00:45:27,289 --> 00:45:29,509
That he could play chess
to this level,
1081
00:45:29,552 --> 00:45:31,641
where he could compete
on an equal footing
1082
00:45:31,685 --> 00:45:33,252
with the brute of a machine,
1083
00:45:33,295 --> 00:45:35,167
but of course, Kasparov can do
1084
00:45:35,210 --> 00:45:36,951
everything else humans can do,
too.
1085
00:45:36,995 --> 00:45:38,257
It was a huge achievement.
1086
00:45:38,300 --> 00:45:39,388
But the truth
of the matter was,
1087
00:45:39,432 --> 00:45:40,868
Deep Blue
could only play chess.
1088
00:45:42,435 --> 00:45:44,524
What we would regard
as intelligence
1089
00:45:44,567 --> 00:45:46,874
was missing from that system.
1090
00:45:46,918 --> 00:45:49,834
This idea of generality
and also learning.
1091
00:45:53,751 --> 00:45:55,404
Cambridge was amazing,
because of course, you know,
1092
00:45:55,448 --> 00:45:56,666
you're mixing with people
1093
00:45:56,710 --> 00:45:58,233
who are studying
many different subjects.
1094
00:45:58,277 --> 00:46:01,410
SILVER: There were scientists,
philosophers, artists...
1095
00:46:01,454 --> 00:46:04,457
STEVENS: ...geologists,
biologists, ecologists.
1096
00:46:04,500 --> 00:46:07,416
You know, everybody is talking
about everything all the time.
1097
00:46:07,460 --> 00:46:10,768
I was obsessed with
the protein folding problem.
1098
00:46:10,811 --> 00:46:13,248
HASSABIS: Tim Stevens used
to talk obsessively,
1099
00:46:13,292 --> 00:46:15,381
almost like religiously
about this problem,
1100
00:46:15,424 --> 00:46:17,165
protein folding problem.
1101
00:46:17,209 --> 00:46:18,863
STEVENS:
Proteins are, you know,
1102
00:46:18,906 --> 00:46:22,083
one of the most beautiful and
elegant things about biology.
1103
00:46:22,127 --> 00:46:24,738
They are the machines of life.
1104
00:46:24,782 --> 00:46:27,001
They build everything,
they control everything,
1105
00:46:27,045 --> 00:46:29,569
they're why biology works.
1106
00:46:29,612 --> 00:46:32,659
Proteins are made from strings
of amino acids
1107
00:46:32,702 --> 00:46:37,055
that fold up to create
a protein structure.
1108
00:46:37,098 --> 00:46:39,884
If we can predict
the structure of proteins
1109
00:46:39,927 --> 00:46:43,104
from just their amino acid
sequences,
1110
00:46:43,148 --> 00:46:46,107
then a new protein
to cure cancer
1111
00:46:46,151 --> 00:46:49,415
or break down plastic
to help the environment
1112
00:46:49,458 --> 00:46:50,808
is definitely something
1113
00:46:50,851 --> 00:46:52,940
that you could begin
to think about.
1114
00:46:53,941 --> 00:46:55,029
I kind of thought,
1115
00:46:55,073 --> 00:46:58,293
"Well, is a human being
clever enough
1116
00:46:58,337 --> 00:46:59,991
"to actually fold a protein?"
1117
00:47:00,034 --> 00:47:02,080
We can't work it out.
1118
00:47:02,123 --> 00:47:04,082
JOHN MOULT: Since the 1960s,
1119
00:47:04,125 --> 00:47:05,953
we thought that in principle,
1120
00:47:05,997 --> 00:47:08,913
if I know what the amino acid
sequence of a protein is,
1121
00:47:08,956 --> 00:47:11,437
I should be able to compute
what the structure's like.
1122
00:47:11,480 --> 00:47:13,700
So, if you could
just press a button,
1123
00:47:13,743 --> 00:47:16,311
and they'd all come
popping out, that would be...
1124
00:47:16,355 --> 00:47:18,009
that would have some impact.
1125
00:47:20,272 --> 00:47:21,577
HASSABIS: It stuck in my mind.
1126
00:47:21,621 --> 00:47:23,405
"Oh, this is
a very interesting problem."
1127
00:47:23,449 --> 00:47:26,844
And it felt to me
like it would be solvable.
1128
00:47:26,887 --> 00:47:29,934
But I thought
it would need AI to do it.
1129
00:47:31,500 --> 00:47:34,025
If we could just solve
protein folding,
1130
00:47:34,068 --> 00:47:35,635
it could change the world.
1131
00:47:50,868 --> 00:47:52,826
HASSABIS: Ever since
I was a student at Cambridge,
1132
00:47:54,001 --> 00:47:55,611
I've never
stopped thinking about
1133
00:47:55,655 --> 00:47:57,135
the protein folding problem.
1134
00:47:59,789 --> 00:48:02,792
If you were
to solve protein folding,
1135
00:48:02,836 --> 00:48:05,665
then the potential
to help solve problems like
1136
00:48:05,708 --> 00:48:09,669
Alzheimer's, dementia
and drug discovery is huge.
1137
00:48:09,712 --> 00:48:11,671
Solving disease is probably
1138
00:48:11,714 --> 00:48:13,325
the most major impact
we could have.
1139
00:48:13,368 --> 00:48:14,587
[CLICKS MOUSE]
1140
00:48:15,457 --> 00:48:16,676
Thousands of very smart people
1141
00:48:16,719 --> 00:48:18,765
have tried
to solve protein folding.
1142
00:48:18,808 --> 00:48:20,985
I just think now
is the right time
1143
00:48:21,028 --> 00:48:22,508
for AI to crack it.
1144
00:48:22,551 --> 00:48:24,292
[THRILLING MUSIC PLAYING]
1145
00:48:24,336 --> 00:48:26,512
[INDISTINCT CONVERSATION]
1146
00:48:26,555 --> 00:48:28,383
RICHARD EVANS: We needed
a reasonable way
1147
00:48:28,427 --> 00:48:29,515
to apply machine learning
1148
00:48:29,558 --> 00:48:30,516
to the protein folding
problem.
1149
00:48:30,559 --> 00:48:32,387
[CLICKING MOUSE]
1150
00:48:32,431 --> 00:48:35,173
We came across
this Foldit game.
1151
00:48:35,216 --> 00:48:38,785
The goal is to move around
this 3D model of a protein
1152
00:48:38,828 --> 00:48:41,701
and you get a score
every time you move it.
1153
00:48:41,744 --> 00:48:43,050
The more accurate
you make these structures,
1154
00:48:43,094 --> 00:48:45,531
the more useful
they will be to biologists.
1155
00:48:46,227 --> 00:48:47,489
I spent a few days
1156
00:48:47,533 --> 00:48:48,838
just kind of seeing
how well we could do.
1157
00:48:48,882 --> 00:48:50,623
[GAME DINGING]
1158
00:48:50,666 --> 00:48:52,407
We did reasonably well.
1159
00:48:52,451 --> 00:48:53,843
But even if you were
1160
00:48:53,887 --> 00:48:55,280
the world's
best Foldit player,
1161
00:48:55,323 --> 00:48:57,499
you wouldn't
solve protein folding.
1162
00:48:57,543 --> 00:48:59,501
That's why we had
to move beyond the game.
1163
00:48:59,545 --> 00:49:00,720
HASSABIS: Games
are always just
1164
00:49:00,763 --> 00:49:03,723
the proving ground
for our algorithms.
1165
00:49:03,766 --> 00:49:07,727
The ultimate goal was not just
to crack Go and StarCraft.
1166
00:49:07,770 --> 00:49:10,034
It was to crack
real-world challenges.
1167
00:49:10,904 --> 00:49:13,037
[THRILLING MUSIC CONTINUES]
1168
00:49:16,127 --> 00:49:18,520
JOHN JUMPER: I remember
hearing this rumor
1169
00:49:18,564 --> 00:49:21,175
that Demis was
getting into proteins.
1170
00:49:21,219 --> 00:49:23,743
I talked to some people
at DeepMind and I would ask,
1171
00:49:23,786 --> 00:49:25,049
"So are you doing
protein folding?"
1172
00:49:25,092 --> 00:49:26,920
And they would
artfully change the subject.
1173
00:49:26,964 --> 00:49:30,097
And when that happened twice,
I pretty much figured it out.
1174
00:49:30,141 --> 00:49:32,839
So I thought
I should submit a resume.
1175
00:49:32,882 --> 00:49:35,668
HASSABIS: All right, everyone,
welcome to DeepMind.
1176
00:49:35,711 --> 00:49:37,626
I know some of you,
this may be your first week,
1177
00:49:37,670 --> 00:49:39,193
but I hope you all set...
1178
00:49:39,237 --> 00:49:40,890
JUMPER: The really appealing
part for me about the job
1179
00:49:40,934 --> 00:49:42,675
was this, like,
sense of connection
1180
00:49:42,718 --> 00:49:44,503
to the larger purpose.
1181
00:49:44,546 --> 00:49:45,591
HASSABIS: If we can crack
1182
00:49:45,634 --> 00:49:48,028
some fundamental problems
in science,
1183
00:49:48,072 --> 00:49:49,160
many other people
1184
00:49:49,203 --> 00:49:50,900
and other companies
and labs and so on
1185
00:49:50,944 --> 00:49:52,467
could build
on top of our work.
1186
00:49:52,511 --> 00:49:53,816
This is your chance now
1187
00:49:53,860 --> 00:49:55,818
to add your chapter
to this story.
1188
00:49:55,862 --> 00:49:57,298
JUMPER: When I arrived,
1189
00:49:57,342 --> 00:49:59,605
I was definitely[CHUCKLES]
quite a bit nervous.
1190
00:49:59,648 --> 00:50:00,736
I'm still trying to keep...
1191
00:50:00,780 --> 00:50:02,912
I haven't taken
any biology courses.
1192
00:50:02,956 --> 00:50:05,393
We haven't spent
years of our lives
1193
00:50:05,437 --> 00:50:07,917
looking at these structures
and understanding them.
1194
00:50:07,961 --> 00:50:09,963
We are just going off the data
1195
00:50:10,007 --> 00:50:11,269
and our machine learning
models.
1196
00:50:12,705 --> 00:50:13,836
JUMPER: In machine learning,
1197
00:50:13,880 --> 00:50:15,795
you train a network
like flashcards.
1198
00:50:15,838 --> 00:50:18,798
Here's the question.
Here's the answer.
1199
00:50:18,841 --> 00:50:20,756
Here's the question.
Here's the answer.
1200
00:50:20,800 --> 00:50:22,323
But in protein folding,
1201
00:50:22,367 --> 00:50:25,457
we're not doing the kind
of standard task at DeepMind
1202
00:50:25,500 --> 00:50:28,155
where you have unlimited data.
1203
00:50:28,199 --> 00:50:30,766
Your job is to get better
at chess or Go
1204
00:50:30,810 --> 00:50:32,986
and you can play
as many games of chess or Go
1205
00:50:33,030 --> 00:50:34,814
as your computers will allow.
1206
00:50:35,510 --> 00:50:36,772
With proteins,
1207
00:50:36,816 --> 00:50:39,732
we're sitting on
a very thick size of data
1208
00:50:39,775 --> 00:50:41,995
that's been determined
by a half century
1209
00:50:42,039 --> 00:50:46,478
of time-consuming experimental
methods in laboratories.
1210
00:50:46,521 --> 00:50:49,698
These painstaking methods
can take months or years
1211
00:50:49,742 --> 00:50:52,353
to determine
a single protein structure,
1212
00:50:52,397 --> 00:50:55,791
and sometimes, a structure
can never be determined.
1213
00:50:55,835 --> 00:50:57,271
[TYPING]
1214
00:50:57,315 --> 00:51:00,274
That's why we're working
with such small datasets
1215
00:51:00,318 --> 00:51:02,233
to train our algorithms.
1216
00:51:02,276 --> 00:51:04,365
EWAN BIRNEY: When DeepMind
started to explore
1217
00:51:04,409 --> 00:51:05,975
the folding problem,
1218
00:51:06,019 --> 00:51:07,977
they were talking to us about
which datasets they were using
1219
00:51:08,021 --> 00:51:09,892
and what would be
the possibilities
1220
00:51:09,936 --> 00:51:11,633
if they did
solve this problem.
1221
00:51:12,373 --> 00:51:13,940
Many people have tried,
1222
00:51:13,983 --> 00:51:16,638
and yet no one on the planet
has solved protein folding.
1223
00:51:16,682 --> 00:51:18,205
[CHUCKLES]
I did think to myself,
1224
00:51:18,249 --> 00:51:19,946
"Well, you know, good luck."
1225
00:51:19,989 --> 00:51:22,731
JUMPER: If we can solve
the protein folding problem,
1226
00:51:22,775 --> 00:51:25,691
it would have an incredible
kind of medical relevance.
1227
00:51:25,734 --> 00:51:27,736
HASSABIS:
This is the cycle of science.
1228
00:51:27,780 --> 00:51:30,043
You do a huge amount
of exploration,
1229
00:51:30,087 --> 00:51:31,914
and then you go
into exploitation mode,
1230
00:51:31,958 --> 00:51:33,568
and you focus and you see
1231
00:51:33,612 --> 00:51:35,353
how good
are those ideas, really?
1232
00:51:35,396 --> 00:51:36,528
And there's nothing better
1233
00:51:36,571 --> 00:51:38,095
than external competition
for that.
1234
00:51:39,835 --> 00:51:43,056
So we decided
to enter CASP competition.
1235
00:51:43,100 --> 00:51:47,234
CASP, we started
to try and speed up
1236
00:51:47,278 --> 00:51:49,802
the solution to
the protein folding problem.
1237
00:51:49,845 --> 00:51:52,065
CASP is when we say,
1238
00:51:52,109 --> 00:51:54,372
"Look, DeepMind
is doing protein folding,
1239
00:51:54,415 --> 00:51:55,590
"this is how good we are,
1240
00:51:55,634 --> 00:51:57,592
"and maybe it's better
than everybody else.
1241
00:51:57,636 --> 00:51:58,680
"Maybe it isn't."
1242
00:51:58,724 --> 00:52:00,073
CASP is a bit like
1243
00:52:00,117 --> 00:52:02,075
the Olympic Games
of protein folding.
1244
00:52:03,642 --> 00:52:06,035
CASP is
a community-wide assessment
1245
00:52:06,079 --> 00:52:08,125
that's held every two years.
1246
00:52:09,561 --> 00:52:10,866
Teams are given
1247
00:52:10,910 --> 00:52:14,261
the amino acid sequences
of about 100 proteins,
1248
00:52:14,305 --> 00:52:17,525
and then they try
to solve this folding problem
1249
00:52:17,569 --> 00:52:20,833
using computational methods.
1250
00:52:20,876 --> 00:52:23,401
These proteins have
already been determined
1251
00:52:23,444 --> 00:52:25,968
by experiments
in a laboratory,
1252
00:52:26,012 --> 00:52:29,276
but have not yet
been revealed publicly.
1253
00:52:29,320 --> 00:52:30,756
And these known structures
1254
00:52:30,799 --> 00:52:33,280
represent the gold standard
against which
1255
00:52:33,324 --> 00:52:36,936
all the computational
predictions will be compared.
1256
00:52:37,937 --> 00:52:39,330
MOULT: We've got a score
1257
00:52:39,373 --> 00:52:42,159
that measures the accuracy
of the predictions.
1258
00:52:42,202 --> 00:52:44,726
And you would expect
a score of over 90
1259
00:52:44,770 --> 00:52:47,425
to be a solution to
the protein folding problem.
1260
00:52:47,468 --> 00:52:48,513
[INDISTINCT CHATTER]
1261
00:52:48,556 --> 00:52:50,036
MAN: Welcome, everyone,
1262
00:52:50,079 --> 00:52:52,038
to our first, uh, semifinals
in the winners' bracket.
1263
00:52:52,081 --> 00:52:54,954
Nick and John
versus Demis and Frank.
1264
00:52:54,997 --> 00:52:57,217
Please join us, come around.
This will be an intense match.
1265
00:52:57,261 --> 00:52:59,306
STEVENS:
When I learned that Demis was
1266
00:52:59,350 --> 00:53:02,048
going to tackle
the protein folding issue,
1267
00:53:02,091 --> 00:53:04,790
um, I wasn't at all surprised.
1268
00:53:04,833 --> 00:53:06,792
It's very typical of Demis.
1269
00:53:06,835 --> 00:53:08,924
You know,
he loves competition.
1270
00:53:08,968 --> 00:53:10,143
And that's the end
1271
00:53:10,187 --> 00:53:12,972
- of the first game, 10-7.
- [ALL CHEERING]
1272
00:53:13,015 --> 00:53:14,103
HASSABIS:
The aim for CASP would be
1273
00:53:14,147 --> 00:53:15,975
to not just
win the competition,
1274
00:53:16,018 --> 00:53:19,892
but sort of, um,
retire the need for it.
1275
00:53:19,935 --> 00:53:23,417
So, 20 targets total
have been released by CASP.
1276
00:53:23,461 --> 00:53:24,810
JUMPER: We were thinking maybe
1277
00:53:24,853 --> 00:53:26,899
throw in the standard
kind of machine learning
1278
00:53:26,942 --> 00:53:28,814
and see how far
that could take us.
1279
00:53:28,857 --> 00:53:30,729
Instead of having a couple
of days on an experiment,
1280
00:53:30,772 --> 00:53:33,558
we can turn around
five experiments a day.
1281
00:53:33,601 --> 00:53:35,342
Great. Well done, everyone.
1282
00:53:35,386 --> 00:53:36,735
[TYPING]
1283
00:53:36,778 --> 00:53:38,693
Can you show me the real one
instead of ours?
1284
00:53:38,737 --> 00:53:39,781
MAN 1: The true answer is
1285
00:53:39,825 --> 00:53:42,044
supposed to look
something like that.
1286
00:53:42,088 --> 00:53:45,047
MAN 2: It's a lot more
cylindrical than I thought.
1287
00:53:45,091 --> 00:53:47,398
JUMPER: The results
were not very good.
1288
00:53:47,441 --> 00:53:48,703
Okay.
1289
00:53:48,747 --> 00:53:49,835
JUMPER: We throw
all the obvious ideas to it
1290
00:53:49,878 --> 00:53:51,793
and the problem laughs at you.
1291
00:53:52,881 --> 00:53:54,361
This makes no sense.
1292
00:53:54,405 --> 00:53:56,015
EVANS: We thought
we could just throw
1293
00:53:56,058 --> 00:53:58,322
some of our best algorithms
at the problem.
1294
00:53:59,366 --> 00:54:00,976
We were slightly naive.
1295
00:54:01,020 --> 00:54:02,282
JUMPER:
We should be learning this,
1296
00:54:02,326 --> 00:54:04,284
you know,
in the blink of an eye.
1297
00:54:05,372 --> 00:54:06,982
The thing
I'm worried about is,
1298
00:54:07,026 --> 00:54:08,375
we take the field from
1299
00:54:08,419 --> 00:54:10,899
really bad answers
to moderately bad answers.
1300
00:54:10,943 --> 00:54:13,946
I feel like we need
some sort of new technology
1301
00:54:13,989 --> 00:54:15,164
for moving around
these things.
1302
00:54:15,208 --> 00:54:17,341
[THRILLING MUSIC CONTINUES]
1303
00:54:20,431 --> 00:54:22,215
HASSABIS: With only
a week left of CASP,
1304
00:54:22,259 --> 00:54:24,348
it's now a sprint
to get it deployed.
1305
00:54:24,391 --> 00:54:25,349
[MUSIC FADES]
1306
00:54:26,654 --> 00:54:28,090
You've done your best.
1307
00:54:28,134 --> 00:54:29,875
Then there's
nothing more you can do
1308
00:54:29,918 --> 00:54:32,399
but wait for CASP
to deliver the results.
1309
00:54:32,443 --> 00:54:34,401
[HOPEFUL MUSIC PLAYING]
1310
00:54:52,593 --> 00:54:53,725
This famous thing of Einstein,
1311
00:54:53,768 --> 00:54:55,030
the last couple of years
of his life,
1312
00:54:55,074 --> 00:54:57,381
when he was here,
he overlapped with Kurt Godel
1313
00:54:57,424 --> 00:54:59,774
and he said one of the reasons
he still comes in to work
1314
00:54:59,818 --> 00:55:01,646
is so that
he gets to walk home
1315
00:55:01,689 --> 00:55:03,517
and discuss things with Godel.
1316
00:55:03,561 --> 00:55:05,911
It's a pretty big compliment
for Kurt Godel,
1317
00:55:05,954 --> 00:55:07,478
shows you how amazing he was.
1318
00:55:09,131 --> 00:55:10,568
MAN: The Institute
for Advanced Study
1319
00:55:10,611 --> 00:55:12,918
was formed in 1933.
1320
00:55:12,961 --> 00:55:14,223
In the early years,
1321
00:55:14,267 --> 00:55:16,487
the intense scientific
atmosphere attracted
1322
00:55:16,530 --> 00:55:19,359
some of the most brilliant
mathematicians and physicists
1323
00:55:19,403 --> 00:55:22,536
ever concentrated
in a single place and time.
1324
00:55:22,580 --> 00:55:24,582
HASSABIS: The founding
principle of this place,
1325
00:55:24,625 --> 00:55:28,020
it's the idea of unfettered
intellectual pursuits,
1326
00:55:28,063 --> 00:55:30,152
even if you don't know
what you're exploring.
1327
00:55:30,196 --> 00:55:32,154
Will result
in some cool things,
1328
00:55:32,198 --> 00:55:34,896
and sometimes that then
ends up being useful,
1329
00:55:34,940 --> 00:55:36,376
which, of course,
1330
00:55:36,420 --> 00:55:37,986
is partially what I've been
trying to do at DeepMind.
1331
00:55:38,030 --> 00:55:39,988
How many big breakthroughs
do you think are required
1332
00:55:40,032 --> 00:55:41,686
to get all the way to AGI?
1333
00:55:41,729 --> 00:55:43,078
And, you know,
I estimate maybe
1334
00:55:43,122 --> 00:55:44,341
there's about
a dozen of those.
1335
00:55:44,384 --> 00:55:46,125
You know, I hope
it's within my lifetime.
1336
00:55:46,168 --> 00:55:47,605
- Yes, okay.
- HASSABIS: But then,
1337
00:55:47,648 --> 00:55:49,171
all scientists
hope that, right?
1338
00:55:49,215 --> 00:55:51,130
EMCEE: Demis has
many accolades.
1339
00:55:51,173 --> 00:55:54,002
He was elected Fellow to
the Royal Society last year.
1340
00:55:54,046 --> 00:55:55,961
He is also a Fellow
of Royal Society of Arts.
1341
00:55:56,004 --> 00:55:57,528
A big hand for Demis Hassabis.
1342
00:56:02,968 --> 00:56:04,186
[MUSIC FADES]
1343
00:56:04,230 --> 00:56:05,927
HASSABIS: My dream
has always been to try
1344
00:56:05,971 --> 00:56:08,103
and make
AI-assisted science possible.
1345
00:56:08,147 --> 00:56:09,235
And what I think is
1346
00:56:09,278 --> 00:56:11,150
our most exciting project,
last year,
1347
00:56:11,193 --> 00:56:13,152
which is our work
in protein folding.
1348
00:56:13,195 --> 00:56:15,328
Uh, and we call this system
AlphaFold.
1349
00:56:15,372 --> 00:56:18,331
We entered it into CASP
and our system, uh,
1350
00:56:18,375 --> 00:56:20,507
was the most accurate,
uh, predicting structures
1351
00:56:20,551 --> 00:56:24,946
for 25 out of the 43 proteins
in the hardest category.
1352
00:56:24,990 --> 00:56:26,208
So we're state of the art,
1353
00:56:26,252 --> 00:56:27,514
but we still...
I have to make... Be clear,
1354
00:56:27,558 --> 00:56:28,559
we're still a long way from
1355
00:56:28,602 --> 00:56:30,474
solving the protein
folding problem.
1356
00:56:30,517 --> 00:56:31,866
We're working hard
on this, though,
1357
00:56:31,910 --> 00:56:33,738
and we're exploring
many other techniques.
1358
00:56:33,781 --> 00:56:35,479
[SOMBER MUSIC PLAYING]
1359
00:56:49,188 --> 00:56:50,232
Let's get started.
1360
00:56:50,276 --> 00:56:53,148
JUMPER: So kind of
a rapid debrief,
1361
00:56:53,192 --> 00:56:55,455
these are
our final rankings for CASP.
1362
00:56:56,500 --> 00:56:57,544
HASSABIS:
We beat the second team
1363
00:56:57,588 --> 00:57:00,155
in this competition
by nearly 50%,
1364
00:57:00,199 --> 00:57:01,592
but we've still got
a long way to go
1365
00:57:01,635 --> 00:57:04,333
before we've solved
the protein folding problem
1366
00:57:04,377 --> 00:57:07,032
in a sense that
a biologist could use it.
1367
00:57:07,075 --> 00:57:08,990
JUMPER: It is area of concern.
1368
00:57:11,602 --> 00:57:14,213
JANET THORNTON: The quality
of predictions varied
1369
00:57:14,256 --> 00:57:16,737
and they were no more useful
than the previous methods.
1370
00:57:16,781 --> 00:57:19,914
PAUL NURSE: AlphaFold didn't
produce good enough data
1371
00:57:19,958 --> 00:57:22,526
for it to be useful
in a practical way
1372
00:57:22,569 --> 00:57:24,005
to, say, somebody like me
1373
00:57:24,049 --> 00:57:28,227
investigating
my own biological problems.
1374
00:57:28,270 --> 00:57:30,316
JUMPER: That was kind of
a humbling moment
1375
00:57:30,359 --> 00:57:32,753
'cause we thought we'd worked
very hard and succeeded.
1376
00:57:32,797 --> 00:57:34,886
And what we'd found is
we were the best in the world
1377
00:57:34,929 --> 00:57:36,453
at a problem
the world's not good at.
1378
00:57:37,671 --> 00:57:38,933
We knew we sucked.
1379
00:57:38,977 --> 00:57:40,413
[INDISTINCT CHATTER]
1380
00:57:40,457 --> 00:57:42,328
JUMPER: It doesn't help
if you have the tallest ladder
1381
00:57:42,371 --> 00:57:44,635
when you're going to the moon.
1382
00:57:44,678 --> 00:57:47,115
HASSABIS: The opinion of quite
a few people on the team,
1383
00:57:47,159 --> 00:57:51,468
that this is sort of
a fool's errand in some ways.
1384
00:57:51,511 --> 00:57:54,079
And I might have been wrong
with protein folding.
1385
00:57:54,122 --> 00:57:55,559
Maybe it's too hard still
1386
00:57:55,602 --> 00:57:58,431
for where we're at
generally with AI.
1387
00:57:58,475 --> 00:58:01,173
If you want to do
biological research,
1388
00:58:01,216 --> 00:58:03,044
you have to be
prepared to fail
1389
00:58:03,088 --> 00:58:06,570
because biology
is very complicated.
1390
00:58:06,613 --> 00:58:09,790
I've run a laboratory
for nearly 50 years,
1391
00:58:09,834 --> 00:58:11,096
and half my time,
1392
00:58:11,139 --> 00:58:12,619
I'm just
an amateur psychiatrist
1393
00:58:12,663 --> 00:58:18,103
to keep, um, my colleagues
cheerful when nothing works.
1394
00:58:18,146 --> 00:58:22,542
And quite a lot of the time
and I mean, 80, 90%,
1395
00:58:22,586 --> 00:58:24,413
it does not work.
1396
00:58:24,457 --> 00:58:26,720
If you are
at the forefront of science,
1397
00:58:26,764 --> 00:58:30,115
I can tell you,
you will fail a great deal.
1398
00:58:32,465 --> 00:58:33,466
[CLICKS MOUSE]
1399
00:58:35,163 --> 00:58:37,165
HASSABIS:
I just felt disappointed.
1400
00:58:38,689 --> 00:58:41,605
Lesson I learned is that
ambition is a good thing,
1401
00:58:41,648 --> 00:58:43,694
but you need
to get the timing right.
1402
00:58:43,737 --> 00:58:46,784
There's no point being
50 years ahead of your time.
1403
00:58:46,827 --> 00:58:48,133
You will never survive
1404
00:58:48,176 --> 00:58:49,917
fifty years of
that kind of endeavor
1405
00:58:49,961 --> 00:58:51,963
before it yields something.
1406
00:58:52,006 --> 00:58:53,268
You'll literally die trying.
1407
00:58:53,312 --> 00:58:55,096
[TENSE MUSIC PLAYING]
1408
00:59:08,936 --> 00:59:11,286
CUKIER:
When we talk about AGI,
1409
00:59:11,330 --> 00:59:14,376
the holy grail
of artificial intelligence,
1410
00:59:14,420 --> 00:59:15,508
it becomes really difficult
1411
00:59:15,552 --> 00:59:17,815
to know what
we're even talking about.
1412
00:59:17,858 --> 00:59:19,643
HASSABIS: Which bits
are we gonna see today?
1413
00:59:19,686 --> 00:59:21,645
MAN: We're going
to start in the garden.
1414
00:59:21,688 --> 00:59:23,081
[MACHINE BEEPS]
1415
00:59:23,124 --> 00:59:25,649
This is the garden looking
from the observation area.
1416
00:59:25,692 --> 00:59:27,433
Research scientists
and engineers
1417
00:59:27,476 --> 00:59:30,871
can analyze and collaborate
and evaluate
1418
00:59:30,915 --> 00:59:33,004
what's going on in real time.
1419
00:59:33,047 --> 00:59:34,614
CUKIER: So in the 1800s,
1420
00:59:34,658 --> 00:59:37,008
we'd think of things like
television and the submarine
1421
00:59:37,051 --> 00:59:38,139
or a rocket ship to the moon
1422
00:59:38,183 --> 00:59:40,228
and say these things
are impossible.
1423
00:59:40,272 --> 00:59:41,490
Yet Jules Verne
wrote about them and,
1424
00:59:41,534 --> 00:59:44,406
a century and a half later,
they happened.
1425
00:59:44,450 --> 00:59:45,451
HASSABIS: We'll be
experimenting
1426
00:59:45,494 --> 00:59:47,888
on civilizations really,
1427
00:59:47,932 --> 00:59:50,587
civilizations of AI agents.
1428
00:59:50,630 --> 00:59:52,719
Once the experiments
start going,
1429
00:59:52,763 --> 00:59:54,242
it's going to be
the most exciting thing ever.
1430
00:59:54,286 --> 00:59:56,984
- So how will we get sleep?
- [MAN LAUGHS]
1431
00:59:57,028 --> 00:59:58,682
I won't be able to sleep.
1432
00:59:58,725 --> 01:00:00,684
LEGG: Full AGI
will be able to do
1433
01:00:00,727 --> 01:00:03,861
any cognitive task
a person can do.
1434
01:00:03,904 --> 01:00:08,387
It will be at a scale,
potentially, far beyond that.
1435
01:00:08,430 --> 01:00:10,302
STUART RUSSELL:
It's really impossible for us
1436
01:00:10,345 --> 01:00:14,828
to imagine the outputs
of a superintelligent entity.
1437
01:00:14,872 --> 01:00:18,963
It's like asking a gorilla
to imagine, you know,
1438
01:00:19,006 --> 01:00:20,181
what Einstein does
1439
01:00:20,225 --> 01:00:23,402
when he produces
the theory of relativity.
1440
01:00:23,445 --> 01:00:25,491
LEGG: People often ask me
these questions like,
1441
01:00:25,534 --> 01:00:29,495
"What happens if you're wrong,
and AGI is quite far away?"
1442
01:00:29,538 --> 01:00:31,453
And I'm like,
I never worry about that.
1443
01:00:31,497 --> 01:00:33,847
I actually
worry about the reverse.
1444
01:00:33,891 --> 01:00:37,242
I actually worry
that it's coming faster
1445
01:00:37,285 --> 01:00:39,723
than we can
really prepare for.
1446
01:00:39,766 --> 01:00:42,029
[ROBOTIC ARM WHIRRING]
1447
01:00:42,073 --> 01:00:45,859
HADSELL: It really feels
like we're in a race to AGI.
1448
01:00:45,903 --> 01:00:49,907
The prototypes and the models
that we are developing now
1449
01:00:49,950 --> 01:00:51,822
are actually transforming
1450
01:00:51,865 --> 01:00:54,215
the space of what
we know about intelligence.
1451
01:00:54,259 --> 01:00:57,305
[WHIRRING]
1452
01:00:57,349 --> 01:00:58,785
LEGG: Recently,
we've had agents
1453
01:00:58,829 --> 01:01:00,047
that are powerful enough
1454
01:01:00,091 --> 01:01:03,442
to actually start
playing games in teams,
1455
01:01:03,485 --> 01:01:06,140
then competing
against other teams.
1456
01:01:06,184 --> 01:01:08,795
We're seeing
co-operative social dynamics
1457
01:01:08,839 --> 01:01:10,492
coming out of agents
1458
01:01:10,536 --> 01:01:13,321
where we haven't
pre-programmed in
1459
01:01:13,365 --> 01:01:15,584
any of these sorts
of dynamics.
1460
01:01:15,628 --> 01:01:19,240
It's completely learned
from their own experiences.
1461
01:01:20,807 --> 01:01:23,288
When we started,
we thought we were
1462
01:01:23,331 --> 01:01:25,725
out to build
an intelligence system
1463
01:01:25,769 --> 01:01:28,336
and convince the world
that we'd done it.
1464
01:01:28,380 --> 01:01:29,947
We're now starting
to wonder whether
1465
01:01:29,990 --> 01:01:31,296
we're gonna build systems
1466
01:01:31,339 --> 01:01:32,906
that we're not convinced
are fully intelligent,
1467
01:01:32,950 --> 01:01:34,691
and we're trying to convince
the world that they're not.
1468
01:01:34,734 --> 01:01:35,779
[CHUCKLES]
1469
01:01:35,822 --> 01:01:36,780
[CELL PHONE DINGS]
1470
01:01:38,651 --> 01:01:40,000
Hi, Alpha.
1471
01:01:40,044 --> 01:01:41,523
ALPHA: Hello there.
1472
01:01:41,567 --> 01:01:43,917
LOVE: Where are we today?
1473
01:01:43,961 --> 01:01:46,659
You're at the Museum of
Modern Art in New York City.
1474
01:01:48,400 --> 01:01:53,013
Kind of.
Um, what painting is this?
1475
01:01:53,057 --> 01:01:55,494
This isThe Creation of Adam
by Michelangelo.
1476
01:01:55,537 --> 01:01:58,410
I don't think that painting
is in New York City.
1477
01:01:58,453 --> 01:02:01,543
You are right.
It's in the Vatican City.
1478
01:02:01,587 --> 01:02:02,806
LOVE: Do you think
that's where we are?
1479
01:02:04,677 --> 01:02:05,809
ALPHA: No.
1480
01:02:05,852 --> 01:02:07,593
LOVE: Then how
am I looking at this?
1481
01:02:10,509 --> 01:02:11,640
ALPHA: You're looking at
1482
01:02:11,684 --> 01:02:12,946
a digital copy
of the painting.
1483
01:02:12,990 --> 01:02:13,991
Yes.
1484
01:02:15,035 --> 01:02:16,341
MAN 1: Who lives
in this house?
1485
01:02:16,384 --> 01:02:19,170
[EXCITING MUSIC PLAYING]
1486
01:02:19,213 --> 01:02:20,345
ALPHA: An astronaut.
1487
01:02:26,046 --> 01:02:28,005
What's this astronaut's name?
1488
01:02:28,048 --> 01:02:29,397
ALPHA: His name is Mark.
1489
01:02:29,441 --> 01:02:32,052
This is a famous equation
by Albert Einstein.
1490
01:02:32,096 --> 01:02:35,577
It shows that energy and mass
are equivalent.
1491
01:02:35,621 --> 01:02:37,666
How would you explain it
to a five year old?
1492
01:02:40,931 --> 01:02:43,542
ALPHA: Well, imagine
you have a big rock,
1493
01:02:43,585 --> 01:02:45,326
and you want to know
how much it weighs.
1494
01:02:45,370 --> 01:02:47,589
MAN 2: Is Mark in danger?
1495
01:02:47,633 --> 01:02:49,722
ALPHA: No.
Mark is not in danger.
1496
01:02:49,766 --> 01:02:50,767
Why is that?
1497
01:02:54,422 --> 01:02:56,163
ALPHA: Because the dinosaur
is not real.
1498
01:02:56,207 --> 01:02:57,295
[MEN CHUCKLING]
1499
01:02:57,338 --> 01:02:58,470
LOVE: All right.
1500
01:02:58,513 --> 01:03:00,515
Why could you
not answer my question?
1501
01:03:00,559 --> 01:03:01,908
ALPHA: I think I couldn't
answer your question
1502
01:03:01,952 --> 01:03:04,041
because I don't know
enough about the subject.
1503
01:03:04,911 --> 01:03:06,130
Okay.
1504
01:03:06,173 --> 01:03:08,654
How could you learn
more about the subject?
1505
01:03:08,697 --> 01:03:09,960
ALPHA: I can learn more
about the subject
1506
01:03:10,003 --> 01:03:12,876
by reading books,
articles, and websites.
1507
01:03:12,919 --> 01:03:14,268
You can teach me.
1508
01:03:14,312 --> 01:03:15,835
HASSABIS: My conclusion is,
1509
01:03:15,879 --> 01:03:17,619
if you now think about
what we're doing,
1510
01:03:17,663 --> 01:03:20,057
is learning from all humans,
1511
01:03:20,100 --> 01:03:22,581
all their knowledge at once
put on the Internet,
1512
01:03:22,624 --> 01:03:24,931
you would actually
know a lot about the world.
1513
01:03:24,975 --> 01:03:27,716
Like a significant portion
of everything humans can do.
1514
01:03:27,760 --> 01:03:29,631
And now,
I think it's more like,
1515
01:03:29,675 --> 01:03:30,937
"Well, it might just work."
1516
01:03:30,981 --> 01:03:33,113
This is a big moment.
1517
01:03:33,157 --> 01:03:34,636
Who is this?
1518
01:03:34,680 --> 01:03:36,551
ALPHA: This is God
reaching out to Adam.
1519
01:03:36,595 --> 01:03:37,988
RUSSELL: The advent of AGI
1520
01:03:38,031 --> 01:03:40,860
will divide human history
into two parts.
1521
01:03:40,904 --> 01:03:43,950
The part up to that point
and the part after that point.
1522
01:03:43,994 --> 01:03:47,258
LOVE: Why is he reaching out
to touch Adam?
1523
01:03:47,301 --> 01:03:48,912
ALPHA: God is
reaching out to touch Adam
1524
01:03:48,955 --> 01:03:50,217
to give him life.
1525
01:03:50,261 --> 01:03:52,219
RUSSELL:
It will give us a tool
1526
01:03:52,263 --> 01:03:57,529
that can completely reinvent
our entire civilization.
1527
01:03:57,572 --> 01:03:59,400
LOVE: What does
this painting mean to you?
1528
01:04:01,750 --> 01:04:03,752
ALPHA: The painting
means a lot to me.
1529
01:04:03,796 --> 01:04:04,797
Okay. Like what?
1530
01:04:09,062 --> 01:04:10,281
[MUSIC FADES]
1531
01:04:10,324 --> 01:04:11,456
ALPHA: I think
the painting is a reminder
1532
01:04:11,499 --> 01:04:12,674
that we are all
connected to each other
1533
01:04:12,718 --> 01:04:13,937
and that we are
all part of something
1534
01:04:13,980 --> 01:04:15,112
bigger than ourselves.
1535
01:04:16,461 --> 01:04:17,766
That's pretty nice.
1536
01:04:19,029 --> 01:04:21,379
LEGG: When you cross
that barrier of
1537
01:04:21,422 --> 01:04:23,947
"AGI might happen
one day in the future"
1538
01:04:23,990 --> 01:04:26,645
to "No, actually, this could
really happen in a time frame
1539
01:04:26,688 --> 01:04:28,690
"that is sort of, like,
on my watch, you know,"
1540
01:04:28,734 --> 01:04:30,475
something changes
in your thinking.
1541
01:04:30,518 --> 01:04:32,694
MAN: ...learned to orient
itself by looking...
1542
01:04:32,738 --> 01:04:35,045
HASSABIS: We have to be
careful with how we use it
1543
01:04:35,088 --> 01:04:37,177
and thoughtful about
how we deploy it.
1544
01:04:37,221 --> 01:04:39,788
[GRIPPING MUSIC BUILDING]
1545
01:04:39,832 --> 01:04:41,138
HASSABIS:
You'd have to consider
1546
01:04:41,181 --> 01:04:42,487
what's its top level goal.
1547
01:04:42,530 --> 01:04:45,011
If it's to keep humans happy,
1548
01:04:45,055 --> 01:04:48,928
which set of humans?
What does happiness mean?
1549
01:04:48,972 --> 01:04:52,018
A lot of our collective goals
are very tricky,
1550
01:04:52,062 --> 01:04:54,891
even for humans to figure out.
1551
01:04:54,934 --> 01:04:58,503
CUKIER: Technology always
embeds our values.
1552
01:04:58,546 --> 01:05:01,680
It's not just technical,
it's ethical as well.
1553
01:05:01,723 --> 01:05:02,899
So we've got
to be really cautious
1554
01:05:02,942 --> 01:05:04,291
about what
we're building into it.
1555
01:05:04,335 --> 01:05:06,076
MAN: We're trying to find
a single algorithm which...
1556
01:05:06,119 --> 01:05:07,816
SILVER: The reality is
that this is an algorithm
1557
01:05:07,860 --> 01:05:11,037
that has been created
by people, by us.
1558
01:05:11,081 --> 01:05:13,213
You know, what does it mean
to endow our agents
1559
01:05:13,257 --> 01:05:15,607
with the same kind of values
that we hold dear?
1560
01:05:15,650 --> 01:05:17,652
What is the purpose
of making these AI systems
1561
01:05:17,696 --> 01:05:19,045
appear so humanlike
1562
01:05:19,089 --> 01:05:20,742
so that they do
capture hearts and minds
1563
01:05:20,786 --> 01:05:21,961
because they're kind of
1564
01:05:22,005 --> 01:05:24,703
exploiting a human
vulnerability also?
1565
01:05:24,746 --> 01:05:26,531
The heart and mind
of these systems
1566
01:05:26,574 --> 01:05:28,054
are very much
human-generated data...
1567
01:05:28,098 --> 01:05:29,055
WOMAN: Mmm-hmm.
1568
01:05:29,099 --> 01:05:30,491
...for all the good
and the bad.
1569
01:05:30,535 --> 01:05:32,015
LEVI:
There is a parallel
1570
01:05:32,058 --> 01:05:34,017
between
the Industrial Revolution,
1571
01:05:34,060 --> 01:05:36,758
which was an incredible
moment of displacement
1572
01:05:36,802 --> 01:05:42,373
and the current technological
change created by AI.
1573
01:05:42,416 --> 01:05:43,722
[CHANTING] Pause AI!
1574
01:05:43,765 --> 01:05:45,724
LEVI: We have to think
about who's displaced
1575
01:05:45,767 --> 01:05:48,596
and how we're going
to support them.
1576
01:05:48,640 --> 01:05:50,076
This technology
is coming a lot sooner,
1577
01:05:50,120 --> 01:05:52,426
uh, than really
the world knows or kind of
1578
01:05:52,470 --> 01:05:55,908
even we 18, 24 months
ago thought.
1579
01:05:55,952 --> 01:05:57,257
So there's
a tremendous opportunity,
1580
01:05:57,301 --> 01:05:58,389
tremendous excitement,
1581
01:05:58,432 --> 01:06:00,391
but also
tremendous responsibility.
1582
01:06:00,434 --> 01:06:01,740
It's happening so fast.
1583
01:06:02,654 --> 01:06:04,003
How will we govern it?
1584
01:06:05,135 --> 01:06:06,223
How will we decide
1585
01:06:06,266 --> 01:06:08,181
what is okay
and what is not okay?
1586
01:06:08,225 --> 01:06:10,923
AI-generated images are
getting more sophisticated.
1587
01:06:10,967 --> 01:06:14,535
RUSSELL: The use of AI
for generating disinformation
1588
01:06:14,579 --> 01:06:17,016
and manipulating
human psychology
1589
01:06:17,060 --> 01:06:20,237
is only going to get
much, much worse.
1590
01:06:21,194 --> 01:06:22,587
LEGG: AGI is coming,
1591
01:06:22,630 --> 01:06:24,632
whether we do it here
at DeepMind or not.
1592
01:06:25,459 --> 01:06:26,765
CUKIER: It's gonna happen,
1593
01:06:26,808 --> 01:06:29,028
so we better create
institutions to protect us.
1594
01:06:29,072 --> 01:06:30,595
It's gonna require
global coordination.
1595
01:06:30,638 --> 01:06:32,727
And I worry that humanity is
1596
01:06:32,771 --> 01:06:35,382
increasingly getting worse
at that rather than better.
1597
01:06:35,426 --> 01:06:37,123
LEGG: We need
a lot more people
1598
01:06:37,167 --> 01:06:40,039
really taking this seriously
and thinking about this.
1599
01:06:40,083 --> 01:06:42,999
It's, yeah, it's serious.
It worries me.
1600
01:06:44,043 --> 01:06:45,871
It worries me. Yeah.
1601
01:06:45,914 --> 01:06:48,613
RUSSELL: If you received
an email saying
1602
01:06:48,656 --> 01:06:50,832
this superior
alien civilization
1603
01:06:50,876 --> 01:06:52,791
is going to arrive on Earth,
1604
01:06:52,834 --> 01:06:54,575
there would be
emergency meetings
1605
01:06:54,619 --> 01:06:56,273
of all the governments.
1606
01:06:56,316 --> 01:06:58,144
We would go into overdrive
1607
01:06:58,188 --> 01:07:00,103
trying to figure out
how to prepare.
1608
01:07:00,146 --> 01:07:01,626
- [MUSIC FADES]
- [BELL TOLLING FAINTLY]
1609
01:07:01,669 --> 01:07:03,976
The arrival of AGI will be
1610
01:07:04,020 --> 01:07:06,935
the most important moment
that we have ever faced.
1611
01:07:06,979 --> 01:07:09,155
[BELL CONTINUES
TOLLING FAINTLY]
1612
01:07:14,378 --> 01:07:17,555
HASSABIS: My dream
was that on the way to AGI,
1613
01:07:17,598 --> 01:07:20,688
we would create
revolutionary technologies
1614
01:07:20,732 --> 01:07:23,082
that would be
of use to humanity.
1615
01:07:23,126 --> 01:07:25,171
That's what I wanted
with AlphaFold.
1616
01:07:26,694 --> 01:07:28,653
I think
it's more important than ever
1617
01:07:28,696 --> 01:07:31,047
that we should solve
the protein folding problem.
1618
01:07:32,004 --> 01:07:34,224
This is gonna be really hard,
1619
01:07:34,267 --> 01:07:36,791
but I won't give up
until it's done.
1620
01:07:36,835 --> 01:07:37,879
You know,
we need to double down
1621
01:07:37,923 --> 01:07:40,317
and go as fast as possible
from here.
1622
01:07:40,360 --> 01:07:41,796
I think we've got
no time to lose.
1623
01:07:41,840 --> 01:07:45,757
So we are going to make
a protein folding strike team.
1624
01:07:45,800 --> 01:07:47,541
Team lead for the strike team
will be John.
1625
01:07:47,585 --> 01:07:48,673
Yeah, we've seen Alpha...
1626
01:07:48,716 --> 01:07:50,283
You know,
we're gonna try everything,
1627
01:07:50,327 --> 01:07:51,328
kitchen sink, the whole lot.
1628
01:07:52,198 --> 01:07:53,330
CASP14 is about
1629
01:07:53,373 --> 01:07:55,158
proving we can
solve the whole problem.
1630
01:07:56,333 --> 01:07:57,725
And I felt that to do that,
1631
01:07:57,769 --> 01:08:00,337
we would need to incorporate
some domain knowledge.
1632
01:08:00,380 --> 01:08:01,860
[EXCITING MUSIC PLAYING]
1633
01:08:01,903 --> 01:08:03,731
We had some
fantastic engineers on it,
1634
01:08:03,775 --> 01:08:05,733
but they were
not trained in biology.
1635
01:08:08,475 --> 01:08:10,260
KATHRYN TUNYASUVUNAKOOL:
As a computational biologist,
1636
01:08:10,303 --> 01:08:12,131
when I initially joined
the AlphaFold team,
1637
01:08:12,175 --> 01:08:14,220
I didn't immediately feel
confident about anything.
1638
01:08:14,264 --> 01:08:15,352
[CHUCKLES] You know,
1639
01:08:15,395 --> 01:08:17,223
whether we were
gonna be successful.
1640
01:08:17,267 --> 01:08:21,097
Biology is so
ridiculously complicated.
1641
01:08:21,140 --> 01:08:25,101
It just felt like this very
far-off mountain to climb.
1642
01:08:25,144 --> 01:08:26,754
MAN: I'm starting to play with
the underlying temperatures
1643
01:08:26,798 --> 01:08:27,973
to see if we can get...
1644
01:08:28,016 --> 01:08:29,148
As one of the few people
on the team
1645
01:08:29,192 --> 01:08:31,846
who's done work
in biology before,
1646
01:08:31,890 --> 01:08:34,849
you feel this huge sense
of responsibility.
1647
01:08:34,893 --> 01:08:36,112
"We're expecting you to do
1648
01:08:36,155 --> 01:08:37,678
"great things
on this strike team."
1649
01:08:37,722 --> 01:08:38,897
That's terrifying.
1650
01:08:40,464 --> 01:08:42,727
But one of the reasons
why I wanted to come here
1651
01:08:42,770 --> 01:08:45,556
was to do
something that matters.
1652
01:08:45,599 --> 01:08:48,472
This is the number
of missing things.
1653
01:08:48,515 --> 01:08:49,951
What about making use
1654
01:08:49,995 --> 01:08:52,563
of whatever understanding
you have of physics?
1655
01:08:52,606 --> 01:08:54,391
Using that
as a source of data?
1656
01:08:54,434 --> 01:08:55,479
But if it's systematic...
1657
01:08:55,522 --> 01:08:56,784
Then, that can't be
right, though.
1658
01:08:56,828 --> 01:08:58,308
If it's systematically wrong
in some weird way,
1659
01:08:58,351 --> 01:09:01,224
you might be learning that
systematically wrong physics.
1660
01:09:01,267 --> 01:09:02,355
The team is already
1661
01:09:02,399 --> 01:09:04,749
trying to think
of multiple ways that...
1662
01:09:04,792 --> 01:09:06,229
TUNYASUVUNAKOOL:
Biological relevance
1663
01:09:06,272 --> 01:09:07,795
is what we're going for.
1664
01:09:09,057 --> 01:09:11,364
So we rewrote
the whole data pipeline
1665
01:09:11,408 --> 01:09:13,279
that AlphaFold uses to learn.
1666
01:09:13,323 --> 01:09:15,586
HASSABIS: You can't
force the creative phase.
1667
01:09:15,629 --> 01:09:18,241
You have to give it space
for those flowers to bloom.
1668
01:09:19,242 --> 01:09:20,286
We won CASP.
1669
01:09:20,330 --> 01:09:22,070
Then it was
back to the drawing board
1670
01:09:22,114 --> 01:09:24,116
and like,
what are our new ideas?
1671
01:09:24,160 --> 01:09:26,945
Um, and then it's taken
a little while, I would say,
1672
01:09:26,988 --> 01:09:28,686
for them to get back
to where they were,
1673
01:09:28,729 --> 01:09:30,340
but with the new ideas.
1674
01:09:30,383 --> 01:09:31,515
And then now I think
1675
01:09:31,558 --> 01:09:33,952
we're seeing the benefits
of the new ideas.
1676
01:09:33,995 --> 01:09:35,736
They can go further, right?
1677
01:09:35,780 --> 01:09:38,130
So, um, that's a really
important moment.
1678
01:09:38,174 --> 01:09:40,959
I've seen that moment
so many times now,
1679
01:09:41,002 --> 01:09:42,613
but I know
what that means now.
1680
01:09:42,656 --> 01:09:44,484
And I know
this is the time now to press.
1681
01:09:44,528 --> 01:09:45,877
[EXCITING MUSIC CONTINUES]
1682
01:09:45,920 --> 01:09:48,009
JUMPER: Adding side-chains
improves direct folding.
1683
01:09:48,053 --> 01:09:49,663
That drove
a lot of the progress.
1684
01:09:49,707 --> 01:09:51,012
- We'll talk about that.
- Great.
1685
01:09:51,056 --> 01:09:54,799
The last four months,
we've made enormous gains.
1686
01:09:54,842 --> 01:09:56,453
EVANS: During CASP13,
1687
01:09:56,496 --> 01:09:59,499
it would take us a day or two
to fold one of the proteins,
1688
01:09:59,543 --> 01:10:01,762
and now we're folding, like,
1689
01:10:01,806 --> 01:10:03,938
hundreds of thousands
a second.
1690
01:10:03,982 --> 01:10:05,636
Yeah, it's just insane.
[CHUCKLES]
1691
01:10:05,679 --> 01:10:06,985
KAVUKCUOGLU: Now,
this is a model
1692
01:10:07,028 --> 01:10:09,901
that is
orders of magnitude faster,
1693
01:10:09,944 --> 01:10:12,251
while at the same time
being better.
1694
01:10:12,295 --> 01:10:13,644
We're getting
a lot of structures
1695
01:10:13,687 --> 01:10:15,254
into the high-accuracy regime.
1696
01:10:15,298 --> 01:10:17,517
We're rapidly improving
to a system
1697
01:10:17,561 --> 01:10:18,823
that is starting to really
1698
01:10:18,866 --> 01:10:20,477
get at the core and heart
of the problem.
1699
01:10:20,520 --> 01:10:21,695
HASSABIS: It's great work.
1700
01:10:21,739 --> 01:10:23,088
It looks like
we're in good shape.
1701
01:10:23,131 --> 01:10:26,222
So we got, what, six,
five weeks left? Six weeks?
1702
01:10:26,265 --> 01:10:29,616
So what's, uh... Is it...
You got enough compute power?
1703
01:10:29,660 --> 01:10:31,531
MAN: I... We could use more.
1704
01:10:31,575 --> 01:10:32,924
[ALL LAUGHING]
1705
01:10:32,967 --> 01:10:34,360
TUNYASUVUNAKOOL:
I was nervous about CASP
1706
01:10:34,404 --> 01:10:36,580
but as the system
is starting to come together,
1707
01:10:36,623 --> 01:10:37,972
I don't feel as nervous.
1708
01:10:38,016 --> 01:10:39,496
I feel like things
have, sort of,
1709
01:10:39,539 --> 01:10:41,193
come into perspective
recently,
1710
01:10:41,237 --> 01:10:44,240
and, you know,
it's gonna be fine.
1711
01:10:47,330 --> 01:10:48,853
NEWSCASTER: The Prime Minister
has announced
1712
01:10:48,896 --> 01:10:51,290
the most drastic limits
to our lives
1713
01:10:51,334 --> 01:10:53,858
the U.K. has ever seen
in living memory.
1714
01:10:53,901 --> 01:10:55,033
BORIS JOHNSON:
I must give the British people
1715
01:10:55,076 --> 01:10:56,904
a very simple instruction.
1716
01:10:56,948 --> 01:10:59,037
You must stay at home.
1717
01:10:59,080 --> 01:11:02,519
HASSABIS: It feels like we're
in a science fiction novel.
1718
01:11:02,562 --> 01:11:04,869
You know, I'm delivering food
to my parents,
1719
01:11:04,912 --> 01:11:08,220
making sure
they stay isolated and safe.
1720
01:11:08,264 --> 01:11:10,570
I think it just highlights
the incredible need
1721
01:11:10,614 --> 01:11:12,877
for AI-assisted science.
1722
01:11:17,098 --> 01:11:18,361
TUNYASUVUNAKOOL:
You always know that
1723
01:11:18,404 --> 01:11:21,015
something like this
is a possibility.
1724
01:11:21,059 --> 01:11:23,888
But nobody ever really
believes it's gonna happen
1725
01:11:23,931 --> 01:11:25,585
in their lifetime, though.
1726
01:11:25,629 --> 01:11:26,934
[COMPUTER BEEPS]
1727
01:11:26,978 --> 01:11:29,154
- JUMPER: Are you recording yet?
- RESEARCHER: Yes.
1728
01:11:29,197 --> 01:11:31,025
- Okay, morning, all.
- Hey.
1729
01:11:31,069 --> 01:11:32,679
Good. CASP has started.
1730
01:11:32,723 --> 01:11:36,074
It's nice I get to sit around
in my pajama bottoms all day.
1731
01:11:36,117 --> 01:11:37,597
TUNYASUVUNAKOOL: I never
thought I'd live in a house
1732
01:11:37,641 --> 01:11:39,164
where so much was going on.
1733
01:11:39,207 --> 01:11:41,427
I would be trying to solve
protein folding in one room,
1734
01:11:41,471 --> 01:11:42,559
and my husband would be trying
1735
01:11:42,602 --> 01:11:43,908
to make robots walk
in the other.
1736
01:11:45,388 --> 01:11:46,911
[EXHALES]
1737
01:11:46,954 --> 01:11:49,392
One of the hardest proteins
we've gotten in CASP thus far
1738
01:11:49,435 --> 01:11:51,219
is the SARS-CoV-2 protein
1739
01:11:51,263 --> 01:11:52,220
called ORF8.
1740
01:11:52,264 --> 01:11:54,919
ORF8 is
a coronavirus protein.
1741
01:11:54,962 --> 01:11:56,964
It's one of the main proteins,
um,
1742
01:11:57,008 --> 01:11:58,749
that dampens
the immune system.
1743
01:11:58,792 --> 01:12:00,054
TUNYASUVUNAKOOL:
We tried really hard
1744
01:12:00,098 --> 01:12:01,752
to improve our prediction.
1745
01:12:01,795 --> 01:12:03,493
Like, really, really hard.
1746
01:12:03,536 --> 01:12:05,582
Probably the most time
that we have ever spent
1747
01:12:05,625 --> 01:12:07,105
on a single target.
1748
01:12:07,148 --> 01:12:08,933
To the point where
my husband is, like,
1749
01:12:08,976 --> 01:12:12,197
"It's midnight.
You need to go to bed."
1750
01:12:12,240 --> 01:12:16,419
So I think we're at
Day 102 since lockdown.
1751
01:12:16,462 --> 01:12:19,944
My daughter
is keeping a journal.
1752
01:12:19,987 --> 01:12:22,120
Now you can go out
as much as you want.
1753
01:12:25,036 --> 01:12:27,212
JUMPER: We have received
the last target.
1754
01:12:27,255 --> 01:12:29,649
They've said they will be
sending out no more targets
1755
01:12:29,693 --> 01:12:31,347
in our category of CASP.
1756
01:12:32,652 --> 01:12:33,653
So we're just making sure
1757
01:12:33,697 --> 01:12:35,481
we get
the best possible answer.
1758
01:12:40,530 --> 01:12:43,315
MOULT: As soon as we started
to get the results,
1759
01:12:43,359 --> 01:12:48,233
I'd sit down and start looking
at how close did anybody come
1760
01:12:48,276 --> 01:12:50,583
to getting the protein
structures correct.
1761
01:12:54,848 --> 01:12:56,937
[ROBOT SQUEAKING]
1762
01:12:59,113 --> 01:13:00,201
[INCOMING CALL BEEPING]
1763
01:13:00,245 --> 01:13:01,551
- Oh, hi there.
- MAN: Hello.
1764
01:13:01,594 --> 01:13:03,770
[ALL CHUCKLING]
1765
01:13:03,814 --> 01:13:07,078
It is an unbelievable thing,
CASP has finally ended.
1766
01:13:07,121 --> 01:13:09,472
I think it's at least time
to raise a glass.
1767
01:13:09,515 --> 01:13:11,212
Um, I don't know
if everyone has a glass
1768
01:13:11,256 --> 01:13:12,823
of something
that they can raise.
1769
01:13:12,866 --> 01:13:14,955
If not, raise,
I don't know, your laptops.
1770
01:13:14,999 --> 01:13:17,088
- Um...
- [LAUGHTER]
1771
01:13:17,131 --> 01:13:18,611
I'll probably make a speech
in a minute.
1772
01:13:18,655 --> 01:13:20,483
I feel like I should but I
just have no idea what to say.
1773
01:13:21,005 --> 01:13:24,269
So... let's see.
1774
01:13:24,312 --> 01:13:27,054
I feel like
a reading of email...
1775
01:13:27,098 --> 01:13:28,534
is the right thing to do.
1776
01:13:28,578 --> 01:13:29,883
[ALL CHUCKLING]
1777
01:13:29,927 --> 01:13:31,232
TUNYASUVUNAKOOL:
When John said,
1778
01:13:31,276 --> 01:13:33,191
"I'm gonna read an email,"
at a team social,
1779
01:13:33,234 --> 01:13:35,498
I thought, "Wow, John,
you know how to have fun."
1780
01:13:35,541 --> 01:13:38,370
We're gonna read an email now.
[LAUGHS]
1781
01:13:38,414 --> 01:13:41,634
Uh, I got this
about four o'clock today.
1782
01:13:42,722 --> 01:13:44,724
Um, it is from John Moult.
1783
01:13:45,725 --> 01:13:47,031
And I'll just read it.
1784
01:13:47,074 --> 01:13:49,381
It says,
"As I expect you know,
1785
01:13:49,425 --> 01:13:53,603
"your group has performed
amazingly well in CASP 14,
1786
01:13:53,646 --> 01:13:55,387
"both relative to other groups
1787
01:13:55,431 --> 01:13:57,911
"and in absolute
model accuracy."
1788
01:13:57,955 --> 01:13:59,783
[PEOPLE CLAPPING]
1789
01:13:59,826 --> 01:14:01,219
"Congratulations on this work.
1790
01:14:01,262 --> 01:14:03,047
"It is really outstanding."
1791
01:14:03,090 --> 01:14:05,266
The structures were so good,
1792
01:14:05,310 --> 01:14:07,443
it was... it was just amazing.
1793
01:14:07,486 --> 01:14:09,096
[TRIUMPHANT INSTRUMENTAL
MUSIC PLAYING]
1794
01:14:09,140 --> 01:14:10,750
After half a century,
1795
01:14:10,794 --> 01:14:12,230
we finally have a solution
1796
01:14:12,273 --> 01:14:14,928
to the protein folding
problem.
1797
01:14:14,972 --> 01:14:17,409
When I saw this email,
I read it,
1798
01:14:17,453 --> 01:14:19,585
I go, "Oh, shit!"
1799
01:14:19,629 --> 01:14:21,587
And my wife goes,
"Is everything okay?"
1800
01:14:21,631 --> 01:14:24,242
I call my parents, and just,
like, "Hey, Mum.
1801
01:14:24,285 --> 01:14:26,244
"Um, got something
to tell you.
1802
01:14:26,287 --> 01:14:27,550
"We've done this thing
1803
01:14:27,593 --> 01:14:29,813
"and it might be kind of
a big deal." [LAUGHS]
1804
01:14:29,856 --> 01:14:31,641
When I learned of
the CASP 14 results,
1805
01:14:32,642 --> 01:14:34,034
I was gobsmacked.
1806
01:14:34,078 --> 01:14:35,819
I was just excited.
1807
01:14:35,862 --> 01:14:38,909
This is a problem
that I was beginning to think
1808
01:14:38,952 --> 01:14:42,086
would not get solved
in my lifetime.
1809
01:14:42,129 --> 01:14:44,741
NURSE: Now we have a tool
that can be used
1810
01:14:44,784 --> 01:14:46,612
practically by scientists.
1811
01:14:46,656 --> 01:14:48,440
SENIOR: These people
are asking us, you know,
1812
01:14:48,484 --> 01:14:50,224
"I've got this protein
involved in malaria,"
1813
01:14:50,268 --> 01:14:52,139
or, you know,
some infectious disease.
1814
01:14:52,183 --> 01:14:53,227
"We don't know the structure.
1815
01:14:53,271 --> 01:14:55,186
"Can we use AlphaFold
to solve it?"
1816
01:14:55,229 --> 01:14:56,970
JUMPER: We can easily predict
all known sequences
1817
01:14:57,014 --> 01:14:58,276
in a month.
1818
01:14:58,319 --> 01:14:59,973
All known sequences
in a month?
1819
01:15:00,017 --> 01:15:01,279
- Yeah, easily.
- Mmm-hmm?
1820
01:15:01,322 --> 01:15:02,585
JUMPER:
A billion, two billion.
1821
01:15:02,628 --> 01:15:03,673
Um, and they're...
1822
01:15:03,716 --> 01:15:05,196
So why don't we just do that?
Yeah.
1823
01:15:05,239 --> 01:15:07,111
- We should just do that a lot.
- Well, I mean...
1824
01:15:07,154 --> 01:15:09,243
That's way better.
Why don't we just do that?
1825
01:15:09,287 --> 01:15:11,115
SENIOR: So that's
one of the options.
1826
01:15:11,158 --> 01:15:12,638
- HASSABIS: Right.
- There's this...
1827
01:15:12,682 --> 01:15:15,119
We should just...
Right, that's a great idea.
1828
01:15:15,162 --> 01:15:17,513
We should just run
every protein in existence.
1829
01:15:18,296 --> 01:15:19,471
And then release that.
1830
01:15:19,515 --> 01:15:20,994
Why didn't someone
suggest this before?
1831
01:15:21,038 --> 01:15:22,126
Of course that's
what we should do.
1832
01:15:22,169 --> 01:15:23,954
Why are we thinking about
making a service
1833
01:15:23,997 --> 01:15:25,651
and then people submit
their protein?
1834
01:15:25,695 --> 01:15:26,913
We just fold everything.
1835
01:15:26,957 --> 01:15:28,654
And then give it to
everyone in the world.
1836
01:15:28,698 --> 01:15:31,483
Who knows how many discoveries
will be made from that?
1837
01:15:31,527 --> 01:15:33,790
BIRNEY: Demis called us up
and said,
1838
01:15:33,833 --> 01:15:35,618
"We want to make this open.
1839
01:15:35,661 --> 01:15:37,837
"Not just make sure
the code is open,
1840
01:15:37,881 --> 01:15:39,578
"but we're gonna make it
really easy
1841
01:15:39,622 --> 01:15:42,668
"for everybody to get access
to the predictions."
1842
01:15:45,062 --> 01:15:47,238
THORNTON: That is fantastic.
1843
01:15:47,281 --> 01:15:49,327
It's like drawing back
the curtain
1844
01:15:49,370 --> 01:15:52,852
and seeing the whole world
of protein structures.
1845
01:15:52,896 --> 01:15:55,202
[ETHEREAL MUSIC PLAYING]
1846
01:15:55,246 --> 01:15:56,987
SCHMIDT:
They released the structures
1847
01:15:57,030 --> 01:15:59,772
of 200 million proteins.
1848
01:15:59,816 --> 01:16:01,818
These are gifts to humanity.
1849
01:16:07,650 --> 01:16:10,914
JUMPER: The moment AlphaFold
is live to the world,
1850
01:16:10,957 --> 01:16:13,873
we will no longer be
the most important people
1851
01:16:13,917 --> 01:16:15,222
in AlphaFold's story.
1852
01:16:15,266 --> 01:16:16,833
HASSABIS: Can't quite believe
it's all out.
1853
01:16:16,876 --> 01:16:18,356
PEOPLE: Aw!
1854
01:16:18,399 --> 01:16:20,314
WOMAN: A hundred
and sixty-four users.
1855
01:16:20,358 --> 01:16:22,578
HASSABIS:
Loads of activity in Japan.
1856
01:16:22,621 --> 01:16:24,928
RESEARCHER 1:
We have 655 users currently.
1857
01:16:24,971 --> 01:16:26,930
RESEARCHER 2: We currently
have 100,000 concurrent users.
1858
01:16:26,973 --> 01:16:28,192
Wow!
1859
01:16:31,108 --> 01:16:33,893
Today is just crazy.
1860
01:16:33,937 --> 01:16:36,504
HASSABIS: What an absolutely
unbelievable effort
1861
01:16:36,548 --> 01:16:37,723
from everyone.
1862
01:16:37,767 --> 01:16:38,550
We're gonna all remember
these moments
1863
01:16:38,594 --> 01:16:40,030
for the rest of our lives.
1864
01:16:40,073 --> 01:16:41,727
I'm excited about AlphaFold.
1865
01:16:41,771 --> 01:16:45,601
For my research, it's already
propelling lots of progress.
1866
01:16:45,644 --> 01:16:47,385
And this is
just the beginning.
1867
01:16:47,428 --> 01:16:48,908
SCHMIDT: My guess is,
1868
01:16:48,952 --> 01:16:53,043
every single biological
and chemistry achievement
1869
01:16:53,086 --> 01:16:55,698
will be related to AlphaFold
in some way.
1870
01:16:55,741 --> 01:16:57,874
[TRIUMPHANT INSTRUMENTAL
MUSIC PLAYING]
1871
01:17:13,367 --> 01:17:15,413
AlphaFold is an index moment.
1872
01:17:15,456 --> 01:17:18,068
It's a moment
that people will not forget
1873
01:17:18,111 --> 01:17:20,244
because the world changed.
1874
01:17:39,655 --> 01:17:41,482
HASSABIS:
Everybody's realized now
1875
01:17:41,526 --> 01:17:43,746
what Shane and I have known
for more than 20 years,
1876
01:17:43,789 --> 01:17:46,618
that AI is going to be
the most important thing
1877
01:17:46,662 --> 01:17:48,446
humanity's ever gonna invent.
1878
01:17:48,489 --> 01:17:50,230
TRAIN ANNOUNCER:
We will shortly be arriving
1879
01:17:50,274 --> 01:17:52,058
at our final destination.
1880
01:17:52,102 --> 01:17:53,581
[ELECTRONIC MUSIC PLAYING]
1881
01:18:02,068 --> 01:18:04,767
HASSABIS: The pace of
innovation and capabilities
1882
01:18:04,810 --> 01:18:06,507
is accelerating,
1883
01:18:06,551 --> 01:18:09,293
like a boulder rolling down
a hill that we've kicked off
1884
01:18:09,336 --> 01:18:12,644
and now it's continuing
to gather speed.
1885
01:18:12,688 --> 01:18:15,299
NEWSCASTER: We are at
a crossroads in human history.
1886
01:18:15,342 --> 01:18:16,735
AI has the potential
1887
01:18:16,779 --> 01:18:19,172
to transform our lives
in every aspect.
1888
01:18:19,216 --> 01:18:23,786
It's no less important than
the discovery of electricity.
1889
01:18:23,829 --> 01:18:26,484
HASSABIS: We should be looking
at the scientific method
1890
01:18:26,527 --> 01:18:28,834
and trying to understand
each step of the way
1891
01:18:28,878 --> 01:18:30,096
in a rigorous way.
1892
01:18:30,140 --> 01:18:32,664
This is a moment
of profound opportunity.
1893
01:18:32,708 --> 01:18:34,753
SUNAK:
Harnessing this technology
1894
01:18:34,797 --> 01:18:37,713
could eclipse anything
we have ever known.
1895
01:18:40,411 --> 01:18:42,152
[ELECTRONIC DEVICE BEEPS]
1896
01:18:42,195 --> 01:18:43,675
HASSABIS: Hi, Alpha.
1897
01:18:44,676 --> 01:18:45,764
ALPHA: Hi.
1898
01:18:47,157 --> 01:18:48,419
What is this?
1899
01:18:50,682 --> 01:18:53,729
ALPHA: This is a chessboard.
1900
01:18:53,772 --> 01:18:56,514
If I was to play white, what
move would you recommend?
1901
01:18:59,865 --> 01:19:00,953
ALPHA: I would recommend
1902
01:19:00,997 --> 01:19:02,781
that you move your pawn
from E2 to E4.
1903
01:19:05,871 --> 01:19:08,787
And now if you were black,
what would you play now?
1904
01:19:11,572 --> 01:19:13,618
ALPHA: I would play
the Sicilian Defense.
1905
01:19:15,838 --> 01:19:16,882
That's a good choice.
1906
01:19:19,406 --> 01:19:21,452
- ALPHA: Thanks.
- [CHUCKLES]
1907
01:19:23,715 --> 01:19:25,891
So what do you see?
What is this object?
1908
01:19:28,546 --> 01:19:30,504
ALPHA:
This is a pencil sculpture.
1909
01:19:32,811 --> 01:19:35,031
What happens if I move
one of the pencils?
1910
01:19:37,990 --> 01:19:39,470
ALPHA: If you move
one of the pencils,
1911
01:19:39,513 --> 01:19:42,081
the sculpture will fall apart.
1912
01:19:42,125 --> 01:19:44,301
I'd better leave it alone,
then.
1913
01:19:44,344 --> 01:19:45,868
ALPHA: That's probably
a good idea.
1914
01:19:45,911 --> 01:19:47,391
[HASSABIS CHUCKLES]
1915
01:19:50,568 --> 01:19:52,744
HASSABIS:
AGI is on the horizon now.
1916
01:19:54,833 --> 01:19:56,661
Very clearly
the next generation
1917
01:19:56,704 --> 01:19:58,141
is going to live
in a future world
1918
01:19:58,184 --> 01:20:01,057
where things will be radically
different because of AI.
1919
01:20:02,493 --> 01:20:05,496
And if you want to steward
that responsibly,
1920
01:20:05,539 --> 01:20:09,239
every moment is vital.
1921
01:20:09,282 --> 01:20:12,677
This is the moment I've been
living my whole life for.
1922
01:20:19,162 --> 01:20:21,120
It's just
a good thinking game.
1923
01:20:22,469 --> 01:20:24,602
[UPLIFTING INSTRUMENTAL
MUSIC PLAYING]