Google's DeepMind Wants To Make Human-Level Artificial Intelligence, Says Its Chief Business Officer

Channel: Alex Kantrowitz

Published at: 2023-09-14

YouTube video id: nTzb8QszTeE

Source: https://www.youtube.com/watch?v=nTzb8QszTeE

a top executive at the heart of Google's
Deep Mind efforts to advance artificial
intelligence joins us right after this
welcome to Big technology podcast a show
for cool-headed nuance conversation of
the tech world and Beyond we're joined
by a very special guest today Colin
Murdoch is here he's the chief business
officer at Google's Deep Mind we're
going to talk a little bit about Theory
we're going to talk about practice um
and how Google is trying to break take
The Cutting Edge and artificial
intelligence and productize it Colin
welcome to the show Alex it's fantastic
to be here I'm really looking forward to
this
conversation great so would you like to
start easy or
hard uh wherever you like Alex let's
jump right let's let's just jump in uh
why build artificial general
intelligence I mean this is something
that is a stated goal of Deep Mind to
build AI on par with human intelligence
or even something that's surpasses it
why do it that's right well I mean just
stepping back for a moment I think it's
really important to think about what
artificial general intelligence is or
AGI is because you'll be familiar with a
lot of AI systems today you build an AI
system to solve a particular problem and
that works exceptionally well we've seen
huge kind of breakthroughs in
fundamental AI research which has driven
really important impact in the world
through this form of AI what we hope
though with artificial general
intelligence is to build a system that
can solve multiple different problems so
one AI system that can solve multiple
different problems and much like us
humans Alex that means we can take
learnings and the AGI system can take
learnings from one setting and apply it
to a new setting and we expect that
therefore uh means that this AGI system
can create more creative and
transformational solutions and we know
this is possible actually because humans
are a form of artificial well not
artificial in fact real general
intelligence and look at the incredible
things that we've been able to achieve
and that's why we at Deep mine think
it's a really worthy to pursuit to
develop a form of artificial general
intelligence that will help hopefully
tackle some of society's biggest
challenges things like climate change
and you know problems and questions in
healthcare that's actually the core of
what we're up to and it's incredibly
important and I think incredibly
interesting yeah but it's it's going to
do more than than just that I mean you
know you can direct I'm sure you can
direct it to do you know climate uh work
and Healthcare but there's a whole host
of different things that you know an AGI
you know will will I imagine will be
able to do from your website you say uh
by building and collaborating with AGI
we should be able to gain a deeper
understanding of our world so that's
even a level deeper than you just
mentioned resulting in significant
advances for Humanity how how will this
technology a provide a deeper
understanding of our world but B you
know if if we can apply you know if we
can think like a human then where are we
GNA how practically will it be able to
achieve some of these you know goals
that you just
discussed so I think uh the application
in science is a really interesting area
to think about um science is a
incredibly complex area that we as
humans over the years have made
incredible advancements in and those
advancements in science have really
enabled us to build the societies we
have today have the health that we have
today have the food production that we
have today but at some level it feels
like we're meeting the limits of the
knowledge that human alone can create
through this process and a concrete
example of this is um proteins uh
they're the building blocks of life
they're what makes you and I work alcts
right now if we didn't have proteins the
little machines in our bodies uh we
wouldn't function and scientists for
years have been trying to determine the
structure of proteins actually because
if they go wrong if they're misformed um
if the structure isn't quite right that
can cause things like disease um and all
sorts of um maladies across you know
whole range of different areas the
challenge is for humans we've been
trying to do that using experimental
means so it takes years of painstaking
research and millions of dollars of
specialist equipment to determine the
structure of just one of these
proteins which is why actually experts
for about 50 years now actually these
are really really uh Advanced scientists
have been trying to use computational
approaches to determine the structure of
a protein because proteins are made up
of these little units that encoded for
in our DNA and we actually know the
sequence of those units the challenge is
how do they turn into this
three-dimensional structure so there's a
really important problem there that is
the core of a lot of really important
biology understanding that we've not
been able to solve as humans and this is
a good example of where AI has been able
to step in and I can tell you a bit more
about that you're building up too
yeah Alpha fold Bingo yes I mean so so
I'm going to wa wait a moment because so
Alpha fold we're going to talk about
about it more in the second half but
you've been able to decode proteins with
uh Deep Mind research using this alfold
system but that is that is still a
narrow intelligence so you're actually
taking it a step deeper if you're trying
to build an artificial general
intelligence so talk a little bit more
about why that's
necessary well there are many problems
that uh we're able to uncover uh um
begin to solve with things like Alpha
fold but if you step back and think how
humans we solve problems we come across
one problem we solve that and that
typically opens up a new problem and if
you step back and think about the
universe as a whole and our world as a
whole the size and scale of that problem
space is immense and I suspect there are
problems that we've not even thought of
today and I hope with a system like AGI
artificial general intelligence is that
once we solve one problem that typically
opens up uh and shows this kind of
branching set of new problems the AGI
system will be able to kind of trans
translate across that problem space at a
speed and scale that we can't even
imagine today and and in many ways it's
a hard question because we're bounded by
our existing human intelligence so we're
abanded by what we currently know but if
we can build a system like this I think
we'll be able to explore this problem
space in an exceptional way and the
reason why I use Al fold as example is
because I think science and scientific
discovery is actually a really
fascinating and important area that is
this kind of enless set of problems that
we hope in AGI system we working with
humans be able to help us
cck doesn't this seed a lot of control
or a lot of the sort of meaning of what
it is to be human to the
machines what we see actually um in
Alpha fold as a good example um and even
when I think when it gets to uh AGI is
that we'll see computers and people
working together um the great example
that actually uh from a couple of years
ago that still resonates to me is a
system called alphao now go is a a board
game which very popular in some parts of
the world I think our listeners are
familiar but yeah you can go ahead yeah
fantastic um and people studied this for
thousands of years and we developed an
AI system that was able to play alago um
and we set it against various human
experts and you know in some cases the
human experts won but over time actually
alphago became increasingly powerful
what we discovered however was that once
we then made alago more generally
available the humans actually used alago
to improve their performance and that's
often the case so the AI system help the
human improve and you put these two
things together and in many many cases
it's the AI system plus the human that
ends up being the most fruitful the most
powerful outcome so there's definitely a
path there I think of a sort of uh co-
Evolution or co-development between
these two forms of intelligence
yeah but again these are narrow
intelligences right they are built for
one specific task so when we you know if
we're able to achieve an artificial
general intelligence which is basically
going to be able to think and sort of
perceive the world and plan on par with
human obviously it will exceed a human
because it can have the entire power of
computing behind it we're we're limited
by our brains and we get tired um that's
much more powerful I mean it does it
don't you think that's like a completely
different different level and and again
like you know we talk about a
partnership but what what's there to say
that that you know we're again not I
mean there's a going to it does seem
like there's a point where the machine
will just like not need the human
feedback to make these discoveries so
that's the questions are we going to see
a good chunk of what it means to be a
person to be a human to these machines
and if so maybe that's good maybe it
evolves Humanity to a different place
but yeah what do you think about that
well I'm sly excited to imagine how an
increasingly capable system will help
help humanity and also help Humanity
expand our capacities I one way I think
about it is that you know I've got three
children um I'm an adult so I've been in
around longer for them there a form of
uh artificial general or general
intelligence so am I uh but we can work
together even though I happen to know
more than my children we can work
together they sometimes teach me things
that I'm surprised of as well so um I
think these are different forms of
intelligence that I hope can work
together um and actually in Harmony in
many
ways should so it's interesting that you
mentioned your children it's almost like
creating new life in some ways um should
the entities being responsible for
creating these new life this new life in
some way be be businesses I mean should
we have businesses in trust you know
trusted with the stewardship of this new
you know fascinating form of
intelligence should we reach it there
and if so why so I think way I think
rather than life I think the way I think
about it as a as a tool I think that's
probably the framework I use when I'm
thinking about these sort Technologies
and then I think as we think about
building these these really powerful and
important tools um then I think
businesses do have a really important
part to play for sure um I think uh
government has a really important part
to play I think regulation has a really
important part to play I think Society
has a really important part to play this
is a new technology a new set of tooling
that's going to have a really important
and positive impact on the world and one
we've got to develop in a responsible
way because it is a very powerful
technology we've got to take great care
with it as well so that is why actually
at Google dmin um we have and it's
always been part of what we do this uh
kind of responsible approach or
pioneering respons as we call it it's
always been in the DNA of what we do I
think all of these actors need to work
together in harmony actually to to
develop something that's really
important and impactful for
society yeah but you did just compare
the the intelligence to you know it's a
tool but you also compare the
intelligence to that of humans so it
can't be both of those things or maybe
it can um it seems like in some ways we
are like both um you know it seems like
we're both in awe of like what these
things can do and still not fully in
comprehension as a species of what we're
working on do you think that's a fair
assessment
I think uh we're at the early stages of
a very long ladder if you like so we're
on the first rung and uh predicting
exactly where research itself will go is
always a precarious task um I'm very
hopeful if that's what you mean by an a
of the potential for this technology to
really help lift Humanity to new levels
to help things like uh climate change to
help in things like health I think
that's really important to keep in mind
um we've got to continue to interrogate
these systems to understand how they
work to make sure we're doing it in a
responsible way to make sure we get the
right review in place each step of the
way so that we do understand we do roll
them out in a way that makes sense for
society overall then we got to do both
those things we're going to be bold in
how we develop it but also responsible
and take
care
okay so let me ask you a little bit
about what the path is to getting there
right so you talked about how it you
know a general artificial intelligence
will sort of be able to um learn learn
and apply lessons from different fields
or different areas of experience so what
do you what type of uh research and
advancements need to happen in order to
get us closer to that
point well we are still in early days
but let me give you some examples of
what's currently working what we call
generalization and I can talk about some
of the areas of active research we think
are going to need uh for us to get there
so um just stepping back for a moment
what I what I what I mean by the ability
to generalize and you'll hear there some
a lot in this field there the ability to
take learnings from one setting and
apply them in another setting and we
recently developed for example um an
algorithm called muzero which was
originally developed actually to play
the games of Chess and go and then we
realized we were able to take this
algorithm and apply it to I'm going to
say the game of YouTube video
compression that's maybe funny I say
that but we were able to take an
algorithm develop for games that was a
master in chess and use that to
dramatically reduce the bandwidth
requirement to stream YouTube videos and
we did that by understanding that
actually a video is a series of
individual pictures and if you imagine
the transition between each of those
pictures is like a step in a game that
gave us the Insight that this algorithm
would generalize from uh playing chess
and go to uh YouTube video compression
uh another example actually is an
algorithm we call flamingo
um and we were able to uh use Flamingo
as part of an app called Lookout which
is an app developed by Google uh if
you're partially cited and you need help
you can use your mobile phone to take a
picture of something and then you can
also look out what's in that picture and
can help you can kind of be your eyes
and we realized that algorithm could
could could be used and redeployed and
generaliz uh into uh adding the
descriptions into videos that are
uploaded to YouTube shorts so creators
creating videos on YouTube shorts they
have less time um they upload the video
the algorithm that helps people find
those videos need something to go on to
help people discover them uh so we
discovered Flamingo was also able to
look at that video for you entally watch
all the videos for you and add data
metadata to those videos such that when
you're searching for those videos it's
much easier much much easier to find the
videos that you need when you consider
the billions of videos that are viewed
every day on on the on YouTube shorts
that's really significant so those are
those are just I'm calling those out
because those are two examples where
we're beginning to see these forms of
generalization and of course generative
AI is actually a great example as well
so these new uh these new tools where
you're able to interact with them in a
way that is kind of Fairly
conversational um and get a really
surprisingly and um powerful response
that is one way uh we're beginning to
see this move to morge more general
intelligence and maybe I could just jump
into tell you a little bit about how for
example uh that recent working
generative AI is allowing us to move
closer to AGI and then I can tell you a
bit more about some that's fascinating
fantastic so what's maybe surprising to
know if you've been following the field
in generative AI you've really seen it
burst onto the scene in the last you
know 18 to 24 months is that some of the
underlying breakthroughs were developed
about about 5 years ago and what's
happened in the last you know 18 24
months though these systems have been
really really scaled up and by that mean
uh the size of the model the number of
parameters in the model which contain uh
the model power has Dr grown
dramatically um and these systems have
been trained on kind of larger and
larger data sets and what's happened is
that by scaling up these models we've
seen these emerging capabilities appear
and but that I mean it's not always
possible to predict exactly what
capabilities would appear but we've had
these new capabilities appear that have
demonstrated really powerful
generalizability so you can then take a
system that's been trained in this way
and you can ask it to summarize a
document or write you an email and it
wasn't necessarily trained expressly on
these tasks but it's able to achieve
these tasks because of the training
process and the scaling up has happened
and that's been as as I'm sure you've
been following and many others have been
following that's been a massively uh
important breakthrough in the last 18 to
24
months and but these systems are still
not complete uh they still get things
wrong um they maybe can't plan in the
right way they maybe can't remember what
you did yesterday and help you today so
things like memory the ability to
remember between episodes planning the
ability to imagine a whole range of
different future scenarios and plan
effectively in that setting those are
two areas of active research that are
really important and at a kind of zoomed
out level another is what we call kind
of Concepts and transfer learning so as
humans we're able to build this kind of
deep conceptual understanding and that
actually forms a kind of really strong
foundation for us to take knowledge that
we generated in one setting and transfer
that to another so Concepts and transfer
learning planning and memory all really
active areas of research which I think
will help us push the next French here
and and and actually by the way we
haven't necessarily reached the limit of
making these models bigger either we're
not no one's quite sure where that limit
is and so that's also a really important
active area of research just making
these things bigger and bigger um where
will that where will that go and where
will that land and what more can we get
from
that yeah we've had Yan Lun on the show
and I've been speaking with Yan for
since 2015 2016 so seven or eight years
on this point about what intelligence is
from the eye of artificial intelligence
researcher and he's always said that
it's the ability to predict and to plan
and it is very telling right now that
the research now is is all about
teaching these AIS to predict into to
plan it in fact speaking about the
Gemini the new Gemini model um I'm
pretty sure people from Deep Mind have
talked about I think I'm going to just
cite this that the algorithm should be
better at planning and problem solving
so that seems to be where we're going so
first of all I'm going to get you know I
have a few questions for you about
Gemini but just let talk about it on a
broad level
how do you teach an AI to plan and
predict so um there's a whole range of
different active research tasks here and
and to be clear there isn't an answer
yet which is why it's still active
research but one of the ways we motivate
This research is by making sure we have
tasks that require
planning uh so we spend a lot of time
and investment in building a whole Suite
of different evaluations and tasks which
then provide the target if you like for
our research and our research programs
to focus on and that's a really
interesting definition of intelligence
and one of the definitions of
intelligence that we use at Deep mine
and was actually created by one of the
founders of uh Google deep mine Shane
lag is intelligence is the ability to
perform well across a range of different
tasks and I really like that definition
because it's I think it's very
descriptive and it's very easy to
operationalize into a research program
and so this sense of building multiple
different evaluations and tasks that
provide then a way for us to measure our
performance and progress against whether
it's planning or adding memory um is a
is really Central to actually the way we
conduct research and then behind that
it's a creative process uh so what
you're trying to do is bring together
people from a whole range of different
disciplines from Neuroscience from
different areas of AI research to uh
come together and you have ideas about
how we can make progress and then use
the incredible engineering Talent we
have and the compu resources we have to
experiment and take steps forward that's
how we do it at a at a kind of meta
level and deep mine started largely with
some breakthroughs in gaming so how is
that applicable for because you know I
think about predicting and planning and
it seems like if you're playing you know
sophisticated games like go then that's
basically going to take you in that
direction yeah you great point because
games are a fantastic Proving Ground for
these algorithms they're fantastic
because they're actually hard for humans
um they have a they have ability for us
to measure how good performance is
there's normally a score of some sort so
we can Benchmark the algorithm's
performance versus the humans
performance and there's a whole raft of
different existing games out there that
can push and pull the algorithm AI
capability in different directions and
by the way you can develop new games and
I think maybe the third the third maybe
the final point is that games can run
faster than clock time so you can do
many many durations in a kind of
simulated game much much faster than you
could experiment in the real world which
is why there's such an incredible uh
Proving Ground and development ground
you're absolutely right for these
algorithms and we continue to invest
deeply in kind of game like environments
for exactly those reasons maybe one
other important point there also a
really uh useful way of testing
algorithms out to test kind of their
limits um and we can check their kind of
technical safety to make sure they're
doing what we expect them to do so
they're a nice way of developing and
testing an algorithm before they break
out into the real world and and maybe a
nice example here actually we often use
this technique of uh first developing an
algorithm in a game to your point to do
planning um and there's an example here
in robotics if you try and train an
algorithm directly on a real robot it's
going to take you a long time because a
robot can take quite some time to
complete the task and in the beginning
it may be all over the place like I
maybe like a young child learning to
walk what we what we do is we create a
simulation of that robotic environment
be a robotic arm stacking blocks and we
train the algorithm in that simulated
environment until it gets good in that
simulated environment and we take the
algorithm and then we apply it to the
real robot stacking blocks we discover
it's actually then pretty good out of
the blocks and in the real world the
robot can then begin to build in the
training
there so the way that I picture this
happening is like it feels like most of
uh the general public has started to get
a chance to like start talking with AI
VI of these large language models so you
know when I when I try to conceptualize
like what this might look like down the
road I start thinking that like when I'm
speaking with uh Chad GPT or aard or a
bing it starts to remember who I am it's
starts to be able to accomplish tasks
for me it starts to be able to help me
plan you know is that is that sort of
like the next step here is that where
this research is building
toward that's right so you're able to
converse with these do agents today as
you've discovered and you can have
actually quite a meaningful important
conversation but it might have not
remembered what you did last week for
example they've got limited kind of
context Windows as is as you may have
heard it called but what you really want
it's to remember as you've noted what
did I do yesterday what did I last week
what's my preference when it comes to
kind of looking at a given film for
example um because next time I ask to
watch a film you it wants to know what I
watched before or maybe what my
preferences were so that ability to
remember more about our previous
interactions actually becomes really
important you want these things have
like the memory of a goldfish it's like
you sit you're talking with it and then
five minutes later it's like hey just
remind me what what you said like
totally forgot so that's like one step
yeah yeah absolutely right it's a really
important area for us to kind of expand
the memory of these systems sometimes we
refer to this as episodic memory so they
remember um episodes important episodes
in the past so they they're able to
bring to bear that important um
understanding then when it comes to
planning about the Future these systems
need to be able to uh stop and reason
about the right sequence of steps to
take so um you know for example I want
to plan a holiday you know I want to go
here then I want to go here and I want
to go uh there um and the series of
things I want to do may change over time
I may get on the flight I may if you
could ask one of these systems today
catch that they can they can come out
with a pretty good uh pretty good
response on some of these things but
they aren't able to plan based on what
you've done in the past um and you know
what would what would a reasonable kind
of itinerary be that's changing over
time I'm actually going hot over my
family very soon this is very live on my
mind I don't think the AI systems can
really get to a level that I would
really want them to at this point right
and so we think about where this going
we talked a little bit about um you know
being able to predict and plan we talked
about um you know I get we we sort of
hinted at multimodality right like
having a model that's generalized so be
being able to like do text but also and
like a human we can talk we can read we
can see you can process and most of
these models have just been text or
computer vision or computational and it
does seem like the next step is really
going to be bringing them all together
that seems like a massive technological
feat but my understanding is that that
is something that's being worked on
inside Google with this new Gemini model
I mean those two descriptions that I
just read are are both Gemini so talk a
little bit about what Gemini is and how
it's going to take us on on that road
the Gemini is uh one one of our latest
research programs and you're absolutely
right one of the really important errors
it's touching on is what we call multi
modality um it's a bit like the human
senses you just described we can kind of
use all our human senses together and
combined to uh achieve the goal we're
we're setting out to so it will bring in
things like text it will bring in things
like images and it able to input those
things but also output both those things
so you might have a question about
something you can see you can share that
image and you can also ask a question
about it and you may want to then adapt
some something in that image uh by uh
saying please edit this element of the
image and it can do that for you so
bringing together these different
modalities is something that is a really
cool important part of that Gemini
program as well as the kind of memory
and planning architectures that we
discussed earlier and and maybe a final
important component is you know we're
hoping to develop models of different
sizes and scales so there'll be kind of
different sizes of these Gemini models
which can then be applied to different
use cases depending on what
important I mean has there been anything
about training Gemini that's that
surprised you or is this kind of like
where you think it's supposed to where
yet it should have been going the whole
way um I'm I'm not deep in the Gemini
research program myself um but what I
would generally share is that not it's
not Gemini specific is that when it
comes to training these large models um
I think people in general have been
surprised that as you make these models
bigger they get more capable and they
start to uh demonstrate these
capabilities that you wouldn't
necessarily have planned or expected and
in the field this is generally referred
to this there emergent these emerging
capabilities and I'm not sure if we've
fully got to the end of that process yet
so there's a there's a kind of almost a
constant state of surprise as these new
capabilities
emerge right so I was speaking with some
folks uh at Google and trying to figure
out like what to ask you about and
someone brought up um talking about
modes of training so I'm C I I want to
ask you about the Deep mine approach
versus uh this new approach that's or
maybe not new but definitely is gaining
share and people's minds called uh
constitutional AI so I'm just going to
read you what constitutional and our
listeners what constitutional AI is from
a recent New York Times article and I
want to get your take on whether that's
the right way to train these models so
it says constitutional AI Begins by
giving an AI model a written list of
principles a constitution and
instructing it to follow those
principles as closely
as possible a second a AI model is then
used to evaluate how well the first
model follows its Constitution and
corrects it when necessary you know I'm
curious what you think about this um
this approach and whether that's
something that you know Google would
consider employing and if not why
not so this this is kind of uh I would
generally think about this approach and
other approaches like this there's a way
of ensuring these models are behaving in
the way that we want them to behave um
and we think about do definitely think
about that very deeply it's very
important to everything we do there are
different ways to do that um one way is
actually by having um an AI system like
the one you've described provide
feedback to the model that you're
training about whether it's behaving in
the way that the designers would like
their system to behave and that's that's
certainly something um it's all part of
the overall approach another important
way actually is that you have humans
providing feedback to the model this is
a a process called RF that folks might
be familiar with where human human rers
interact with these models and are
observing the Constitution and provide
feedback to the model on WEA and how
well the uh model is performing against
that Constitution and actually at the
moment that's a really important part of
I think the core research process
because humans are actually very good at
this there's a kind of secondary benefit
of that is that we are um beginning to
understand how we can begin to embed
more and more human feedback into the
model process so I think in general
terms yeah this is a really important
part of how we approach research to make
sure the model is well aligned with the
sort of if you like Constitution that
the designers and the society ultimately
would like these models to be behaving
in accordance
with one last question for you before we
go to Break um why does everybody in
this field or some of the leaders in the
field just constantly compare uh this
work to the nuclear weapon project I
mean I I can't go a day without hearing
uh an AI illuminary talk about how like
they're the next Oppenheimer for
instance I mean this is from a New
Yorker story where Sam Altman said uh
you know let's see he um he compared the
company to the Manhattan Project as if
he was chatting about tomorrow's weather
forecast he said the US effort to build
an atomic bomb during the second world
war has been a project on the scale of
open Ai and he just tweeted that he was
uh hoping the oppen Oppenheimer movie
would inspire a generation of kids to be
physicists and and sort of Miss the mark
on that and he wants you know a new
movie of of that of that scale basically
you know put me in a movie or something
of that nature why why what's going on
with all these these comparisons so i'
I've not seen the openen H movie yet but
I'm look I'm looking forward to seeing
it I think the comparison that I often
hear is actually of the Apollo project
the space project and I think the reason
uh examples like that Apollo projects
and don't hear that I mean you don't
hear these these nuclear comparisons I
feel like they're all over the place I
don't hear anyone talking about this as
as a Space Project well maybe maybe I
hear it more because that's actually how
Google de we often think about it and
talk about it I think the but I think
the kind of fundamental first principles
analogy is that these are these are
projects where you're Gathering
Together uh large group of very very
talented people with a very clear focus
and a common belief um and if you can do
that then you can make incredible
progress I think these sorts of projects
may be kind of offering that sort of
inspiration uh to you know folks working
in this
area Colin Murdoch is here with us he's
the chief business officer at Google
deep mine when we come back we're going
to talk a little bit about the business
side of these models uh and especially
how they're being applied within Google
back right after this and we're back
here with Colin Murdoch he's the ch
Chief business officer at Google's Deep
Mind um Colin how what is um the state
of the merger between Deep Mind and
Google brain which just came together to
sort of be able to work together in a
way that hasn't happened for
years that's right so we've just formed
uh Google Deep Mind from the team that
was at Deep Mind and the team that was
at at brain and actually uh these teams
have been working together for quite
some time in the background I think the
the
recent merger if you like to form this
super unit has come a really important
time in the overall development of AI
we're kind of in this you know super era
or Golden Era of AI development and we
just thought it was a right time now for
that reason start to bring together the
talent but also the compute and the
resources so that we could make sure we
were focused and organized in the right
way for the next phase and I've actually
been at well deep mine now Google deep
mine for about 9 years years and the
pace and the change and the kind of
Frontier that we're working at means
we're constantly needing to refine the
way we organize to make sure that it
fits where we are in the kind of
Technology Evolution cycle um and you
know it's going great um I'm really
enjoying kind of getting to know the
entire new team and we're making good
progress right and so it's so
interesting because you know deep Minds
era areas have been the gaming working
on protein folding which we're about to
talk about um Google brain you know
maybe more search related so how much of
your activities are now going to be
focused on the core Google Business
versus some of this other type of
research I I I think it was interesting
to know uh even at Deep Mind and
actually this is very close to uh my
role is that we we have for a long time
being um taking the technology that's
been developed in our fundamental
research programs and apply that to
Google's products and services so that's
all that's actually been a cool part of
both these groups and actually is now a
fundamental part of what we do at Google
deep M so we're both advancing the state
of the art in the technology applying
that to really big problems in science
and then using those breakthroughs to
drive value and impact across these you
know was often bidon user products at
Google and that's absolutely right Alex
it's fundamental to the the new setup at
Google
demon mhm so um where do you see the
bigger business opportunity is it going
to be I mean you're the chief business
officer so is it going to be search or
we talked a little bit about artificial
general intelligence I mean you have
Alpha F right now that's that's out in
Market like where is where's the future
of the business on this front so search
is of course an incredibly important
part of Google's portfolio I I expect it
I expect it to continue to be a very
important part of Google's portfolio so
we'll continue to do everything we can
to drive value and search let me tell
you about how I think about it because
this is a you know a technology trans
transfer process and that's not easy
going from research to real world impact
at any means at all even when even when
you're operating kind of Google deep
mine working with Google I think about
it actually as a matching process so on
the one side we have all this amazing
research and these research
breakthroughs um and there's a team of
people that are developing those and on
the other side we maintain relationships
with all the great businesses and
business units across the whole of
Google and in fact the alphabet group as
a whole so we can deeply understand
what's important to them and moving
their business forward so we've got this
set of Solutions on one side and the set
of problems on the other side and then
we try and match these two things
together sometimes joke is a about like
running a dating service where you're
trying to match problems and solutions
um so we go ahead and do that um and
then we try that out and if it works we
go ahead and launch so there's there's a
there's a process there that's what I
want to share it's quite important to
share there's quite a systematic process
it's one of matching uh technology
solution and product problem as defined
by the business
search is an important area uh we've
done a lot of work with YouTube as well
so uh for example I I talked about that
a bit earlier we've worked with YouTube
to help um create better tooling for
YouTube shorts so you can more easily
find the videos you want we work with
YouTube to reduce the bandwidth
requirement to watch these videos we've
even worked with internal teams to
create better coding tools so we can
kind of really power up all the
developers at Google um we've worked
with other teams at Google to do things
like predict the output from wind farms
that Google is part of so we can make
more efficient use of energy so there's
a there's a whole range of different
applications and I think that's
important to recognize yes search I
think will continue to be really
important um and also I expect us to
kind of weave the technology into all
parts of Google so we can really help
lift the whole
business yeah so Tu a little a little
bit about your process between taking
you know a research breakthrough and
then actually putting it into
production so um so it starts it starts
in the kind of research setting we have
a group of researchers that are working
towards ultimately artificial general
intelligence and as a result of that
they're developing all these new
algorithms and breakthroughs along the
way we have a dedicated team of um
technical Specialists and product
managers who are constantly tracking
that Evolution this kind of gold mine if
you like of research breakthroughs and
then those same people are also in
constant contact with the business
owners with the product owners that sit
across the Google and alphabet ecosystem
deeply understanding their world deeply
understanding their priorities and the
problems that are important to them and
trying to find a way of casting those
problems in a way that match up with the
algorithm that has been developed in the
research process the example I mentioned
earlier actually is is probably a good
one so muzero this algorithm was
originally developed to play games like
chess and go um person in this team was
talking to uh the Youtube team um and
they were talking about how it would be
valuable if they could reduce the size
of a video because it could be streamed
to more people more places around the
world um and it's not immediately
obvious to anyone that a algorithm
developed to play games of Chess and go
can then be applied to reducing or
compress ing a video this is where the
really creative component comes in
because they realized if you think about
a video as a series of images and steps
in a game Sorry and steps between those
images you can think about that as a
game with each each kind of uh picture
in the video there's a stage in the game
and each step between those pictures is
a step in the game and so that was
enough intuition for them to go why
don't we try mu zero on video
compression so that starts the ation
that's the initial kind of matching
phase the next step is to take that
algorithm and work with the product team
in this case the YouTube team to take
some videos and try it out try it on
real data because it doesn't always work
it's a hypothesis you know we run a
portfolio here of these things and some
of them work out and some of them don't
so that's the next step we used to call
it incubation we can still do and the
final step is that if it works and if it
meets the bar and important of the
product team at Google then we work with
them to help make it launch so we start
at the beginning with a matching process
we then kind of incubate and prototype
and then finally if it works we go ahead
and launch and that's a kind of
individual project but zooming out at a
portfolio we have this kind of constant
humming and rolling portfolio of these
projects to make sure we've got this
flow back and flow forward of innovation
between the two two
areas cool so why don't we talk a little
bit about about we have 10 minut about
10 minutes left so I want to talk a
little bit about how this works in a
couple areas um I definitely want to
cover protein folding which is like I
think the most interesting breakthrough
that deep mind has come out with um to
date and then um then maybe we can end
on self-driving cars but let's start
with with protein folding you know I
think that Alpha fold and maybe we've
done it earlier in this in this
conversation sort of gets talked about
as sort of a thing that's that exists
and you know okay it happened and then
people move on but I actually want to
hear a little bit about like
what is going on with with Alpha fold
talk a little bit about about the
Breakthrough itself and and how it's
being applied right now fantastic yeah
so we we we did touch on it earlier on
but just as a brief recap Alpha fold is
a way of determining the structure the
3D structure of proteins which ends up
being really important in a whole range
of different fields and this is
something that folks have been trying to
do for many years but took years to
determine the structure of just one
protein but with Alpha fold we've gone
from years to minutes to even seconds
sometimes to determine the structure of
a protein so what does that mean in
practice well um we've used Alpha fold
now to map all 200 million proteins
known to science or 200 million proteins
known to science we've made that
available to
everyone and actually someone estimated
recently that that's probably saved
about a billion years of PhD time uh
because you know probably on average you
spend about a PhD to determine the
structure of just one
that's available to everyone now we've
had hundreds of thousands of biologists
and Sciences from around the world that
are now tapping that database to be able
to advance their particular work and
their particular
domain let me tell you some stories
about how people are using this um
there's actually a team I think at the
University of Colorado that are using
aftera predictions as part of their work
so they're focused on the problem of
anti itic resistance we kind of take
antibiotics for for granted in most
parts of the world and that's a great
thing um but the bacteria are developing
and there is increasing cases I think in
the us alone there is probably millions
of cases a year of antibiotic resistant
diseases and that's a an important also
quite a scary problem so there's a team
of scientists working on how to uh
address antibiotic resistance there's a
particular bacteria involved in
antibiotic resistance and they've been
trying to determine the proteins on this
bacteria for a number of years a number
of years quite some years but hadn't yet
made an advancement um with Alpha fold
and the protein structures from alpha
fold they were able to solve that
particular protein in minutes they've
gone from years to minutes really
unblocking and experimenting that
research I think that is a you know that
alone is an incredible incredible
example of how alpha fold is impacting
in the world there's another equally
important example there's a group
working on developing malaria vaccines
you know disease that devastates
hundreds of thousands of lives every
year and they've been able to use these
alphafold structure predictions with
different proteins but still Alpha fold
structure predictions to speed up their
Research into malaria vaccines so a
couple of examples in in healthcare and
there's other groups focused on
neglected diseases uh where you know
would may have been too expensive to do
this the traditional way but with Al F
predictions they're making advancements
um a slightly different example but I
think equally important and quite cool
is there is a group at the center for
enzyme Innovation which I think is at a
university here in the UK which is uh
focused on developing enzymes that can
eat the Plastics eat the Plastics that
clog up our landfills and our oceans and
they've been able to use these protein
structure predictions to speed up their
work into producing um plastic eating
enzymes so we've got we've got like and
those are those are just a kind of
sampling of the different ways that
Alpha's been used today it's quite
difficult to keep up there's a kind of
new new group almost every week coming
up with a way of using these uh these
predictions in their
work yeah that's that's wild so as Alpha
fold applications expand I mean as
Google comes up with
more you know programs like this does it
change the nature of Google's business I
mean Alpha fold and Google search are
very different so talk a little bit
about how that fits together yeah and
it's it's uh you know it's a good point
these these two things are quite
different um and Alpha fold is a good
example here of know I described some of
the ways that is having impact in the
world when I looked at Alpha fold to
your point I was like well how does this
work with Google search it's not obvious
right how does how does how do these two
things knit together what's the match
there um so took a step back with my
team and um thought about other ways we
could employe and deplo this and it
seemed we we Lo we looked across a range
of different areas and business
opportunities by the way from
agriculture to all sorts of areas but in
the end we concluded that actually there
is a great opportunity here in drug
Discovery you know it takes 10 plus
years to develop a drug and then often
when it goes into clinical trials it
fails there's a very very high failure
rate and so you've spent all that time
and money in investment and it doesn't
actually make it through and solve the
clinical need concerned about so having
understood this kind of scale and
importance of the problem and the
opportunity that then gave us impetus to
form a new company uh so we we formed a
new company which is a sister company
now to Google deepmind it's part of the
overall alphabet group it's called
isomorphic Labs it's about 2 years old
and its mission is to use AI to
reimagine drug Discovery and the team is
making fantastic progress I'm really
excited to see how that work will help
reimagine that whole process um now
there's definitely more research to do
there that's not just kind of alphafold
and done there are kind of alpha fold
scale problems along the way that's a
really good example of where we've been
able to set up something new based on a
breakr like Alpha fold and I think there
could be other advances that come in
science that may trigger a similar sort
of
arrangement so then does Google just
become like an incubator for AI
companies or I mean what is the nature
of of Google again if it's like you know
it's can it just keep existing across
all these different business
lines I I'm not I'm not well placed
necessarily to talk about the overall
corporate strategy for Google right um
but in in in terms of interesting
because it gets into so many different
areas yeah yeah I mean in terms of how I
think about it what's important for me
to be doing is really focusing on the
most important problems that Google has
and the rest to the alphabet companies
and making sure we're building and
developing AI systems that can help
solve those and being alert by the way
like the isomorphic lab's example when
there is something that's important and
significant and material enough that I'm
also surfacing that and saying hey do
you know what I think there's a really
important opportunity in business here
let's talk about it yeah interesting
okay five minutes left self-driving cars
um I'm in San Francisco right now here
for a visit and hopped in a whmo a week
go uh and it was unbelievable I mean the
way that these these cars can drive
around and no driver feels totally safe
I mean maybe that's misplaced confidence
but it drove really well um how how
close are we to
having these type of cars on the streets
everywhere and is it really a matter of
needing more breakthroughs from the
research side or is it is it just a
business will I feel like you're
perfectly placed to answer that one yeah
yeah well I don't know that's tricky for
me because I'm not I'm not inside the
I'm not inside the wayo business so I'm
notar able to forecast or project
exactly where where weo goes next um I
I've also had a ride on one of the cars
and it was about gosh five or six years
ago now so um and my ride was pretty
good back then actually like so I'd like
to have another go just to help
calibrate myself on I guess the speed of
progress and that would help me predict
where we're heading in the future um I'm
here in the UK so weo hasn't hasn't kind
of made it to the UK yet but I'm looking
forward to have a ride then so yeah I
I'm not sure about exactly AO but I'm
looking forward to you know monitoring
and tracking and set of bracing their
progress for
sure and from a research perspective I
mean it seems like I again maybe you're
not you're not on the ground so you
can't share this but it from a research
perspective it it seems like that that
should be solved at this point I mean I
guess if they're already on the
ground yeah as you said I'm I'm not I'm
not on the ground with weo so I'm not
exactly sure where their research
programs are they got a lot of smart
peopleo let's let's end with this we
just had a year where people have been
going bananas over large language models
I got a question today when I mentioned
I was going to be interviewing you
people want to know what is the next
model breakthrough that's not an llm
that people aren't paying attention to
but will be as impactful as what we've
seen with these models
I'm really excited I don't know exactly
what the breakthrough will be but I'm
really excited about the union of these
llms plus reinforcement learning and I'm
excited about that because I think
there's a lot more to come from
reinforcement learning and I know at
Google dmme we have great deal of
expertise in that so I expect to see the
fusion of those two things creat some
really powerful and important
breakthroughs K Murdoch thanks so much
for joining you're welcome great to be
here all right thanks everybody for
listening thank you Nate guatney for
handling the audio LinkedIn for having
me as part of your podcast Network and
all of you the listeners great to have
this conversation with Colin here for
you hope you've enjoyed and we'll see
you next time on big technology
podcast