Hannah: This is DeepMind, the podcast.
And I’m Hannah Fry.
Over the last year I have been getting
an inside look at current research
into artificial research, or AI.
And we’ve been talking
to scientists, researchers and engineers
about how things stand
and where we’re headed.
Tracing the fast moving story
of one of the biggest
challenges in science today.
So if you want to be inspired
on your own AI journey,
then you’ve come to the right place.
Up until now in this series,
we have largely looked at what
AI is capable of in the lab,
or in the world of games.
But the ambition of people
working in AI
is to help solve problems
in the real world.
Right now people
are trying to use AI
to help with everything
from predicting traffic jams
to monitoring endangered species.
And here at DeepMind they’ve also
been working on a few things.
Now we could call them
case studies,
but where would the fun be in that
so I’ll tell you what -
let’s have one of
those big trailer things - shall we?
In this episode we learn how AI
might potentially help
save the sight
of thousands of eye disease sufferers.
Pearse Keane: We get the output
from the first neural network
and we can interrogate that as doctors
making decisions for our patients.
Hannah: Help break down the enigma
of protein folding.
Sandy Nelson: We can learn
what the processes
or descriptions
of the process
so that we can take a sequence
and then predict its structure.
Hannah: And impact on
our growing energy demands.
Sims Witherspoon: If that waste heat
isn't’ taken care of,
somehow it will literally
melt your laptop.
Hannah: First I want you
to meet Sims Witherspoon.
Sims is originally
from South Carolina,
she’s now the programme
manager at DeepMind
and she is a big believer
in the potential benefits
that AI can bring.
Sims Witherspoon: When people think
about DeepMind,
if they think about games,
they’re largely thinking
about the research side,
but trying to solve
for intelligence
is literally to use that intelligence
to make the world a better place.
The world has no shortage
of problems we need to solve.
We’ve got some really big ones,
some smaller one,
but for the areas
of immense complexity -
things where there’s lots of data,
and you know lots of permutations
and huge combinatorial
space of possibilities,
sometimes that’s really daunting for
human brains to try to figure out.
Hannah: Stuff in the real world
is just really complicated.
Sims Witherspoon: Yeah, it’s really
complicated!
And you know real world data
is really messy
but if we can use AI to try to find
a path through that complexity
then we can solve our problems
faster than we could
if we were trying
to do it on our own.
Hannah: Nowhere is that more true
than in medicine.
In 2016 DeepMind partnered up
with Moorfields Hospital NHS foundation
trust in London to try and apply
deep learning to eye scans.
With the number of people
suffering from sight loss
in the UK
set to double by 2050,
the trial had the potential
to be hugely impactful.
Pearse Keane is a consultant
ophthalmologist
at Moorfields Eye Hospital and National
Institute for Health Research
and knows firsthand about
the demands placed on doctors.
Pearse Keane: one of the huge problems
we have in ophthalmology
not just in the UK
but all around the world
is the huge number of patients
that we have to deal
with so in particular
in the national health service
we get nearly 10 million clinic
appointments in the UK every year
and that’s actually 10%
of all clinic appointments
across the whole NHS
and it’s the number that’s increased
by more than a third
in the past 5 years.
Hannah: Why has it increased?
Pearse Keane: I think it’s increased
because of the ageing population.
I think it’s increased because certain
diseases like diabetes are on the rise
and so we just have to deal
with a lot of eye problems
related to that and so for example
I can tell you about just one condition
which is age-related
macular degeneration,
or AMD, and AMD is the commonest
cause of blindness in the UK,
commonest cause of blindness in Europe
and and in North America as well.
And for just that one condition,
nearly 200 people develop
the blinding forms of AMD
every single day just in the UK.
And so the challenge that we have
is that those people have to be seen
and treated in an urgent fashion,
the problem then is that in for example
in 2016 Moorfields
Eye Hospital in London
where I work received
7,000 urgent referrals
from the community as possible wet AMD -
the blinding form of this condition.
But after those 7,000 referrals
only 800 patients actually had
the severe form of the disease.
Hannah: With so many people
getting urgent referrals
it is inevitable that patients
are going to have to wait for weeks
to be seen by a specialist and during
that time you have thousands of people
who think they have an eye disease
that will threaten their sight
who don’t actually need to worry,
and hundreds of people
with a curable condition
whose sight could be saved
but is slipping away while they wait.
Pearse told me about one of
his patients Elaine Manner.
Pearse Keane: Elaine lost her sight
from macular degeneration in her
left eye completely
more than 10 years ago,
before there was good treatment
and in 2013
she started to develop
blurring the vision in her good eye.
She went to her high street optometrist
who looked in her eye
and said I think you are
developing AMD in your good eye,
you need to be seen
and treated urgently
because now we have
good treatments for this.
She got an appointment
to another hospital on the NHS
and it was 6 weeks later,
so can you imagine if you were at home
and you were losing your sight
in your good eye
and you’re told you have to wait 6 weeks
when there is a treatment
that’s available.
And if that was my family member
I would want them treated in 6 days,
not in 6 weeks.
Hannah: Here is where the AI comes in -
and the idea is simple -
patients who are referred to Moorfields
will have already had pictures taken
of the back of their eye by their doctor
or their optometrists
in their high street opticians.
These are 2 and 3 dimensional images
known as OCT scans
that can show up
any one of 50 different diseases.
If you can use artificial intelligence
to filter through those images first,
and triage the patients, you can flag
the people with a serious disease -
get them in front of a doctor sooner,
give them an earlier diagnosis,
earlier treatment,
and potentially save their sight.
Pearse Keane: Before artificial
intelligence and before the success -
the recent successes of deep learning,
the traditional approach
to programming an algorithm
to recognise a photo
of a cat for example
would be you would write all the code
to describe the future of a cat -
a cat has whiskers, a cat has a tail,
then you’d say some cats
don’t have a tail,
some cats don’t have fur, etc. etc.
and you would try and write thousands
or hundreds of thousands of lines
of code to describe that.
With deep learning we don’t do that.
With deep learning we show many examples
often thousands or hundreds of thousands
of pictures of cats to a neutral network
and it will extract the futures
of interested self
and learn how to recognise a cat.
Or we simply do the same thing
but with eye diseases.
Hannah: The machine doesn’t care
whether it’s looking at cats
or the backs of eyes,
it’s the same person
Pearse Keane: yeah,
to a large extent yes,
and I think the reason why this
collaboration has been successful so far
is because Moorfields eye hospital
is one of the oldest,
one of the largest eye hospitals
in the world
and we have huge numbers of OCT scans
to train these ah neural networks.
Hannah: Does it work then?
Pearse Keane: I think the results are
amazing, I think they’re jaw dropping.
I think that the algorithm
that we have created is on par
with world leading experts at Moorfields
in triaging these OCT scans.
Hannah: But there is quite a big
difference between spotting cats
in pictures
and picking out eye disease.
For something so important, how do you
know the algorithm is getting it right?
How can a consultant be sure
to see what the algorithm saw?
Or feel confident to overrule it
if they don’t agree.
Well the key Pearse told me
is about building an AI
that doesn’t just tell you
what it’s found but also shows you.
And to do that, the AI needs not
one neural network, but two.
Pearse Keane: So the first neural
network is trained to identify
all the disease features on the scan,
and the second neural network is trained
to take those diseased features
and to use them
to make a diagnosis on the scan.
Hannah: So it’s first going through
and kind of highlighting areas
that don’t look totally normal -
anything that looks suspicious,
[Pearse: yep]
and then the second one is coming in,
explaining what is going on
in all of those,
and using that to come
to a final decision.
[Pearse: exactly, yeah].
And you can see all of those areas
that that first neural network
has highlighted.
Pearse Keane: Yes, so that’s one of
the great advantages of this approach -
we get the output
from the first neural network
and we can interrogate that as doctors
making decisions for our patients,
so if you’ve got bleeding
in the retina,
or if you’ve got leakage of fluid
or water logging in the retina,
it will highlight
all of those features,
so if you see that a patient
has diabetic eye disease,
then you can see the very typical
features that have led it
to make that decision,
which gives a lot of I think reassurance
for health care professionals
who would be using this.
Hannah: There’s a double whammy
with this approach,
not only can it reassure
the consultants,
helping them with the diagnoses
they are already doing,
but there is hope
that the AI might one day
also be able to advance
our understanding of the eye itself.
In 2018, another group of researchers
decided to see if they could use
deep learning on images of the retina
to predict the sex of the patient.
Now the best an eye doctor
could manage would be a 50 / 50 guess,
but to their astonishment,
the algorithm got 97% right.
No ophthalmologist in the world
has any idea what it is
this algorithm
is picking up on in the photograph.
Or any theory as to why
the male and female eye
might be structurally different.
But the AI has found something
that they’re now trying to understand.
And in the Moorfield study, even when
the algorithm gets the diagnosis wrong,
it might still be picking up
on something
that the professionals
hadn’t spotted.
Pearse Keane: What was interesting
was that when we looked at the cases
that the algorithm got wrong,
we actually had to take a step back
because it seemed like
some of those cases
were very ambiguous,
challenging cases
where maybe the algorithm
had made the right answer
and our gold standard
was at least open to the debate.
Hannah: Really?
Pearse Keane: So really kind of like jaw
dropping the results
that we were getting.
Hannah: Jaw-dropping indeed.
So that’s AI dipping its toe
into the world of medicine,
but how ‘bout one of the most
fundamental problems in science.
Sandy Nelson: When I spoke to very
senior research
about what he thought was the most
significant problems in biology,
he, his top problem was understanding
the brain and how that works.
His second problem that he thought
was the most important
was understanding
how proteins fold.
Hannah: This is Sandy Nelson -
a product manager
for DeepMind’s science programme,
and as Sandy told me it’s hard
to overstate the importance of proteins.
It is the most cited topic
in 50 million scientific papers.
Sandy: So many of the terms
we are used to
when we are thinking
about medical conditions
are actually underlying proteins,
so we think about the immune system
and how that works,
well that’s proteins.
We think about hormones,
we know that regularly
so many functions in our body,
so of course drugs are a lot
to do about small molecules
interacting with proteins, but there’s
many other ways in which proteins
are important for thinking
about say, disease.
So we know for example
Alzheimer's
and some of those
neurodegenerative diseases
are to do with proteins,
or proteins are implied.
Hannah: Proteins are the building
blocks of all living systems.
Stretched out straight, they’re just
big long chains of amino acids,
a bit like a ribbon,
but they fold in on themselves
and make these giant
three-dimensional structures
stuck together
with peptide bombs.
Now the number of different ways
a protein could fold is vast.
Think origami here,
except mind-bogglingly complicated
human microbiological origami
with 10 to the power
of 300 possibilities.
And scientists care
a great deal
about exactly what shape those final
folded proteins end up as.
Sandy: Part of the reason
proteins are so useful
for taking part in so many
biochemical processes
because they are specific.
They can target very, very specific
points in some process
and that specificity comes from
the uniqueness of their shape,
so when we think about proteins
are the go-to molecule
for anything you need to do in
in a living animal,
and if you want to try
and understand
why some of the proteins
have gone wrong,
or to create some kind
of intervention,
understanding that process
of creating structure from sequence
is a first step on
maybe designing proteins
or understanding
why it might go wrong.
Hannah: The function
of the protein
whether it’s to detect
light in the eye
or fight disease
or speed up reactions rates
is determined by its unique
three dimensional structure.
And the question is how does the protein
go from one state to the other?
From the ribbon
to the final folded structure?
What is the objective
here then?
Is it that in the end,
you want to create something
where I tell you
a sequence of amino acids
and you tell me what
the structure will look like.
Sandy: So to the simplest level, yes.
If you could do it as accurately
as it can be done in a lab,
that saves
a huge amount of effort.
Hannah: In theory, you can just observe
the shape of the final folded structure.
The most common way
of going this
is by bombarding crystals
of the protein with x-rays,
and inferring its shape from the way
these beams are scattered.
But that is hard to do.
It can cost hundreds
of thousands of dollars
for each protein structure,
and take months or even years of work.
It’s so hard in fact that Max Perutz
won a Nobel prize in 1962
just for figuring it out
for one single protein - hemoglobin.
There is an alternative though.
The final structure of the protein
is actually determined
by the chain of component parts.
The forces and charges that are acting
on each of those individual amino acids,
so in theory you could use the physics
to predict how the protein ribbon
is going to fold.
But it’s going to take
a lot of number crunching.
Hannah: So if you had a gigantic enough
computer,
you know, super-computer level
I could give you a string of amino acids
and you could tell me
what shape it would end up as,
but the problem is we just don’t have
the computing power
to crunch through it.
Sandy: Not at the level
of modelling all the forces.
So we can explain why the protein folds
the way it does using
our understanding
of chemistry and physics
but because of the size
and complexity of the molecules,
there are so many forces,
we can’t model everything.
Hannah: And here’s where AI comes into.
Sandy: we think that there’s
another level of abstraction
where we think we can maybe find a
summary description of all those forces.
And that’s again too hard
to come at through analysis,
but maybe we can learn that
because we’ve got a huge data
set that says well
this sequence folds this way
and we know that
that’s reliably the case.
So using machine learning,
maybe we can learn
what the processes
or description of the process
so that we can take a sequence
and then predict its structure.
Hannah: Here is a problem
with a very clear objective.
Correctly predict how a chain
of amino acids is going to fold,
and a vast, vast number of possible ways
to get there.
AI is perfectly placed
to cut through that complexity.
The only problem is that even
with AI on your side,
these things are
so enormously complicated
that you still can’t cut a clear path
to predicting how a protein might fold
based only on the physics.
Thankfully though there is a trick
that you can use to simplify the problem
and give your AI a head start.
The fact that proteins are so diverse
can help you constrain the problem,
although I should warn you,
as a mathematician,
I found this stuff pretty hard
to get my head around,
so I’m going to try and walk you
through it nice and slowly.
Proteins, like organisms
have a long evolutionary history.
They can sometimes be small random
mutations in the string of amino acids.
Every now and then a mutant protein
will differ from its normal version
on just one of its corners
where if you unraveled it back
into the ribbon of amino acids,
the markers of that mutation
would show up in more than one spot.
You can imagine this as though
you’ve got your folded ribbon
scrunched up in some
complicated shape in your hand
and then you take a felt tip pen
to one corner of it.
Now if you unfolded your ribbon
and flattened it out,
you would see that the pen would have
stained various spots along its length.
So working backwards then, if you start
with a flattened ribbon and notice
something strange
in a few different places marks
the hint
at a consistent mutation you know
that however the protein ends up
being folded, you found a big clue -
those stains must have to be next
to each other in the final protein.
Collect up all of those clues and you
have greatly simplified your problem.
Is it like you’ve got this this vast
sort of landscape of options
and you’re trying to build walls
to pen yourself in.
Sandy: Yes, that’s exactly right.
Because these proteins are so large,
they could fold in
so many different shapes
so we need to find clues that allow us
to eliminate whole massive shapes
so we can concentrate
just on a much smaller number.
Hannah: You’re making the problem
smaller?
Sandy: That’s right.
Hannah: You’re listening to a podcast
from the people at DeepMind.
Now every two years there is a big
protein folding competition called CASP
- critical assessment
of structure prediction competition.
Over the course of three months,
academics from around the world
compete to predict the structures
of amino acids using algorithms.
The structures of these particular
amino acids have already
been confirmed
through traditional observation,
so it’s possible to judge
who comes closest,
and in 2018, DeepMind entered
its AI programme AlphaFold.
Sandy: We had a look at how
other people did protein folding
and and we saw how they used
evolutionary information,
and what other people
had been doing
was they had been looking
at sort of binary constraint
that said these two amino acids
should be in contact,
shouldn’t be in contact,
whereas what DeepMind did is we looked
at the probability of different
distances between those amino acids
so that’s really like just saying well
we tried to retain some more information
or learn a better function
for describing that relationship
between proximity
of amino acids.
Hannah: So in terms of the fences
that you’re building on your landscape -
am I going quite far
with this analogy now?
Sandy: That’s fine.
Absolutely a good comparison.
Hannah: You were making sure that you
weren’t throwing away any information.
Sandy: That’s right -
our fences
were ah more subtly defined
or a bit more clearly delineated
or they were less fence-like
and more like
just a sort of
Hannah: hillock?
Sandy: Hillock. That’s right!
Hannah: Nice. But that actually ended up
making the prediction more accurate.
Sandy: Yes, that’s right. So it’s so
it’s a very, very complicated function
but we were able to learn that,
and so once we’re able to learn
to kind of essentially retain
that extra information,
that’s one of the key things
that made our system more successful.
Hannah: With the problem reduced,
the AI could get to work doing
what it does best -
for three nail-biting months,
the DeepMind Alpha fold team
worked on the competition,
turning sequences of amino acids
into predictions
of three dimensional
folded shapes.
Sandy: We didn't have any strong signal
which told us how well
we were performing.
We could see that we were not perfect
in many cases,
so it was very hard to find out how well
we were doing compared to other people.
There’s so many fantastic researchers
that were publishing great results
until we actually went through
that sort of organised assessment,
very very hard to really figure out
how well we were doing.
Hannah: And then finally it
was the moment of truth -
of the 43 strings of amino acids
they were given,
the team came closest to correctly
predicting the structure for 25 of them.
The team that came in second
only managed three -
a staggering result
by anyone’s standards.
You’re kind of downplaying this because
I was talking to a few academics
when this this result came out
and of all the results
that have come out of DeepMind,
this is the one
that has got the scientific community
of DeepMind most excited.
Sandy: Yes.
And and that’s because
this is a classical scientific domain.
It’s a grand challenge in science
that many people have worked on
so it’s something that many
scientists care about deeply,
they can see what the potential
ah impact is
and it’s been known
to be a very hard problem.
So we’ve been able to make
a step change on a hard problem
that’s been worked on
for over 50 years.
Hannah: In terms of interventions then,
is this just something that biologists
and scientists will get very excited
about in terms of like blue sky
research - understanding protein,
or is this something that can end up
having an impact
in real people’s lives.
Sandy: So I guess it’s similar
to all sort of biomedical research.
It’s fundamental
so it has huge leverage -
so essentially it will affect
many many things,
but it needs to be translated
into something specific for it
to have immediate impact
on people’s lives,
so for example, if we think about
the drug discovery process,
part of that process goes on in labs
and is very abstract,
and all to do with chemistry,
but ultimately that process
does produce medicines
that we can buy or prescribe
that will ultimately affect our lives.
So this is at the start - the early part
of that process, for example.
Hannah: Although the long term
implication of protein
folding have the potential
to impact all of us,
it’s not exactly a topic
that most of us are coming
gave to face with on a daily basis,
but one issue that we are all facing
is that of climate change.
You remember Sims who you met earlier?
Well her team decided
to focus the efforts
on one specific climate challenge -
energy consumption in data storage.
And they started by looking for a place
where burning far more energy
than we need to,
because it turns out your emails
are one of the things
that are warming the planet.
Sims: If you think about the things
that we all do online every day.
Whether that’s sending an email,
doing a google search,
looking at dog videos on YouTube,
you know the number one videos -
cat videos but for me it’s dog videos.
Hannah: And me, and me.
Sims: Nice.
Hannah: To be fair.
Sims: Nice.
All of that requires compute power,
and the information
that we you know data we send,
data that we store,
when information is disseminated,
all of that runs through
a physical space - ie Data centre,
and it takes a lot of energy
to do all of those actions
that we rely on
on the internet.
Hannah: Because I mean there are actual
sort of physical warehouses
[Sims: yes] that are holding
all of those cat videos.
Sims: Oh absolutely, there, yes,
I mean yes, there are physical spaces.
And if you think about
the amount of energy they consume,
a data centre you know
in a large industrial kind of setting
can consume the same amount
of energy as a small town.
I mean these things are massive
and they require a lot of energy to run,
um, they also require
a lot of energy to cool.
Hannah: All those emails
sitting in your inbox -
the four dog videos
you’re streaming simultaneously,
the request that you’ve sent to the
server to download this very podcast -
every one of those things
requires computing power
in a data centre somewhere.
Collectively, data centres
now use 3% of the world’s energy -
the equivalent of a whole new country
that just popped up
on the map a few years ago.
And all that computing generates heat -
lots and lots of heat.
Sims: If you imagine how hot your laptop
gets when you’re streaming Netflix
or you know the four videos online,
imagine that but multiply
it times a million.
If that waste heat
isn’t taken care of somehow,
it will literally melt your laptop,
or in the case of a data centre,
it will melt your server.
That’s why your laptop has a fan,
that’s why data centres have cooling
that needs to happen there -
we have to keep them at a temperature
so they don’t melt,
and you and I can get
our dog videos off YouTube [laughs].
Hannah: And I guess just cooling down
those data centres
takes up a vast amount of energy.
Sims: Yes, it does. We are you know
we’re talking about chillers
required that are the size of busses
in order to keep them cool.
Hannah: And this is where AI comes in.
Sims: So imagine you are trying to
control the cooling of a data centre.
And a human being who you know
is usually a facility manager,
a data centre operator,
just has two kind of dials to control,
and that was all you had to do
to control the entire centre,
now that is a vast oversimplification.
Hannah: Like a fan and air con -
Sims: Yeah, exactly. Just those two.
You could figure out
the best you know
is it just air con, is it just fan?
Is it both? Is it neither?
like that’s not that many options,
right? You could figure that out.
But when it turns into a huge number
of pieces of equipment
with set points on every single one
which are all things you can change
by some degree, that then interact,
all the sudden you’ve got a vast number,
literally a number of options
that is in the billions,
and that’s just too much for ah
facility manger, a data centre operator,
a human being to try and control,
so this is where we think AI is
it’s the perfect space for AI.
Because AI can ingest
a vast amount of information -
more than the human brain can,
and can help us figure out
which of those permutations,
which of those combinations actually
is the optimal path forward.
Hannah: How does AI cut through
all of this this complexity though?
Sims: we can ask a model to figure out -
okay we want to keep the data centre
at a certain temperature,
but we want to use less energy
to do that.
Here are all the ways you can manipulate
the system - please figure it out.
Hannah: The setting might look
quite different to a game of Chess or Go
but the principle ideas here
are exactly the same.
Again you have a very clear objective,
namely keep the centre cool
while using as little energy
as possible,
and a vast, vast number of possibilities
of how to get there to the AI
has to find a path through,
and once it does -
Hannah: The AI tells you
how to all of the dials should be set
across the across the centre -
Sims: Exactly.
What set points to change,
and by how much to change them.
Hannah: And does it work?
Sims: It does work!
That’s the best part about it.
We saw that with direct AI control ie
you know getting those recommendations
and having AI feed them directly back
into the physical infrastructure
of the data centre
going through
lots of safety constraints,
we saw a 30% reduction
in the amount of energy
required to cool
Google data centres.
Hannah: Which is massive!
Sims: Which is massive!
And really exciting number -
Hannah: Now I have to confess,
I’ve seen the graph of what happens
when you put the AI in direct control
of all of the dials,
and it is staggering, I mean
you’ve got this this sort of bumpy line
that goes along about how much energy
is being used, and then it’s all -
it looks like there is grass where the
pound crashes after some terrible news.
It just drops off a cliff
and then you kind of have it bumping
along the bottom of the graph
at which point you take the AI
away from being in control
and it’s it switches back
over to human control
and it jumps straight back up
to where it was before, it’s amazing!
Hannah: Is the AI running the cooling
system in the data centres right now?
Sim: Yes it is, which is fantastic,
and hoping to roll them out
to even more in the future
now that we’ve kind of proved
that this works
and works well with more data,
with more practice in other words,
the AI gets better over time,
so 30% is a fantastic number,
but it’s increasing you know,
rules and heuristics
don’t get better over time, but AI does.
That's the best part
about these systems.
Hannah: And that is why there is so much
excitement about AI’s
potential here at DeepMind,
and the AI labs around the world.
If you would like to find out more about
applying AI to energy,
health care and scientific problems,
or explore the world
of AI research beyond DeepMind,
you’ll find plenty of useful links
in the show notes for each episode.
And if there are stories or resources
that you think other listeners
would find helpful, then let us know.
You can message us on Twitter, or email
the team at podcast@deepmind.com.
You can also use that address to send us
your questions
or feedback on the series.
Now, shall we have another break?
