Using AI to accelerate scientific discovery - Demis Hassabis (Crick Insight Lecture Series)

hello
well welcome everybody it's almost
normal
almost but when i look around the room
and see all these masks isn't quite
normal but this is the start of
normality so let's celebrate it and
we've got a great start with demis
hacibus who um all of you will have
heard the name and i'm going to put a
little bit of color to the name because
he has got rather a colorful cv
sorry dennis all right
um and it is sort of interesting it
started at age 13 when he became a
master at chess
i also play a little bit of chess but
i've never actually asked for a chess
game with demis i wonder why
but i know why did his ans levels got an
entrance into cambridge at 1516
but um they thought he should grow up a
bit more before going and he joined a
gaming company
um and
and designed a game
syndicate yes isn't that right yes
and um and that sort of stayed with him
as you'll see in a bit later did
computer science um got a first at
cambridge thought he said it he said
famously said it was like being in a
holiday camp at cambridge
and then um
worked
worked in another gaming company and set
up elixir studios where he worked for a
number of
years before going to do and listen to
this a phd in neuroscience in ucl in
cognitive neuroscience writing a um
paper
on
neuroscience of imagination
which did extremely um well
somewhat later not very much later
founded deepmind which all of you will
be um familiar with bought by uh google
um after uh um
some years actually
and um that resulted and i think you
will say a bit about it demis went to to
alpha go which could um
beat the beat the chess world champion
and then turned himself to something
serious okay and that was solving
the age-old problem that we've known
about for many years from the birth of
molecular biology and i remember it
being stated of how you go from primary
sequence
through of course the simple problem
into the primary amino acid sequence
from dna how can you determine how
proteins fold up use
ai particularly machine learning to
actually um solve that problem which he
will talk about mainly today
now all of this achievement and that is
an extraordinary achievement which i
won't say too much about um led to a lot
of accolades he's a fellow of the um
academy of engineering a fellow of the
royal science society um and um advises
government including on um sage
now if this all sounds as if he's a bit
of an overachieving nerd
i mean
which is certainly true
he is also normal
he loves football and believe it or not
it's a fun of liverpool football club
[Music]
okay so using ai to
you know find out something okay
which which he will tell you
which he will tell you about but i want
to just say a few words about this
because um i feel really um strongly
about this we try and understand life
usually we call it the mechanistic basis
in terms of primarily chemistry and of
course also physics i mean that is um
you know the mechanistic reduction
approach that we i generally use but for
me
that mechanistic
explanation
critical as it is only makes sense at
the biological level when it's embedded
in understanding how information is
being managed in that complex chemical
system and i go further than that i
would actually say as well as thinking
of life as a
chemical and a physical activity uh
science activity it is also an
informational science now that doesn't
just apply to the obvious example of
moving um from dna as encoding
information
critical and central as that was
and of course we named the institute
qrik after after that but also so we
associate that particularly with watson
and crick but also
work um from jacob mono that looked at
control systems um primarily in bacteria
in the 50s and 60s and continuing of
course looking at the complexity of
information management which makes up
life
and for me the future will be built on
the combination of understanding the
chemistry and physics of life coupled
with understanding the information
management within life and what we've
seen up until now and dennis will talk
about is how machine learning can help
understand how living things how to
interpret living things but i also think
artificial intelligence thinking more
generally and apply beyond the machine
learning will actually lead to greater
understanding of how life works a
somewhat different objective coupled
with physics and chemistry the reality
is i don't think i know how that will
work i don't think demis knows how that
will work but i am sure it will work and
it will be central so with that i'm
going to hand over to demis very much
thanks paul for that really uh lovely
introduction and it's uh wonderful to be
here it's also wonderful to be in a in a
full mostly full lecture hall uh finally
again after all these years
so um it's a real pleasure to be at the
crick it's always a pleasure to be there
our offices are just around the corner i
really think the king's cross area is a
real hotbed for scientific discovery and
intellectual discovery it's fantastic to
see how it's grown in the last last
decade or so
and of course paul and his team have
done a wonderful job of setting up this
amazing institute which i think is a
real jewel in the crown for uk science
um and it's a real honor and pleasure to
sort of be speaking on kwik's fifth uh
anna anniversary with this lecture
series
so um as paul mentioned what i'm going
to talk about is uh using ai to
accelerate scientific discovery uh our
course will given especially i'm at the
crick i will be spending most of the
lecture talking about alpha fold and its
impact um and i know many of you it's
really pleasing to hear you using alpha
folds um and obviously there will be
lots of time in the q a to discuss that
i'm also going to cover a little bit
about how we got there the journey to
getting to alpha fold
we spent 10 years working really hard on
the underlying machine learning and ai
algorithms that allowed us to get to the
point where we were able to tackle
something like
protein folding
so
um
first of all with deepmind we we founded
deepmind in 2010 in london and the
vision behind it was to create a kind of
an apollo program effort for building
what we call artificial general
intelligence or agi
and
you may have come across this term agi
and we we use it internally to
distinguish the type of work we work on
from sort of normal common narrow ai
that you might use every day things like
recommendation systems and other things
um so what we're really after at
deepmind is building this kind of
general version of artificial
intelligence and um there's not really a
sort of set definition of this but the
definite the working definition we use
at deepmind is a system that's capable
of performing a human level or above
across almost all cognitive tasks and
domains
so that's
what we mean by artificial general
intelligence and you might ask why is
human level uh you know why use humans
as a reference point well um you know
for now it's the only example of general
intelligence we know of in the universe
and of course it's been sufficient to
create you know this amazing modern
civilization that we all live in around
us so
that's pretty general and uh pretty
amazing and so uh it would seem that if
we can at least mimic those capabilities
um then we would have a very useful tool
so our mission statement for deepmind so
we're this we think about the effort
we're doing at d minor as an apollo
program uh we're now over a thousand
research scientists and engineers um
mostly based as i said in kings cross
just across the road and our mission is
to
solve intelligence uh to under to
advance science and humanity and by
solving intelligence what we mean is
fundamentally understand natural
intelligence and then mimic that in an
artificial construct
now it why is um
agi important well if we could solve it
um in this general way then
our our vision for deepmind and why i
personally work on ai for my whole
career is that um one could potentially
use it as a tool to apply to almost
anything by its very nature now
historically this you know working on
ai's obviously
has been going on from at least the
1950s people like alan turing
mccarthy and these kinds of people
started it off and um historically
they've been two i would say major
approaches to trying to build um
artificial intelligence
the classical approach one could say is
expert systems um so those are systems
famously like deep blue that beat gary
kasparov for chess in the 90s and
what you can think of is they rely on
hard-coded knowledge really the
engineers and the scientists designing
the system they have to understand what
the solution is and then they program
that solution into the machine as a set
of rules uh logic rules um and then the
machine executes those rules um but the
problem with these systems is they can't
deal with the unexpected uh and they're
basically limited to these
pre-programmed solutions so of course
they can't really solve anything new
that the designers of that system the
engineers of that system don't already
know how to do right so that's sort of
it's clearly a subset of what um the
system designers must already know how
to do
and um you know they were born out of
mathematics and inspired by logic
systems
and that's been mostly the tradition i
would say the mainstream tradition of ai
for um you know 50 the first 50 60 years
now on the other hand there's a
different approach which we you know one
could call learning systems and the idea
here is that these ai systems learn the
solutions for themselves directly from
first principles directly from data
directly from experience and
they can generalize and the emphasis on
generalization to new tasks in new
situations that they haven't seen before
and of course the promise of these
systems is that they will be able to
solve things we as the designers do not
know how to solve yet um so that's the
potentially amazing thing about uh these
types of learning systems and um you
know there's a lot of where's the
inspiration come for these kinds of
systems neural networks reinforcement
learning things i'll talk about in a
minute um well a lot of that was
inspired by systems neuroscience not
copying the brain because obviously we
don't actually understand the brain well
enough to do that um but inspired by
our neuroscience ideas and also
validated by things we find out in
neuroscience and that's why i ended up
doing my phd in neuroscience even though
um i was obsessively doing that to to to
get inspiration for new ai ideas
so um so the learning systems is of
course this is how
the human brain does it and and as i
said the emphasis on this approach is
generality and learning
so what's an example of a learning
system and you know there are many types
of learning systems but the one that we
are known for at deepmind and we
specialize in specifically is
reinforcement learning uh and this is
the idea of learning from first
principles through a sophisticated form
of trial and error and uh it's pretty
simple to explain and that's what i'm
gonna explain with this cartoon diagram
what happens is um the ai system which
we usually call an agent on the left
hand side here finds itself in an
environment and that environment could
be the real world in which case the
agent's probably like a robot physical
robot or it could be a virtual world in
which case uh or simulation and or game
in which case the agent is some kind of
avatar
and
the agent gets information about the
environment through its observations
usually we use vision but you could use
other modalities hearing touch and so on
and based on these observations which
are always noisy and incomplete
one has to the agent the first task of
the agent is to build a model of how the
environment works right so that's the
first thing now the agent's normally got
a goal that it's trying to achieve in
the environment that's usually the goal
is usually specified by the designers of
the system you know maybe it's to win a
game or maximize how many points it gets
and um and so the second job of the
agent system is to in the time it has
available to it to think it has to
select the best action from its set of
actions available to at that moment in
time that will best get it incrementally
towards its goal
and it does this by
using the model it has of the
environment to sort of predict what will
happen if it takes that action what will
happen to the environment uh what's the
likely new observation
and is that going to help to get toward
the agent towards its goal
and so once the thinking time's over the
agent
outputs the action that gets executed um
that may or may not make a change the
environment and that drives a new
observation
so this is a dynamic system and um what
this means is is the agent is an active
learner right it's not just passively
looking at data what the actions it
takes uh uh is it partly determines what
the next experience the next data that
it's going to get is right so it's a
kind of active learning system again
much like animals and
and us as humans
now this cartoon diagram is obviously
looks very simple uh it is simple in
some senses but the complexity behind
actually solving all the all the deep
technical questions that arise from
these systems are of course very
complicated and something that's still
ongoing
now i'm going to talk a little bit about
alphago which was um i still think
probably the most famous example of a
reinforcement learning system working at
scale so this combines neural networks
deep learning with reinforcement
learning um in our program alphago that
was our program to play the ancient game
of go
now go
this is for those who don't know this is
what go board looks like i definitely
recommend you trying it out if you
haven't if you don't know how to play it
but it's an amazing absolutely
incredible game
the rules are incredibly simple you can
place your stones there's two players
black and white they place their stones
on the vertices of this 19 by 19 board
and the idea of the game is you're
supposed to wall off empty wall off
spaces of territory on the board so
you're kind of building walls the other
person is building walls as well and the
person that has the most um territory at
the end of the game wins
um so this is you know being played for
thousands of years uh and professionally
hundreds of years in asia and it sort of
occupies the intellectual echelon that
chess does in the west
in china and korea and in japan
now why is go so hard for computers to
play
so after chess was sort of cracked in
the mid 90s
the kind of everest if you like or the
holy grail for game ai was go um and
they used to try to initially of course
what that what the ai people tried to do
researchers tried to do was use the same
uh expert system
uh a approach that worked for chess and
they tried to use that for go but the
problem is it doesn't work for go
and for several reasons uh one is that
the search base is just uh uh much more
massive than chess so it's 10 to the 170
possible positions just to put that in
context they're only about 10 to the 80
atoms in the observable universe so you
know there's no way you can exhaustively
sort of search through and find the
solution to what you should play in go
so it really resists brute force
approaches which expert systems tend to
use and then even more difficult is that
it was thought to be impossible to write
down an evaluation function so an
evaluation function is the key function
to tell the system whether it's winning
or the opponent's winning and by how
much in a specific situation
and for chess this is relatively easy
straightforward to do because chess is a
materialistic game so a minimum you know
first order approximation you just count
up the pieces and what they're worth and
that will tell you a little bit about
who's winning obviously in go it's a lot
more esoteric game and there's only one
type of piece so there's it's just way
harder to
specify what the right rules are to
evaluate a position and in fact one can
see that if you interview top go players
um what they'll say is they you know
basically that this move felt right
right they they you know there's this
sort of notion of relying on their
intuition rather than um calculation
which chess players tend to do
and so um so this you know why this is
why go resisted uh ai approaches
classical ai approaches for 20 years um
post deep blue
so we took a different approach which is
that um let's let the the alphago system
itself learn its own evaluation function
and learn it through playing against
itself hundreds of thousands of games
right let's not try and uh program in
what its evaluation should be let's let
it learn for itself
so how did we do that so this is a kind
of little bit of a simplification of the
whole system and actually we we after
alphago there was future generations of
the alphago program we since improved it
to alphago zero and then alpha zero
which can play any two player game to
world championship standard not just go
but chess japanese chess any two player
game with the same single system but
they all basically work in the same kind
of way and this is how they work what we
want to do is train a neural network
that through self-play playing against
itself
it is able to evaluate the position
correctly so this evaluation function
and also pick probable moves what are
the most likely moves that one should
think about and think about playing in a
particular position
so you start off with imagine version
one of this system v01 um it plays a
hundred thousand games against itself to
begin with it will be almost playing
randomly right it doesn't know anything
about go other than the rules so there's
a hundred thousand games and then you
have this little database basically of a
hundred thousand games positions um and
you know what happened at the end of the
game
and then what you do is you train a
version two of a new network your
network version two and uh on that data
and you train it to do things like
predict what moves were gonna be played
in a particular position by the version
one system and also who ended up winning
the game
so you can then kind of calibrate your
evaluation function right so midway
through the game who's winning does that
tally up with who actually ended up
winning the game at the end
so then you you have this version two
and what you do with version two is you
then have a little mini tournament with
version one so you play a hundred game
match off and if version two beats
version one by thresh some threshold
amount which we set to 55 percent win
rate then
you you know that's the sort of
statistically significant so you can be
pretty sure v2 is stronger than v1 so
now you replace v1 with the v2 system as
the kind of master system and you now do
a new hundred thousand games of v2
playing against itself and you generate
a new data set of 100 000 games now of
course this hundred thousand games will
be slightly higher quality right so
they'll be slightly better than the
version one ones so you're now learning
from your own self-generated data but
it's a slightly higher quality data set
and so then you of course just repeat
this you train up a version three based
on this version two data you have the
match off if
for some reason the new version does not
beat the old version what you do is you
just continue to generate another
hundred thousand games with the old
version and so then now you have 200 000
games to train the the next new version
and eventually the new version will be
the old version
and it turns out after about 17 or 18
cycles of this you get to world
championship level
starting from random
um so it's kind of amazing to see the
evolution you can track it in real time
it's just really kind of an amazing
thing to see when you see this for the
first time
so what does that do more pictorially if
you imagine that this sort of tree of
all possibilities in a go position so
this is like a gaming tree and you
imagine that each of these little little
graphics is a go position
and um and so you know this this this
tree fans out in an incredible amount
very quickly and and you know there's 10
to the power 170 positions so how does
one
you know how can one find a good path
through this enormous search pace well
what happens is we use the neural
network to guide the what's the we use a
monte carlo tree search in this case for
alphago to guide the search so it
doesn't need to look only looks at a
tiny fraction of all the possibilities
that are you know possible from those
positions it only looks at the most
plausible most fruitful ones uh the ones
you know that i've colored in blue here
and then eventually when when it's time
to play a move after a few minutes um it
will you know pick the best one from
those um those those blue options that
it um it searched uh in this case this
this this path in pink
so um so that's how you can imagine how
the system works is this model this
neural network uh helps to reduce down
massively by obviously many many many
orders of magnitude uh the amount of
searching that needs to be done
so we then took this alpha go system to
a challenge match uh with lisa dole 18
times world champion legend of the game
lisa doll in seoul in 2016 uh and we
famously sort of won that game 4-1 over
200 million viewers and um people
researchers both in in sort of the ai
field and also go players both worlds
sort of said that this was a decade
before um they were expecting anything
like this so until alphago came along no
program had even beaten a professional
player let alone a world championed a
sort of legend of the game
but the important thing was actually not
so much of course it's really important
that we won the match but but it but it
was actually what was even more
interesting was how alphago won the
match and um i haven't got time to go
into the details here but it basically
came up with its own original strategies
because of course it wasn't constrained
by what we as human players and
programmers knew so um it sort of came
up with its own ideas about what was a
good go strategy and good go motifs and
most famously this happened in in move
on in game two of this match on move 37
and uh and and the is this move move 37
is this uh uh black stone here in circle
by the red that's what alphago played
and um it was unthinkable if you if you
look at the commentators and what they
said about this everyone thought this
was a terrible move and the computer
didn't know what it was doing because if
you're a go expert you would never at
this early on in the game play at stone
on what's called the fifth line this
this line in red here that i've outlined
and just suffice to say that's just an
unthinkable crazy move and you would get
told off by your go teacher if you ever
moved there uh this early on and yet
alphago did that and then what was
really amazing was about 100 moves later
this stone happened to be in exactly the
right place to decide the the main fight
that happened across the board it was it
was positioned perfectly to decide that
fight pivotally decide that fight almost
as if alpha go had sort of foreseen that
100 moves before
so this this game has now gone down in
go legend it's in all the books everyone
studies it so this is uh and people you
know now play on the fifth line right so
um so that was kind of amazing to see um
and you know if you're interested in
alphago i can recommend the the
award-winning documentary of it which uh
is on youtube now and you can see all of
the kind of uh the drama that was
involved in that
so
um that was you know games and
we used games as a training ground for
our algorithms because they're super
efficient to use
you can run millions of them online on
the in the cloud
it's also very easy to specify the goals
because um there's a clear metric you
know winning a game or maximizing the
points so um also of course as part of
my games background that i was thinking
this would be
the right thing to to to use um and so
for the past decade uh since we started
dmi you know from 2010 it's there's been
amazing progress in ai and machine
learning and of course all of you aware
of that um really amazing like if i i i
just can't believe like um
back in 2010 virtually nobody was
talking about ai we couldn't we could we
virtually couldn't get two pennies
together to start deep mind right we
sort of had to beg borrow and scrape
around to just get the initial small
amounts of funding
and now you know everybody 12 years
later 10 12 years later
everyone seems to be talking about ai
but for me what's really exciting is i
think we're on the cusp of the most
exciting era of all the last 10 years
have been very exciting
things like alphago but i think we
finally made enough progress now to
tackle what we were really trying to do
which is of course we love games and i
love games but that's not the aim of
what we were trying to do at deepmind
this apollo program was not just to win
at games it was to apply ai build the
design these general algorithms that
could be applied to important real world
problems of course um specifically in
scientific discovery which is
my personal passion of reason for
building ai but of course it can be
applied to many other things
industrially and commercially as well
so i think what's really exciting is
we're at that start of that era now and
i'm going to talk a little bit about uh
what makes a suitable problem for the
types of techniques we've currently
developed and of course this itself is a
moving feast because
we're continuing to extend what these ai
systems are capable of doing but right
now the source of systems i've just
described to you what makes a suitable
problem for them well it's these three
kind of principles that i look for when
we're considering a new problem one is
is the problem can it be described or
encapsulated as a sort of massive
combinatorial search space or state
space
um we like that
a criteria because that shows you you
know if there's a system something like
that like go then brute force methods
expert systems and brute force methods
will have a lot of difficulty in in that
area so we look for that kind of
criteria second is is there a clear
objective function or metric that we can
optimize against that the learning
system can optimize against
incrementally um so again of course in
games that could be like points
and then third are there lots of data to
learn from or and ideally it's an and um
an accurate and efficient simulator with
which you can generate more data so uh
self-generated data and synthetic data
is also fine if it's drawn from the
right distribution
so those are the three criteria and um
it turns out that actually there's a lot
of things that fit this so
it's you know maybe this sounds quite
restrictive to you but actually there's
a lot of things that can be couched in
these terms
a couple of other ancillary things we
look for is is is an extra something
that has an external benchmark or test
games obviously are great for that
because you can play world champions and
and other very impressive players and
that can calibrate for you externally
how well your system's doing
and obviously we want to look for
problems that have a big downstream
impact
so it turns out that protein folding
ticks all of those boxes and then some
and
obviously all of you in the room will
know what this is but i know there's
there's there's people uh listening
online who maybe are not in biology so
the protein folding problem is of course
the problem of going from an amino acid
sequence sort of one dimensional
sequence of letters uh to a 3d protein
structure and this has been a kind of
50-year quest uh ever since christian
vincent i think famously uh conjectured
in his nobel prize lecture in 1972 that
this should be possible
three-dimensional structure of a native
protein should be fully determined by
the amino acid sequence i do feel a bit
like ferma's last theorem of like yeah
this should be possible but it's just a
bit too small you know margins a bit too
small will leave you to figure it all
out and then there's 50 years of
obviously attempts at trying to do this
and um
but you know this really does fit uh and
the the kind of criteria i just
mentioned earlier on of course um
first thing is the state space and uh
famously levantal another another big
scientist of the same era uh put forward
this paradox of you know he calculated
um there are you know maybe for an
average protein there are 10 to the
power 300 possible confirmations um so
but then how is it that in nature uh you
could this you know protein spoil you
know
spontaneously fault within milliseconds
in in many cases uh and obviously
randomly sampling all the possible
confirmations would be totally
intractable so this is one of those
massive state spaces right even bigger
than um goes um so the big question is
can this be uh can the the structure
prediction problem be solved
computationally
now for me personally this has been a
long road to this problem to deciding
when to tackle the problem i think
that's one of the key things in science
but also in startups is you don't want
to be too far ahead of your time you
want to be ahead of your time say five
years but you don't want to be 50 years
ahead of your time otherwise you're just
going to be in for a lot of pain and my
sad story about that is always like
charles babbage you know i think he was
maybe 100 years too ahead of his time
and um you know it's pretty tragic story
if you read about his life even though
he obviously did amazing things
um so for me i first came across the
problem of protein folding actually as a
student at cambridge in the 90s because
i had a friend who uh ended up doing his
whole career in in in protein structure
uh still at the lmb now um we talk about
protein folding is the most important
problem to crack at any available
opportunity you know in the bars playing
pool whatever this is what he would talk
about so stuck in my mind is like a
really interesting problem and also even
back then obviously i was doing ai in my
games at that point uh like theme park
uh and the games that paul mentioned but
i i kind of thought this might be you
know a good thing for ai at some point
and then the second time i came across
protein folding was um with the game
fold it some of you will know about
where i was doing my postdoc at mit in
the late 2000s 2008-9
and i think it just came out and
of course i was fascinated about this
idea of citizen science games i still
think there's a lot more actually that
could be done there like can you use all
of the motivation and time gamers spend
on their games if we could catch our
scientific problems in a game maybe we
could kind of harness all of that all of
that uh
leisure time uh to do useful science so
it's pretty cool idea and i think fold
is still one of the best examples of
that and what was intriguing was that um
the best players obviously were not
biologists they were just kind of gamers
could fault discovered some real
structures
i think for some quite important
proteins i think there were a couple of
nature papers actually published on on
on structures that the foldit players
had found and um
and what that said to me and that this
is a little uh screenshot of it is you
know it's like a puzzle game like people
bent the backbone and they tried to
minimize the energy um and and and of
course um they would sometimes do
encounter intuitive moves so some they
would make a move that would increase
the energy temporarily and then only for
them to do another fold and then that
would reduce it overall and and so if
you think about what were these players
doing as sort of amateur biologists well
presumably as they were quite good
gamers and they got quite into this game
and they were using their intuition in a
way their pattern matching skills to
figure out what the right moves were and
some of them had got really good at
figuring out uh their intuition for
protein structures so that really um
then resonated with me when we did
alphago of like what had we done there
well we just mimicked the intuition of
these incredible go masters right who
spent their entire lives playing go like
you could not find anyone more expert at
with their pattern matching skills than
a go player and um so i just felt like
it must be then possible that gave me
some sort of hope that it would be
possible to sort of build a a system to
at least match the intuition of these
folded players so pretty much the day
after we got back from seoul in i think
was march 2016 we we decided that it was
the right time to start the alpha fall
project um back then so it's been been
five years
now the other key thing uh the reason we
we decided to pick protein folding was
the existence of casp um which is the
kind of
i would say the gold standard benchmark
for for for protein structure prediction
it's run every two years um since 1994
and it's run by this amazing team john
moult and his team uh you know john
founded it back then has kept it running
for 30 years incredible and the key
thing about it is a blind prediction
assessment uh as i'm sure most of you in
the room will know which is that you you
know these are newly found experimental
structures they've not been published
yet but they're entered into the
competition so at the point where you're
trying to solve it that there is you
don't the the known the structure is not
known
so there's no way you can overfit or do
anything accidental that sometimes
happens in machine learning it's it's
it's truly blind so it's a fantastic
test if you like then of course later in
the year you do this in the summer and
then later in the year they they reveal
the real structures and then score your
your predictions against that
so
um progress at casp had been very slow
historically so really for the the sort
of um the the previous decade before we
started work on this area these are the
results of
as measured by you can it's measured by
the score gdt score which you can think
of roughly as a percentage accuracy um
and you can see the sort of cast
competitions from 2006 up to 2016.
this is the score of the winning team uh
on the hardest category free modelling
category and you can see there's been
sort of basically no progress and um and
then when we entered for the first time
in in in 2018 and cast 13 um we suddenly
jumped the the you know the the winning
score by nearly 50 percent um by
applying cutting-edge machine learning
uh as the core part of the system for
the first time uh in a in a in a
structured prediction system
uh and then we we architected uh uh we
sort of took those learnings and that
experience we tried to push that further
we realized that was hitting those
particular techniques were hitting a a
ceiling so then we took the knowledge
and the know-how but we re-architected
the system from the beginning with that
knowledge um to then try and reach
atomic accuracy which we managed to do
with uh in the in the next competition
in cast 14 in 2020
so
atomic accuracy was always our our goal
uh we achieved that at cast 14 in
november 2020 um we this measured by uh
uh the mean that the sort of median
error um was less than one angstrom so
um within an atom error and uh therefore
that was competitive that's what we've
been told by the casp organizers uh they
would consider competitive or comparable
with experimental methods and more
importantly potentially useful in
practice for uh experimentalists and
others to to use and rely on so we alpha
fall to got a 0.96 angstrom error and
that was three times more accurate than
the next best system in cast 14 even
though by this point they had all been
using our cast 13 and alpha fold one
techniques obviously we published by
then so um so now most of the even the
most of the the rest of the teams were
using machine learning at this point
almost all of them um but alpha fold two
was was was still three times better
than that with this new system
we couldn't you know there was amazing
time where the first you get the first
results back in sort of september
october and we we were just sort of
blown away by by the beauty of the the
accuracy of the predictions you can see
here the ground truths in green the
predictions are
in blue uh and even in some cases like
this uh protein from sars uh uh from
covet uh is is is even the side chains
are pretty good in some uh in some
situations
so um those were the examples uh that we
got back got this incredible score of of
of 0.96 angstroms this is the alpha fold
2 architecture it's a incredibly sort of
innovative machine learning architecture
i i haven't got time to go into the the
kind of gory technical details of this
um but if you're interested in those
then obviously check out the full
methods paper uh that we published last
year but um i just want to give you a
flavor of the key technical advances um
the key takeaways so first of all this
is the most complex system we have ever
built at deepmind i think um by quite
some way um there was also alpha star
which we did for this starcraft game but
this this is the most complex system i
would say and it's um it's which is
which is saying something because we
build a lot of complex systems and uh
there's 32 component algorithms and uh
one measure of how complex it is is that
there's 60 pages of supplementary
information quite then some preventative
information with the with the paper that
we wrote so describing all of the
different aspects uh that were required
and what that what the you know the key
thing there is that there was no silver
bullet there wasn't just like one uh
algorithmic trick that would then solve
this problem
but we needed like a whole bunch of
insights and innovations that needed to
be carefully integrated and blended
together
but the key takeaways were firstly these
are the key advances over the alpha fold
one system that were all essential for
getting this in this atomic accuracy
performance uh it's a full end to end
system uh with including a recycling
stage that sort of recycles the
prediction and it really refines the
structure over a few cycles now end to
end means
we start off with the amino acid
sequence and we give you back the 3d
structure with alpha fault one that
wasn't the case with alpha fold one we
we gave you back an intermediate thing
the distagram so the distances between
the residues and then we had to use a
different numerical method solution to
actually give you back the 3d structure
so there were sort of two parts to alpha
fold one and we managed to combine that
into one end to end system and in
machine learning one of the things
one of the sort of things that's known
is that the more end to end you can make
it the more you can allow the learning
to flow through the whole system the the
better it is for the system so um so
we're always planning to do this at some
point we finally managed to do that with
alpha volt2 the other key thing is we
switched out the kind of neural network
we were using we used this
attention-based neural network a very
fancy one
to infer the graph structure between the
residues so that actually kind of builds
up the graph structure before that we
were using convolutional neural networks
in alpha fold one which was borrowed
from computer vision um but the problem
with convolutions is that they make some
certain assumptions which are fine for
computer vision you know neighboring
pixels are sort of related to each other
but actually that's not if you think
about it it's not good for protein
folding because often residues that are
near each other or even foreign residues
that are far away could end up being
close to each other in the final 3d
structure right so in fact we were
giving it the wrong biases by giving
architectural biases by giving using
convolutional neural networks so that
was key and then that's perhaps the
most vital thing of all was building in
some evolutionary and physical
constraints into the architecture things
like
bond angles and things like that
without impacting the learning which is
another really hard thing in machine
learning to do is to mix prior knowledge
that you have about the system
but not impact the training and the
learning
so
i think it's a really great alpha fall
too is a really great example of how to
blend those two types of knowledge
so it's been a massive research effort
for us probably our longest project
we've done five years you know roughly
20 people
hugely multi-disciplinary team amazing
team just want to give a shout out to
them but we of course it's not just
machine learners we also have biologists
chemists and physicists on the team um
so we needed all of those different
types of interdisciplinary knowledge in
order to crack this
and then the final point that i think is
interesting to make is that i mentioned
at the start of the talk we our overall
mission is to build agi this general
system that can do anything um so
normally and with our games work we're
really interested in in general systems
so we don't want domain specific
knowledge right so alpha zero as i
mentioned can play any two player game
from scratch without knowing anything
about those games um but in when you're
trying to apply those techniques to a
specific problem that has important
downstream consequences like alpha fold
then actually we don't worry that we
don't worry about generality we just
throw the kitchen sink at it we just
want the performance right we want it to
work
and and and we we want it to be um to
reach a sort of performance threshold
where it'll be useful and of course
without without a folding
protein structure prediction um that's
absolutely the case so that's why you
see so many of these um so it's a
general these sort of general techniques
but also include some domain specific
knowledge in this case and of course
that's absolutely fine for something
like alpha volt2
and then of course now once we've built
a system like that we always go back to
it and see can some of this main
specific aspects that we put in be
removed or replaced with a more general
system and that's how he went from alpha
go that could only play go to alpha zero
that could play any two-player game is
we we sort of pulled out the
ghost-specific things out of alphago and
and
just kept seeing if we could replace it
with something more general
so the speed and scale of this um you
know what this is another thing that we
do is we often uh in again in machine
learning and our engineering stuff we
try to meet the performance we're trying
to reach first then once we get there we
then spend time go back to looking at
the system and see if we can optimize it
to make it more efficient um so by the
end of doing alpha volt2 we it's
actually incredibly fast system uh
training the whole network only takes
about two weeks uh on roughly eight tpus
which is um
tensor processing units which roughly
about 150 gpus which is a very modest
amount of compute actually for modern
day machine learning systems so this is
not a
story about compute power it's just it's
actually what was key was the
architectural innovations
so this is really fast for training but
it's even faster for inference for
asking getting the answer back for a
particular structure takes in the order
of minutes uh or even seconds for an
average protein on a single uh gpu
single gpu machine
so when um sort of over christmas uh
just after the competition around you
know december 2020 we were thinking
about okay how are we going to give
broad access to all of you and
biologists uh to the predictions um
and normally the way you do it with
these types of uh
systems is you set up a server and then
you know you can submit your structure
to the server and it will give you back
a structure but we thought we could do
something better than that given the
speed of the system is we could actually
uh just just fold entire proteomes and
and that was within reach so this sort
of moment of realization and then in
fact we did do the human proteome over
christmas so we set all our machines
running uh left them for christmas came
back in january and there was the human
proteome
so which is one of the magical things
about you know computational methods uh
they can carry on working while you have
your christmas lunch
so um
so that's what we decided to do so we we
we did our first version of human
proteome of course we we had to we had
to check it and analyze it and build
analysis tools for that so that took um
a few months um uh so we had these
really good predictions um then we
wanted to kind of uh build a confidence
measure of how good were these
predictions uh and i'll explain that in
a minute but basically experimental
coverage of the human proteome is about
17
uh after you know 30 years of of
painstaking and amazing experimental
work and we more than doubled that in
one go to 36 with very high accuracy
predictions and around 58 of the human
protein was covered by what we would um
call high accuracy uh uh predictions and
what was interesting is even the
the rest of the um proteins that were
you know had a lower accuracy or
confidence around them um they seemed to
map and this is still an ongoing piece
of research we're doing with some
collaborators to uh areas of the
proteins that are thought to be
unstructured in isolation so in fact one
could maybe turn it on its head and
think about uh alpha fold as a as a
disorder predictor
if it can't predict uh something very
well then perhaps it is disordered in
isolation
so this is um you know the most complete
and accurate picture ever of the of the
human proteome um so we published
another paper on that with all the
analyses of it
the key thing is as we we knew we wanted
uh biologists to use this and perhaps
people scientists and researchers who
were not familiar with machine learning
models um so we we wanted to make sure
we you know in this case that confidence
measures uh were really important
and normally they were nice to have for
machine learning models like how
confident is your model about his
prediction but here we felt that they
were critical and uh so we we came up
with a a a a nice sort of novel measure
of confidence per residue confidence
metric which we call pldt and um over 90
on that one can think of is very high
accuracy with even the side chains would
be good and uh sort of competitive with
experimental methods uh that's kind of
like the dark blue
if it's above 70 we believe that's high
accuracy and you can think qualitatively
that means that probably the backbone is
pretty reliable um but maybe not you
know the side chains and things and then
less than 50 uh you know as i mentioned
earlier could be a good predictor of
disorder but obviously more research is
needed before we could assert that
confidently
wide stop at the human proteome of
course we then decided to uh doing
further 20 proteomes for model organisms
all sorts of different important
research species
but also some pathogens um
and then we collaborated we had this
amazing collaboration with embol ebi the
european bioinformatics institute based
in cambridge um and to build this into a
standard database tool
and of course the ebi host a lot of the
world's most important databases things
like uniprot so they were perfect
partners for this to leverage all their
expertise in building databases and make
it as simple as possible for biologists
like all of you a lot to
use as part of your standard process
and um so we put all of these
predictions up
in the database and released it uh with
the methods paper uh sort of free and
unrestricted access to every structure
for whatever use one wants to use it for
and
we felt in this case that was the best
way to maximize the scientific impact
and benefit to the research community
and of course um hopefully with all the
what all the researchers do
with it will um downstream benefit
humanity as a whole
which goes back to obviously our
original uh and still mission
we're now up to a million predictions in
the database today so we've expanded it
to uh everything in swissprot uh
obviously we've curated proteins of
interest um we've also been very
interested in helping out the who uh and
various non-profit foundations who look
at neglected tropical diseases because
often they don't have the structures for
those things and there are and you know
pharma companies don't generally invest
in those diseases so it's especially
useful for them so we've been working
with the who very carefully on that and
and asking them what they want for for
global health priorities so we provided
them with a couple hundred thousand new
structures on uh uh organisms for
negative diseases
um and then
the community has already done amazing
things with alpha fold just in six
months of us releasing all of this and
the open source code and the databases
um uh uh colleagues of owls and bull
have have have used it to help model
their big complex biology like the
nuclear poor complex uh i mentioned
already people have shown alpha fold is
better than the state of the art in
protein disorder predicting uh we've
worked with g7 who to um give them back
all the structures of the top 30
pathogens they've identified for um that
could could potentially cause a future
pandemic
and has actually been pretty useful for
experimentalists to help
resolve uh their experimental data on
protein structures and we expect there
to be you know hundreds of other
applications in the in the coming years
um you know impact alpha has been very
good
000 researchers have used the database
already um
from 190 countries millions of
structures views and so on obviously
we've had some nice accolades
and what we plan to do from here is over
the next year um to we plan to fold
every protein in uniprot so every
protein sort of known to science over
100 million proteins and counting um and
obviously we're continuing to
collaborate with the ebi on improving
the database and adding new features and
if you have any um uh suggestions
yourselves we have lots of feedback
forms in the database and we do read all
the emails so please tell us if you have
any suggestions or you found some uh
some uh structures that you think look
strange
and then we will incorporate that in the
next you know try and improve that in
the next version of alpha fault
so just to finish then i want to
obviously thank uh the the pdb uh who
obviously without those initial
experimental structures we couldn't have
trained alpha fold uh and and obviously
the experimental biology community at
large
um thanks to everybody who made alpha
possible obviously the methods team and
the and the human protein
human proteome team our collaborators at
embal and also the cast community
but just to finish i'll just talk a
little bit about the future looking work
so we're now pressing forward on many
other new dimensions this is just the
beginning for us i would say of what we
can apply ai and computational methods
to been looking at protein complexes
many of you have used our new alpha fold
system alpha fold multimer that can deal
with multimeters pretty well disorder
proteins point mutations ligand docking
protein interaction protein design this
is just some of the things that we are
currently looking at and just to connect
back with what paul said at the
beginning and actually paul and i have
been discussing this on and off for like
25 years now or something and maybe
we've sort of joked maybe finally is the
time to do these things is i think we're
kind of the dooring dawning of a new era
perhaps in digital biology i think at
its most fundamental level biology is an
information processing system um but
life is phenomenally complex as all of
you will know better than me and it's an
emergent process so i my suspicion is
that it's too complex to ever be sort of
describable in kind of neat mathematical
equations i i know some people try that
and mathematicians are trying that but i
i just don't think they'll be kind of a
newton's laws of motion
for for a cell um i just think it's too
emergent and too too interactive and too
dynamic
but potentially it's the perfect type of
regime for ai to work in right sort of
figuring out all the you know the
patterns in all these weak signals so i
actually think ai might be the right
description language for biology just as
the same way that math is the right
description language for physics
and i think and i hope that alpha fold
is a kind of proof of concept of that a
major proof of concept on that and
perhaps you know herald's the beginning
of this kind of new era of what i would
call digital biology and the dream would
one day be able to create something like
a full virtual cell simulation that you
could interrogate and do experiments in
silico and then you know validate that
experimentally
i mentioned about scientific discovery
we've actually had an amazing sort of
year
it's not just alpha fold we've had all
these other big sort of high profile
papers and breakthroughs in many other
areas of science quantum chemistry pure
mathematics fusion genomics um you can
read the papers if you're interested i
haven't got time obviously to go into
all of them but it's it's been as you
can imagine a very fun time for us over
the last year to to sort of take off our
dream list of of problems in science and
areas and science that are super
fascinating and at the cutting edge
themselves and i think there's so much
more uh to come so i'll just end by
saying you know the way i've always
viewed ai is um you know i wanted to
build this tool kind of this ultimate
general purpose tool to help scientists
see further a little bit like the hubble
telescope has allowed you know
cosmologists to see the universe
much more deeply and much further thank
you
[Applause]
wonderful demis thank you very much
really good
okay
so we've got questions
in
the lecture theater and questions also
online we'll start in the lecture
theater
and there's two microphones and um
just come up and
ask away
number one hello um i don't know if you
can hear me fascinating talk um i was
wondering if you could talk a little bit
about how ai
reinforces biases in society and how
that might have an impact on health and
disease and what deep mind is doing to
address that
yes great question i think
one always has to be very careful of
that obviously this is a very um
hot topic right now in research of um
addressing biases in the system part of
that comes from the data sources so that
that's a really important thing to look
at and analyze itself the data creation
where the data comes from it's
interesting there's a lot of active
research going on in things like
generating synthetic data to perhaps
rebalance the data bias that one might
get from the real data um but that but
the generation itself can also be biased
so there's you have to you have to sort
of be aware of this at every stage
i also think that
this is why you need you know we spend a
lot of time broadening our diverse uh
set of people that work on these systems
because i think that
even though these systems are learning
systems so they learn for themselves
from data they're still going to be
always be an echo in the design of the
structure or the goal that you give it
from the designers right so even though
it's not as direct as it used to be with
the expert systems there's still going
to be an echo of like the value systems
of the people and the culture that
they're in that designed it in order to
uh set off the learning system what it's
going to learn so i think one just has
to have that at the forefront of your
mind the whole time and get a diverse
set of inputs as early as possible uh in
the design of these systems uh and the
engineering of these systems and you
know we're trying very hard to do that
deepmind and sponsoring a lot of things
in the ecosystem uh diversity
scholarships and other things to also
bring up the next generation to get into
this area
next one at the back yeah
uh so my my question kind of has three
parts but it's it's one question
so the uh so you mentioned intuition
several times in the talk and i was just
wondering how common it is that uh as
humans we solve
the kinds of problems that ai is used
for
based on the three criteria you gave
how common is it for us to solve those
problems using um intuition and then the
second part of the question is
uh why is conscious uh computation and
in our sense uh slower than uh intuition
is
and uh
what does that tell us about
consciousness
well these are the easy questions yeah
uh great questions so intuition you
might remind me all of them as i answer
them intuition um
so you know i have a working definition
of internship i don't want to start a
huge row about because obviously these
are philosophy you know this sort of in
the realms of philosophy what is
intuition what is consciousness but my
work operational definition of intuition
is knowledge i think there's anything
really mystical about it's knowledge
that we've gained through experience
right if you talk about human intuition
but it's not consciously accessible so
our conscious parts of the brain can't
access it and we can't describe it right
that's all it is and i think in
scientific endeavor but also in our
normal lives if you if i tell you you
know describe to me how you swim or ride
a break in a way that i could program
into an expert system incredibly
difficult right because because it's
below the level of what we're
consciously
able to access right and that's
traditionally why those those those
types of things have been very difficult
for roboticists to do and so on
so i think of course we use that insight
as scientists i think and often we call
it things like having good taste or good
judgment you know and i think the best
scientists do have that and i think that
what that is is these all these signals
that we're bombarded with from our
experiments or reading papers or
listening to speakers um you know if
you're really good scientists your your
intuitive part of your brain is going to
synthesize some interesting new uh
conjecture from that so i do think we we
use our intuition a lot and but i don't
think there's anything too mystical
about it i think that ai systems can
also
find those types of patterns and signals
too
i think pattern matching is a or pattern
recognition is a key part of intuition
so that's the first question
consciousness so i think you said um you
know conscious expression well i i
you know i don't think that
consciousness is necessary for
intelligence um i think that if you were
to push me on it i would say that
they're sort of doubly dissociable so i
can imagine intelligence systems that do
intelligent things that don't feel
conscious to us at all and i would
classify all the systems i've shown you
today as that right none of them i don't
worry about any of them as being
partially conscious some people do some
of my team do worry about not letting
alpha go play go games and whether
that's that's you know it's sad because
of that
but i don't think it is although there
is something a little bit because goes
considered to be an art form so it is in
in in asia right so it is a little bit
like are we turning off picasso that
could make more picassos uh paintings
you know go games so but i you know that
they're sort of it's it's that's you
know not to be taken too seriously um so
and i think the other way is true too so
uh
you know i think that if you look at um
the higher animals you know dogs and
dolphins and things like that i think
it's pretty clear i would argue that
they are conscious right they have
consciousness they seem to dream if you
look at a pet dog or something they have
complex social dynamics um
but they're not as intelligent as humans
and maybe in some cases one wouldn't
call them general intelligences right in
the sense of they could do sort of
generalized to anything they're quite
specific to their ecological niche um so
it's a sort of interesting dissociation
but actually i think that building ai in
the way we're trying to build in this
general and general way and analyzing
that is going to be one of the best ways
to
find out more about what consciousness
is because i think if we compare these
systems to the human brain and see what
the differences are i would say that um
uh you know we might see what the sort
of special essence is of perhaps human
consciousness um that makes us different
peter
i wonder as fascinated he brought in
evolution and i wonder if you tell us
how important evolution is in your
models yes um what we think here as
biologists is our lives made
immensely more difficult because of the
perversity of evolution as francois
jacob famously said nature is a tinkerer
not an engineer whereas in a sense
you're you're an engineer
can you comment on
whether
nature could have done things in many
different ways and achieved the same
solution in other words the inverse of
what you're doing or how and how
important is evolutionary constraint
in your prediction
well i mean the evolution constraint is
comes in pretty important with a
multi-sequence alignment uh part of the
system um so it is key it's not um vital
like you can you know we did these
ablation studies where you can remove
all the different bits and see how well
it still does so it's not um
it's it's not completely essential but
it's important for the very you know the
very high performance so that's where we
the evolutionary part comes in um in
terms of your more general question i
mean i think evolution is an incredibly
powerful
simple algorithm i think that's clear
what that is what that's what's you know
giving rise to all of this
um and that is fascinating and uh but it
isn't efficient right so it is it is
sort of as you say a tinkering system
and it needed billions of years and you
might not you know who knows if you
re-round the experiment would you get
human intelligence or not um it's not
clear um so
uh perhaps there was you know a great
you know people talk about these great
filters that had to be uh
got through like um going from you know
going to eukaryotes and so on so i think
um we would like something a little bit
more direct than uh evolution although
we do also have a team that that does
evolutionary uh programming so actually
mimics uh some of the aspects of
evolution uh to just to bring in that
diversity element because it is another
way of exploring uh and finding novelty
is obviously to randomly try things but
it's a little bit of a brute force
algorithm i would say so we're probably
after something a little bit more
efficient if possible okay
um online
there's a lot online
in many different directions and one
direction is really what's alpha fold
three you already gave us some ideas of
where you want to go
and one
thing is dynamics obviously at the
moment it's all static structures so
what about dynamics how far do you think
you can go
could you go
to really look at whole pathways
predict how biological pathways might
work respond to inputs
and then also along what alpha 4 can do
do you think you will be able at some
stage to predict function from structure
yeah and that's a summary of a lot of
questions
thank you yes absolutely this is we see
this as the beginning of a big journey
of um
you can sort of think of it as moving up
the hierarchy of complexity so you know
obviously
uh uh we we started off with this this
grand challenge and and in the sort of
static case we would love to understand
protein protein interactions um
and protein ligand binding and you know
all of these things um uh more you know
the dynamic way these systems work and
uh we have some promising early work on
that uh and also we're we're trying some
fundamental things too like can we
understand uh protein folding from the
physics aspect right so this is a sort
of it's almost like a translation
machine we've built so far with alpha
thought too right you have a language
with the amino acid sequence and then
you want a different language a 3d
structure so you one can think of it
almost like a translation model even
though it's not how it works um but what
you know can we actually model all the
physics and the physics energies and so
on so we have some people who are
pushing hard on that and that might
unlock things like
you know activation energies of enzymes
and things like that should be obviously
super useful and things like protein
design so um and then we are starting to
look actually potentially collaborating
with paul and others on pathways and and
and building up slowly towards this
dream of a virtual cell the question is
that's the end goal how does one get
there um you know after falls may be one
of those components but how what's the
next stages what would be the interim
stages if that was like a 10-year goal
uh and i think we are you know we've got
some promising ideas for that now i you
know i listed some of them uh
at the end of my talk and um
some of those will manifest themselves
in alpha fall three of course there's
also just the continually improving of
the current system and making sure that
you know we get that more and more
accurate and you know it's not perfect
in all cases and some of you may have
some examples of where it doesn't work
well we'd love to understand what those
are and improve that in a new system um
in the same way we did with alphago so
one of the ways we do that
is
another version of alphago alphago zero
we we in that diagram i showed you about
always insisting that the new version
can beat the old version um there's we
added something to that in later on in
alphago which was the not only did it
have to beat the old version it had to
do better on this special set of problem
positions that we knew the earlier
system couldn't go got confused in let's
say right so that and we collected those
uh automatically because we could we
could detect when the alphago system got
confused very rarely but we collected
literally about 100 positions eventually
was about a thousand positions and and
initially you know the alphago system
that beat lisa dole got scored zero out
of a thousand on those positions even
though it's very strong in general and
then by the end of it we had a second
match against the chinese uh world
champion about a year later and by then
it was getting about not only was it
much stronger beating the older version
it was getting 950 out of a thousand of
those difficult positions right now i
can imagine i would like to create with
all the help of all of you a a sort of
database of problem proteins that we we
know alpha fold two doesn't do well on
uh and then we would sort of train
against those specifically as well as
the overall accuracy
okay so
um to the back first
hi thanks a lot um
this is kind of related to peter
radcliff's question i'm sure less
articulate
in the second iteration but um i've
enjoyed doing a small amount of
mutagenesis or e-mutagenesis with
alpha-fold with my protein of interest
and as i add more amino acids into odd
positions i start to wonder if um
it can predict
um a synthetic sequence given it's only
learnt on
the proteome that exists
yes well it's interesting question it
seems to do quite well on de novo
proteins so there were there were a
couple actually in the cast competition
and it did particularly well on on those
surprisingly somewhat surprisingly given
that you know obviously the evolutionary
information is a key part of the inputs
right through the msa
but um one thing i would caution against
is is is relying too much on the on the
mutated predictions because
with alpha fold two one of the things
it's weaker at is point mutation
predictions that um you know change the
confirmation in a radical way which
obviously sometimes happens in disease
and we're trying to figure out that's
one of the main things we're working on
because we it's one of the main things
we get asked for you can imagine
useful for biologists and we're working
really hard on it it's a hard problem
because in a way one can think of alpha
fold has been trained to be robust to
small changes because we you know you
you want it to be if it's sort of
exploring the la energy landscape you
sort of don't want it to have these
weird delta functions where you change
one one one residue and suddenly you
know you're on some other huge
completely different part of the region
of space
uh that would be not such good behavior
in normal cases where you know a residue
probably doesn't make much difference
but of course in diseases there's the
occasional mutations that would make a
huge difference
and
um
in a way we got to sort of tell alpha
fault to pay special attention to that
somehow and kind of detect that it needs
to in a way say okay this isn't a normal
thing that i should just sort of be
resilient to robust to uh i actually you
know it needs to actually
take that really heavily overweight it
in some way one could say in its
attention network so that's one way of
thinking about it
and then related to this is is another
question we add is can we in some
situations you know can you get multiple
confirmations out of alpha fault not
just one uh and again you know misfolded
proteins and other things there's
another thing we'd like to sort of ask
alpha fold again can you give me an
alternate the next best you know the
next most energy uh
efficient confirmation
and that's you know we can sometimes do
that but it's not reliable enough yet i
would say so
um
yeah hopefully in the next year or so
we'll have something a lot better for
your emutogenesis experiments
right another one at the back then we'll
come to the front then we'll go digital
okay um thank you for that fantastic
talk um so so you mentioned during the
talk um you know this premise that in
vivo proteins fault you know instantly
essentially um however they they don't
necessarily always fold just by
themselves so there are contributions
from you know chaperones that will you
know contribute to the folding um so to
what extent do you think uh modeling uh
the the contribution of chaperones to um
stabilizing sort of intermediate um you
know com confirmations could contribute
to um refining you know alpha folds uh
for for problematic um structures yeah
no it's a great question i think um i
think eventually
in the limit we will have to
model the context right the chaperones
and other things i think this goes back
to the earlier question from online
about the dynamics of the situation okay
if we want to get up to virtual cell or
anything pathways even we're going to
have to take into account all these
dynamic things that go on around it
um
that was one of the reasons i think you
know chaperones and things like that was
also one of the reasons my you know
people were telling us alpha fold would
never work you know it was basically
essentially this conjecture was wrong
from that and vincent right that that's
the negative view of it i guess would be
that you can't actually just go from the
amino acid sequence to the structure
without taking into account all these
things which clearly happen in in in
real biology
but um networks are pretty clever
somehow understanding context right that
even if you don't explicitly give it to
them if it's sort of there in the data
you know imagine that chaperones do
certain systematic set of things with
particular type of proteins even if you
didn't put them into the directly into
the inputs
the the the sort of consequence of those
things might be modelable so uh you know
the best example we have of that is
things like uh proteins that sort of
have pockets and to you know bind to
certain things heme binding proteins and
other things is the one we usually use
example even if you don't give it the
heme or what what it's supposed to be
binding it it kind of makes the pocket
just with the missing ion you know it's
just sort of
knows somehow that that that shape that
that kind of protein should should have
that type of um capacity
even though obviously it doesn't know
anything at this point about the
dynamics
um so
it's sort of this is the idea of like
machine learning and ai picking up weak
signals that we as human designers would
never be able to program in things like
that right but somehow those there are
enough weak signals let's say in the
data to to allow you to approximate that
those influences
thank you
from mike please
hi that was a awesome talk um i guess i
have a more general question so
deepmind's goal is artificial general
intelligence and i guess the main method
that you use is reinforcement learning
do you think that everything that kind
of encompasses
intelligence comes down to maximizing
points or minimizing risks
no i yeah so we i should just be clear
we we use many techniques we don't mind
uh so deep learning is key it's why we
call deep mind because we we bet on deep
learning back in 2010 before sort of
anyone else knew about it obviously it's
everyone uses that now
um i guess what i was meaning is one of
the more unique things about deepmind is
we put a lot of emphasis on
reinforcement learning this trial and
error learning this active learning um
i don't think everything is as easy to
specify as absolutely you know like not
like the kind of points and and um
and uh you know minimizing risks it's
that's a whole research area in itself
is how to specify goals um you know
another approach is the the ai system
could like learn what the right value
function is
maybe from a meta meta reward that you
give it
but it's you know clearly as you get
closer to
human society things it's very difficult
for us to specify what the really the
objective functions are so um maybe
another approach would be to get
feedback from from the human user or the
human expert to kind of train it in the
way uh that that's desirable for for the
for the
task at hand so this course is obviously
a massive area of research and in fact
one of you know when i put up the rl
diagram that was one of the super
complex things is that little box saying
goal well how do you specify it how do
you make sure how do you um make sure it
doesn't deviate from it in in ways that
you don't want it to right this is the
risks part you specify a goal how can
you make sure that that's the only thing
it does that's actually what you wanted
um
and what's you know uh a nuanced enough
language in which to uh specify that
goal is also going to be complicated the
more real world we get with our our um
applications but right now we're
sticking to things that are you know
quite easy to specify i would say like
you know 3d structures of proteins or
energy systems all of those science
things i showed you have quite reason we
picked most of them as they have quite
clear metrics that um already the
scientists in that area are trying to
optimize for so it's easy for us but
this is a big question we're going to
have to overcome in the next few years
um virtual okay we have some questions
about potential negative impacts of
general purpose ai
um be it by malicious designers
that misuse them or people who
badly design their systems so what do
you think should be done to safeguard
against this or what can be done yeah
i'm
you know i worry about those things too
i think there are different sets of
worries there's two sorts of sets of
worries uh around ai systems one is what
they might do themselves you know i
think we just touched on that with if
you misspecify the goal and so on but i
think the obvious bigger worry is
malicious humans using these systems in
the wrong way and um i think part of
that is making sure the general public
and politicians and
governments understand better what these
systems are what is ai for example i
think there's a lot of misnomers out
there about even the definition of what
is ai um and what isn't um and then
beyond that is we i think we're gonna
have to set up a bunch of principles
perhaps even regulations at some point
uh with government about how
these systems should be deployed um or
used uh once they hit applications
because obviously that's when that's
when they have real world consequences i
think you know when you're doing
research and games and so on i mean one
of the reasons we pick games it was it
was a safe place to experiment in right
um it's a sandbox basically so it's the
perfect um uh sandbox to wish to to
investigate these ideas and still is so
we still by the way i want to get clear
we still use games and simulations to
for all of our research um partially
because of this because it's a safe
domain um but as you start doing more
applications self-driving cars whatever
that is then obviously at that point um
uh rules and regulations and things that
govern those areas already whether
that's transport or healthcare whatever
need to be updated to deal with this new
type of technology just in the same way
they were
updated to deal with mobile and internet
and digital in the first place 20 years
ago
i think there needs to be another
overhaul careful thoughtful overhaul
that doesn't stop useful helpful
innovation but um curtails this sort of
bad usage uh cases of uh of these
systems or bad training you know i think
we touched earlier on the first question
on bias and things like that and there's
so many
of course one thing i worry about is
because ai is so um
popular and buzzword now and say in the
startup world you know people can raise
millions of dollars just but everyone
says they're working on ai and it's
ridiculous like 99 of them i would say
are not um
or don't even know what it is and so
firstly it's giving a bad name to the
whole field but also uh it can lead to
cutting of corners of things that
obviously shouldn't be done but if one
were to do it you could make money on it
um doing face recognition or something
but it would just be unethical
okay back please
um so thank you for the talk uh how easy
would you say this is to completely
reverse engineer so
i want a stick man or molecular motor
yeah spit me out an amino acid code
well this is a again something we're
really actively looking at and i think
others are as well
can you put say alpha fold into the
design protein design loop i think is
what you're talking about a stick man
protein are you meaning okay so
um
so i think there are various ways to try
this like you could sort of use it as an
oracle like okay here's i'm gonna change
the amino acid sequence in this way tell
me what the structure is and you keep
sort of you could imagine making a cycle
out of that there have been a few
archive papers on that we've tried that
ourselves we don't think that's quite
the right way to do it we think there's
more that is needed uh to be this sort
of true generative model almost reverse
model
this does get back actually to
actually answer that question on
function from online about well
the dream would be to say the function
that you want and then you just get the
amino acid sequence that will produce
the right protein structure for it but i
don't think you know from what i
understand we don't understand enough
yet how structure and function relate
obviously there is relation but a lot of
big
biologist scientists i talked to say
there's much more to it than structure
of course all the dynamics we've been
discussing so thank you so um
so i think there's the i would say it
doesn't quite work yet but clearly uh
this is something on our radar when i
say protein design that's obviously the
kinds of things we're looking at um
it should be very useful in future for
that the type of thing you're talking
about
front mic
so all sorts of really interesting
problems in biology that would be
susceptible to ai so practical question
how do we collaborate more between the
crick and deepmind
well yeah we we collaborate quite a lot
already and and um
we're actually potentially even setting
up a small little wet lab here to to
help validate some of the predictions
that we're doing i'm just talking to
paul and sam about that um so then um
you know we we can we can that will also
facilitate collaborations
um we also started a new company that
will take this further i call isomorphic
labs that will just take the biology
part of this further beyond what like
deepmind can do as just a more of a
general ai um research organization so
uh once that gets sort of settled and
built up i think that that entity will
also be able to collaborate more on
specific biology um issues but if you've
got any you know questions i'm going to
be around later and then some of my team
are here too and you know we're just
across the road so it's easy it's easy
geographically to collaborate it's
always about problem is you can imagine
we've since we've done alpha fall and
and released it we've been inundated
with with collapse sort of collaboration
ideas and and we're still a relatively
small team especially working on these
types of things so it's all about
prioritizing what would actually make
the most difference
and and you know sort of stack ranking
that but feel free to let us know what
our your idea is
thanks
back mike please
hi thanks for talk um
so all the deep mind ais have been like
changes that no one expected big
paradigm shifts and leaps ahead
um do you think that that is going to
continue to be the case the way forward
do you think it's going to be more slow
and iterative from now on and if it
isn't those big leaps what is the
biological challenge that you think is
most dependent on a kind of big
leap or changing way of thinking like
that
do you mean an ai itself or ai applied
to biology i applied to biology oh okay
yeah um
well i i i think there are going to be a
lot more leaps actually so um or it's
going to feel like that that there will
be leaps so um i think clearly the big
one is can we get to dynamic systems i
think that's the next i mean i think i
guess we've i've answered that a lot of
other questions i think that's the real
and can we get down to is are our
systems really learning uh can they
learn physics properties of what's going
on um
so i think those are the next sort of
big challenges and um
you know we'll see if that's possible
obviously no one knows if that's
possible yet but i suspect that it will
be
thank you thank you i think we probably
i'm getting worried about dennis's
health here
the last two questions here
um hi so i wanted to ask you about the
explainability of ai yes because as we
know deep learning is to some extent a
black box yes
and i think explainability of that is
quite important when we are getting
close to the domains like healthcare
and even like folding the proteins so i
wanted to ask to what extent do you
actually understand decision making in
alpha fault
yeah and what should be
you know in the future kind of like
spread of the
resources towards getting the great
result
versus actually understanding how
decisions were made by the diplomatic
diplomatic network
yeah great question i'm glad you asked
that actually because so i kind of have
a multi-part answer to this so
first of all um
absolutely you know these systems are
the the kind of
the the sort of cliche at the moment is
that they are black box systems which is
true right you you sort of put these
inputs you train them and then they give
you what you're measuring them by is the
output not really how it got there um
but we're also science so we're
engineers and scientists at dmi so we
would also like to understand how you
know that what how is it doing this um
and perhaps one day you could imagine
reverse engineering out some principles
that are sort of human explainable about
what it's doing um now what i would say
is initially with all
ai is an engineering science i call it
an engineering science so how does that
differ from a natural science like you
know what all of you do with biology or
physics or chemistry is that the
phenomena you're studying is already
exists it's out there right so and and
and it's phenomenally complex to to to
study and break down and reduce down
into its principles um with engineering
science we have to build the artifact
first right and then we can analyze it
but the analysis part there's no reason
to think it would be any easier than for
for for natural phenomena right with
these incredibly i mean i sort of just
gave you a feel for the alpha faults
you know complexity it's it's it's
crazily complex and so
um
you know it's going to take the same
amount of effort now until now
i don't think a lot of don't think
enough effort and i don't think a lot of
effort has gone into understanding these
black boxes but that's partly because
until very recently they didn't really
do anything very interesting right so so
if one's gonna spend five years uh
analyzing a black box well you better
you better it better be the case that
the black box is doing something
worthwhile right so so until like things
like alpha go i don't think there was
anything really worthy of the amount of
effort that would take
now um but even alphago game systems so
we the best work i have example of that
so far is alpha zero which plays chess
um
that we just published an archive paper
with some of the people our programmers
on that system but also with vladimir
kramnik who's the ex-world chess
champion he was fascinated by alpha zero
how it played and he wanted to help us
analyze the alpha zero system and i i um
of course there's a x chess player i was
fascinated by that too so i we went
ahead with that project because in chess
as i mentioned in the beginning that's
one of the most studied areas of domains
of ai it's you know chess programs have
been made since the days of touring and
shannon right they even famously
programmed you know creating all the
chess programs and of course we know
what um rules should look like for chess
these heuristics that the the the
classical systems use so we were
wondering could we back could we extract
out of our alpha zero network those
rules things like king safety queen
mobility p you know number of p the
value of the pieces uh and so i i sort
of point you at that point we just
released that recently as yes it turns
out we could actually reverse engineer
out what alpha zero thought about those
situations
and then compare that to what classical
machines could do you remember the name
of the paper the title uh it was like i
come from i remember if you look at me
there's only one paper we've done
together so
it's quite recent um and so yeah so i
think we need to basically uh there's a
whole much more research to come and i
think we're a transitory moment in ai in
that i think in the next 10 years you'll
see loads more tools visualization tools
analysis tools for analyzing important
systems it's just only now have we got
systems i think that are worth like an
hour fold to that people will even be
interested to understand the how of
um so my suspicion is is that this is a
it's not a fundamental problem with
these citizens they're black boxes they
are currently but i think they can be
interrogated and my final example of
that is my the neuroscientist hat on is
what i tell and we have a lot of we have
a whole neuroscience team uh at deepmind
as well and um
what i say to them is that we should
understand these artificial networks at
least as well as we understand brands
and currently we understand them less
well because brains are sort of you know
human brains a bit of a black box too if
i tell you you know how are you coming
up with these questions
it's a bit of a black box right you can
tell me some theories but who knows
right what's really happening but of
course we can from you know do fmri and
and single cell recording all these
things now so we're finding out more but
in the in the artificial brain situation
we have a we can do we have a lot more
freedom to do all sorts of things um we
did a bunch of ablation studies on alpha
fold two right so that's the first step
of analyzing something is we so we spent
a good few months breaking down which
parts of the components of the system
were needed turns out all of them um but
why so there's that's a little bit of
understanding um but we should we should
be as a lower bound we should understand
as much about these artificial networks
as we do about um brains you know
natural brains and currently we're we're
definitely less understanding than that
so there's a lot of head room to go so
people are interested in that question
you know we have a whole team looking at
building those kinds of analysis tools
we call it virtual brain analytics um
and there's you know i think it's a
fascinating project you know research
area in itself great thank you great
thank you it's an excellent question to
finish i say so we will finish there um
tremendous um talk tremendous energy
still going at an hour and a half still
some way to go after it he um demise is
going to be involved in one of our
smaller discussion groups so we're going
to go straight there after it but now
thanks dennis wonderful performance
wonderful work thanks
you