Kind: captions
Language: en
the following is a conversation with
demus hasabis
ceo and co-founder of deepmind
a company that has published and builds
some of the most incredible artificial
intelligence systems in the history of
computing including alfred zero that
learned
all by itself to play the game of gold
better than any human in the world and
alpha fold two that solved protein
folding
both tasks considered nearly impossible
for a very long time
demus is widely considered to be one of
the most brilliant and impactful humans
in the history of artificial
intelligence and science and engineering
in general
this was truly an honor and a pleasure
for me to finally sit down with him for
this conversation and i'm sure we will
talk many times again in the future
this is the lex friedman podcast to
support it please check out our sponsors
in the description and now dear friends
here's demis
hassabis
let's start with a bit of a personal
question
am i an ai program you wrote to
interview people until i get good enough
to interview you
well i'll be impressed if if you were
i'd be impressed by myself if you were i
don't think we're quite up to that yet
but uh maybe you're from the future lex
if you did would you tell me is that is
that a good thing to tell a language
model that's tasked with interviewing
that it is in fact um ai maybe we're in
a kind of meta turing test uh probably
probably it would be a good idea not to
tell you so it doesn't change your
behavior right this is a kind of
heisenberg uncertainty principle
situation if i told you you behave
differently yeah maybe that's what's
happening with us of course this is a
benchmark from the future where they
replay 2022 as a year before ais were
good enough yet and now we want to see
is it going to pass exactly
if i was such a
program would you be able to tell do you
think so to the touring test question
you've talked about
the benchmark for solving intelligence
what would be the impressive thing
you've talked about winning a nobel
prize in a system winning a nobel prize
but i still return to the touring test
as a compelling test the spirit of the
touring test is a compelling test
yeah the turing test of course it's been
unbelievably influential and turing's
one of my all-time heroes but i think if
you look back at the 1950 papers
original paper and read the original
you'll see i don't think he meant it to
be a rigorous formal test i think it was
more like a thought experiment almost a
bit of philosophy he was writing if you
look at the style of the paper and you
can see he didn't specify it very
rigorously so for example he didn't
specify the knowledge that the expert or
judge would have um not you know how
much time would they have to investigate
this so these important parameters if
you were gonna make it uh a true sort of
formal test
um and you know some by some measures
people claimed the turing test passed
several you know a decade ago i remember
someone claiming that with a with a kind
of very bog standard normal uh
logic model um because they pretended it
was a it was a kid so the the judges
thought that the machine you know was
was a was a child so um that would be
very different from an expert ai person
uh interrogating a machine and knowing
how it was built and so on so i think um
you know we should probably move away
from that as a formal test and move more
towards a general test where we test the
ai capabilities on a range of tasks and
see if it reaches human level or above
performance on maybe thousands perhaps
even millions of tasks eventually and
cover the entire sort of cognitive space
so i think
for its time it was an amazing thought
experiment and also 1950s obviously it
was barely the dawn of the computer age
so of course he only thought about text
and now um we have a lot more different
inputs
so yeah maybe the better thing to test
is the generalizability so across
multiple tasks but i think it's also
possible as as systems like god show
that
eventually that might map right back to
language so you might be able to
demonstrate your ability to generalize
across tasks
by then communicating your ability to
generalize across tasks which is kind of
what we do through conversation anyway
when we jump around
ultimately what's in there in that
conversation is not just you moving
around knowledge
it's you moving around like these
entirely different modalities of
understanding that ultimately map to
your ability to
to uh operate successfully in all these
domains which you can think of as tasks
yeah i think certainly we as humans use
language as our main generalization
communication tool so i think we end up
thinking in language and expressing our
solutions in language um so it's going
to be very powerful
uh uh mode in which to uh explain you
know the system to explain what it's
doing um but i don't think it's the only
uh uh modality that matters so i think
there's gonna be a lot of you know
there's there's a lot of different ways
to express uh capabilities uh other than
just language
yeah visual
robotics body language
um
yeah action is the interactive aspect of
all that that's all part of it but
what's interesting with gato is that
it's a it's it's it's sort of pushing
prediction to the maximum in terms of
like you know mapping arbitrary
sequences to other sequences and sort of
just predicting what's going to happen
next so prediction seems to be
fundamental to intelligence
and what you're predicting
doesn't so much matter yeah it seems
like you can generalize that quite well
so obviously language models predict the
next word um gato predicts potentially
any uh action or any token uh and it's
just the beginning really it's our most
general agent one could call it so far
but um you know that itself can be
scaled up massively more than we've done
so far obviously we're in the in the
middle of doing that but the big part of
solving agi is creating benchmarks that
help us get closer and closer sort of
creating benchmarks that test the
journalizability and it's just still
interesting that this fella alan turing
was one of the first and probably still
one of the only people that was trying
maybe philosophically but was trying to
formulate a benchmark that could be
followed it is
even though it's it's fuzzy it's still
sufficiently rigorous to where you can
run that test and i still think
something like the touring test will at
the end of the day
be the thing that truly
impresses other humans
so that you can have a close friend
who's an ai system
for that friend to be a good friend
they're going to have to be able to
uh play starcraft
and they're going to have to do all of
these tasks um get you a beer so the
robotics tasks
uh play games with you use language
humor all those kinds of things but that
ultimately can boil down to language it
feels like not in terms of the ai
community but in terms of the actual
impact of general intelligence on the
world it feels like language will be the
place where it truly shines i think so
because it's such an important kind of
input output for us i think you're right
i think the turing test that what the
the kind of the the philosophy behind it
which is the idea of can can a machine
mimic the behaviors of a human and men
and i would say wider than just language
and text
then you know in terms of actions and
everything else creativity all these
things then um if it can sort of match
or exceed human cognitive capabilities
then i think we have a you know true
intelligence so i thought from that
perspective you're right i think he did
formulate the right uh kind of uh setup
i just i think there'll be a kind of
humor
in the ai systems of the future looking
back to this conversation
and thinking about the touring test
and uh also thinking about
by that time they would know which year
they were finally able to sort of cross
the threshold of human level
intelligence and think
how funny it is that we humans were
still
confused about this whole problem
that has been long ago been solved
well hello to the ai agents of the
future anyway so going back to your
to your journey when did you fall in
love with programming first
well it's pretty uh pretty young age
actually so um
you know i started off uh actually games
was my first love so starting to play
chess when i was around four years old
and then um it was actually with
winnings from a chess competition that i
managed to buy my first chess computer
when i was about eight years old it was
a zx spectrum which was hugely popular
in the uk at the time
and uh it's amazing machine because i
think it trained a whole generation of
programmers in the uk because it was so
accessible you know you literally
switched it on and there was the basic
prompt and you could just get going and
um my parents didn't really know
anything about computers so but because
it was my money from a chess competition
i could i could say i i wanted to buy it
uh and then you know i just went to
bookstores got books on programming and
um started typing in you know the
programming code and and then of course
um once you start doing that you start
adjusting it and then making your own
games and that's when i fell in love
with computers and realized that they
were a very magical device um in a way i
kind of i would have been able to
explain this at the time but i felt that
they were sort of almost a magical
extension of your mind i always had this
feeling and i've always loved this about
computers that you can set them off
doing something some task for you you
can go to sleep come back the next day
and it's solved
um you know that feels magical to me so
i mean all machines do that to some
extent they all enhance our natural
capabilities obviously cars make us
allow us to move faster than we can run
but this was a machine to extend the
mind
and and then of course ai is the
ultimate expression of what a machine
may be able to do or learn so
very naturally for me that thought
extended into into ai quite quickly
remember the the programming language
that was first
started
special to the machine no it was just
the base it was just i think it was just
basic uh on the zx spectrum i don't know
what specific form it was and then later
on i got a commodore amiga which uh
was a fantastic machine no you're just
showing off so yeah well lots of my
friends had atari st's and i i managed
to get amigas it was a bit more powerful
and uh and that was incredible and used
to do um programming in assembler and
and uh also amos basic this this
specific form of basic it was incredible
actually as well all my coding skills
and when did you fall in love with ai so
when did you first
start to gain an understanding that you
can not just write programs that
do some mathematical operations for you
while you sleep but something that's
a keen to
bringing an entity to life
sort of
a thing that can figure out something
more complicated than uh
than a simple mathematical operation
yeah so there was a few stages for me
all while i was very young so first of
all as i was trying to improve at
playing chess i was captaining various
england junior chess teams and at the
time when i was about you know maybe 10
11 years old i was gonna become a
professional chess player that was my
first
thought um that dream was there sure she
tried to get to the highest level yeah
so i was um you know i got to when i was
about 12 years old i got to master stand
and i was second highest rated player in
the world to judith polgar who obviously
ended up being an amazing chess player
and uh world women's champion and when i
was trying to improve at chess where you
know what you do is you obviously first
of all you're trying to improve your own
thinking processes so that leads you to
thinking about thinking how is your
brain coming up with these ideas why is
it making mistakes how can you how can
you improve that thought process but the
second thing is that you it was just the
beginning this was like in the in the
early 80s mid 80s of chess computers if
you remember they were physical boards
like the one we have in front of us and
you pressed down the you know the
squares and i think kasparov had a
branded version of it that i i i got and
um you were you know used to they're not
as strong as they are today but they
were they were pretty strong and you
used to practice against them um to try
and improve your openings and other
things and so i remember i think i
probably got my first one i was around
11 or 12. and i remember thinking um
this is amazing you know how how has
someone programmed uh uh this this chess
board to play chess uh and uh it was
very formative book i bought which was
called the chess computer handbook by
david levy which came out in 1984 or
something so i must have got it when i
was about 11 12 and it explained fully
how these chess programs were made i
remember my first ai program being uh
programming my amiga it couldn't it
wasn't powerful enough to play chess i
couldn't write a whole chess program but
i wrote a program for it to play othello
reversey it's sometimes called i think
in the u.s and so a slightly simpler
game than chess but i used all of the
principles that chess programs had alpha
beta search all of that and that was my
first ai program i remember that very
well was around 12 years old so that
that that brought me into ai and then
the second part was later on uh when i
was around 1617 and i was writing games
professionally designing games uh
writing a game called theme park which
um had ai as a core gameplay component
as part of the simulation um and it sold
you know millions of copies around the
world and people loved the way that the
ai even though it was relatively simple
by today's ai standards um was was
reacting to the way you as the player
played it so it was called a sandbox
game so it's one of the first types of
games like that along with simcity and
it meant that every game you played was
unique
is there something you could say just on
a small tangent
about
really impressive ai from a game design
human enjoyment perspective
really impressive ai that you've seen in
games and maybe what does it take to
create ai system and how hard of a
problem is that so a million questions
just as a brief tangent
well look i think um
games uh games have been significant in
my life for three reasons so first of
all to to i was playing them and
training myself on games when i was a
kid then i went through a phase of
designing games and writing ai4 games so
all the games i i professionally wrote
uh had ai as a core component and that
was mostly in the in the 90s and the
reason i was doing that in games
industry was at the time the games
industry i think was the cutting edge of
technology so whether it was graphics
with people like john carmack and quake
and those kind of things or ai i think
actually all the action was going on in
games and and we've seen we're still
reaping the benefits of that even with
things like gpus which you know i find
ironic was obviously invented for
graphics computer graphics but then
turns out to be amazingly useful for ai
it just turns out everything's a matrix
multiplication it appears you know in
the whole world
so um so i think games at the time had
the most cutting edge ai and a lot of
the the games uh uh we you know i was
involved in writing so there was a game
called black and white which was one
game i was involved with in the early
stages of which i still think is the
most um
impressive uh example of reinforcement
learning in a computer game so in that
game you know you trained a little pet
animal uh and
yeah and it sort of learned from how you
were treating it so if you treated it
badly then it became mean yeah and then
it would be mean to to your villagers
and your and your population the sort of
uh the little tribe that you were
running uh but if you were kind to it
then it would be kind and people were
fascinated by how that was and so was i
to be honest with the way it kind of
developed and um especially the mapping
to good and evil yeah it made you made
you realize made me realize that you can
sort of in the way in the choices you
make can define
uh the
where you end up and that means
all of us are capable of the good
uh evil it all matters in uh the
different choices along the trajectory
to those places that you make it's
fascinating i mean games can do that
philosophically to you and it's rare it
seems rare yeah well games are i think a
unique medium because um you as the
player you're not just passively
consuming the the entertainment right
you're actually actively involved as an
as a as an agent so i think that's what
makes it in some ways can be more
visceral than other other mediums like
you know films and books so the second
so that was you know designing ai and
games and then the third use uh uh i've
we've used of ai is in deep mind from
the beginning which is using games as a
testing ground for proving out ai
algorithms and developing ai algorithms
and that was a that was a sort of um a
core component of our vision at the
start of deepmind was that we would use
games very heavily uh as our main
testing ground certainly to begin with
um because it's super efficient to use
games and also you know it's very easy
to have metrics to see how well your
systems are improving and what direction
your ideas are going in and whether
you're making incremental improvements
and because those games are often rooted
in something that humans did for a long
time beforehand
there's already a strong
set of rules like it's already a damn
good benchmark yes it's really good for
so many reasons because you've got
you've got you've got clear measures of
how good humans can be at these things
and in some cases like go we've been
playing it for thousands of years um and
and uh often they have scores or at
least win conditions so it's very easy
for reward learning systems to get a
reward it's very easy to specify what
that reward is um and uh also at the end
it's easy to you know to test uh
externally you know
how strong is your system by of course
playing against you know the world's
strongest players at those games so it's
it's so good for so many reasons and
it's also very efficient to run
potentially millions of simulations in
parallel on the cloud so um i think
there's a huge reason why we were so
successful back in you know starting out
2010 how come we were able to progress
so quickly because we'd utilize games
and um you know at the beginning of deep
mind we also hired some amazing game
engineers uh who i knew from my previous
uh lives in the games industry and uh
and that helped to bootstrap us very
quickly and plus it's somehow super
compelling almost at a philosophical
level of man versus machine
over over a chessboard or a go board
and especially given that the entire
history of ai is defined by people
saying it's going to be impossible to
make a machine that
beats a human being in chess
and then once that happened
people were certain when i was coming up
in ai that go
is not a game that could be solved
because of the combinatorial complexity
it's just too it's it's it's you know
no matter how much moore's law you have
compute is just never going to be able
to crack the game of go yeah and so that
then there's something compelling about
facing sort of taking on the
impossibility of that task from the
ai
researcher perspective engineer
perspective and then as a human being
just observing this whole thing
your
beliefs about what you thought was
impossible
being broken apart
it's it's uh humbling
to realize we're not as smart as we
thought
it's humbling to realize that the things
we think are impossible now perhaps will
be done
in the future there's something
really powerful about a game ai system
being a human being in a game that
drives that message
uh home for like millions billions of
people especially in the case of go sure
well look i think it's a i mean it has
been a fascinating journey and and
especially as i i think about it from i
can understand it from both sides both
as the ai
you know creators of the ai um but also
as a games player originally so you know
it was a it was a really interesting it
was i mean it was a fantastic um but
also somewhat bittersweet moment the
alphago match for me um uh seeing that
and and and being obviously heavily
heavily involved in that um but you know
as you say chess has been uh the i mean
kasparov i think rightly called it the
drosophila of of intelligence right so
it's sort of i i love that phrase and
and i think he's right because chess has
been um hand in hand with ai from the
beginning of the the whole field right
so i think every ai practitioner
starting with turing and claude shannon
and all those uh the sort of forefathers
of of of of the field um tried their
hand at writing a chess program uh i've
got original audition of claude
shannon's first chess program i think it
was 1949 uh the the original sort of uh
paper and um they all did that and
turing famously wrote a chess program
that but all the computers around there
were obviously too slow to run it so he
had to run he had to be the computer
right so he literally i think spent two
or three days running his own program by
hand with pencil and paper and playing
playing a friend of his uh with his
chess program so
of course deep blue was a huge moment uh
beating
off um but actually when that happened i
remember that very very vividly of
course because it was you know chess and
computers and ai all the things i loved
and i was at college at the time but i
remember coming away from that being
more impressed by kasparov's mind than i
was by deep blue because here was
kasparov with his human mind not only
could he play chess more or less to the
same level as this brute of a
calculation machine um but of course
kasparov can do everything else humans
can do ride a bike talk many languages
do politics all the rest of the amazing
things that kasparov does and so with
the same brain yeah and and yet deep
blue uh brilliant as it was at chess it
had been hand coded for chess and um
actually had distilled the knowledge of
chess grand masters uh into into a cool
program but it couldn't do anything else
like it couldn't even play a strictly
simpler game like tic-tac-toe so um
something to me was missing from um
intelligence from that system that we
would regard as intelligence and i think
it was this idea of generality and and
also learning yeah um so and that's what
we tried to do out with alphago yeah we
alphago and alpha zero mu zero and then
got on all the things that uh we'll get
into some parts of there's just a
fascinating trajectory here but let's
just stick on chess briefly uh on the
human side of chess you've proposed that
from a game design perspective the thing
that makes chess
compelling as a game
uh is that there's a creative tension
between a bishop
and the knight
can you explain this first of all it's
really interesting to think about what
makes the game compelling
makes it stick across centuries
yeah i was sort of thinking about this
and actually a lot of even amazing chess
players don't think about it necessarily
from a games designer point of view so
it's with my game design hat on that i
was thinking about this why is chess so
compelling
and i think a critical uh reason is the
the dynamicness of of of the different
kind of chess positions you can have
whether they're closed or open and other
things comes from the bishop and the
night so if you think about how
different the the the capabilities of
the bishop and knight are in terms of
the way they move and then somehow chess
has evolved to balance those two
capabilities more or less equally so
they're both roughly worth three points
each so you think that dynamics was
always there and then the rest of the
rules are kind of trying to stabilize
the game well maybe i mean it's sort of
i don't know his chicken and egg
situation probably both came together
but the fact that it's got to this
beautiful equilibrium where you can have
the bishop and knight they're so
different in power um but so equal in
value across the set of the universe of
all positions right somehow they've been
balanced by humanity over hundreds of
years um i think gives gives the game
the creative tension uh that you can
swap the bishop and knights uh for a
bishop for a knight and you you they're
more or less worth the same but now you
aim for a different type of position if
you have the knight you want a closed
position if you have the bishop you want
an open position so i think that creates
a lot of the creative tension in chess
so some kind of controlled creative
tension
from an ai perspective
do you think ai systems convention
design games that are optimally
compelling to humans
well that's an interesting question you
know sometimes i get asked about
ai and creativity and and this and the
way i answered that is relevant to that
question which is that i think they're
different levels of creativity one could
say so i think um if we define
creativity as coming up with something
original right that's that's useful for
a purpose then you know i think the kind
of lowest level of creativity is like an
interpolation so an averaging of all the
examples you see so maybe a very basic
ai system could say you could have that
so you show it millions of pictures of
cats and then you say give me an average
looking cat right generate me an average
looking cat i would call that
interpolation then there's extrapolation
which something like alphago showed so
alphago played you know millions of
games of go against itself
and then it came up with brilliant new
ideas like move 37 in game two bringing
a motif strategies and go that that no
humans had ever thought of even though
we've played it for thousands of years
and professionally for hundreds of years
so that that i call that extrapolation
but then that's still there's still a
level above that which is you know you
could call out the box thinking or true
innovation which is could you invent go
right could you invent chess and not
just come up with a brilliant chess move
or brilliant go move but can you can you
actually invent chess or something as
good as chess or go and i think one day
uh ai could but what's missing is how
would you even specify that task to a a
program right now and the way i would do
it if i was best telling a human to do
it or a games designer a human games
designer to do it is i would say
something like go i would say um
come up with a game that only takes five
minutes to learn which go does because
it's got simple rules but many lifetimes
to master right or impossible to master
in one lifetime because so deep and so
complex um and then it's aesthetically
beautiful uh and also uh it can be
completed in three or four hours of
gameplay time which is you know useful
for our us you know in in a human day
and so um you might specify these side
of high level concepts like that and
then you know with that and maybe a few
other things uh one could imagine that
go satisfies uh those those constraints
um but the problem is is that we we're
not able to specify abstract notions
like that high-level abstract notions
like that yet to our ai systems um and i
think there's still something missing
there in terms of um high-level concepts
or abstractions that they truly
understand and that you know combinable
and compositional um so for the moment
i think ai is capable of doing
interpolation extrapolation but not true
invention so coming up with rule sets
uh and optimizing
with complicated objectives around those
rule sets we can't currently do
but you could take a specific rule set
and then run a kind of self-play
experiment to see how long
just observe how an ai system from
scratch learns how long is that journey
of learning and maybe
if it satisfies some of those other
things you mentioned in terms of
quickness to learn and so on and you
could see a long journey to master for
even an ai system then you could say
that this is a promising game
um but it would be nice to do almost
like alpha codes or programming rules so
generating rules that kind of
uh
that automate even that part of the
generation of rules so i have thought
about systems actually um that i think
would be amazing in in for a games
designer if you could have a system that
um takes your game plays it tens of
millions of times maybe overnight and
then self balances the rules better so
it tweaks the the rules and the maybe
the equations and the and the and the
parameters so that the game uh is more
balanced the units in the game or
some of the rules could be tweaked so
it's a bit of like a giving a base set
and then allowing a monte carlo tree
search or something like that to sort of
explore it right and i think that would
be super super a powerful tool actually
for for balancing auto balancing a game
which usually takes
thousands of hours from hundreds of
games human games testers normally to to
balance some one you know game like
starcraft which is you know blizzard are
amazing at balancing their games but it
takes them years and years and years so
one could imagine at some point when
this uh this stuff becomes uh efficient
enough to you know you might be able to
do that like overnight
do you think a game that is optimal
designed by an ai system
would look very much like uh planet
earth
maybe maybe it's only the sort of game i
would love to make is is and i've tried
you know my in my game's career the
games design career you know my first
big game was designing a theme park an
amusement park then uh with games like
republic i tried to you know have games
where we designed whole cities and and
allowed you to play in so and of course
people like will wright have written
games like sim earth uh trying to
simulate the whole of earth pretty
tricky but um i see earth i haven't
actually played that one so what is it
does it incorporative evolution or yeah
it has evolution and it's sort of um it
tries to it sort of treats it as an
entire biosphere but from quite a high
level
so
nice to be able to sort of zoom in zoom
out zoom in exactly so obviously he
couldn't do that was in the night i
think he wrote that in the 90s so it
couldn't you know it wasn't it wasn't
able to do that but that that would be
uh obviously the ultimate sandbox game
of course
on that topic do you think we're living
in a simulation
yes well so okay so i'm gonna jump
around from the absurdly philosophical
to the short term sure very very happy
to so i think uh my answer to that
question is a little bit complex because
uh there is simulation theory which
obviously nick bostrom i think famously
first proposed um
and uh i don't quite believe it in in
that sense so um in the in the sense
that uh are we in some sort of computer
game or have our descendants somehow
recreated uh uh earth in the you know
21st century and and some for some kind
of experimental reason i think that um
but i do think that we that that we
might be that the best way to understand
physics and the universe is from a
computational perspective so
understanding it as an information
universe and actually information being
the most fundamental unit of uh reality
rather than matter or energy so a
physicist would say you know matter or
energy you know e equals m c squared
these are the things that are are the
fundamentals of the universe i'd
actually say information um which of
course itself can be can specify energy
or matter right matter is actually just
you know we're we're just out the way
our bodies and all the molecules in our
body arrange is information so i think
information may be the most fundamental
way to describe the universe and
therefore you could say we're in some
sort of simulation because of that um
but i don't i do i'm not i'm not really
a subscriber to the idea that um you
know these are sort of throw away
billions of simulations around i think
this is actually very critical and
possibly unique this simulation
particular one yes so but and you just
mean
treating the universe
as a computer
that's
processing and modifying information
is is a good way to solve the problems
of physics of chemistry of biology
and perhaps of humanity and so on yes i
think understanding physics in terms of
information theory uh might be the best
way to to really uh understand what's
going on here
from our understanding of a universal
turing machine from our understanding of
a computer do you think there's
something outside of the capabilities of
a computer that is present in our
universe you have a disagreement with
roger penrose
the nature of consciousness he he thinks
that consciousness is more than just a
computation
uh do you think all of it the whole
shebang is can be can be a competition
yeah i've had many fascinating debates
with uh sir roger penrose and obviously
he's he's famously and i read you know
emperors of new mind and and um
and his books uh his classical books uh
and they they were pretty influential
and you know in the 90s and um he
believes that there's something more you
know something quantum that is needed to
explain consciousness in the brain um i
think about what we're doing actually at
deepmind and what my career is being
we're almost like true rings champion so
we are pushing turing machines or
classical computation to the limits what
are the limits of what classical
computing can do now um and at the same
time i've also studied neuroscience to
see and that's why i did my phd in was
to see also to look at you know is there
anything quantum in the brain from a
neuroscience or biological perspective
and um and so far i think most
neuroscientists and most mainstream
biologists and neuroscientists would say
there's no evidence of any quantum uh
systems or effects in the brain as far
as we can see it's it can be mostly
explained by classical uh classical
theories so
and then so there's sort of the the
search from the biology side and then at
the same time there's the raising of the
water uh at the bar from what classical
turing machines can do uh uh and
and you know including our new ai
systems and uh as you alluded to earlier
um you know i think ai especially in the
last decade plus has been a continual
story now of surprising uh events uh and
surprising successes knocking over one
theory after another of what was thought
to be impossible you know from go to
protein folding and so on and so i think
um
i would be very hesitant to bet against
how far the uh universal turing machine
and classical computation paradigm can
go and and my betting would be
that all of certainly what's going on in
our brain uh can probably be mimicked or
or approximated on a on a classical
machine um not you know not requiring
something metaphysical or quantum and
we'll get there with some of the work
with alpha fold
which i think begins the journey of
modeling this beautiful and complex
world of biology so you think all the
magic of the human mind comes from this
just a few pounds of mush
of a biological computational mush
that's
akin to some of the neural networks
not directly but in spirit that deep
mind has been working with well look i
think it's um you say it's a few you
know of course it's this is the i think
the biggest miracle of the universe is
that um it is just a few pounds of mush
in our skulls and yet it's also our
brains are the most complex objects in
the in that we know of in the universe
so there's something profoundly
beautiful and amazing about our brains
and
i
think that it's an incredibly uh
incredible efficient machine and and uh
uh
and it's a is you know phenomenal
basically and i think that building ai
one of the reasons i want to build ai
and i've always wanted to is i think by
building an intelligent artifact like ai
and then comparing it to the human mind
um that will help us unlock the
uniqueness and the true secrets of the
mind that we've always wondered about
since the dawn of history like
consciousness dreaming uh creativity uh
emotions what are all these things right
we've we've wondered about them since
since the dawn of humanity and i think
one of the reasons and you know i love
philosophy and philosophy of mind is we
found it difficult is there haven't been
the tools for us to really other than
introspection to from very clever people
in in history very clever philosophers
to really investigate this
scientifically but now suddenly we have
a plethora of tools firstly we have all
the neuroscience tools fmri machines
single cell recording all of this stuff
but we also have the ability computers
and ai to build uh intelligent systems
so i think that um
uh
you know i think it is amazing what the
human mind does and um and and i'm kind
of in awe of it really and uh and i
think it's amazing that without human
minds we're able to build things like
computers and and actually even you know
think and investigate about these
questions i think that's also a
testament to the human mind yeah the
universe built the human mind that now
is building computers that help
us understand both the universe and our
own human mind right that's exactly it i
mean i think that's one you know one
could say we we are
maybe we're the mechanism by which the
universe is going to try and understand
itself yeah
it's beautiful so let's let's go to the
basic building blocks of biology that i
think
is another angle at which you can start
to understand the human mind the human
body which is quite fascinating which is
from the basic building blocks start to
simulate start to model
how from those building blocks you can
construct bigger and bigger more complex
systems maybe one day the entirety of
the human biology so
here's another problem that thought to
be impossible to solve which is protein
folding and alpha fold or
specific alpha fold 2
did just that it solved protein folding
i think it's one of the biggest
breakthroughs
uh certainly in the history of
structural biology but uh in general in
in science
um
maybe from a high level
what is it and how does it work
and then we can ask some fascinating
sure questions after sure um so maybe
like to explain it uh to people not
familiar with protein folding is you
know i first of all explain proteins
which is you know proteins are essential
to all life every function in your body
depends on proteins sometimes they're
called the workhorses of biology and if
you look into them and i've you know
obviously as part of alpha fold i've
been researching proteins and and
structural biology for the last few
years you know they're amazing little
bio nano machines proteins they're
incredible if you actually watch little
videos of how they work animations of
how they work
and um proteins are specified by their
genetic sequence called the amino acid
sequence so you can think of those their
genetic makeup and then in the body uh
in in nature they when they when they
fold up into a 3d structure so you can
think of it as a string of beads and
then they fold up into a ball now the
key thing is you want to know what that
3d structure is
because the structure the 3d structure
of a protein
is what helps to determine what does it
do the function it does in your body
and also if you're interested in drug
drugs or disease you need to understand
that 3d structure because if you want to
target something with a drug compound or
about to block that something the
protein is doing uh you need to
understand where it's going to bind on
the surface of the protein so obviously
in order to do that you need to
understand the 3d structure so the
structure is mapped to the function the
structure is mapped to the function and
the structure is obviously somehow
specified by the by the amino acid
sequence and that's the in essence the
protein folding problem is can you just
from the amino acid sequence the
one-dimensional
string of letters can you
immediately computationally predict the
3d structure right and this has been a
grand challenge in biology for over 50
years so i think it was first
articulated by christian anfinsen a
nobel prize winner in 1972 uh as part of
his nobel prize winning lecture and he
just speculated this should be possible
to go from the amino acid sequence to
the 3d structure we didn't say how so
i you know it's been described to me as
equivalent to fermat's last theorem but
for biology right you should as somebody
that uh very well might win the nobel
prize in the future but outside of that
you should do more of that kind of thing
in the margins just put random things
that will take like 200 years to solve
set people off for 200 years it should
be possible exactly and just don't give
any interest exactly i think everyone's
exactly should be i'll have to remember
that for future so yeah so he set off
you know with this one throwaway remark
just like fermat you know he he set off
this whole 50-year uh
uh uh field really of computational
biology and and they had you know they
got stuck they hadn't really got very
far with doing this and and um until now
until alpha fold came along this is done
experimentally right very painstakingly
so the rule of thumb is and you have to
like crystallize the protein which is
really difficult some proteins can't be
crystallized like membrane proteins and
then you have to use very expensive
electron microscopes or x-ray
crystallography machines really
painstaking work to get the 3d structure
and visualize the 3d structure so the
rule of thumb in in experimental biology
is that it takes one phd student their
entire phd to do one protein uh and with
alpha fold two we were able to predict
the 3d structure in a matter of seconds
um and so we were you know over
christmas we did the whole human
proteome or every protein in the human
body all 20 000 proteins so the human
proteins like the equivalent of the
human genome but on protein space and uh
and sort of revolutionize really what uh
a structural biologist can do because
now um they don't have to worry about
these painstaking experimentals you know
should they put all of that effort in or
not they can almost just look up the
structure of their proteins like a
google search
and so there's a data set on which it's
trained and how to map this amino acids
because first of all it's incredible
that a protein this little chemical
computer is able to do that computation
itself in some kind of distributed way
and do it very quickly
that's a weird thing and they evolved
that way because you know in the
beginning
i mean that's a great invention just the
protein itself yes i mean and then they
there's i think probably a history of
like uh they evolved
to have many of these proteins and those
proteins figure out how to be computers
themselves
in such a way that you can create
structures that can interact in
complexes with each other in order to
form high level functions i mean it's a
weird system that they figured it out
well for sure i mean we you know maybe
we should talk about the origins of life
too but proteins themselves i think are
magical and incredible uh uh uh as i
said little little bio-nano machines and
um
and and actually levantal who is another
scientist uh uh a contemporary of
anfinsen uh he he coined this eleventh
house what became known as levantal's
paradox which is exactly what you're
saying he calculated roughly a protein
an average protein which is maybe 2 000
amino acids
bases long is um
is is can fold in maybe 10 to the power
300 different conformations so there's
10 to the power 300 different ways that
protein could fold up and yet somehow in
nature physics solves this solves this
in a matter of milliseconds so proteins
fold up in your body in you know
sometimes in fractions of a second so
physics is somehow solving that search
problem and just to be clear in many of
these cases maybe you correct me if i'm
wrong there's often a unique way
for that sequence to form itself yes so
among that huge number of possibilities
yes it figures out a way how to
stability
uh
in some cases there might be a
misfunction so on which leads to a lot
of the disorders and stuff like that but
yes most of the time it's a unique
mapping and that unique mapping is not
obvious no exactly that's just what the
problem is exactly so there's a unique
mapping usually in a healthy in if it's
healthy and as you say in disease
so for example alzheimer's one one one
conjecture is that it's because of a
misfolded protein a protein that folds
in the wrong way amyloid beta protein so
um and then because it falls in the
wrong way it gets tangled up right in
your in your neurons so
um it's super important to understand
both healthy functioning and also
disease is to understand uh you know
what what these things are doing and how
they're structuring of course the next
step is sometimes proteins change shape
when they interact with something so um
they're not just static necessarily in
in biology
maybe you can give some interesting sort
of beautiful things to you about these
early days of alpha fold of of solving
this problem because
unlike games this is
real physical systems that are less
amenable to
self-play type of mechanisms
the the size of the data set is smaller
that you might otherwise like so you
have to be very clever about certain
things is there something you could
speak to um
what was very hard to solve and what are
some beautiful aspects about the the
solution yeah i would say alpha fold is
the most complex and also probably most
meaningful system we've built so far so
it's been an amazing time actually in
the last you know two three years to see
that come through because um as we
talked about earlier you know games is
what we started on uh building things
like alphago and alpha zero but really
the ultimate goal was to um not just to
crack games it was just to to to build
use them to bootstrap general learning
systems we could then apply to real
world challenges specifically my passion
is scientific challenges like protein
folding and then alpha fold of course is
our first big proof point of that and so
um you know in terms of the data uh and
the amount of innovations that had to go
into it we you know it was like more
than 30 different component algorithms
needed to be put together to crack the
protein folding um i think some of the
big innovations were that um
kind of building in some hard coded
constraints around physics and
evolutionary biology um to constrain
sort of things like the bond angles uh
uh in the in the in the protein and
things like that um
a lot but not to impact the learning
system so still allowing uh the system
to be able to learn the physics uh
itself um from the examples that we had
and the examples as you say there are
only about 150 000 proteins even after
40 years of experimental biology only
around 150 000 proteins have been the
structures have been found out about so
that was our training set which is um
much less than normally we would like to
use
but using various tricks things like
self distillation so actually using
alpha folds predictions um some of the
best predictions that it thought was
highly confident in we put them back
into the training set right to make the
training set bigger
that was critical to to alpha fold
working so there was actually a huge
number of different um uh innovations
like that that were required to to
ultimately crack the problem after fold
one what it produced was a distagram so
a kind of
a matrix of the pairwise distances
between all of the molecules in the in
the in the protein and then there had to
be a separate optimization process to uh
create the 3d structure
and what we did for alpha volt2 is make
it truly end to end so we went straight
from the amino acid sequence of of of
bases to
the 3d structure directly without going
through this intermediate step and in
machine learning what we've always found
is that the more end to end you can make
it the better the system and it's
probably because um we you know the in
the end the system is better at learning
what the constraints are than than we
are as the human designers of specifying
it so anytime you can let it flow end to
end and actually just generate what it
is you're really looking for in this
case the 3d structure you're better off
than having this intermediate step which
you then have to hand craft the next
step for so
so it's better to let the gradients and
the learning flow all the way through
the system um from the end point the end
output you want to the inputs so that's
a good way to start a new problem
handcraft a bunch of stuff add a bunch
of manual constraints with a small
intent learning piece or a small
learning piece and grow that learning
piece until it consumes the whole thing
that's right and so you can also see you
know this is a bit of a method we've
developed over doing many sort of
successful outfits we call them alpha x
projects right is and the easiest way to
see that is the evolution of alphago to
alpha zero so alphago was um a learning
system but it was specifically trained
to only play go right so uh and what we
wanted to do with first version of go is
just get to world champion performance
no matter how we did it right and then
and then of course alphago zero we we we
removed the need to use human games as a
starting point right so it could just
play against itself from random starting
point from the beginning so that removed
the the need for human knowledge uh
about go and then finally alpha zero
then generalized it so that any things
we had in there the system including
things like symmetry of the go board uh
were removed so the alpha zero could
play from scratch any two player game
and then mu0 which is the final
latest version of that set of things was
then extending it so that you didn't
even have to give it the rules of the
game it would learn that for itself so
it could also deal with computer games
as well as board games so that line of
alpha golf goes zero alpha zero mu zero
that's the full trajectory of what you
can take from
uh imitation learning
to full self
supervised learning yeah exactly and
learning learning uh the entire
structure of the environment you put in
from scratch right and and and and
bootstrapping it uh through self-play uh
yourself but the thing is it would have
been impossible i think or very hard for
us to build alpha zero or mu0 first out
of the box
even psychologically because you have to
believe in yourself for a very long time
you're constantly dealing with doubt
because a lot of people say that it's
impossible exactly so it was hard enough
just to do go as you were saying
everyone thought that was impossible or
at least a decade away um from when we
when we did it back in 2015 24 you know
2016 and um
and so yes it would have been
psychologically probably very difficult
as well as the fact that of course we
learnt a lot by building alphago first
right so it's i think this is why i call
ai in engineering science it's one of
the most fascinating science disciplines
but it's also an engineering science in
the sense that unlike natural sciences
um the phenomenon you're studying it
doesn't exist out in nature you have to
build it first so you have to build the
artifact first and then you can study
how how and pull it apart and how it
works this is tough to uh
ask you this question because you
probably will say it's everything but
let's let's try let's try to think to
this because you're in a very
interesting position where deepmind is
the place of some of the most uh
brilliant ideas in the history of ai but
it's also a place of brilliant
engineering
so how much of solving intelligence this
big goal for deepmind how much of it is
science how much is engineering so how
much is the algorithms how much is the
data how much is the
hardware compute infrastructure how much
is it the software computer
infrastructure yeah um what else is
there how much is the human
infrastructure
and like just the humans interact in
certain kinds of ways in all the space
of all those ideas how much does maybe
like philosophy how much what's the key
if um
uh
if if you were to sort of look back like
if we go forward 200 years look back
what was the key thing that solved
intelligence is that ideas
i think it's a combination first of all
of course it's a combination of all
those things but the the ratios of them
changed over over time
so yeah so um even in the last 12 years
so we started deep mine in 2010 which is
hard to imagine now because 2010 it's
only 12 short years ago but nobody was
talking about ai uh you know if you
remember back to your mit days you know
no one was talking about it i did a
postdoc at mit back around then and it
was sort of thought of as a well look we
know ai doesn't work we tried this hard
in the 90s at places like mit mostly
losing using logic systems and
old-fashioned sort of good old-fashioned
ai we would call it now um people like
minsky and and and patrick winston and
you know all these characters right and
used to debate a few of them and they
used to think i was mad thinking about
that some new advance could be done with
learning systems and um i was actually
pleased to hear that because at least
you know you're on a unique track at
that point right even if every all of
your you know professors are telling you
you're mad that's true and of course in
industry uh you can we couldn't get you
know as difficult to get two cents
together uh and which is hard to imagine
now as well given it's the biggest sort
of buzzword in in vcs and and
fundraising's easy and all these kind of
things today so
back in 2010 it was very difficult and
what we the reason we started then and
shane and i used to discuss um uh uh
what were the sort of founding tenets of
deep mind and it was very various things
one was um algorithmic advances so deep
learning you know jeff hinton and cohen
just had just sort of invented that in
academia but no one in industry knew
about it uh we love reinforcement
learning we thought that could be scaled
up but also understanding about the
human brain had advanced um quite a lot
uh in the decade prior with fmri
machines and other things so we could
get some good hints about architectures
and algorithms and and sort of um
representations maybe that the brain
uses so as at a systems level not at a
implementation level um and then the
other big things were compute and gpus
right so we could see a compute was
going to be really useful and it got to
a place where it became commoditized
mostly through the games industry and
and that could be taken advantage of and
then the final thing was also
mathematical and theoretical definitions
of intelligence so things like ai xi aix
which uh shane worked on with his
supervisor marcus hutter which is a sort
of theoretical uh proof really of
universal intelligence um which is
actually a reinforcement learning system
um in the limit i mean it assumes
infinite compute and infinite memory in
the way you know like a turing machine
proof but i was also waiting to see
something like that too to you know like
turing machines uh and and computation
theory that people like turing and
shannon came up with underpins modern
computer science um uh you know i was
waiting for a theory like that to sort
of underpin agi research so when i you
know met shane and saw he was working on
something like that you know that to me
was a sort of final piece of the jigsaw
so
in the early days i would say that
ideas were the most important uh you
know and for us it was deep
reinforcement learning scaling up deep
learning um of course we've seen
transformers so huge leaps i would say
you know three or four from for if you
think from 2010 until now uh huge
evolutions things like alphago um and um
and and maybe there's a few more still
needed but as we get closer to ai agi um
i think engineering becomes more and
more important and data because scale
and of course the the recent you know
results of gpt3 and all the big language
models and large models including our
ones uh has shown that scale is a is and
large models are clearly going to be
unnecessary but perhaps not sufficient
part of an agi solution and
throughout that like you said and i'd
like to give you a big thank you you're
one of the pioneers in this is
sticking by ideas like reinforcement
learning that this can actually work
given actually
limited success in the past
and also
which we still don't know but
proudly
having the best researchers in the world
and talking about solving intelligence
so talking about whatever you call it
agi or something like this
that speaking of mit that's that's just
something not you wouldn't bring up no
uh not not maybe you did in uh like 40
50 years ago
but that was
um
ai was a place where you do tinkering
very small scale not very ambitious
projects and
maybe the biggest ambitious projects
were in the space of robotics and doing
like the darpa challenge sure but the
task of solving intelligence and
believing you can
that's really really powerful so
in order for engineering to do its work
to have great engineers build great
systems you have to have that belief
that threats throughout the whole thing
that you can actually solve some of
these impossible challenges yeah that's
right and and back in 2010 you know our
mission statement um and still is today
you know it was used to be uh solving
step one solve intelligence step two use
it to solve everything else yes so if
you can imagine pitching that to a vc in
2010 you know the kind of looks we we
got we managed to you know find a few uh
kooky people to back us but it was uh it
was tricky and and i and i got to the
point where we we wouldn't mention it to
any of our professors because they would
just eye roll and think we you know
committed career suicide and and uh and
and you know so it was there's a lot of
things that we had to do but we always
believed it and one reason you know by
the way one reason we i believe i've
always believed in reinforcement
learning is that
that if you look at neuroscience that is
the way that the you know primate brain
learns one of the main mechanisms is the
dopamine system implement some form of
td learning a very famous result in the
late 90s uh where they saw this in
monkeys and uh and as a you know proper
game prediction error so we you know
again in the limit this is this is what
i think you can use neuroscience for is
is you know any at mathematics you when
you're when you're doing something as
ambitious as trying to solve
intelligence and you're you're you know
it's blue sky research no one knows how
to do it you you you need to use any
evidence or any source of information
you can to help guide you in the right
direction or give you confidence you're
going in the right direction so so that
that was one reason we pushed so hard on
that and that's and just going back to
your early question about organization
the other big thing that i think we
innovated with at deepmind to encourage
invention and and uh and innovation was
the multi-disciplinary organization we
built and we still have today so
deepmind originally was a confluence of
the of the most cutting-edge knowledge
in neuroscience with machine learning
engineering and mathematics right and
and gaming
and then since then we built that out
even further so we have philosophers
here and and uh by you know ethicists
but also other types of scientists
physicists and so on um and that's what
brings together i tried to build a sort
of um new type of bell labs but in this
golden era right uh
and and a new expression of that um to
try and uh foster this incredible sort
of innovation machine so talking about
the humans in the machine
the mind itself is a learning machine
with a lots of amazing human minds in it
coming together to try and build these
uh learning systems
if we return to
the big ambitious dream of alpha fold
that may be the early steps on a very
long journey in um
in biology
do you think the same kind of approach
can use to predict the structure and
function of more complex biological
systems so multi-protein interaction
and then
i mean you can go out from there just
simulating bigger and bigger systems
that eventually simulate something like
the human brain or the human body just
the big mush the mess of the beautiful
resilient mesobiology do do you see that
as a long-term vision i do and i think
um
you know if you think about what are the
things top things i wanted to apply ai
ai2 once we had powerful enough systems
biology and curing diseases and
understanding biology uh was right up
there you know top of my list that's one
of the reasons i personally pushed that
myself and with alpha fold but i think
alpha fold uh amazing as it is is just
the beginning um and and and i hope it's
evidence of uh what could be done with
computational methods so um you know
alpha fold solve this this huge problem
of the structure of proteins but biology
is dynamic so really what i imagine from
here we're working on all these things
now is protein protein interaction uh
protein ligand binding so reacting with
molecules um then you want to get build
up to pathways and then eventually a
virtual cell that's my dream uh maybe in
the next 10 years and i've been talking
actually to a lot of biologists friends
of mine paul nurse who runs the qrik
institute amazing biologist nobel prize
winning biologist we've been discussing
for 20 years now virtual cells could you
build a virtual simulation of a cell and
if you could that would be incredible
for biology and disease discovery
because you could do loads of
experiments on the virtual cell and then
only at the last stage validate it in
the wet lab so you could you know in
terms of the search space of discovering
new drugs you know it takes 10 years
roughly to go from uh uh to to go from
uh you know identifying a target to uh
having a drug candidate um maybe that
could be shortened to you know by an
order of magnitude with if you could do
most of that that that work in silico so
in order to get to a virtual cell
we have to build up uh uh understanding
of different parts of biology and the
interactions and and um so you know
every every few years we talk about this
with i talked about this with paul and
then finally last year after alpha fault
i said now is the time we can finally go
for it and and alpha falls the first
proof point that this might be possible
uh and he's very excited when we have
some collaborations with his with his
lab they're just across the road
actually from us as you know wonderful
being here in king's cross with the
quick institute across the road and um
and i think the next steps you know i
think there's going to be some amazing
advances in biology built on top of
things like alpha fold uh we're already
seeing that with the community doing
that after we've open sourced it and
released it um and uh you know i also i
often say that i think
uh if you think of mathematics is the
perfect description language for physics
i think ai might be end up being the
perfect description language for biology
because
biology is so messy it's so emergent so
dynamic and complex um i think i find it
very hard to believe we'll ever get to
something as elegant as newton's laws of
motions to describe a cell right it's
just too complicated um so i think ai is
the right tool for this you have to uh
you have to start at the basic building
blocks and use ai to run the simulation
for all those building blocks so have a
very strong way to do prediction of what
given these building blocks what kind of
biology how the
the function
and the evolution of that biological
system
it's almost like a cellular automata you
have to run you can't analyze it from a
high level you have to take the basic
ingredients figure out the rules yeah
and let it run but in this case the
rules are very difficult to figure out
yes yes learn them that's exactly it so
it's the biology is too complicated to
figure out the rules it's it's it's too
emergent too dynamic say compared to a
physics system like the motion of a
planet yeah right and and so you have to
learn the rules and that's exactly the
type of systems that we're building so
you you mentioned you've open sourced
alpha fold and even the data involved
to me personally also
really happy and a big thank you for
open sourcing mijoko
uh the physics simulation engine that's
that's often used for robotics research
and so on so i think that's a pretty
gangster move uh so what what's the
what's i mean this uh
very few companies or people would do
that kind of thing what's the philosophy
behind that you know it's a case-by-case
basis and in both those cases we felt
that was the maximum benefit to humanity
to do that and and the scientific
community in one case the robotics uh
physics community with mojoco so
purchased it we purchased
to obs we purchased it for the express
principle to open source it so
um so
you know i hope people appreciate that
it's great to hear that you do and then
the second thing was and mostly we did
it because the person building it is uh
uh would not it was not able to cope
with supporting it anymore because it
was it got too big for him his amazing
professor uh who who built it in the
first place so we helped him out with
that and then with alpha folds even
bigger i would say and i think in that
case we decided that there were so many
downstream applications of alpha fold um
that we couldn't possibly even imagine
what they all were so the best way to
accelerate uh drug discovery and also
fundamental research would be to to um
give all that data away and and and the
and the and the system itself um you
know it's been so gratifying to see what
people have done that within just one
year which is a short amount of time in
science and uh it's been used by
over 500 000 researchers have used it we
think that's almost every biologist in
the world i think there's roughly 500
000 biologists in the world professional
biologists have used it to to look at
their proteins of interest
we've seen amazing fundamental research
done so a couple of weeks ago front
cover there was a whole special issue of
science including the front cover which
had the nuclear pore complex on it which
is one of the biggest proteins in the
body the nuclear poor complex is a
protein that governs all the nutrients
going in and out of your cell nucleus so
they're like little hole gateways that
open and close to let things go in and
out of your cell nucleus so they're
really important but they're huge
because they're massive doughnut rings
shaped things and they've been looking
to try and figure out that structure for
decades and they have lots of you know
experimental data but it's too low
resolution there's bits missing and they
were able to like a giant lego jigsaw
puzzle use alpha fold predictions plus
experimental data and combined those two
independent sources of information uh
actually four different groups around
the world were able to put it together
the sec more or less simultaneously
using alpha fault predictions so that's
been amazing to see and pretty much
every pharma company every drug company
executive i've spoken to has said that
their teams are using alpha fold to
accelerate whatever drugs uh uh they're
trying to discover so i think the
knock-on effect has been enormous in
terms of uh the impact that uh
alpha-fold has made and it's probably
bringing in it's creating biologists
it's bringing more people into the field
um
both on the excitement and both on the
technical skills involved
and um
it's almost like uh a gateway drug to
biology yes it is you get more
computational people involved too
hopefully and and i think for us you
know the next stage as i said you know
in future we have to have other
considerations too we're building on top
of alpha fold and these other ideas i
discussed with you about protein protein
interactions and and genomics and other
things and not everything will be open
source some of it will will do
commercially because that will be the
best way to actually get the most
resources and impact behind it in other
ways some other projects will do
non-profit style um and also we have to
consider for future things as well
safety and ethics as well like but you
know synthetic biology there are you
know there is dual use and we have to
think about that as well with alpha fold
we you know we consulted with 30
different bioethicists and and other
people expert in this field to make sure
it was safe before um we released it so
there'll be other considerations in
future but for right now you know i
think alpha fold is a kind of a gift
from us to to to the scientific
community so i'm pretty sure
that
something like alpha fold
uh would be part of nobel prizes in the
future
but us humans of course are horrible
with credit assignment so we'll of
course give it to the humans
do you think there will be a day
when ai system
can't be denied
that it earned that nobel prize do you
think we'll see that in 21st century it
depends what type of ais we end up
building right whether they're um
you know goal seeking agents who
specifies the goals uh who comes up with
the hypotheses
who you know who determines which
problems to tackle right so i think it's
about an announcement yeah so it's
announcing the results exactly as part
of it um so i think right now of course
it's it's it's it's amazing human
ingenuity that's behind these systems
and then the system in my opinion is
just a tool you know it'd be a bit like
saying with galileo and his telescope
you know the ingenuity the the the
credit should go to the telescope i mean
it's clearly galileo building the tool
which he then uses
so i still see that in the same way
today even though these tools learn for
themselves um they're i think i think of
things like alpha fold and that the
things we're building as the ultimate
tools for science and for acquiring new
knowledge to help us as scientists
acquire new knowledge i think one day
there will come a point where
an ai system may solve or come up with
something like general relativity of its
own bat not just by
averaging everything on the internet or
averaging everything on pubmed
although that would be interesting to
see what that would come up with um so
that to me is a bit like our earlier
debate about creativity you know
inventing go rather than just coming up
with a good go move and um so i think uh
solving i think to to you know if we
wanted to give it the credit of like a
nobel type of thing then it would need
to invent go uh and sort of invent that
new conjecture out of the blue um rather
than being specified by the the human
scientists or the human creators so i
think right now that's it's definitely
just a tool although it is interesting
how far you get by averaging everything
on the internet like you said because
you know
a lot of people do see science as you're
always standing on the shoulders of
giants and
the question is how much are you really
reaching
up above the shoulders of giants maybe
it's just assimilating different kinds
of
results of the past
with ultimately this new perspective
that gives you this breakthrough idea
but that idea may not be
novel in the way that we can't be
already discovered on the internet maybe
the nobel prizes
of the next 100 years are already all
there on the internet to be discovered
they could be they could be i mean i
think um
this is one of the big mysteries i think
is that uh
uh i i first of all i believe a lot of
the big new breakthroughs that are going
to come in the next few decades and even
in the last decade are going to come at
the intersection between different
subject areas where um there'll be some
new connection that's found between what
seemingly with disparate areas and and
one can even think of deep mind as i
said earlier as a sort of
interdisciplinary between neuroscience
ideas and ai engineering ideas uh
originally and so um so i think there's
that and then one of the things we can't
imagine today is and one of the reasons
i think people we were so surprised by
how well large models worked is that
actually
it's very hard for our human minds our
limited human minds to understand what
it would be like to read the whole
internet right i think we can do a
thought experiment and i used to do this
of like well what if i read the whole of
wikipedia
what would i know and i think our minds
can just about comprehend maybe what
that would be like but the whole
internet is beyond comprehension so i
think we just don't understand what it
would be like to be able to hold all of
that in mind potentially right and then
active at once and then maybe what are
the connections that are available there
so i think no doubt there are huge
things to be discovered just like that
but i do think there is this other type
of creativity of true spark of new
knowledge new idea never thought before
about can't be average from things that
are known um that really of course
everything come you know nobody creates
in a vacuum so there must be clues
somewhere but just a unique way of
putting those things together i think
some of the greatest scientists in
history have displayed that i would say
although it's very hard to know going
back to their time what was exactly
known uh when they came up with those
things although
you're making me really think because
just the thought experiment of deeply
knowing a hundred wikipedia pages
i don't think i can um
i've been really impressed by wikipedia
for for technical topics yeah so if you
know a hundred pages or a thousand pages
i don't think who can visually truly
comprehend what's
what kind of intelligence that is that's
a pretty powerful intelligence if you
know how to use that and integrate that
information correctly yes i think you
can go really far you can probably
construct thought experiments based on
that
like simulate different ideas so if this
is true let me run this thought
experiment then maybe this is true it's
not really invention it's like just
taking literally the knowledge and using
it to construct a very basic simulation
of the world i mean some argue it's
romantic in part but einstein would do
the same kind of things with a thought
experiment yeah one could imagine doing
that systematically across millions of
wikipedia pages plus pubmed all these
things i think there are
many many things to be discovered like
that they're hugely useful you know you
could imagine and i want us to do some
of those things in material science like
room temperature superconductors or
something on my list one day i'd like to
like you know have an ai system to help
build better optimized batteries all of
these sort of mechanical things mr i
think a systematic sort of search could
be uh
guided by a model could be um could be
extremely powerful so speaking of which
you have a paper on nuclear fusion
uh magnetic control of tokamak plasmas
to deep reinforcement learning so you uh
you're seeking to solve nuclear fusion
with deep rl
so it's doing control of high
temperature plasmas can you explain this
work
and uh can ai eventually solve nuclear
fusion
it's been very fun last year or two and
very productive because we've been
taking off a lot of my
dream projects if you like of things
that i've collected over the years of
areas of science that i would like to i
think could be very transformative if we
helped accelerate and uh really
interesting problems scientific
challenges in of themselves
this is energy so energy yes exactly so
energy and climate so we talked about
disease and biology as being one of the
biggest places i think ai can help with
i think energy and climate uh is another
one so maybe they would be my top two um
and fusion is one one area i think ai
can help with now fusion has many
challenges mostly physics material
science and engineering challenges as
well to build these massive fusion
reactors and contain the plasma and what
we try to do whenever we go into a new
field
to apply our systems is we look for um
we talk to domain experts we try and
find the best people in the world to
collaborate with um
in this case in fusion we we
collaborated with epfl in switzerland
the swiss technical institute who are
amazing they have a test reactor that
they were willing to let us use which
you know i double checked with the team
we were going to use carefully and
safely
i was impressed they managed to persuade
them to let us use it and um and it's a
it's an amazing test reactor they have
there and they try all sorts of pretty
crazy experiments on it and um the the
the what we tend to look at is if we go
into a new domain like fusion what are
all the bottleneck problems uh like
thinking from first principles you know
what are all the bottleneck problems
that are still stopping fusion working
today and then we look at we you know we
get a fusion expert to tell us and then
we look at those bottlenecks and we look
at the ones which ones are amenable to
our ai methods today yes right and and
and then and would be interesting from a
research perspective from our point of
view from an ai point of view and that
would address one of their bottlenecks
and in this case plasma control was was
perfect so you know the plasma it's a
million degrees celsius something like
that it's hotter than the sun
and there's obviously no material that
can contain it so they have to be
containing these magnetic very powerful
superconducting magnetic fields but the
problem is plasma is pretty unstable as
you imagine you're kind of holding a
mini sun mini star in a reactor so you
know you you kind of want to predict
ahead of time
what the plasma's going to do so you can
move the magnetic field within a few
milliseconds you know to to basically
contain what it's going to do next so it
seems like a perfect problem if you
think of it for like a reinforcement
learning prediction problem so uh you
know your controller you're gonna move
the magnetic field and until we came
along you know they were they were doing
it with with traditional operational uh
research type of uh controllers uh which
are kind of handcrafted and the problem
is of course they can't react in the
moment to something the plasma's doing
that they have to be hard-coded and
again knowing that that's normally our
go-to solution is we would like to learn
that instead and they also had a
simulator of these plasma so there were
lots of criteria that matched what we we
like to to to use
so can ai eventually solve nuclear
fusion well so we with this problem and
we published it in a nature paper last
year uh we held the fusion that we held
the plasma in specific shapes so
actually it's almost like carving the
plasma into different shapes and control
and hold it there for the record amount
of time so um so that's one of the
problems of of fusion sort of um solved
so i have a controller that's able to no
matter the shape uh contain it continue
yeah contain it and hold it in structure
and there's different shapes that are
better for for the energy productions
called droplets and and and so on so um
so that was huge and now we're looking
we're talking to lots of fusion startups
to see what's the next problem we can
tackle uh in the fusion area
so another fascinating place
in a paper title pushing the frontiers
of density functionals by solving the
fractional electron problem so you're
taking on
modeling and simulating the quantum
mechanical behavior of electrons yes
um
can you explain this work and can ai
model and simulate arbitrary quantum
mechanical systems in the future yeah so
this is another problem i've had my eye
on for you know a decade or more which
is um
uh sort of simulating the properties of
electrons if you can do that you can
basically describe how elements and
materials and substances work so it's
kind of like fundamental if you want to
advance material science um and uh you
know we have schrodinger's equation and
then we have approximations to that
density functional theory these things
are you know are famous and um people
try and write approximations to to these
uh uh to these functionals and and kind
of come up with descriptions of the
electron clouds where they're gonna go
how they're gonna interact when you put
two elements together uh and what we try
to do is learn a simulation uh uh
learner functional that will describe
more chemistry types of chemistry so um
until now you know you can run expensive
simulations but then you can only
simulate very small uh molecules very
simple molecules we would like to
simulate large materials um and so uh
today there's no way of doing that and
we're building up towards uh building
functionals that approximate
schrodinger's equation and then allow
you to describe uh what the electrons
are doing
and all materials sort of science and
material properties are governed by the
electrons and and how they interact so
have a good summarization of the
simulation through the functional
um
but one that is still
close to what the actual simulation
would come out with so what um
how difficult is that to ask what's
involved in that task is it running
those
those complicated simulations yeah and
learning the task of mapping from the
initial conditions and the parameters of
the simulation learning what the
functional would be yeah so it's pretty
tricky and we've done it with um you
know the nice thing is we there are we
can run a lot of the simulations that
the molecular dynamics simulations on
our compute clusters and so that
generates a lot of data so in this case
the data is generated so we like those
sort of systems and that's why we use
games simulator generated data
and we can kind of create as much of it
as we want really um and just let's
leave some you know if any computers are
free in the cloud we just run we run
some of these calculations right compute
cluster calculation that's all the the
free compute times used up on quantum
mechanics quantum mechanics exactly
simulations and protein simulations and
other things and so um and so you know
when you're not searching on youtube for
video cat videos we're using those
computers usefully and quantum chemistry
that's the idea
and and putting them for good use and
then yeah and then all of that
computational data that's generated we
can then try and learn the functionals
from that which of course are way more
efficient
once we learn the functional than um
running those simulations would be
do you think one day ai may allow us to
do something like basically crack open
physics so do something like travel
faster than the speed of light
my ultimate aim has always been with ai
is
um the reason i am personally working on
ai for my whole life it was to build a
tool to help us understand stand the
universe so i wanted to and that means
physics really and the nature of reality
so
um
uh i don't think we have systems that
are capable of doing that yet but when
we get towards agi i think um that's one
of the first things i think we should
apply agi to
i would like to test the limits of
physics and our knowledge of physics
there's so many things we don't know
there's one thing i find fascinating
about science and you know as a huge
proponent of the scientific method as
being one of the greatest ideas
humanity's ever had and allowed us to
progress with our knowledge
but i think as a true scientist i think
what you find is the more you find out
uh you the more you realize we don't
know
and and i always think that it's
surprising that more people don't aren't
troubled you know every night i think
about all these things we interact with
all the time that we have no idea how
they work time
consciousness gravity
life we can't i mean these are all the
fundamental things of nature i think the
way we don't really know what they are
to live life we uh pin certain
assumptions on them and kind of treat
our assumptions as if they're a fact
yeah that allows us to sort of box them
off somehow yeah box them off
but the reality is when you think of
time
you should remind yourself you should
put it off the sh
take it off the shelf and realize like
no we have a bunch of assumptions
there's still a lot of there's even now
a lot of debate there's a lot of
uncertainty about exactly what is time
uh is there an error of time you know
there's there's a lot of fundamental
questions you can't just make
assumptions about and maybe
ai allows you to um
not put anything on the shelf
yeah not make any uh hard assumptions
and really open it up and see what
exactly i think we should be truly
open-minded about that and uh exactly
that not be dogmatic to a particular
theory
um it'll also allow us to build better
tools experimental tools eventually
that can then test certain theories that
may not be testable today about as
things about like
what we spoke about at the beginning
about the computational nature of the
universe how one might if that was true
how one might go about testing that
right and and how much uh you know there
are people who've conjectured people
like uh scott aronson and others about
uh you know how much information can a
specific planck unit of space and time
contain right so one might be able to
think about testing those ideas if you
had um
ai helping you build some new exquisite
uh uh experimental tools this is what i
imagine you know many decades from now
we'll be able to do
and what kind of
questions can be answered through
running a simulation
of of them so there's a bunch of physics
simulations you can imagine that could
be run
in an uh so some kind of efficient way
much like you're doing in the quantum
simulation work
and perhaps even the origin of life so
figuring out how
going even back before the work of alpha
fault begins of how this whole whole
thing
um emerges from a rock yes from a static
thing would what do you do you think ai
will allow us to is that something you
have your eye on it's trying to
understand the origin of life first of
all yourself
what do you think
um
how the heck did life originate on earth
yeah well maybe we i'll come to that in
a second but i think the ultimate
use of ai is to
kind of use it to accelerate science to
the maximum so i
um think of it a little bit like the
tree of all knowledge if you imagine
that's all the knowledge there is in the
universe to attain
and we sort of barely scratched the
surface of that so far in even though
you know we've we've done pretty well
since the enlightenment right as
humanity and i think ai will turbo
charge all of that like we've seen with
alpha fold and i want to explore as much
of that tree of knowledge as it's
possible to do and um and i think that
involves ai helping us with with with
understanding or finding patterns um but
also potentially designing and building
new tools experimental tools so i think
that's all uh
and also running simulations and
learning simulations all of that we're
already we're sort of doing it at a at a
at a you know baby steps level here but
i can imagine that in in in the decades
to come as uh you know what's the full
flourishing of of that line of thinking
it's going to be truly incredible i
would say if i visualize this tree of
knowledge something tells me that that
knowledge for tree of knowledge for
humans is much smaller
in the set of all possible trees of
knowledge is actually quite small giving
our cognitive
limitations
limited cognitive capabilities that even
with with the tools we build we still
won't be able to understand a lot of
things and that's perhaps what non-human
systems might be able to reach farther
not just as tools
but in themselves understanding
something that they can bring back yeah
it could well be so i mean there's so
many things that that are sort of
encapsulated in what you just said there
i think first of all um
there's there's two different things
there's like what do we understand today
yeah what could the human mind
understand and what is the totality of
what is there to be understood yeah
right and so there's three consensus you
know you can think of them as three
larger and larger trees or exploring
more branches of that tree and i i think
with ai we're going to explore that
whole lot now the question is is uh you
know if you think about what is the
totality of what could be understood um
there may be some fundamental physics
reasons why certain things can't be
understood like what's outside the
simulation or outside the universe maybe
it's not understandable from within the
universe
so that's there may be some hard
constraints like that you know it could
be smaller constraints like
um we think of space time as fundamental
us our human brains are really used to
this idea of a three-dimensional world
with time right
maybe but our tools could go beyond that
they wouldn't have that limitation
necessary they could think in 11
dimensions 12 dimensions whatever is
needed but um we could still maybe
understand that in several different
ways the example i always give is um
when i you know play gary kasparov at
speed chess or we've talked about chess
and these kind of things um you know he
if you if you if you're reasonably good
at chess you can um you can't come up
with the move gary comes up with in his
move but he can explain it to you and
you can understand and you can
understand post hoc the reasoning yeah
so so i think there's a there's an even
further level of like well maybe you
couldn't have invented that thing but
but using like going back to using
language again perhaps you can
understand and appreciate that same way
like you can appreciate you know vivaldi
or mozart or something without you can
appreciate the beauty of that without um
being able to to construct it yourself
right invent the music yourself so i
think we see this in all forms of life
so it'll be that times you know
a million but it would you can imagine
also one sign of intelligence is the
ability to explain things clearly and
simply right you know people like
richard feynman another one of my
all-time heroes used to say that right
if you can't you know if you can explain
it something simply then you that's a
that's the best sign a complex topic
simply then that's one of the best signs
of you understanding it yeah so i can
see myself talking trash in the ai
system in that way yes uh
it gets frustrated how dumb i am and
trying to explain something to me i was
like well that means you're not
intelligent because if you were
intelligent you'd be able to explain it
simply yeah of course you know there's
also the other option of course we could
enhance ourselves and and without
devices we we are already sort of
symbiotic with our compute devices right
with our phones and other things and you
know this stuff like neural link and etc
that could be could could advance that
further um so i think there's lots of
lots of really amazing possibilities uh
that i could foresee from here well let
me ask you some wild questions so out
there
looking for friends
do you think there's a lot of alien
civilizations out there
so i guess this also goes back to your
origin of life question too because i
think that that's key
um
my personal opinion looking at all this
and and you know it's one of my hobbies
physics i guess so so i i you know it's
something i think about a lot and talk
to a lot of experts on and and and read
a lot of books on and i think
my feeling currently is that that we are
alone i think that's the most likely
scenario given what what evidence we
have so um and the reasoning is i think
that
you know we've tried since uh things
like seti program and i guess since the
dawning of the the space age uh we've
you know had telescopes open radio
telescopes and other things and if you
think about um and try to detect signals
now if you think about the evolution of
humans on earth we could have easily
been um a million years ahead of our
time now or million years behind quite
easily with just some slightly different
quirk thing happening hundreds of
thousands years ago uh you know things
could have been slightly different if
the bto had hit the dinosaurs a million
years earlier maybe things would have
evolved uh we'd be a million years
ahead of where we are now so what that
means is if you imagine where humanity
will be in a few hundred years let alone
a million years especially if we
hopefully um
you know solve things like climate
change and other things and we continue
to flourish
and we build things like ai and we do
space traveling and all of the stuff
that that humans have dreamed of for
forever right and sci-fi has talked
about forever um
we will be spreading across the stars
right and void neumann famously
calculated you know it would only take
about a million years if you send out
von neumann probes to the nearest you
know the nearest uh uh other solar
systems and and then they built all they
did was build two more versions of
themselves and set those two out to the
next nearest systems uh you you know
within a million years i think you would
have one of these probes in every system
in the galaxy so it's not actually in
cosmo cosmological time that's actually
a very short amount of time
so and and you know we've people like
dyson have thought about constructing
dyson spheres around stars to collect
all the energy coming out of the star
you know that there would be
constructions like that would be visible
across base um probably even across a
galaxy so and then you know if you think
about all of our radio television uh
emissions that have gone out since since
the you know 30s and 40s um imagine a
million years of that and now hundreds
of civilizations doing that when we
opened our ears at the point we got
technologically sophisticated enough in
the space age we should have
heard a cacophony of voices we should
have joined that cacophony of voices and
what we did we opened our ears and we
heard nothing
and many people who argue that there are
aliens would say well we haven't really
done exhaustive search yet and maybe
we're looking in the wrong bands and and
we've got the wrong devices and we
wouldn't notice what an alien form was
like to be so different to what we're
used to but you know i'm not i don't
really buy that that it shouldn't be as
difficult as that like we i think we've
searched enough there should be if it
were everywhere if it was it should be
everywhere we should see dyson's fears
being put up sun's blinking in and out
you know there should be a lot of
evidence for those things and then there
are other people argue well the sort of
safari view of like well we're a
primitive species still because we're
not space faring yet and and and we're
you know there's some kind of globe like
universal rule not to interfere star
trek rule but like look look we can't
even coordinate humans to deal with
climate change and we're one species
what is the chance that of all of these
different human civilization you know
alien civilizations they would have the
same priorities and and and and agree
across you know these kind of matters
and even if that was true and we were in
some sort of safari for our own good to
me that's not much different from the
simulation hypothesis because what does
it mean the simulation hypothesis i
think in its most fundamental level it
means what we're seeing is not quite
reality right it's something there's
something more deeper underlying it
maybe computational now if we were in a
if we were in a sort of safari park and
everything we were seeing was a hologram
and it was projected by the aliens or
whatever that to me is not much
different than thinking we're inside of
another universe because we still can't
see true reality right i mean there's
there's other explanations it could be
that
the way they're communicating is just
fundamentally different that we're too
dumb to understand the much better
methods of communication they have it
could be i mean i mean it's silly to say
but
our own thoughts could be the methods by
which they're communicating like the
place from which our ideas writers talk
about this like the muse yeah
it sounds like very kind of uh
wild but it could be thoughts it could
be
some interactions with our mind that we
think are originating from
us is actually something that uh
is coming from other life forms
elsewhere consciousness itself might be
that it could be but i don't see any
sensible argument to the why why would
all of the alien species be using this
way yes some of them will be more
primitive they would be close to our
level you know there would there should
be a whole sort of normal distribution
of these things right some would be
aggressive some would be you know
curious others would be very stoical and
philosophical because you know maybe
they're a million years older than us
but it's not it shouldn't be like what i
mean one one alien civilization might be
like that communicating thoughts and
others but i don't see why you know
potentially the hundreds there should be
would be uniform in this way right it
could be a violent dictatorship that the
the people the alien civilizations that
uh become successful
become um
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gain the ability to be destructive an
order of magnitude more destructive
but of course the the sad thought
well
either humans are very special we took a
lot of leaps that arrived at what it
means to be human yeah
um
there's a question there which was the
hardest which was the most special but
also if others have reached this level
and maybe many others have reached this
level
the great filter
that prevented them from going farther
to becoming a multi-planetary species or
reaching out into the stars
and those are really important questions
for us whether
um
whether there's other alien
civilizations out there or not this is
very useful for us to think about if we
destroy ourselves
how will we do it and how easy is it to
do yeah well you know these are big
questions and i've thought about these a
lot but the the the interesting thing is
that if we're if we're alone
that's somewhat comforting from the
great filter perspective because it
probably means the great filters were
are past us and i'm pretty sure they are
so that by in going back to your origin
of life question there are some
incredible things that no one knows how
happened like obviously the first
life form from chemical soup that seems
pretty hard but i would guess the
multicellular i wouldn't be that
surprised if we saw single
single cell sort of life forms elsewhere
uh bacteria type things but
multicellular life seems incredibly hard
that step of you know capturing
mitochondria and then sort of using that
as part of yourself you know when you've
just eaten it would you say that's the
biggest
the most uh like
if if you had to choose one sort of uh
hitchhiker's got this galaxy one
sentence summary of like oh those clever
creatures did this that would be the
multilist i think that was probably the
one that that's the biggest i mean
there's a great book called the 10 grand
great inventions of evolution by nick
lane and he speculates on 10 10 of these
you know what could be great filters um
i think that's one i think the the
advent of of intelligence and and
conscious intelligence and in order you
know to us to be able to do science and
things like that is huge as well i mean
it's only evolved once as far as you
know uh in in earth history so that
would be a later candidate but there's
certainly for the early candidates i
think multicellular life forms is huge
by the way what it's interesting to ask
you if you can hypothesize about
what is the origin of intelligence is it
uh
that we started
cooking meat over fire
is it that we somehow figured out that
we could be very powerful when we start
collaborating so cooperation between
um our ancestors
so that we can overthrow the alpha male
uh what is it richard i talked to
richard randham who thinks we're all
just beta males who figured out how to
collaborate to defeat the one the
dictator the authoritarian alpha male
um that control the tribe um is there
other explanation did was there um 2001
space out any type of monolith yeah that
came down to earth well i i think um i
think all of those things you suggest
for good candidates fire and and and
cooking right so that's clearly
important
you know energy efficiency yeah cooking
our meat and then and then being able to
to to be more efficient about eating it
and getting it consuming the energy um i
think that's huge and then utilizing
fire and tools i think you're right
about the the tribal cooperation aspects
and probably language as part of that
yes um because probably that's what
allowed us to outcompete neanderthals
and and perhaps less cooperative species
so um so that may be the case tool
making spears axes i think that let us i
mean i think it's pretty clear now that
humans were responsible for a lot of the
extinctions of megafauna um especially
in in the americas when humans arrived
so uh you can imagine once you discover
tool usage how powerful that would have
been and how scary for animals so i
think all of those could have been
explanations for it you know the
interesting thing is that it's a bit
like general intelligence too is it's
very costly to begin with to have a
brain
and especially a general purpose brain
rather than a special purpose one
because the amount of energy our brains
use i think it's like 20 of the body's
energy and it's it's massive and when
you're thinking chest one of the funny
things that that we used to say is as
much as a racing driver uses for a whole
you know formula one race if just
playing a game of you know serious high
level chess which you you know you
wouldn't think just sitting there um
because the brain's using so much uh
energy so in order for an animal an
organism to justify that there has to be
a huge payoff and the problem with with
half a brain or half you know
intelligence saying iqs of you know
of like a monkey brain it's
it's not clear you can justify that
evolutionary until you get to the human
level brain and so but how do you how do
you do that jump it's very difficult
which is why i think it's only been done
once from the sort of specialized brains
that you see in animals to this sort of
general purpose chewing powerful brains
that humans have um and which allows us
to invent the modern modern world um and
uh you know it takes a lot to to cross
that barrier and i think we've seen the
same with ai systems which is that uh
maybe until very recently it's always
been easier to craft a specific solution
to a problem like chess than it has been
to build a general learning system that
could potentially do many things because
initially uh that system will be way
worse than uh less efficient than the
specialized system so one of the
interesting
quirks of the human mind of this evolved
system is that it appears to be
conscious
this thing that we don't quite
understand but it seems very
very special its ability to have a
subjective experience that it feels like
something
to eat a cookie the deliciousness of it
or see a color and that kind of stuff do
you think in order to solve intelligence
we also need to solve consciousness
along the way do you think agi systems
need to have consciousness in order to
be
truly intelligent yeah we thought about
this a lot actually and um i think that
my guess is that consciousness and
intelligence are double dissociable so
you can have one without the other both
ways and i think you can see that with
consciousness in that i think some
animals and pets if you have a pet dog
or something like that you can see some
of the higher animals and dolphins
things like that are uh have
self-awareness and uh very sociable um
seem to dream um you know those kinds of
a lot of the traits one would regard as
being kind of conscious and self-aware
um and but yet they're not that smart
right uh so they're not that intelligent
by by say iq standards or something like
that yeah it's also possible that our
understanding of intelligence is flawed
like putting an iq to it sure maybe the
thing that a dog can do
is actually gone very far along the path
of intelligence and we humans are just
able to
play chess and maybe write poems right
but if we go back to the idea of agi and
general intelligence you know dogs are
very specialized right most animals are
pretty specialized they can be amazing
at what they do but they're like kind of
elite sports sports people or something
right so they do one thing extremely
well because their entire brain is is
optimized they have somehow convinced
the entirety of the human population to
feed them and service them so in some
way they're controlling yes exactly well
we co-evolved to some crazy degree right
uh including the the way the dogs you
know even even wag their tails and
twitch their noses right we find we're
finding inexorably cute yeah um but i
think um you can also see intelligence
on the other side so systems like
artificial systems that are amazingly
smart at certain things like maybe
playing go and chess and other things
but they don't feel at all in any shape
or form conscious in the way that you
know you do to me or i do to you and um
and i think actually
building ai
is uh these intelligent constructs uh is
one of the best ways to explore the
mystery of consciousness to break it
down because um we're going to have
devices that are
pretty smart at certain things or
capable of certain things but
potentially won't have any semblance of
self-awareness or other things and in
fact i would advocate
if there's a choice building systems in
the first place ai systems that are not
conscious to begin with uh are just
tools um until we understand them better
and the capabilities better so on that
topic just not
as the ceo of deep mind
just as a human being let me ask you
about this one particular anecdotal
evidence of the google engineer
who made a comment
or
believed that there's some aspect of a
language model
the lambda language model that exhibited
sentience
so you said you believe there might be a
responsibility to build systems that are
not essential and this experience of a
particular engineer i think i'd love to
get your general opinion on this kind of
thing but i think it will happen more
and more and more
which uh not when engineers but when
people out there that don't have an
engineering background start interacting
with increasingly intelligent systems
we anthropomorphize them they they start
to have deep impactful
um interactions with us in a way that we
miss them yeah when they're gone
and
we sure feel like they're
living entities self-aware entities and
maybe even we project sentience onto
them so what what's your thought about
this particular
uh system was is uh
have you ever met a language model
that's sentient
no i no no what do you make of the case
of when you kind of
feel
that there's some elements of sentience
to this system yeah so this is you know
an interesting question and uh uh
obviously a very fundamental one so
first thing to say is i think that
none of the systems we have today i i
would say even have one iota of uh
semblance of consciousness or sentience
that's my personal feeling interacting
with them every day so i think that's
way premature to be discussing what that
engineer talked about i appreciate i
think at the moment it's more of a
projection of the way our own minds work
which is to see
uh uh uh sort of purpose and direction
in almost anything that we you know our
brains are trained to interpret uh
agency basically in things uh even the
an inanimate thing sometimes and of
course with a a language system because
language is so fundamental to
intelligence that's going to be easy for
us to anthropomorphize that
i mean back in the day even the first uh
you know the dumbest sort of template
chatbots ever eliza and and and and the
ilk of the original chatbots back in the
60s fooled some people under certain
circumstances right they pretended to be
a psychologist so just basically rabbit
back to you the same question you asked
it back to you um
and uh some people believe that so i
don't think we can this is why i think
the turing test is a little bit flawed
as a formal test because it depends on
the sophistication of the of the judge
um whether or not they are qualified to
make that distinction so
i think we should uh talk to you know
the the top philosophers about this
people like daniel dennett and uh david
chalmers and others who've obviously
thought deeply about consciousness of
course consciousness itself hasn't been
well there's no agreed definition if i
was to you know uh speculate about that
uh you know i kind of the definite the
working definition i like is it's the
way information feels when you know it
gets processed i think maybe max tegmark
came up with that i like that idea i
don't know if it helps us get towards
any more operational thing but but it's
it's it's i think it's a nice way of
viewing it um i think we can obviously
see from neuroscience certain
prerequisites that are required like
self-awareness i think is necessary but
not sufficient component this idea of a
self and other and set of coherent
preferences that are coherent over time
you know these things are maybe memory
um these things are probably needed for
a sentient or conscious being um but but
the reason that the difficult thing i
think for us when we get and i think
this is a really interesting
philosophical debate is when we get
closer to agi and and and you know
and and much more powerful systems than
we have today
um how are we going to make this
judgment and one way which is the turing
test is sort of a behavioral judgment is
is the system exhibiting all the
behaviors um that a human sentient uh or
a sentient being would would would
exhibit um is it answering the right
questions is it saying the right things
is it indistinguishable from a human um
and so on
but i think there's a second thing that
makes us as humans regard each other as
sentient right why do we why do we think
this and i debated this with daniel
dennett and i think there's a second
reason that's often overlooked which is
that we're running on the same substrate
right so if we're exhibiting the same
behavior uh more or less as humans and
we're running on the same you know
carbon-based biological substrate the
squishy you know few pounds of of flesh
in our skulls then the most parsimonious
i think explanation is that you're
feeling the same thing as i'm feeling
right but we will never have that second
part the substrate equivalence with a
machine
right so we will have to only judge
based on the behavior and i think the
substrate equivalence is a critical part
of why we make assumptions that we're
conscious and in fact even with with
animals high-level animals why we think
they might be because they're exhibiting
some of the behaviors we would expect
from a sentient animal and we know
they're made of the same things
biological neurons so we're gonna have
to come up with
explanations uh or models of the gap
between substrate differences between
machines and humans did to get anywhere
beyond the behavioral but to me sort of
the practical question is
very interesting and very important when
you have millions perhaps billions of
people believing that you have ascension
ai believing what that google engineer
believed
which i just see is an obvious
very near-term future thing certainly on
the path to agi
how does that change the world
what's the responsibility of the ai
system to help those millions of people
and also what's the ethical thing
because
you can you can make a lot of people
happy
by creating a meaningful deep experience
with a system
that's faking it before it makes it yeah
and i i don't
is a are we the right or who is to say
what's the right thing to do should ai
always be tools like why why why are we
constraining ais to always be tools as
opposed to
friends yeah i think well i mean these
are you know you know fantastic
questions and and also critical ones and
we've been thinking about this uh since
the start of d minor before that because
we planned for success and you know how
how you know you know however remote
that looked like back in 2010 and we've
always had sort of these ethical
considerations as fundamental at
deepmind um and my current thinking on
the language models is and and large
models is they're not ready we don't
understand them well enough yet um and
you know in terms of analysis tools and
and guard rails what they can and can't
do and so on to deploy them at scale
because i think you know there are big
still ethical questions like should an
ai system always announce that it is an
ai system to begin with probably yes um
it what what do you do about answering
those philosophical questions about the
feelings uh people may have about ai
systems perhaps incorrectly attributed
so i think there's a whole bunch of
research that needs to be done first um
to responsibly before you know you can
responsibly deploy these systems at
scale that would be at least be my
current position
over time i'm very confident we'll have
those tools like interpretability
questions um
and uh analysis questions uh and then
with the ethical quandary you know i
think there
it's important to
uh look beyond just science that's why i
think philosophy social sciences even
theology other things like that come
into it where um what you know arts and
humanities what what does it mean to be
human and the spirit of being human and
and to enhance that and and the human
condition right and allow us to
experience things we could never
experience before and improve the the
overall human condition and humanity
overall you know get radical abundance
solve many scientific problems solve
disease so this is the era i think this
is the amazing era i think we're heading
into if we do it right um but we've got
to be careful we've already seen with
things like social media how dual use
technologies can be misused by firstly
by by by bad you know p bad actors or
naive actors or crazy actors right so
there's that set of just the common or
garden misuse of existing dual use
technology and then of course there's an
additional uh uh thing that has to be
overcome with ai that eventually it may
have its own agency so it could be uh uh
uh good or bad in in in of itself so i
think these questions have to be
approached very carefully um using the
scientific method i would say in terms
of hypothesis generation careful control
testing not live a b testing out in the
world because with powerful dual
technologies like ai
if something goes wrong it may cause you
know a lot of harm before you can fix it
um it's not like a you know an imaging
app or game app where you know that if
if something goes wrong it's relatively
easy to fix and and the harm's
relatively small so i think
it comes with you know the the the usual
uh cliche of like with a lot of power
comes a lot of responsibility and i
think that's the case here with things
like ai given the the enormous
opportunity in front of us and i think
we need a lot of voices uh and as many
inputs into things like the design of
the systems and the values
they should have and what goals should
they be put to um i think as wide a
group of voices as possible beyond just
the technologies is needed uh to input
into that and to have a say in that
especially when it comes to deployment
of these systems which is when the
rubber really hits the road it really
affects the general person in the street
rather than fundamental research and
that's why i say
i think as a first step it would be
better if we have the choice to build
these systems as tools to give and i'm
not saying that it should never they
should never go beyond tools because of
course the potential is there um for it
to go way beyond just tools uh but um i
think that would be a good first step
in order for us to you know allow us to
carefully experiment understand what
these things can do so the leap between
tool to sentient entity being is one
should take very careful yes
let me ask a dark personal question
so you're one of the most brilliant
people in the ag community also one of
the most
kind
and uh if i may say sort of loved people
in the community that said
uh
creation of a super intelligent ai
system
would be one of the most
powerful
things in the world tools or otherwise
and again as the old saying goes power
corrupts and absolute power crops
absolutely
you are
likely
to be one of the people
i would say probably the most likely
person to be in the control of such a
system
do you think about
the corrupting nature of power when you
talk about these kinds of systems that
um as all dictators
and people have caused atrocities in the
past always think they're doing good
but they don't do good because the
powers polluted their mind about what is
good and what is evil do you think about
this stuff or are we just focused on
language modeling no i think about them
all the time and you know i think
what are the defenses against that i
think one thing is to remain very
grounded and sort of humble uh no matter
what you do or achieve and i try to do
that i might you know my best friends
are still my set of friends from my
undergraduate cambridge days my family's
you know and and friends are very
important
um
i've always i think trying to be a
multi-disciplinary person it helps to
keep you humble because no matter how
good you are at one topic someone will
be better than you at that and it and
always relearning a new topic again from
scratch is or new field is very humbling
right so for me that's been biology over
the last five years you know huge area
topic and and and it's been and i just
love doing that but it helps to keep you
grounded like it keeps you open-minded
and
and then the other important thing is to
have a really good amazing set of uh
people around you at your company or
your organization who are also very
ethical and grounded themselves and help
to keep you that way
and then ultimately just to answer your
question i hope we're going to be a big
part of of birthing ai and that being
the greatest benefit to humanity of any
tool or technology ever and and getting
us into a world of radical abundance and
curing diseases and
and and solving many of the big
challenges we have in front of us and
then ultimately you know help the
ultimate flourishing of humanity to
travel the stars and find those aliens
if they are there and if they're not
there find out why they're not there
what what is going on here in the
universe um this is all to come and and
that's what i've always dreamed about um
but i don't think i think ai is too big
an idea it's not going to be uh there'll
be a certain set of pioneers who get
there first i hope
we're in the vanguard so we can
influence how that goes and i think it
matters who builds who which which
cultures they come from and what values
they have uh the builders of ai systems
because i think even though the ai
system is going to learn for itself most
of its knowledge there'll be a residue
in the system of the culture and the
values of the creators of the system um
and there's interesting questions to to
discuss about that geopolitically you
know different cultures as we're in a
more fragmented world than ever
unfortunately i think in terms of global
cooperation
we see that in things like climate where
we can't seem to get our act together uh
globally to cooperate on these pressing
matters i hope that will change over
time perhaps you know if we get to an
era of radical abundance we don't have
to be so competitive anymore maybe we
can be more cooperative
if resources aren't so scarce it's true
that
in terms of
power corrupting and leading to
destructive things it seems that some of
the atrocities of the past happen when
there's a significant
constraint on resources i think that's
the first thing i don't think that's
enough i think scarcity is one thing
that's led to competition destruct you
know sort of zero sum game thinking i
would like us to all be in a positive
sum world and i think for that you have
to remove scarcity i don't think that's
enough unfortunately to get world peace
because there's also other corrupting
things like wanting power over people
and this kind of stuff which is not
necessarily satisfied by by just
abundance but i think it will help um
and i think uh but i think ultimately ai
is not going to be run by any one person
or one organization i think it should
belong to the world belong to humanity
um and i think maybe many there'll be
many ways this will happen and
ultimately um
everybody should have a say in that
do you have advice
for uh young people in high school and
college maybe um
if they're interested in ai or
interested in having a
big impact on the world what they should
do to have a career they can be proud of
her to have a life they can be proud of
i love giving talks to the next
generation what i say to them is
actually two things i i think the most
important things to learn about and to
find out about when you're when you're
young is what are your true passions is
first of all there's two things one is
find your true passions and i think you
can do that by the way to do that is to
explore as many things as possible when
you're young and you you have the time
and you and you can take those risks um
i would also encourage people to look at
the finding the connections between
things
in a unique way i think that's a really
great way to find a passion second thing
i would say advise is know yourself so
spend a lot of time
understanding how you work best like
what are the optimal times to work what
are the optimal ways that you study um
what are your how do you deal with
pressure
sort of test yourself in various
scenarios and try and improve your
weaknesses but also find out what your
unique skills and strengths are and then
hone those so then that's what will be
your super value in the world later on
and if you can then combine those two
things and find passions that you're
genuinely excited about that intersect
with what your unique strong skills are
then you're you know you're on to
something incredible and and you know i
think you can make a huge difference in
the world so let me ask about know
yourself this is fun this is fun quick
questions about day in the life the
perfect day the perfect productive day
in the life of demise's house yeah maybe
uh maybe these days you're um
there's a lot involved yeah it may be a
slightly younger
you could focus on a demonstration
project maybe um
how early do you wake up are you night
owl do you wake up early in the morning
what are some interesting habits
uh how many dozens of cups of coffees do
you drink a day what's the computer um
that you use
uh what's the setup how many screens
what kind of keyboard are we talking uh
emacs vim are we talking something more
modern so it's a bunch of those
questions so maybe uh day in the life
what what's the perfect day involved
well these days it's quite different
from say 10 20 years ago back 10 20
years ago it would have been you know a
whole day of
research individual research or
programming doing some experiment
neuroscience computer science experiment
reading lots of research papers uh and
then perhaps at night time you know um
reading science fiction books or or uh
playing uh some games but lots of focus
so like deep focused work on whether
it's uh
programming or reading research paper
yes yes so that would be a lot of
debrief you know uh focused work these
days for the last sort of i guess you
know five to ten years i've actually got
quite a structure that works very well
for me now which is that um i'm a night
complete night out always have been so i
optimized for that so you know i get you
know i basically do a normal day's work
get into work about 11 o'clock and sort
of do work to about seven uh in the
office uh and i will arrange
back-to-back meetings for the entire
time of that and with as many me as many
people as possible so that's my
collaboration management part of the day
then i go home uh spend time with the
family and friends uh have dinner uh uh
relax a little bit and then i start a
second day of work i call it my second
day work around 10 pm 11 p.m and that's
the time till about the small hours of
the morning four five in the morning
where i will do my thinking and reading
a research writing research papers um
sadly don't have time to code anymore
but it's it's not efficient to to do
that uh these days uh given the amount
of time i have um but that's when i do
you know maybe do the long kind of
stretches of of thinking and planning
and then probably you know using using
email or other things i would set i
would fire off a lot of things to my
team to deal with the next morning for
actually thinking about this overnight
we should go for this project or arrange
this meeting the next day when you're
thinking through a problem are you
talking about a sheet of paper or the
patent pen is there some independent
structure yeah i like processes i still
like pencil and paper best for working
out things but um these days it's just
so efficient to read research papers
just on the screen i still often print
them out actually i still prefer to
mark out things and i find it goes into
the brain quick better and sticks in the
brain better when you're you're still
using physical pen and pencil and paper
so you take notes with the i have lots
of nodes electronic ones and also um
whole stacks of notebooks that
um that i use at home yeah on some of
these most challenging next steps for
example stuff
none of us know about that you're
working on you're thinking
there's some deep thinking required
there right like what what is the right
problem what is the right approach
because you're gonna have to invest a
huge amount of time for the whole team
they're going to have to pursue this
thing what's the right way to do it is
is rl going to work here or not yes um
what's the right thing to try what's the
right benchmark to use yeah we need to
construct a benchmark from scratch all
those kinds of things yes so i think all
those kind of things in the night time
phase but also much more um i find i've
always found the quiet hours of the
morning um when everyone's asleep it's
super quiet outside um i love that time
it's the golden hours like between like
one and three in the morning um put some
music on some inspiring music on and
then um think these deep thoughts so
that's when i would read you know my
philosophy books and uh spinoza's my you
know recent favorite can all these
things i i i you know read about a great
uh uh
a scientist of history how they did
things how they thought things so that's
when you do all your create that's when
i do all my creative thinking and it's
good i think i think people recommend
you know you do your your your sort of
creative thinking in one block and the
way i organize the day that way i don't
get interrupted because obviously no one
else is up uh at those times so i can i
can go uh you know as i can sort of get
super deep and super into flow the other
nice thing about doing it night time
wise is if i'm really uh onto something
or i've i've got really deep into
something i can choose to extend it and
i'll go into six in the morning whatever
and then i'll just pay for it the next
day yeah cause i'll be a bit tired and i
won't be my best but that's fine i can
decide looking at my schedule the next
day that and given where i'm at with
this particular thought or creative idea
that i'm going to pay that cost the next
day so so i think that's that's more
flexible than morning people who do that
you know they get up at four in the
morning they can also do those golden
hours then but then their start of their
schedule day starts at breakfast you
know 8 a.m whatever they have their
first meeting and then it's hard you
have to reschedule a day if you're in
flow yeah that's going to be i don't
have to see that special threat of
thoughts that
the
you're too passionate about you that
this is where some of the greatest ideas
could potentially come is when you just
lose yourself late into yeah
and for the meetings i mean you're
loading in really hard problems in a
very short amount of time so you have to
do some kind of first principles
thinking here it's like what's the
problem what's the state of things
what's the right next step yes you have
to get really good at context switching
which is one of the hardest things
because especially as we do so many
things if you include all the scientific
things we do scientific fields we're
working in these are entire you know
complex fields in themselves and you you
have to sort of keep up to abreast of
that but i enjoy it i've always been uh
a sort of generalist in a way and that's
actually what happened with my games
career after chess i i i one of the
reasons i stopped playing chess was that
i got into computers but also i started
realizing there were many other great
games out there to play too so
i've always been that way inclined
multidisciplinary and there's too many
interesting things in in the world to
spend all your time just on one thing
so you mentioned spinoza gotta ask the
big
ridiculously big question about life
what do you think is the meaning of this
whole thing
uh why are we humans here you've already
mentioned that perhaps the universe
created us
is that why you think we're here
to understand how the universe yeah i
think my answer to that would be and at
least the the life i'm living is to gain
and uh to gain and understand the
knowledge you know to gain knowledge and
understand the universe that's what i
think uh i can't see any higher purpose
than that if you think back to the
classical greeks you know the virtue of
gaining knowledge it's uh i think it's
that it's one of the few true virtues is
to understand um the world around us and
the context and humanity better and um
and i think if you do that you become
more compassionate and more
understanding yourself and and more
tolerant and all these i think all these
other things may flow from that and to
me
you know understanding the nature of
reality that is the biggest question
what is going on here is sometimes the
colloquial way i say what is really
going on here
uh it's so mysterious i feel like we're
in some huge puzzle and and it's but the
world is also seems to be the universe
seems to be structured in a way you know
why is it structured in a way that
science is even possible that you know
methods the scientific method works
things are repeatable
um it feels like it's almost structured
in a way to be conducive to gaining
knowledge so i feel like and you know
why should computers be even possible
isn't that amazing that uh computational
electronic devices can can can can be
possible and they're made of sand our
most you know common element that we
have you know silicon that on the on the
earth's crust they could be made of
diamond or something then we would have
only had one computer yeah right so it's
a lot of things are kind of slightly
suspicious to me it sure as heck sounds
this puzzle sure sounds like something
we talked about earlier what it takes to
to design a game
that's really fun to play for prolonged
periods of time
and it does seem like this puzzle like
you mentioned the more you learn about
it the more you realize how little you
know
so it humbles you but excites you by the
possibility of learning more it's one
heck of a one heck of a puzzle we got
going on here um so like i mentioned of
all the people in the world you're very
likely to be the one who creates the agi
system
um that achieves human level
intelligence and goes beyond it so if
you got a chance and very well you could
be the person that goes into the room
with the system and have a conversation
maybe you only get to ask one question
if you do
what question would you ask her
i would probably ask um what is the true
nature of reality
i think that's the question i don't know
if i'd understand the answer because
maybe it would be 42 or something like
that but um that's the question i would
ask
and then there'll be a deep sigh from
the systems like all right how do i
explain to the excuse me exactly all
right let me i don't have time
to explain uh maybe i'll draw you a
picture that it is
i mean how do you even begin
um
to answer that question
well i think it would um what would you
what would you think the answer could
possibly look like i think it could it
could start looking like
uh
uh more fundamental explanations of
physics would be the beginning you know
more careful specification of that
taking you walking us through by the
hand as to what one would do to maybe
prove those things out maybe giving you
glimpses of
what things you totally missed in the
physics of today
exactly just here here's glimpses of no
like there's a much
uh
a much more elaborate world or a much
simpler world or something
a much deeper maybe simpler explanation
yes of things right than the standard
model of physics which we know doesn't
work but we still keep adding to so um
and and that's how i think the beginning
of an explanation would look and it
would start encompassing many of the
mysteries that we have wondered about
for thousands of years like you know
consciousness
uh life and gravity all of these things
yeah giving us a glimpses of
explanations for those things yeah
well um
damas dear one of the special
human beings in this giant puzzle of
ours and it's a huge honor that you
would take a pause from the bigger
puzzle to solve this small puzzle of a
conversation with me today it's truly an
honor and a pleasure thank you thank you
i really enjoyed it thanks lex
thanks for listening to this
conversation with demas establish to
support this podcast please check out
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and now let me leave you with some words
from edskar dykstra
computer science is no more about
computers
than astronomy is about telescopes
thank you for listening and hope to see
you next time