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Google DeepMind CEO Demis Hassabis on AI, Creativity, and a Golden Age of Science | All-In Summit | All-In Podcast | YouTubeToText
YouTube Transcript: Google DeepMind CEO Demis Hassabis on AI, Creativity, and a Golden Age of Science | All-In Summit
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A genius who may hold the cards of our future.
future.
CEO of Google DeepMind, which is the
engine of the company's artificial intelligence.
intelligence.
After his Nobel and a nighthood from
King Charles, he became a pioneer of
artificial intelligence.
We were the first ones to start doing it
seriously in the modern era. Alph Go was
the big watershed moment, I think, not
just for Deep Mind at my company, but
for AI in general. This was always my
aim with AI from a kid, which is to to
use it to accelerate scientific discovery.
discovery.
Ladies and gentlemen, please welcome
Google Deep Minds Deis Hassabis. [Applause]
[Applause] Welcome.
Welcome.
Great to be here.
Thanks. Thanks for following Tucker,
Mark Cuban at all. Um, first off,
congrats on winning the Nobel Prize.
for the incredible breakthrough of
AlphaFold. Maybe you may have done this
before, but I know everyone here would
love to hear your recounting of how you
where you were when you won the Nobel
Prize. How'd you find out?
Well, it was very surreal moment
obviously. Um, you know, it's that
everything about it is surreal. The way
they tell you, they tell you like 10
minutes before it all goes live. It's
just, you know, you can't really it's
you're sort of shell shocked when you
get that call from Sweden. It's the call
that every scientist dreams about. And
um and then the seal ceremonies the
whole week in Sweden with the royal
family. It's amazing. Obviously, it's
been going for 120 years. Uh and the
most amazing bit is they bring out the
this Nobel book from the from the vaults
in the safe and you get to sign your
name next to you know all the other
greats. So it's quite an incredible
moment sort of leafing back to the other
pages and seeing Fineman and Mary Cury
and Einstein and Neils Bore and you just
carry on going backwards and you get to
put your name on that in that book. It's incredible.
incredible.
Did you have an inkling you had been
nominated and that this might be coming
your way?
Well, you you you get you hear rumors.
It's amazingly locked down actually in
today's age how they keep it so so
quiet, but um it's sort of like a a
national treasure for Sweden. And um and
so you hear, you know, maybe Alpha Fold
is the kind of thing that that would be
uh worthy of that recognition. And it
has they look for impact as well as the
scientific breakthrough uh impact in the
real world. And that can take 20 30
years to to arrive. So you just never
know, you know, whether how soon it's
going to be and and and whether it's
going to be at all. So it's a surprise.
Well, congrats.
Yeah. Thank you.
Um and thank you. You let me take a
picture with it a few weeks ago when we
So that's something I'll cherish. Um
what is Deep Mind within Alphabet?
Alphabet is a sprawling organization,
sprawling business units. What is Deep
Mind? What are you responsible for?
Well, we sort of see DeepMind now and
Google Deep Mind as it's become. We sort
of merged a couple of years back all of
the different AI efforts across Google
and Alphabet including Deep Mind. Put it
all together, the kind of bringing the
the strengths of all the different
groups together into one division. And
um really the way I describe it now is
that we're the engine room of the whole
of Google and the whole of Alphabet. So
Gemini, our main model that we're
building, but also many of the other
models that we also build, the the video
models and interactive world models, uh
we plug them in all across Google now.
So pretty much every product, every
surface area has um uh one of our ai
models in it. So you know, billions of
people now interact with Gemini models,
whether that's through AI overview, AI
mode, or the Gemini app. Uh and that's
just the beginning. You know, we're kind
of incorporating into workspace into
Gmail and so on. So it's a fantastic
opportunity really for us to do cutting
edge research, but then immediately ship
it to billions of users.
And uh how many people what's the
profile? Are these scientists,
engineers? What's the makeup of your
There's around 5,000 people in in in my
or in Google Deep Mind and and you know,
it's predominantly I guess 80% plus
engineers and PhD researchers. So, uh
yeah, about you know, three three or 4 thousand.
thousand.
So, there's an evolution of models, a
lot of new models coming out and also
new classes of models. Um the other day
you released this Genie World model. Yes.
Yes.
So, what is the Genie World model and um
I think we got a video of it. Is it
worth looking at and we can talk about
it live?
Yeah, we can watch. Sure.
Because I think you have to see it to
understand it because it's so
extraordinary. Um, can we pull up the
video and then uh Demis can narrate a
little bit about what we're looking at.
What you're seeing are not games or
videos, they're worlds.
Each one of these is an interactive
environment generated by Genie 3, a new
frontier for world models.
With Genie3, you can use natural
language to generate a variety of worlds
and explore them interactively.
All with a single text prompt.
Yeah. So all of these videos, all these
interactive worlds that you're seeing,
so you're seeing someone actually can
control the video. It's not a static
video. It's just being generated by a
text prompt. And then people are able to
control the 3D environment using the
arrow keys and the spacebar. So
everything you're seeing here is being
fully all these pixels are being
generated on the fly. They don't exist
until the player or the the the person
interacting with it goes to that part of
the world. So, um, all of this richness,
um, and then you'll see in a second. So,
this is fully generated. This is not a
real video. This is generated someone
painting their room and they're painting
some stuff on the wall. And then the the
player is going to look to the right.
Uh, and then look back.
So, now this part of the world didn't
exist before, so now it exists. And then
they look back and they see the same
painting marks they they left just
earlier. And again, this is fully every
pixel you can see is fully generated.
And then you can type things like person
in a chicken suit or a jet ski and it
will just uh in real time uh include
them in the scene.
So um I think
you know it's quite mind-blowing really.
I think what's hard to gro when looking
at this because we've all played video
games that have a 3D element to them
when you're in an immersive world,
but there's no objects that have been
created. There's no rendering engine.
You're not using Unity or Unreal which
are the 3D rendering engines. Yeah,
Yeah,
this is actually just 2D images that are
being rendered like created on the fly
by the AI.
This model is reverse engineering
intuitive physics. So, you know, it's
watched many millions of videos and
YouTube videos and other things about
the world. And just from that, it's kind
of reverse engineered how a lot of the
world works. It's not perfect yet, but
it can generate um a consistent minute
or two of um interaction as you as the
user uh in many many different worlds.
There there's some videos later on where
you can control, you know, a dog on a
beach or a jellyfish or that's not
limited to just human things
cuz the way a 3D rendering engine works
is you type in the programmer programs
all the laws of physics. How does light
reflect off of an object? You create a
3D object, light reflects off, and then
so what I see visually is rendered by
the software because it's got all the
programming on how to create physics,
how to do physics.
But this this was just trained off of
video and it figured it all out.
Yeah, it was trained off of video and
some synthetic data from from game
engines and it's just reverse engineered
it. For me, it's it's it's very close to
my heart, this project, but it's also
quite mind-blowing because in the 90s,
in my early career, I used to write uh
video games and AI for video games and
graphics engines. And I remember how
hard it was to do this by hand, program
all the polygons and the physics
engines. Um, and it's amazing to just
see this, do it effortlessly, all of the
reflections on the water and the the way
materials flow, um, and and and objects
behave. And it's just doing that all out
of the box. I think it's hard to
describe like how much complexity was
solved for with that model. Uh it's it's
it's really really really mind-blowing.
Where does this lead us? So fast forward
this model to gen five.
Yeah. So so the reason we're building
these kind of models is um we feel and
we've always felt obviously progressing
on the normal language models like with
our Gemini model but from the beginning
with Gemini we wanted it to be
multimodal. So we wanted it to input any
take any kind of input images audio
video and it can output anything and uh
and and so we've been very interested in
this because you for an AI to be truly
general to build AGI we feel that the
AGI system needs to understand the world
around us and the physical world around
us not just the abstract world of
languages or mathematics and of course
that's what's critical for robotics to
work. It's probably what's missing from
it today. And also things like smart
glasses, a smart glass assistant that
helps you in your everyday life. It's
got to understand the physical context
that you're in and and how the world the
intuitive physics of the world works. So
we think that building these types of
models uh these genie models and also VO
our the best text to video models um
those are expressions of us uh building
world models that understand the
dynamics of the world the physics of the
world. If you can generate it then um
that's that's an expression of your
system understanding uh those dynamics
and that leads to a world of robotics
ultimately um one one one aspect one
application but maybe we can talk about
that what is the state-of-the-art with
the vision language action models today
so a generalized system a box a machine
that can observe the world with a camera
and then I can use language I can use
text or speech to tell I want you to do
it. And then it knows how to act
physically to do something in the
physical world for me.
That's right. So, if you if you look at
our uh Gemini Gemini live version of of
Gemini where you can hold up your phone
to the world around you, uh I'd
recommend any of you try it. It's kind
of magical what it already understands
about the physical world. Um you can
think of the next step as as
incorporating that in some sort of more
handy device like glasses. Um and then
it will be an everyday assistant. and
it'll be able to recommend things to you
uh as you're walking the streets or we
can embed it into Google Maps. Um and
then with robotics uh we've we've built
a something called Gemini robotics
models which are sort of fine-tuned
Gemini with extra robotics data. And
what's really cool about that is and and
we released some demos of this over the
summer was um you can have you know
we've got these tabletop setups of two
hands uh interacting with objects on a
table, two robotic hands and you can
just talk to the robot. So you can say
you know put the the yellow object into
the red bucket or whatever it is and it
will just it will it will interpret that
instruction that language instruction
into motor movements and that's the
power of a multimodal model rather than
just a robotic specific model is that it
will be able to bring in real world
understanding to the way you interact
with it. So in the end it will be the UI
UX that you you need for as well as the
understanding the robotic the robots
need to to navigate the world safely. I
asked Sundar this, does that mean that
ultimately you could build what would be
the equivalent of call it either a Unix
like an operating system layer or like
an Android for generalized robotics at
which point if it works well enough
across enough devices, there will be a
proliferation of robotics uh devices and
and companies and products that will
suddenly take off in the world because
this software exists to do this generally.
generally.
Exactly. That's certainly one strategy
we're pursuing is a is a kind of Android
play if you like a cross as a kind of
robotics almost an OS layer cross
robotics. Um but there's also some quite
interesting things about vertically
integrating our latest models uh with
specific robot uh uh types and robot
designs and some kind of endto-end
learning of that too. So both are
actually pretty interesting and we're
pursuing uh both strategies.
Do you think that there's humanoid
robots as a good kind of um form factor?
Is that does that make sense in the
world? Because some folks have
criticized it as being good for humans
cuz we're meant to do lots of different
things, but if we want to solve a
problem, there may be a different form
factor to fold laundry or do dishes or
clean the house or whatever.
Yeah, I think I think there's going to
be a place for both. So, so actually I
used to be of the opinion maybe five 5
10 years ago that we'll have form
specific robots for certain tasks and I
think in industry industrial robots will
definitely be like that where you can
optimize the robot for the specific task
whether it's a laboratory or a
production line you'd want quite
different types of robots. Uh on the
other hand, for um uh uh general use or
personal use robotics um and just
interacting with the the ordinary world,
uh the humanoid form factor could be
pretty important because of course we've
designed the physical world around us uh
to be for for humans. And so steps,
doorways, all the things that we've
designed for ourselves, um rather than
changing all of those in the real world,
it might be easier to design the form
factor to work seamlessly uh with the
way we've already designed the world. So
I think there's an argument to be made
that the humanoid form factor could be
very important for for those types of
tasks. Um but I think there is a place
also for specialized robotic forms.
Do you have a view on hundreds of
millions, millions, thousands over the
next 5 years, seven years? I mean, do
you have a like in your head, do you
have a vision on
Yeah, I I do and I spend quite a lot of
time on this and I think we're we're
we're still I I feel we're still a
little bit early on robotics. I think in
the next couple of years there'll be a
sort of real wow moment with robotics,
but um I think the algorithms need a bit
more development. Uh the general purpose
uh uh uh models that these these these
robotics models are built on still need
to be better and more reliable uh and
and better understanding the world
around it. Um and I think that will come
in the next couple of years. And then
also on the on the hardware side, the
key is I think eventually we will have
millions of robots uh helping helping
helping society and and increasing
productivity. But the key there is when
you talk to hardware experts is at what
point uh do you have the right level of
hardware to go for the scaling uh option
because effectively when you start
building factories around trying to make
tens of thousands hundreds of thousands
of particular robot type um you know
it's harder for you to update quickly
iterate the the robot design. So, it's
one of those kind of questions where if
you call it too early, uh, then then
then the next generation of robot might
be invented in 6 months time that's just
more reliable and better and more dextrous.
dextrous.
Sounds like using a computing analogy,
we're kind of in the 70s era PC DOSs
kind of uh,
yeah, potentially. But of course, I
think the the the the maybe that's where
we are, but I think the except that 10
years happens in one year probably. So, right
right
one of those years, right? Exactly.
Yeah. Um so let's talk about other
applications um particularly in in
science. Uh true to your heart as as as a
a
as a scientist as the Nobel Prize
winning scientist um I always felt like
the greatest thing things that we would
be able to do with AI would be the
problems that are intractable to humans
with our current technology and
capabilities and our brains and whatnot
and we can unlock all of this potential.
what are the areas of science and
breakthroughs in science that you're
most excited about and what kinds of
models do we use to get there? Yeah, I
mean a AI to accelerate scientific
discovery and and help with things like
human health is the reason I spent my
whole career on AI and I think um it's
the most important thing we can do with
AI and I feel like if we build AGI in
the right way it will be the ultimate
tool for science and I think we've been
showing at Deep Mind a lot of the way of
that obviously Alphafold uh most
famously but actually we've we've um
applied uh our AI systems to many
branches of science whether it's
material design um helping with
controlling plasma and fusion reactors,
predicting the weather, um solving, you
know, mass olympiad uh uh uh math
problems and um the same types of
systems uh with some uh extra finetuning
can basically uh solve a lot of these
complex problems. So I think we're just
scratching the surface of what AI will
be able to do and there are some things
that are missing. So uh AI today I would
say doesn't have true creativity in the
sense that it can't come up with a new
conjecture yet or new hypothesis. It can
maybe prove something uh that you give
it uh but it's not able to come up with
a sort of new idea or new theory itself.
So I think that would be one of the
tests actually for
AGI. What is that creativity as a human? Yeah.
Yeah.
What is creativity?
I think it's this sort of intuitive
leaps that we often celebrate with the
best scientists in history and and and
artists of course. Um and you know maybe
it's done through analogy or analogical
reasoning. There are many theories in
psychology and neuroscience and as to
how uh we as human scientists do it. But
a good test for it would be something
like um give one of these modern AI
systems a knowledge cutoff of 1901 and
see if it can come up with special
relativity like Einstein did in 1905.
Right? If it's able to do that then I
think we're on to something really uh
really important where perhaps we're
nearing an AGI. Another example would be
with our Alpha Go program that beat the
world champion at Go. Um, not only did
it win in, you know, back 10 years ago,
it it invented new strategies that had
never been seen before uh for the game
of Go, this famously move 37 in game two
that is now studied. But can an AI
system come up with a game as elegant,
as satisfying, as aesthetically
beautiful as go, not just a new
strategy? And the answer to those things
at the moment is no. So that's one of
the things I think that's missing uh
from uh a true general system an AGI
system is it should be able to do uh
those kinds of things as well.
Can you break down what's missing and
maybe related to the point of view
shared by Daario Sam others about AGI is
a few years away. Do you not subscribe
to that belief and maybe help us
understand what is it
in your understanding of structure in
your understanding of the system
architecture what what's lacking
well so I think the fundamental aspect
of this is um can we mimic these
intuitive leaps rather than incremental
uh advances that that the best human
scientists seem to be able to do. I
always say like what separates a great
scientist from a good scientist is
they're both technically very capable of
course. Um but the great scientist is
more creative and so maybe they'll spot
some pattern from another subject area
that can be uh uh can sort of have an
analogy or some sort of pattern matching
to the area they're trying to solve. And
I think one day AI will be able to do
this, but it doesn't have the reasoning
uh capabilities and and some of the um
uh uh thinking capabilities that um are
going to be needed to to make that kind
of breakthrough. Um I also think that
we're lacking consistency. So you often
hear some of our competitors talk about
uh you know these modern systems that we
have today are PhD intelligences. I
think that's a nonsense. They're not
they're not PhD intelligences. They have
some capabilities that are PhD level. um
but they're not in general uh capable
and that's what exactly what general
intelligence should be of of performing
across the board at the PhD level. In
fact, as we all know interacting with
today's chat bots, if you pose the
question in a certain way, they can make
simple mistakes with even like high
school maths um and and simple counting.
So, uh that shouldn't be possible for a
true AGI system. So I think that we are
maybe you know I would say sort of five
to 10 years away um from having an AGI
system that's capable of doing those
things. Um another thing that's missing
is continual learning. This ability to
like online teach the system something
new um or or some or adjust its behavior
in some way. And so a lot of these I
think core capabilities are still
missing and maybe scaling will get us
there but I feel if I was to bet I think
there are probably one or two missing
breakthroughs that are still required um
and will come over the next uh five five
or so years. In the meantime some of the
reports and the the scoring systems that
are used seem to be demonstrating two
things. One perhaps, and tell me if
we're wrong on this, a convergence of
performance of large language models,
and number two perhaps is a slowing down
or a flatlining of improvements in
performance on each generation. Are
those two statements generally true or
not so much?
No, I mean, we're not we're not seeing
that internally and and um we're still
seeing a huge rate of progress. Um but
also uh we're sort of looking at things
more broadly. You see with our Genie
models and VO models and nano banana is
insane. It's bananas. It's bananas.
Has anyone here can Can I see who's used
it? Has anyone used Nano Banana?
It's incredible, right? I mean, I'm I'm
a nerd who used to use Adobe Photoshop
as a kid and Kai's power tools and I was
telling you Bryce 3D. So, like the
graphic systems and like recognizing
what's going on there was just like
mind-blowing. Well, I think that's the
future of uh a lot of these creative
tools is you're just going to sort of
vibe with it or just talk to them and
they'll be consistent enough where like
with Nana Banana, what's amazing about
it is it's an image generator. It's best
in best, you know, it's state-of-the-art
and bestin-class, but it's one of the
things that makes it so great is it's
consistency. It's able to un instruction
follow what you want changed and keep
everything else the same. And so you can
iterate with it uh and eventually get
the kind of output that you want. And
that's um I think what the future of a
lot of these creative tools is going to
be um and and sort of signals the
direction and people love it and and
they love creating with it.
So democratization of creativity I I
think is really powerful. I remember
having to buy books on Adobe Photoshop
as a kid and then you'd read them to
learn how to remove something from a
from an image and how to fill it in and
feather and all this stuff. Now anyone
can do it with Nano Banana and just they
can explain to the software what they
want it to do and it just does it.
Yeah. I think you're going to see two
things which is that um uh this sort of
democratization of these tools for
everybody to just use and create with
without having to learn, you know,
incredibly complex UX's and UIs uh like
like we had to do in the past. But on
the other hand, I think we and we're
also collaborating with filmmakers and
top creators and artists. Um so they're
helping us design what these new tools
should be, what features would they
want. people like the director Darren
Aronowski who's a good friend of mine,
an amazing director and and he's been
making and his team making films using
VO and some of our other tools and we're
learning a lot by observing them and and
collaborating them. And what we find is
that it's it also superpowers and
turbocharges the best professionals too
cuz they're suddenly um the best
creatives, the professional creatives,
they're suddenly able to be 10x, 100x
more productive. they can just try out
all sorts of ideas they have in mind,
you know, very low cost and then get to
the beautiful thing that they wanted.
So, I actually think it's sort of both
things are true. We're we're
democratizing it for everyday use, uh,
for YouTube creators and so on. But on
the other hand, at the high end, um, the
people who are who understand these
tools and it's and it's not everyone can
get the same output out of these tools,
there's a skill in that as well as um,
the vision and the storytelling and the
narrative style of uh, the top
creatives. And I think it just allows
them, they really enjoy using these
tools. It allows them to iterate way faster.
faster.
Do we get to a world where each
individual describes what sort of
content they're interested in? Play me
music like Dave Matthews and it'll play
some new track. Yes.
Or I want to play a video game set, you
know, in the movie Braveheart and I want
to be in that movie. Yes.
And I just have that experience. Do we
end up there or do we still have a one
to many creative process in society? how
important culturally and I know this is
a little bit philosophical but it's
interesting to me which is
are we still going to have storytelling
where we have one story that we all
share because someone made it
or we each going to start to develop and
pull on our own kind of virtual
I I actually foresee a world and I think
a lot about this having started in the
games industry as a game designer and
programmer is that uh in the '90s is
that you know I think the future of
enter this is what we're seeing is the
beginning of the future of entertainment
maybe some new genre or new art form and
where there's a bit of co-creation. I
still think that you'll have the top
creative visionaries. Um they will be
creating these compelling experiences
and dynamic story lines and they'll be
of higher quality even if they're using
the same tools than the everyday person
can do. But also and so millions of
people will potentially dive into those
worlds, but maybe they'll also be able
to create co-create certain parts of
those worlds and perhaps that you know
the the the main creative uh person is
almost an editor of that world. So
that's the kind of things I'm foreseeing
in the next few years and I'd actually
like to explore ourselves with with with
technologies like Genie.
Right. Incredible. And how are you
spending your time? Are you at is and
maybe you can describe Isomorphic?
Of course.
What isomorphic is and are you spending
a lot of your time there?
I am. So, so I also run Isomorphic which
is our spinout company uh to
revolutionize drug discovery building on
our alpha fold breakthrough in in
protein folding and of course um pro
knowing the structure of a protein is
only one step in the drug discovery
process. So isomorphic you can think of
it as building many uh adjacent alpha
folds to help with things like designing
chemical compounds that don't have any
side effects but bind to the right place
on the protein. And um I think we could
reduce down drug discovery from taking
years, sometimes a decade to do down to
maybe weeks or even days uh over the
next 10 years.
It's incredible. Do you think that's in
clinic soon or is that still in the
discovery phase? And
we're building up the platform right now
and it's uh we have great partnerships
with Eli Liy. I think you had the CEO
speaking earlier and and Novartis which
are fantastic and our own internal drug
programs and I think we'll be entering
sort of pre-clinical phase sometime next
year. So candidates get handed over to
the pharma company and they then take
them forward.
That's right. And we're working on
cancers and immunology and oncology and
we're working with uh uh uh uh places
like MD Anderson.
How much of this requires and I just
want to go back to your point about AGI
as it relates to what you just said.
models can be probabilistic or
deterministic and tell me if I'm
reducing this down too simplistically
that the model takes an input and it
outputs something very specific like
it's got a logical algorithm and it
outputs the same thing every time and it
could be probabilistic where it can
change things and make selections the
probability is 80% I'll select this
letter 90% I'll select this letter next
etc um how much do we have to kind of
develop deterministic models that sync
up with for example the the the physics
or the chemistry
underlying the molecular interactions as
you do your drug discovery modeling. How
much are you building novel
deterministic models that work with the
models that are probabilistic trained on data?
data?
Yeah, it's a great question. Actually,
we for the moment and I think probably
for the next 5 years or so, we're
building what maybe you could call
hybrid models. So, alphafold itself is a
hybrid model where you have the learning
component, this probabilistic component
you're talking about which is you know
based on neuronet networks and
transformers and things and that's
learning from the data you give it uh
you know any data you have available but
also to in a lot of cases with biology
and chemistry there isn't enough data to
learn from. So you also have to build in
some of the rules about chemistry and
physics that you already know about. So
for example with alpha fold um the angle
of bonds between atoms. um so and make
sure that the the alpha fold understood
you couldn't have atoms overlapping with
each other and things like that. Now in
theory it could learn that but it would
waste a lot of the learning capacity. So
actually it's better to kind of have
that as a as a yeah as a as a constraint
in there. Now the trick is with all
hybrid systems is and and Alpha Go was
another hybrid system where a neural
network learning about the game of Go
and what's what kind of patterns are
good and then we had Monte Carlo
research on top which was doing the
planning and so the trick is how do you
marry up a learning system with a a more
handcrafted system bespoke system and
actually have them work well together.
Uh and that's that's pretty tricky to do.
do.
Does that sort of architecture
ultimately lead to the breakthroughs
needed for AGI do you think? Are there
deterministic components that need to be solved?
solved?
I think ultimately you what you want to
do is um when you figure out something
where this one of these hybrid systems,
what you want what you ultimately want
to do is upstream it into the learning
component. So it's always better if you
can do endto-end learning and and and
directly predict the thing that you're
after from the data that you you you're
given. So um so once you figured out
something uh using one of these hybrid
systems, you then try and go back and
reverse engineer what you've done and
see if you can incorporate that learning
uh that that that that information into
the learning system. And this is sort of
what we did with Alpha Zero, the more
general form of Alph Go. So Alph Go had
some uh Go specific knowledge in it. But
then with Alpha Zero, we we got rid of
that including the human data human
games that we learned from and actually
just did self-learning from scratch. And
of course then it was able to learn any
game not just go.
A lot of hype and hoopla has been made
about the demand for energy arising from
AI. Uh this is a big part of the AI
summit we held in Washington DC a few
weeks ago and it seems to be the number
one topic everyone talks about in tech
nowadays. Where's all this power going
to come from? But I ask the question of
you, are there changes in the
architecture of the models or the
hardware or the relationship between the
models and the hardware that brings down
the energy per token of output or the
cost per token of output that ultimately
maybe say mutes the energy demand curve
that's in front of us or do you not
think that that's the case and we're
still going to have a pretty kind of
geometric energy demand curve? Well,
look, interestingly again, I think both
cases are true in the sense that uh
especially us at Google and at DeepMind,
we we focus a lot on very efficient
models uh that are powerful because we
have our own internal use cases of
course where we need to serve say AI
overviews to billions of users uh every
day and it has to be extremely
efficient, extremely low latency and
very cheap to serve and and so we've
we've kind of pioneered many um
techniques that allow us to do that like
distillation where you sort of have a
bigger model internally that trains the
smaller model, right? So, you train the
smaller model to mimic the bigger model.
And over time, you look at the progress
of the last 2 years, uh the model
efficiencies are like 10x, you know,
even 100x better for the same
performance. Now, the the reason that
that isn't reducing demand is because
we're still not got to AGI yet. So, also
the frontier models, you keep wanting to
train and experiment with uh new ideas
at larger and larger scale, whilst at
the same time at the serving side, uh
things are getting more and more
efficient. So both things are true and I
and in in the end I think that from the
energy perspective um I think AI systems
will give back a lot more to energy uh
uh and climate change and these kind of
things than they take in terms of
efficiency of of of grid systems and
electrical systems material design new
types of properties new energy sources.
I think AI uh will help with all of that
over the next 10 years that will far
outweigh um the energy that it uses
today. As the last question, describe
the world 10 years from now.
Wow. Okay. Well, I mean, you know, 10
years uh even even 10 weeks is is a
lifetime in AI. So, um field of 10 years,
years,
right? But I do feel like if we will
have AGI in the next 10 years, you know,
full AGI and um I think that will usher
in a new golden era of science. So, a
kind of new renaissance. Um, and I think
we'll see the benefits of that right
across from from energy to to human health.
health.
Amazing. Please join me in thanking
Nobel laurate Dennis. Thank you. That
was great. Thank you. [Applause]
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