The following is a conversation with Demis Hassabis,

CEO and co founder of DeepMind,

a company that has published and built

some of the most incredible artificial intelligence systems

in the history of computing,

including AlphaZero that learned all by itself

to play the game of go better than any human in the world

and AlphaFold2 that solved protein folding.

Both tasks considered nearly impossible

for a very long time.

Demis is widely considered to be

one of the most brilliant and impactful humans

in the history of artificial intelligence

and science and engineering in general.

This was truly an honor and a pleasure for me

to finally sit down with him for this conversation.

And I’m sure we will talk many times again in the future.

This is the Lux Readman podcast.

To support it, please check out our sponsors

in the description.

And now, dear friends, here’s Demis Hassabis.

Let’s start with a bit of a personal question.

Am I an AI program you wrote to interview people

until I get good enough to interview you?

Well, I’d be impressed if you were.

I’d be impressed by myself if you were.

I don’t think we’re quite up to that yet,

but maybe you’re from the future, Lex.

If you did, would you tell me?

Is that a good thing to tell a language model

that’s tasked with interviewing

that it is, in fact, AI?

Maybe we’re in a kind of meta Turing test.

Probably it would be a good idea not to tell you,

so it doesn’t change your behavior, right?

This is a kind of link.

Heisenberg uncertainty principle situation.

If I told you, you’d behave differently.

Maybe that’s what’s happening with us, of course.

This is a benchmark from the future

where they replay 2022 as a year

before AIs were good enough yet,

and now we want to see, is it gonna pass?

Exactly.

If I was such a program,

would you be able to tell, do you think?

So to the Turing test question,

you’ve talked about the benchmark for solving intelligence.

What would be the impressive thing?

You’ve talked about winning a Nobel Prize

and AIS system winning a Nobel Prize,

but I still return to the Turing test as a compelling test,

the spirit of the Turing test as a compelling test.

Yeah, the Turing test, of course,

it’s been unbelievably influential,

and Turing’s one of my all time heroes,

but I think if you look back at the 1950 paper,

his original paper and read the original,

you’ll see, I don’t think he meant it

to be a rigorous formal test.

I think it was more like a thought experiment,

almost a bit of philosophy he was writing

if you look at the style of the paper,

and you can see he didn’t specify it very rigorously.

So for example, he didn’t specify the knowledge

that the expert or judge would have.

How much time would they have to investigate this?

So these are important parameters

if you were gonna make it a true sort of formal test.

And by some measures, people claim the Turing test passed

several, a decade ago, I remember someone claiming that

with a kind of very bog standard, normal logic model,

because they pretended it was a kid.

So the judges thought that the machine was a child.

So that would be very different

from an expert AI person interrogating a machine

and knowing how it was built and so on.

So I think we should probably move away from that

as a formal test and move more towards a general test

where we test the AI capabilities on a range of tasks

and see if it reaches human level or above performance

on maybe thousands, perhaps even millions of tasks

eventually and cover the entire sort of cognitive space.

So I think for its time,

it was an amazing thought experiment.

And also 1950s, obviously there’s barely the dawn

of the computer age.

So of course he only thought about text

and now we have a lot more different inputs.

So yeah, maybe the better thing to test

is the generalizability, so across multiple tasks.

But I think it’s also possible as systems like Gato show

that eventually that might map right back to language.

So you might be able to demonstrate your ability

to generalize across tasks by then communicating

your ability to generalize across tasks,

which is kind of what we do through conversation anyway

when we jump around.

Ultimately what’s in there in that conversation

is not just you moving around knowledge,

it’s you moving around like these entirely different

modalities of understanding that ultimately map

to your ability to operate successfully

in all of these domains, which you can think of as tasks.

Yeah, I think certainly we as humans use language

as our main generalization communication tool.

So I think we end up thinking in language

and expressing our solutions in language.

So it’s going to be a very powerful mode in which

to explain the system, to explain what it’s doing.

But I don’t think it’s the only modality that matters.

So I think there’s going to be a lot of different ways

to express capabilities other than just language.

Yeah, visual, robotics, body language,

yeah, actions, the interactive aspect of all that.

That’s all part of it.

But what’s interesting with Gato is that

it’s sort of pushing prediction to the maximum

in terms of like mapping arbitrary sequences

to other sequences and sort of just predicting

what’s going to happen next.

So prediction seems to be fundamental to intelligence.

And what you’re predicting doesn’t so much matter.

Yeah, it seems like you can generalize that quite well.

So obviously language models predict the next word,

Gato predicts potentially any action or any token.

And it’s just the beginning really.

It’s our most general agent one could call it so far,

but that itself can be scaled up massively more

than we’ve done so far.

And obviously we’re in the middle of doing that.

But the big part of solving AGI is creating benchmarks

that help us get closer and closer,

sort of creating benchmarks that test the generalizability.

And it’s just still interesting that this fella,

Alan Turing, was one of the first

and probably still one of the only people

that was trying, maybe philosophically,

but was trying to formulate a benchmark

that could be followed.

It is, even though it’s fuzzy,

it’s still sufficiently rigorous

to where you can run that test.

And I still think something like the Turing test

will, at the end of the day,

be the thing that truly impresses other humans

so that you can have a close friend who’s an AI system.

And for that friend to be a good friend,

they’re going to have to be able to play StarCraft

and they’re gonna have to do all of these tasks,

get you a beer, so the robotics tasks,

play games with you, use language,

humor, all of those kinds of things.

But that ultimately can boil down to language.

It feels like, not in terms of the AI community,

but in terms of the actual impact

of general intelligence on the world,

it feels like language will be the place

where it truly shines.

I think so, because it’s such an important

kind of input output for us.

I think you’re right.

I think the Turing test,

the kind of the philosophy behind it,

which is the idea of can a machine mimic

the behaviors of a human?

And I would say wider than just language and text,

then in terms of actions and everything else,

creativity, all these things,

then if it can sort of match or exceed

human cognitive capabilities,

then I think we have a true intelligence.

So from that perspective, you’re right.

I think he did formulate the right kind of setup.

I just, I think there’ll be a kind of humor

in the AI systems of the future looking back

to this conversation and thinking about the Turing test

and also thinking about by that time,

they would know which year they were finally able

to sort of cross the threshold of human level intelligence

and think how funny it is that we humans

were still confused about this whole problem

that has been long ago been solved.

Well, hello to the AI agents of the future.

Anyway, so going back to your journey,

when did you fall in love with programming first?

Well, I was pretty young age actually.

So, I started off, actually games was my first love.

So starting to play chess when I was around four years old

and then it was actually with winnings

from a chess competition that I managed

to buy my first chess computer

when I was about eight years old.

It was a ZX Spectrum, which was hugely popular

in the UK at the time.

And it was amazing machine because I think it trained

a whole generation of programmers in the UK

because it was so accessible.

You know, you literally switched it on

and there was the basic prompt

and you could just get going.

And my parents didn’t really know anything about computers.

So, but because it was my money from a chess competition,

I could say I wanted to buy it.

And then, you know, I just went to bookstores,

got books on programming and started typing in,

you know, the programming code.

And then of course, once you start doing that,

you start adjusting it and then making your own games.

And that’s when I fell in love with computers

and realized that they were a very magical device.

In a way, I kind of, I wouldn’t have been able

to explain this at the time,

but I felt that they were sort of almost

a magical extension of your mind.

I always had this feeling and I’ve always loved this

about computers that you can set them off doing something,

some task for you, you can go to sleep,

come back the next day and it’s solved.

You know, that feels magical to me.

So, I mean, all machines do that to some extent.

They all enhance our natural capabilities.

Obviously cars make us, allow us to move faster

than we can run, but this was a machine to extend the mind.

And then of course, AI is the ultimate expression

of what a machine may be able to do or learn.

So very naturally for me, that thought extended

into AI quite quickly.

Do you remember the programming language

that was first started and was it special to the machine?

No, I think it was just basic on the ZX Spectrum.

I don’t know what specific form it was.

And then later on I got a Commodore Amiga,

which was a fantastic machine.

Now you’re just showing off.

So yeah, well, lots of my friends had Atari STs

and I managed to get Amigas, it was a bit more powerful

and that was incredible and used to do programming

in assembler and also Amos basic,

this specific form of basic, it was incredible actually.

So I learned all my coding skills.

And when did you fall in love with AI?

So when did you first start to gain an understanding

that you can not just write programs

that do some mathematical operations for you

while you sleep, but something that’s akin

to bringing an entity to life,

sort of a thing that can figure out something

more complicated than a simple mathematical operation.

Yeah, so there was a few stages for me

all while I was very young.

So first of all, as I was trying to improve

at playing chess, I was captaining

various England junior chess teams.

And at the time when I was about maybe 10, 11 years old,

I was gonna become a professional chess player.

That was my first thought.

So that dream was there to try to get

to the highest levels of chess.

Yeah, so when I was about 12 years old,

I got to master standard and I was second highest rated

player in the world to Judith Polgar,

who obviously ended up being an amazing chess player

and a world women’s champion.

And when I was trying to improve at chess,

where what you do is you obviously, first of all,

you’re trying to improve your own thinking processes.

So that leads you to thinking about thinking,

how is your brain coming up with these ideas?

Why is it making mistakes?

How can you improve that thought process?

But the second thing is that you,

it was just the beginning, this was like in the early 80s,

mid 80s of chess computers.

If you remember, they were physical balls

like the one we have in front of us.

And you press down the squares.

And I think Kasparov had a branded version of it

that I got.

And you used to, they’re not as strong as they are today,

but they were pretty strong and you used to practice

against them to try and improve your openings

and other things.

And so I remember, I think I probably got my first one,

I was around 11 or 12.

And I remember thinking, this is amazing,

how has someone programmed this chess board to play chess?

And it was very formative book I bought,

which was called The Chess Computer Handbook

by David Levy.

This thing came out in 1984 or something.

So I must’ve got it when I was about 11, 12.

And it explained fully how these chess programs were made.

And I remember my first AI program

being programming my Amiga.

It couldn’t, it wasn’t powerful enough to play chess.

I couldn’t write a whole chess program,

but I wrote a program for it to play Othello or reverse it,

sometimes called I think in the US.

And so a slightly simpler game than chess,

but I used all of the principles that chess programs had,

alpha, beta, search, all of that.

And that was my first AI program.

I remember that very well, I was around 12 years old.

So that brought me into AI.

And then the second part was later on,

when I was around 16, 17,

and I was writing games professionally, designing games,

writing a game called Theme Park,

which had AI as a core gameplay component

as part of the simulation.

And it sold millions of copies around the world.

And people loved the way that the AI,

even though it was relatively simple

by today’s AI standards,

was reacting to the way you as the player played it.

So it was called a sandbox game.

So it was one of the first types of games like that,

along with SimCity.

And it meant that every game you played was unique.

Is there something you could say just on a small tangent

about really impressive AI

from a game design, human enjoyment perspective,

really impressive AI that you’ve seen in games

and maybe what does it take to create an AI system?

And how hard of a problem is that?

So a million questions just as a brief tangent.

Well, look, I think games have been significant in my life

for three reasons.

So first of all, I was playing them

and training myself on games when I was a kid.

Then I went through a phase of designing games

and writing AI for games.

So all the games I professionally wrote

had AI as a core component.

And that was mostly in the 90s.

And the reason I was doing that in games industry

was at the time the games industry,

I think was the cutting edge of technology.

So whether it was graphics with people like John Carmack

and Quake and those kinds of things or AI,

I think actually all the action was going on in games.

And we’re still reaping the benefits of that

even with things like GPUs, which I find ironic

was obviously invented for graphics, computer graphics,

but then turns out to be amazingly useful for AI.

It just turns out everything’s a matrix multiplication

it appears in the whole world.

So I think games at the time had the most cutting edge AI.

And a lot of the games, I was involved in writing.

So there was a game called Black and White,

which was one game I was involved with

in the early stages of,

which I still think is the most impressive example

of reinforcement learning in a computer game.

So in that game, you trained a little pet animal and…

It’s a brilliant game.

And it sort of learned from how you were treating it.

So if you treated it badly, then it became mean.

And then it would be mean to your villagers

and your population, the sort of the little tribe

that you were running.

But if you were kind to it, then it would be kind.

And people were fascinated by how that works.

And so was I to be honest with the way it kind of developed.

And…

Especially the mapping to good and evil.

Yeah.

Made you realize, made me realize that you can sort of

in the choices you make can define where you end up.

And that means all of us are capable of the good, evil.

It all matters in the different choices

along the trajectory to those places that you make.

It’s fascinating.

I mean, games can do that philosophically to you.

And it’s rare.

It seems rare.

Yeah.

Well, games are, I think, a unique medium

because you as the player,

you’re not just passively consuming the entertainment,

right?

You’re actually actively involved as an agent.

So I think that’s what makes it in some ways

can be more visceral than other mediums

like films and books.

So the second, so that was designing AI in games.

And then the third use we’ve used of AI

is in DeepMind from the beginning,

which is using games as a testing ground

for proving out AI algorithms and developing AI algorithms.

And that was a sort of a core component

of our vision at the start of DeepMind

was that we would use games very heavily

as our main testing ground, certainly to begin with,

because it’s super efficient to use games.

And also, it’s very easy to have metrics

to see how well your systems are improving

and what direction your ideas are going in

and whether you’re making incremental improvements.

And because those games are often rooted

in something that humans did for a long time beforehand,

there’s already a strong set of rules.

Like it’s already a damn good benchmark.

Yes, it’s really good for so many reasons

because you’ve got clear measures

of how good humans can be at these things.

And in some cases like Go,

we’ve been playing it for thousands of years

and often they have scores or at least win conditions.

So it’s very easy for reward learning systems

to get a reward.

It’s very easy to specify what that reward is.

And also at the end, it’s easy to test externally

at how strong is your system by of course,

playing against the world’s strongest players at those games.

So it’s so good for so many reasons

and it’s also very efficient to run potentially millions

of simulations in parallel on the cloud.

So I think there’s a huge reason why we were so successful

back in starting out 2010,

how come we were able to progress so quickly

because we’ve utilized games.

And at the beginning of DeepMind,

we also hired some amazing game engineers

who I knew from my previous lives in the games industry.

And that helped to bootstrap us very quickly.

And plus it’s somehow super compelling

almost at a philosophical level of man versus machine

over a chess board or a Go board.

And especially given that the entire history of AI

is defined by people saying it’s gonna be impossible

to make a machine that beats a human being in chess.

And then once that happened,

people were certain when I was coming up in AI

that Go is not a game that can be solved

because of the combinatorial complexity is just too,

it’s no matter how much Moore’s law you have,

compute is just never going to be able

to crack the game of Go.

And so then there’s something compelling about facing,

sort of taking on the impossibility of that task

from the AI researcher perspective,

engineer perspective, and then as a human being,

just observing this whole thing.

Your beliefs about what you thought was impossible

being broken apart,

it’s humbling to realize we’re not as smart as we thought.

It’s humbling to realize that the things we think

are impossible now perhaps will be done in the future.

There’s something really powerful about a game,

AI system beating human being in a game

that drives that message home

for like millions, billions of people,

especially in the case of Go.

Sure.

Well, look, I think it’s,

I mean, it has been a fascinating journey

and especially as I think about it from,

I can understand it from both sides,

both as the AI, creators of the AI,

but also as a games player originally.

So, it was a really interesting,

I mean, it was a fantastic, but also somewhat

bittersweet moment, the AlphaGo match for me,

seeing that and being obviously heavily involved in that.

But as you say, chess has been the,

I mean, Kasparov, I think rightly called it

the Drosophila of intelligence, right?

So, it’s sort of, I love that phrase

and I think he’s right because chess has been

hand in hand with AI from the beginning

of the whole field, right?

So, I think every AI practitioner,

starting with Turing and Claude Shannon and all those,

the sort of forefathers of the field,

tried their hand at writing a chess program.

I’ve got original edition of Claude Shannon’s

first chess program, I think it was 1949,

the original sort of paper.

And they all did that and Turing famously wrote

a chess program, but all the computers around them

were obviously too slow to run it.

So, he had to run, he had to be the computer, right?

So, he literally, I think spent two or three days

running his own program by hand with pencil and paper

and playing a friend of his with his chess program.

So, of course, Deep Blue was a huge moment,

beating Kasparov, but actually when that happened,

I remember that very vividly, of course,

because it was chess and computers and AI,

all the things I loved and I was at college at the time.

But I remember coming away from that,

being more impressed by Kasparov’s mind

than I was by Deep Blue.

Because here was Kasparov with his human mind,

not only could he play chess more or less

to the same level as this brute of a calculation machine,

but of course, Kasparov can do everything else

humans can do, ride a bike, talk many languages,

do politics, all the rest of the amazing things

that Kasparov does.

And so, with the same brain.

And yet Deep Blue, brilliant as it was at chess,

it’d been hand coded for chess and actually had distilled

the knowledge of chess grandmasters into a cool program,

but it couldn’t do anything else.

It couldn’t even play a strictly simpler game

like tic tac toe.

So, something to me was missing from intelligence

from that system that we would regard as intelligence.

And I think it was this idea of generality

and also learning.

So, and that’s obviously what we tried to do with AlphaGo.

Yeah, with AlphaGo and AlphaZero, MuZero,

and then God and all the things that we’ll get into

some parts of, there’s just a fascinating trajectory here.

But let’s just stick on chess briefly.

On the human side of chess, you’ve proposed that

from a game design perspective,

the thing that makes chess compelling as a game

is that there’s a creative tension between a bishop

and the knight.

Can you explain this?

First of all, it’s really interesting to think about

what makes a game compelling,

makes it stick across centuries.

Yeah, I was sort of thinking about this,

and actually a lot of even amazing chess players

don’t think about it necessarily

from a game’s designer point of view.

So, it’s with my game design hat on

that I was thinking about this, why is chess so compelling?

And I think a critical reason is the dynamicness

of the different kind of chess positions you can have,

whether they’re closed or open and other things,

comes from the bishop and the knight.

So, if you think about how different

the capabilities of the bishop and knight are

in terms of the way they move,

and then somehow chess has evolved

to balance those two capabilities more or less equally.

So, they’re both roughly worth three points each.

So, you think that dynamics is always there

and then the rest of the rules

are kind of trying to stabilize the game.

Well, maybe, I mean, it’s sort of,

I don’t know, it’s chicken and egg situation,

probably both came together.

But the fact that it’s got to this beautiful equilibrium

where you can have the bishop and knight

that are so different in power,

but so equal in value across the set

of the universe of all positions, right?

Somehow they’ve been balanced by humanity

over hundreds of years,

I think gives the game the creative tension

that you can swap the bishop and knights

for a bishop for a knight,

and they’re more or less worth the same,

but now you aim for a different type of position.

If you have the knight, you want a closed position.

If you have the bishop, you want an open position.

So, I think that creates

a lot of the creative tension in chess.

So, some kind of controlled creative tension.

From an AI perspective,

do you think AI systems could eventually design games

that are optimally compelling to humans?

Well, that’s an interesting question.

Sometimes I get asked about AI and creativity,

and the way I answered that is relevant to that question,

which is that I think there are different levels

of creativity, one could say.

So, I think if we define creativity

as coming up with something original, right,

that’s useful for a purpose,

then I think the kind of lowest level of creativity

is like an interpolation.

So, an averaging of all the examples you see.

So, maybe a very basic AI system could say

you could have that.

So, you show it millions of pictures of cats,

and then you say, give me an average looking cat, right?

Generate me an average looking cat.

I would call that interpolation.

Then there’s extrapolation,

which something like AlphaGo showed.

So, AlphaGo played millions of games of Go against itself,

and then it came up with brilliant new ideas

like Move 37 in game two, brilliant motif strategies in Go

that no humans had ever thought of,

even though we’ve played it for thousands of years

and professionally for hundreds of years.

So, that I call that extrapolation,

but then there’s still a level above that,

which is, you could call out of the box thinking

or true innovation, which is, could you invent Go, right?

Could you invent chess and not just come up

with a brilliant chess move or brilliant Go move,

but can you actually invent chess

or something as good as chess or Go?

And I think one day AI could, but then what’s missing

is how would you even specify that task

to a program right now?

And the way I would do it if I was telling a human to do it

or a human games designer to do it is I would say,

something like Go, I would say, come up with a game

that only takes five minutes to learn,

which Go does because it’s got simple rules,

but many lifetimes to master, right?

Or impossible to master in one lifetime

because it’s so deep and so complex.

And then it’s aesthetically beautiful.

And also it can be completed in three or four hours

of gameplay time, which is useful for us in a human day.

And so you might specify these sort of high level concepts

like that, and then with that

and then maybe a few other things,

one could imagine that Go satisfies those constraints.

But the problem is that we’re not able

to specify abstract notions like that,

high level abstract notions like that yet to our AI systems.

And I think there’s still something missing there

in terms of high level concepts or abstractions

that they truly understand

and they’re combinable and compositional.

So for the moment, I think AI is capable

of doing interpolation and extrapolation,

but not true invention.

So coming up with rule sets and optimizing

with complicated objectives around those rule sets,

we can’t currently do.

But you could take a specific rule set

and then run a kind of self play experiment

to see how long, just observe how an AI system

from scratch learns, how long is that journey of learning?

And maybe if it satisfies some of those other things

you mentioned in terms of quickness to learn and so on,

and you could see a long journey to master

for even an AI system, then you could say

that this is a promising game.

But it would be nice to do almost like AlphaCode

so programming rules.

So generating rules that automate even that part

of the generation of rules.

So I have thought about systems actually

that I think would be amazing for a games designer.

If you could have a system that takes your game,

plays it tens of millions of times, maybe overnight,

and then self balances the rules better.

So it tweaks the rules and maybe the equations

and the parameters so that the game is more balanced,

the units in the game or some of the rules could be tweaked.

So it’s a bit of like giving a base set

and then allowing Monte Carlo Tree Search

or something like that to sort of explore it.

And I think that would be super powerful tool actually

for balancing, auto balancing a game,

which usually takes thousands of hours

from hundreds of human games testers normally

to balance a game like StarCraft,

which is Blizzard are amazing at balancing their games,

but it takes them years and years and years.

So one could imagine at some point

when this stuff becomes efficient enough

to you might be able to do that like overnight.

Do you think a game that is optimal designed by an AI system

would look very much like a planet earth?

Maybe, maybe it’s only the sort of game

I would love to make is, and I’ve tried in my games career,

the games design career, my first big game

was designing a theme park, an amusement park.

Then with games like Republic, I tried to have games

where we designed whole cities and allowed you to play in.

So, and of course people like Will Wright

have written games like SimEarth,

trying to simulate the whole of earth, pretty tricky,

but I think.

SimEarth, I haven’t actually played that one.

So what is it?

Does it incorporate of evolution or?

Yeah, it has evolution and it sort of tries to,

it sort of treats it as an entire biosphere,

but from quite high level.

So.

It’d be nice to be able to sort of zoom in,

zoom out and zoom in.

Exactly, exactly.

So obviously it couldn’t do, that was in the 90s.

I think he wrote that in the 90s.

So it couldn’t, it wasn’t able to do that,

but that would be obviously the ultimate sandbox game.

Of course.

On that topic, do you think we’re living in a simulation?

Yes, well, so, okay.

So I.

We’re gonna jump around from the absurdly philosophical

to the technical.

Sure, sure, very, very happy to.

So I think my answer to that question

is a little bit complex because there is simulation theory,

which obviously Nick Bostrom,

I think famously first proposed.

And I don’t quite believe it in that sense.

So in the sense that are we in some sort of computer game

or have our descendants somehow recreated earth

in the 21st century and some,

for some kind of experimental reason.

I think that, but I do think that we,

that we might be, that the best way to understand physics

and the universe is from a computational perspective.

So understanding it as an information universe

and actually information being the most fundamental unit

of reality rather than matter or energy.

So a physicist would say, you know, matter or energy,

you know, E equals MC squared.

These are the things that are the fundamentals

of the universe.

I’d actually say information,

which of course itself can be,

can specify energy or matter, right?

Matter is actually just, you know,

we’re just out the way our bodies

and the molecules in our body are arranged as information.

So I think information may be the most fundamental way

to describe the universe.

And therefore you could say we’re in some sort of simulation

because of that.

But I don’t, I do, I’m not,

I’m not really a subscriber to the idea that, you know,

these are sort of throw away billions of simulations around.

I think this is actually very critical and possibly unique,

this simulation.

This particular one.

Yes.

And you just mean treating the universe as a computer

that’s processing and modifying information

is a good way to solve the problems of physics,

of chemistry, of biology,

and perhaps of humanity and so on.

Yes, I think understanding physics

in terms of information theory

might be the best way to really understand

what’s going on here.

From our understanding of a universal Turing machine,

from our understanding of a computer,

do you think there’s something outside

of the capabilities of a computer

that is present in our universe?

You have a disagreement with Roger Penrose

about the nature of consciousness.

He thinks that consciousness is more

than just a computation.

Do you think all of it, the whole shebangs,

can be a computation?

Yeah, I’ve had many fascinating debates

with Sir Roger Penrose,

and obviously he’s famously,

and I read, you know, Emperors of the New Mind

and his books, his classical books,

and they were pretty influential in the 90s.

And he believes that there’s something more,

something quantum that is needed

to explain consciousness in the brain.

I think about what we’re doing actually at DeepMind

and what my career is being,

we’re almost like Turing’s champion.

So we are pushing Turing machines or classical computation

to the limits.

What are the limits of what classical computing can do?

Now, and at the same time,

I’ve also studied neuroscience to see,

and that’s why I did my PhD in,

was to see, also to look at, you know,

is there anything quantum in the brain

from a neuroscience or biological perspective?

And so far, I think most neuroscientists

and most mainstream biologists and neuroscientists

would say there’s no evidence of any quantum systems

or effects in the brain.

As far as we can see, it can be mostly explained

by classical theories.

So, and then, so there’s sort of the search

from the biology side.

And then at the same time,

there’s the raising of the water, the bar,

from what classical Turing machines can do.

And, you know, including our new AI systems.

And as you alluded to earlier, you know,

I think AI, especially in the last decade plus,

has been a continual story now of surprising events

and surprising successes,

knocking over one theory after another

of what was thought to be impossible, you know,

from Go to protein folding and so on.

And so I think I would be very hesitant

to bet against how far the universal Turing machine

and classical computation paradigm can go.

And my betting would be that all of,

certainly what’s going on in our brain,

can probably be mimicked or approximated

on a classical machine,

not requiring something metaphysical or quantum.

And we’ll get there with some of the work with AlphaFold,

which I think begins the journey of modeling

this beautiful and complex world of biology.

So you think all the magic of the human mind

comes from this, just a few pounds of mush,

of biological computational mush,

that’s akin to some of the neural networks,

not directly, but in spirit

that DeepMind has been working with.

Well, look, I think it’s, you say it’s a few, you know,

of course it’s, this is the,

I think the biggest miracle of the universe

is that it is just a few pounds of mush in our skulls.

And yet it’s also our brains are the most complex objects

that we know of in the universe.

So there’s something profoundly beautiful

and amazing about our brains.

And I think that it’s an incredibly,

incredible efficient machine.

And it’s, you know, phenomenon basically.

And I think that building AI,

one of the reasons I wanna build AI,

and I’ve always wanted to is,

I think by building an intelligent artifact like AI,

and then comparing it to the human mind,

that will help us unlock the uniqueness

and the true secrets of the mind

that we’ve always wondered about since the dawn of history,

like consciousness, dreaming, creativity, emotions,

what are all these things, right?

We’ve wondered about them since the dawn of humanity.

And I think one of the reasons,

and, you know, I love philosophy and philosophy of mind is,

we found it difficult is there haven’t been the tools

for us to really, other than introspection,

from very clever people in history,

very clever philosophers,

to really investigate this scientifically.

But now suddenly we have a plethora of tools.

Firstly, we have all of the neuroscience tools,

fMRI machines, single cell recording, all of this stuff,

but we also have the ability, computers and AI,

to build intelligent systems.

So I think that, you know,

I think it is amazing what the human mind does.

And I’m kind of in awe of it really.

And I think it’s amazing that with our human minds,

we’re able to build things like computers

and actually even, you know,

think and investigate about these questions.

I think that’s also a testament to the human mind.

Yeah.

The universe built the human mind

that now is building computers that help us understand

both the universe and our own human mind.

That’s right.

This is actually it.

I mean, I think that’s one, you know,

one could say we are,

maybe we’re the mechanism by which the universe

is going to try and understand itself.

Yeah.

It’s beautiful.

So let’s go to the basic building blocks of biology

that I think is another angle at which you can start

to understand the human mind, the human body,

which is quite fascinating,

which is from the basic building blocks,

start to simulate, start to model

how from those building blocks,

you can construct bigger and bigger, more complex systems,

maybe one day the entirety of the human biology.

So here’s another problem that thought

to be impossible to solve, which is protein folding.

And Alpha Fold or specifically Alpha Fold 2 did just that.

It solved protein folding.

I think it’s one of the biggest breakthroughs,

certainly in the history of structural biology,

but in general in science,

maybe from a high level, what is it and how does it work?

And then we can ask some fascinating questions after.

Sure.

So maybe to explain it to people not familiar

with protein folding is, you know,

first of all, explain proteins, which is, you know,

proteins are essential to all life.

Every function in your body depends on proteins.

Sometimes they’re called the workhorses of biology.

And if you look into them and I’ve, you know,

obviously as part of Alpha Fold,

I’ve been researching proteins and structural biology

for the last few years, you know,

they’re amazing little bio nano machines proteins.

They’re incredible if you actually watch little videos

of how they work, animations of how they work.

And proteins are specified by their genetic sequence

called the amino acid sequence.

So you can think of it as their genetic makeup.

And then in the body in nature,

they fold up into a 3D structure.

So you can think of it as a string of beads

and then they fold up into a ball.

Now, the key thing is you want to know

what that 3D structure is because the structure,

the 3D structure of a protein is what helps to determine

what does it do, the function it does in your body.

And also if you’re interested in drugs or disease,

you need to understand that 3D structure

because if you want to target something

with a drug compound about to block something

the protein’s doing, you need to understand

where it’s gonna bind on the surface of the protein.

So obviously in order to do that,

you need to understand the 3D structure.

So the structure is mapped to the function.

The structure is mapped to the function

and the structure is obviously somehow specified

by the amino acid sequence.

And that’s the, in essence, the protein folding problem is,

can you just from the amino acid sequence,

the one dimensional string of letters,

can you immediately computationally predict

the 3D structure?

And this has been a grand challenge in biology

for over 50 years.

So I think it was first articulated by Christian Anfinsen,

a Nobel prize winner in 1972,

as part of his Nobel prize winning lecture.

And he just speculated this should be possible

to go from the amino acid sequence to the 3D structure,

but he didn’t say how.

So it’s been described to me as equivalent

to Fermat’s last theorem, but for biology.

You should, as somebody that very well might win

the Nobel prize in the future.

But outside of that, you should do more

of that kind of thing.

In the margin, just put random things

that will take like 200 years to solve.

Set people off for 200 years.

It should be possible.

And just don’t give any details.

Exactly.

I think everyone exactly should be,

I’ll have to remember that for future.

So yeah, so he set off, you know,

with this one throwaway remark, just like Fermat,

you know, he set off this whole 50 year field really

of computational biology.

And they had, you know, they got stuck.

They hadn’t really got very far with doing this.

And until now, until AlphaFold came along,

this is done experimentally, right?

Very painstakingly.

So the rule of thumb is, and you have to like

crystallize the protein, which is really difficult.

Some proteins can’t be crystallized like membrane proteins.

And then you have to use very expensive electron microscopes

or X ray crystallography machines.

Really painstaking work to get the 3D structure

and visualize the 3D structure.

So the rule of thumb in experimental biology

is that it takes one PhD student,

their entire PhD to do one protein.

And with AlphaFold 2, we were able to predict

the 3D structure in a matter of seconds.

And so we were, you know, over Christmas,

we did the whole human proteome

or every protein in the human body or 20,000 proteins.

So the human proteomes like the equivalent

of the human genome, but on protein space.

And sort of revolutionized really

what a structural biologist can do.

Because now they don’t have to worry

about these painstaking experimental,

should they put all of that effort in or not?

They can almost just look up the structure

of their proteins like a Google search.

And so there’s a data set on which it’s trained

and how to map this amino acid sequence.

First of all, it’s incredible that a protein,

this little chemical computer is able to do

that computation itself in some kind of distributed way

and do it very quickly.

That’s a weird thing.

And they evolve that way because, you know,

in the beginning, I mean, that’s a great invention,

just the protein itself.

And then there’s, I think, probably a history

of like they evolved to have many of these proteins

and those proteins figure out how to be computers themselves

in such a way that you can create structures

that can interact in complexes with each other

in order to form high level functions.

I mean, it’s a weird system that they figured it out.

Well, for sure.

I mean, you know, maybe we should talk

about the origins of life too,

but proteins themselves, I think are magical

and incredible, as I said, little bio nano machines.

And actually Leventhal, who was another scientist,

a contemporary of Amphinson, he coined this Leventhal,

what became known as Leventhal’s paradox,

which is exactly what you’re saying.

He calculated roughly an average protein,

which is maybe 2000 amino acids base as long,

is can fold in maybe 10 to the power 300

different confirmations.

So there’s 10 to the power 300 different ways

that protein could fold up.

And yet somehow in nature, physics solves this,

solves this in a matter of milliseconds.

So proteins fold up in your body in, you know,

sometimes in fractions of a second.

So physics is somehow solving that search problem.

And just to be clear, in many of these cases,

maybe you can correct me if I’m wrong,

there’s often a unique way for that sequence to form itself.

So among that huge number of possibilities,

it figures out a way how to stably,

in some cases there might be a misfunction, so on,

which leads to a lot of the disorders and stuff like that.

But most of the time it’s a unique mapping

and that unique mapping is not obvious.

No, exactly.

Which is what the problem is.

Exactly, so there’s a unique mapping usually in a healthy,

if it’s healthy, and as you say in disease,

so for example, Alzheimer’s,

one conjecture is that it’s because of misfolded protein,

a protein that folds in the wrong way, amyloid beta protein.

So, and then because it folds in the wrong way,

it gets tangled up, right, in your neurons.

So it’s super important to understand

both healthy functioning and also disease

is to understand, you know, what these things are doing

and how they’re structuring.

Of course, the next step is sometimes proteins change shape

when they interact with something.

So they’re not just static necessarily in biology.

Maybe you can give some interesting,

so beautiful things to you about these early days

of AlphaFold, of solving this problem,

because unlike games, this is real physical systems

that are less amenable to self play type of mechanisms.

Sure.

The size of the data set is smaller

than you might otherwise like,

so you have to be very clever about certain things.

Is there something you could speak to

what was very hard to solve

and what are some beautiful aspects about the solution?

Yeah, I would say AlphaFold is the most complex

and also probably most meaningful system

we’ve built so far.

So it’s been an amazing time actually in the last,

you know, two, three years to see that come through

because as we talked about earlier, you know,

games is what we started on

building things like AlphaGo and AlphaZero,

but really the ultimate goal was to,

not just to crack games,

it was just to build,

use them to bootstrap general learning systems

we could then apply to real world challenges.

Specifically, my passion is scientific challenges

like protein folding.

And then AlphaFold of course

is our first big proof point of that.

And so, you know, in terms of the data

and the amount of innovations that had to go into it,

we, you know, it was like

more than 30 different component algorithms

needed to be put together to crack the protein folding.

I think some of the big innovations were that

kind of building in some hard coded constraints

around physics and evolutionary biology

to constrain sort of things like the bond angles

in the protein and things like that,

a lot, but not to impact the learning system.

So still allowing the system to be able to learn

the physics itself from the examples that we had.

And the examples, as you say,

there are only about 150,000 proteins,

even after 40 years of experimental biology,

only around 150,000 proteins have been,

the structures have been found out about.

So that was our training set,

which is much less than normally we would like to use,

but using various tricks, things like self distillation.

So actually using AlphaFold predictions,

some of the best predictions

that it thought was highly confident in,

we put them back into the training set, right?

To make the training set bigger,

that was critical to AlphaFold working.

So there was actually a huge number

of different innovations like that,

that were required to ultimately crack the problem.

AlphaFold one, what it produced was a distrogram.

So a kind of a matrix of the pairwise distances

between all of the molecules in the protein.

And then there had to be a separate optimization process

to create the 3D structure.

And what we did for AlphaFold two

is make it truly end to end.

So we went straight from the amino acid sequence of bases

to the 3D structure directly

without going through this intermediate step.

And in machine learning, what we’ve always found is

that the more end to end you can make it,

the better the system.

And it’s probably because in the end,

the system’s better at learning what the constraints are

than we are as the human designers of specifying it.

So anytime you can let it flow end to end

and actually just generate what it is

you’re really looking for, in this case, the 3D structure,

you’re better off than having this intermediate step,

which you then have to handcraft the next step for.

So it’s better to let the gradients and the learning

flow all the way through the system from the end point,

the end output you want to the inputs.

So that’s a good way to start on a new problem.

Handcraft a bunch of stuff,

add a bunch of manual constraints

with a small end to end learning piece

or a small learning piece and grow that learning piece

until it consumes the whole thing.

That’s right.

And so you can also see,

this is a bit of a method we’ve developed

over doing many sort of successful alpha,

we call them alpha X projects, right?

And the easiest way to see that is the evolution

of alpha go to alpha zero.

So alpha go was a learning system,

but it was specifically trained to only play go, right?

So, and what we wanted to do with first version of alpha go

is just get to world champion performance

no matter how we did it, right?

And then of course, alpha go zero,

we remove the need to use human games as a starting point,

right?

So it could just play against itself

from random starting point from the beginning.

So that removed the need for human knowledge about go.

And then finally alpha zero then generalized it

so that any things we had in there, the system,

including things like symmetry of the go board were removed.

So the alpha zero could play from scratch

any two player game and then mu zero,

which is the final, our latest version

of that set of things was then extending it

so that you didn’t even have to give it

the rules of the game.

It would learn that for itself.

So it could also deal with computer games

as well as board games.

So that line of alpha go, alpha go zero, alpha zero,

mu zero, that’s the full trajectory

of what you can take from imitation learning

to full self supervised learning.

Yeah, exactly.

And learning the entire structure

of the environment you’re put in from scratch, right?

And bootstrapping it through self play yourself.

But the thing is it would have been impossible, I think,

or very hard for us to build alpha zero

or mu zero first out of the box.

Even psychologically, because you have to believe

in yourself for a very long time.

You’re constantly dealing with doubt

because a lot of people say that it’s impossible.

Exactly, so it’s hard enough just to do go.

As you were saying, everyone thought that was impossible

or at least a decade away from when we did it

back in 2015, 2016.

And so yes, it would have been psychologically

probably very difficult as well as the fact

that of course we learn a lot by building alpha go first.

Right, so I think this is why I call AI

an engineering science.

It’s one of the most fascinating science disciplines,

but it’s also an engineering science in the sense

that unlike natural sciences, the phenomenon you’re studying

doesn’t exist out in nature.

You have to build it first.

So you have to build the artifact first,

and then you can study and pull it apart and how it works.

This is tough to ask you this question

because you probably will say it’s everything,

but let’s try to think through this

because you’re in a very interesting position

where DeepMind is a place of some of the most brilliant

ideas in the history of AI,

but it’s also a place of brilliant engineering.

So how much of solving intelligence,

this big goal for DeepMind,

how much of it is science?

How much is engineering?

So how much is the algorithms?

How much is the data?

How much is the hardware compute infrastructure?

How much is it the software compute infrastructure?

What else is there?

How much is the human infrastructure?

And like just the humans interacting certain kinds of ways

in all the space of all those ideas.

And how much is maybe like philosophy?

How much, what’s the key?

If you were to sort of look back,

like if we go forward 200 years and look back,

what was the key thing that solved intelligence?

Is it the ideas or the engineering?

I think it’s a combination.

First of all, of course,

it’s a combination of all those things,

but the ratios of them changed over time.

So even in the last 12 years,

so we started DeepMind in 2010,

which is hard to imagine now because 2010,

it’s only 12 short years ago,

but nobody was talking about AI.

I don’t know if you remember back to your MIT days,

no one was talking about it.

I did a postdoc at MIT back around then.

And it was sort of thought of as a,

well, look, we know AI doesn’t work.

We tried this hard in the 90s at places like MIT,

mostly using logic systems and old fashioned,

sort of good old fashioned AI, we would call it now.

People like Minsky and Patrick Winston,

and you know all these characters, right?

And used to debate a few of them.

And they used to think I was mad thinking about

that some new advance could be done with learning systems.

And I was actually pleased to hear that

because at least you know you’re on a unique track

at that point, right?

Even if all of your professors are telling you you’re mad.

And of course in industry,

we couldn’t get, it was difficult to get two cents together,

which is hard to imagine now as well,

given that it’s the biggest sort of buzzword in VCs

and fundraisings easy and all these kinds of things today.

So back in 2010, it was very difficult.

And the reason we started then,

and Shane and I used to discuss

what were the sort of founding tenants of DeepMind.

And it was various things.

One was algorithmic advances.

So deep learning, you know,

Jeff Hinton and Co had just sort of invented that

in academia, but no one in industry knew about it.

We love reinforcement learning.

We thought that could be scaled up.

But also understanding about the human brain

had advanced quite a lot in the decade prior

with fMRI machines and other things.

So we could get some good hints about architectures

and algorithms and sort of representations maybe

that the brain uses.

So at a systems level, not at a implementation level.

And then the other big things were compute and GPUs, right?

So we could see a compute was going to be really useful

and had got to a place where it become commoditized

mostly through the games industry

and that could be taken advantage of.

And then the final thing was also mathematical

and theoretical definitions of intelligence.

So things like AIXI, AIXE,

which Shane worked on with his supervisor, Marcus Hutter,

which is this sort of theoretical proof really

of universal intelligence,

which is actually a reinforcement learning system

in the limit.

I mean, it assumes infinite compute and infinite memory

in the way, you know, like a Turing machine proves.

But I was also waiting to see something like that too,

to, you know, like Turing machines and computation theory

that people like Turing and Shannon came up with

underpins modern computer science.

You know, I was waiting for a theory like that

to sort of underpin AGI research.

So when I, you know, met Shane

and saw he was working on something like that,

you know, that to me was a sort of final piece

of the jigsaw.

So in the early days, I would say that ideas

were the most important.

You know, for us, it was deep reinforcement learning,

scaling up deep learning.

Of course, we’ve seen transformers.

So huge leaps, I would say, you know, three or four

from, if you think from 2010 till now,

huge evolutions, things like AlphaGo.

And maybe there’s a few more still needed.

But as we get closer to AI, AGI,

I think engineering becomes more and more important

and data because scale and of course the recent,

you know, results of GPT3 and all the big language models

and large models, including our ones,

has shown that scale and large models

are clearly gonna be a necessary,

but perhaps not sufficient part of an AGI solution.

And throughout that, like you said,

and I’d like to give you a big thank you.

You’re one of the pioneers in this is sticking by ideas

like reinforcement learning, that this can actually work

given actually limited success in the past.

And also, which we still don’t know,

but proudly having the best researchers in the world

and talking about solving intelligence.

So talking about whatever you call it,

AGI or something like this, speaking of MIT,

that’s just something you wouldn’t bring up.

Not maybe you did in like 40, 50 years ago,

but that was, AI was a place where you do tinkering,

very small scale, not very ambitious projects.

And maybe the biggest ambitious projects

were in the space of robotics

and doing like the DARPA challenge.

But the task of solving intelligence and believing you can,

that’s really, really powerful.

So in order for engineering to do its work,

to have great engineers, build great systems,

you have to have that belief,

that threads throughout the whole thing

that you can actually solve

some of these impossible challenges.

Yeah, that’s right.

And back in 2010, our mission statement and still is today,

it was used to be solving step one, solve intelligence,

step two, use it to solve everything else.

So if you can imagine pitching that to a VC in 2010,

the kind of looks we got,

we managed to find a few kooky people to back us,

but it was tricky.

And it got to the point where we wouldn’t mention it

to any of our professors because they would just eye roll

and think we committed career suicide.

And so it was, there’s a lot of things that we had to do,

but we always believed it.

And one reason, by the way,

one reason I’ve always believed in reinforcement learning

is that if you look at neuroscience,

that is the way that the primate brain learns.

One of the main mechanisms is the dopamine system

implements some form of TD learning.

It was a very famous result in the late 90s

where they saw this in monkeys

and as a propagating prediction error.

So again, in the limit,

this is what I think you can use neuroscience for is,

at mathematics, when you’re doing something as ambitious

as trying to solve intelligence

and it’s blue sky research, no one knows how to do it,

you need to use any evidence

or any source of information you can

to help guide you in the right direction

or give you confidence you’re going in the right direction.

So that was one reason we pushed so hard on that.

And just going back to your earlier question

about organization, the other big thing

that I think we innovated with at DeepMind

to encourage invention and innovation

was the multidisciplinary organization we built

and we still have today.

So DeepMind originally was a confluence

of the most cutting edge knowledge in neuroscience

with machine learning, engineering and mathematics, right?

And gaming.

And then since then we’ve built that out even further.

So we have philosophers here and ethicists,

but also other types of scientists, physicists and so on.

And that’s what brings together,

I tried to build a sort of new type of Bell Labs,

but in its golden era, right?

And a new expression of that to try and foster

this incredible sort of innovation machine.

So talking about the humans in the machine,

DeepMind itself is a learning machine

with lots of amazing human minds in it

coming together to try and build these learning systems.

If we return to the big ambitious dream of AlphaFold,

that may be the early steps on a very long journey

in biology, do you think the same kind of approach

can use to predict the structure and function

of more complex biological systems?

So multi protein interaction,

and then, I mean, you can go out from there,

just simulating bigger and bigger systems

that eventually simulate something like the human brain

or the human body, just the big mush,

the mess of the beautiful, resilient mess of biology.

Do you see that as a long term vision?

I do, and I think, if you think about

what are the top things I wanted to apply AI to

once we had powerful enough systems,

biology and curing diseases and understanding biology

was right up there, top of my list.

That’s one of the reasons I personally pushed that myself

and with AlphaFold, but I think AlphaFold,

amazing as it is, is just the beginning.

And I hope it’s evidence of what could be done

with computational methods.

So AlphaFold solved this huge problem

of the structure of proteins, but biology is dynamic.

So really what I imagine from here,

and we’re working on all these things now,

is protein, protein interaction, protein ligand binding,

so reacting with molecules,

then you wanna build up to pathways,

and then eventually a virtual cell.

That’s my dream, maybe in the next 10 years.

And I’ve been talking actually

to a lot of biologists, friends of mine,

Paul Nurse, who runs the Crick Institute,

amazing biologists, Nobel Prize winning biologists.

We’ve been discussing for 20 years now, virtual cells.

Could you build a virtual simulation of a cell?

And if you could, that would be incredible

for biology and disease discovery,

because you could do loads of experiments

on the virtual cell, and then only at the last stage,

validate it in the wet lab.

So you could, in terms of the search space

of discovering new drugs, it takes 10 years roughly

to go from identifying a target,

to having a drug candidate.

Maybe that could be shortened by an order of magnitude,

if you could do most of that work in silico.

So in order to get to a virtual cell,

we have to build up understanding

of different parts of biology and the interactions.

And so every few years we talk about this,

I talked about this with Paul.

And then finally, last year after AlphaFold,

I said, now’s the time we can finally go for it.

And AlphaFold is the first proof point

that this might be possible.

And he’s very excited, and we have some collaborations

with his lab, they’re just across the road actually

from us, it’s wonderful being here in King’s Cross

with the Crick Institute across the road.

And I think the next steps,

I think there’s gonna be some amazing advances

in biology built on top of things like AlphaFold.

We’re already seeing that with the community doing that

after we’ve open sourced it and released it.

And I often say that I think if you think of mathematics

is the perfect description language for physics,

I think AI might be end up being

the perfect description language for biology

because biology is so messy, it’s so emergent,

so dynamic and complex.

I think I find it very hard to believe

we’ll ever get to something as elegant

as Newton’s laws of motions to describe a cell, right?

It’s just too complicated.

So I think AI is the right tool for that.

So you have to start at the basic building blocks

and use AI to run the simulation

for all those building blocks.

So have a very strong way to do prediction

of what given these building blocks,

what kind of biology, how the function

and the evolution of that biological system.

It’s almost like a cellular automata,

you have to run it, you can’t analyze it from a high level.

You have to take the basic ingredients,

figure out the rules and let it run.

But in this case, the rules are very difficult

to figure out, you have to learn them.

That’s exactly it.

So the biology is too complicated to figure out the rules.

It’s too emergent, too dynamic,

say compared to a physics system,

like the motion of a planet, right?

And so you have to learn the rules

and that’s exactly the type of systems that we’re building.

So you mentioned you’ve open sourced AlphaFold

and even the data involved.

To me personally, also really happy

and a big thank you for open sourcing Mojoko,

the physics simulation engine that’s often used

for robotics research and so on.

So I think that’s a pretty gangster move.

So what’s the, I mean, very few companies

or people do that kind of thing.

What’s the philosophy behind that?

You know, it’s a case by case basis.

And in both of those cases,

we felt that was the maximum benefit to humanity to do that.

And the scientific community, in one case,

the robotics physics community with Mojoko, so.

We purchased it.

We purchased it for, yes,

we purchased it for the express principle to open source it.

So, you know, I hope people appreciate that.

It’s great to hear that you do.

And then the second thing was,

and mostly we did it because the person building it

was not able to cope with supporting it anymore

because it got too big for him.

He’s an amazing professor who built it in the first place.

So we helped him out with that.

And then with AlphaFold is even bigger, I would say.

And I think in that case,

we decided that there were so many downstream applications

of AlphaFold that we couldn’t possibly even imagine

what they all were.

So the best way to accelerate drug discovery

and also fundamental research would be to give all

that data away and the system itself.

You know, it’s been so gratifying to see

what people have done that within just one year,

which is a short amount of time in science.

And it’s been used by over 500,000 researchers have used it.

We think that’s almost every biologist in the world.

I think there’s roughly 500,000 biologists in the world,

professional biologists,

have used it to look at their proteins of interest.

We’ve seen amazing fundamental research done.

So a couple of weeks ago, front cover,

there was a whole special issue of science,

including the front cover,

which had the nuclear pore complex on it,

which is one of the biggest proteins in the body.

The nuclear pore complex is a protein that governs

all the nutrients going in and out of your cell nucleus.

So they’re like little gateways that open and close

to let things go in and out of your cell nucleus.

So they’re really important, but they’re huge

because they’re massive donut ring shaped things.

And they’ve been looking to try and figure out

that structure for decades.

And they have lots of experimental data,

but it’s too low resolution, there’s bits missing.

And they were able to, like a giant Lego jigsaw puzzle,

use alpha fold predictions plus experimental data

and combined those two independent sources of information,

actually four different groups around the world

were able to put it together more or less simultaneously

using alpha fold predictions.

So that’s been amazing to see.

And pretty much every pharma company,

every drug company executive I’ve spoken to

has said that their teams are using alpha fold

to accelerate whatever drugs they’re trying to discover.

So I think the knock on effect has been enormous

in terms of the impact that alpha fold has made.

And it’s probably bringing in, it’s creating biologists,

it’s bringing more people into the field,

both on the excitement and both on the technical skills

involved in, it’s almost like a gateway drug to biology.

Yes, it is.

And to get more computational people involved too, hopefully.

And I think for us, the next stage, as I said,

in future we have to have other considerations too.

We’re building on top of alpha fold

and these other ideas I discussed with you

about protein interactions and genomics and other things.

And not everything will be open source.

Some of it we’ll do commercially

because that will be the best way

to actually get the most resources and impact behind it.

In other ways, some other projects

we’ll do nonprofit style.

And also we have to consider for future things as well,

safety and ethics as well.

Like synthetic biology, there is dual use.

And we have to think about that as well.

With alpha fold, we consulted with 30 different bioethicists

and other people expert in this field

to make sure it was safe before we released it.

So there’ll be other considerations in future.

But for right now, I think alpha fold

is a kind of a gift from us to the scientific community.

So I’m pretty sure that something like alpha fold

will be part of Nobel prizes in the future.

But us humans, of course,

are horrible with credit assignment.

So we’ll of course give it to the humans.

Do you think there will be a day

when AI system can’t be denied

that it earned that Nobel prize?

Do you think we will see that in 21st century?

It depends what type of AIs we end up building, right?

Whether they’re goal seeking agents

who specifies the goals, who comes up with the hypotheses,

who determines which problems to tackle, right?

So I think…

And tweets about it, announcement of the results.

Yes, and tweets about results exactly as part of it.

So I think right now, of course,

it’s amazing human ingenuity that’s behind these systems.

And then the system, in my opinion, is just a tool.

Be a bit like saying with Galileo and his telescope,

the ingenuity that the credit should go to the telescope.

I mean, it’s clearly Galileo building the tool

which he then uses.

So I still see that in the same way today,

even though these tools learn for themselves.

There, I think of things like alpha fold

and the things we’re building as the ultimate tools

for science and for acquiring new knowledge

to help us as scientists acquire new knowledge.

I think one day there will come a point

where an AI system may solve

or come up with something like general relativity

of its own bat, not just by averaging everything

on the internet or averaging everything on PubMed,

although that would be interesting to see

what that would come up with.

So that to me is a bit like our earlier debate

about creativity, you know, inventing go

rather than just coming up with a good go move.

And so I think solving, I think to, you know,

if we wanted to give it the credit

of like a Nobel type of thing,

then it would need to invent go

and sort of invent that new conjecture out of the blue

rather than being specified by the human scientists

or the human creators.

So I think right now it’s definitely just a tool.

Although it is interesting how far you get

by averaging everything on the internet, like you said,

because, you know, a lot of people do see science

as you’re always standing on the shoulders of giants.

And the question is how much are you really reaching

up above the shoulders of giants?

Maybe it’s just simulating different kinds

of results of the past with ultimately this new perspective

that gives you this breakthrough idea.

But that idea may not be novel in the way

that it can’t be already discovered on the internet.

Maybe the Nobel prizes of the next 100 years

are already all there on the internet to be discovered.

They could be, they could be.

I mean, I think this is one of the big mysteries,

I think is that I, first of all,

I believe a lot of the big new breakthroughs

that are gonna come in the next few decades

and even in the last decade are gonna come

at the intersection between different subject areas

where there’ll be some new connection that’s found

between what seemingly were disparate areas.

And one can even think of DeepMind, as I said earlier,

as a sort of interdisciplinary between neuroscience ideas

and AI engineering ideas originally.

And so I think there’s that.

And then one of the things we can’t imagine today is,

and one of the reasons I think people,

we were so surprised by how well large models worked

is that actually it’s very hard for our human minds,

our limited human minds to understand

what it would be like to read the whole internet, right?

I think we can do a thought experiment

and I used to do this of like,

well, what if I read the whole of Wikipedia?

What would I know?

And I think our minds can just about comprehend

maybe what that would be like,

but the whole internet is beyond comprehension.

So I think we just don’t understand what it would be like

to be able to hold all of that in mind potentially, right?

And then active at once,

and then maybe what are the connections

that are available there?

So I think no doubt there are huge things

to be discovered just like that.

But I do think there is this other type of creativity

of true spark of new knowledge, new idea,

never thought before about,

can’t be averaged from things that are known,

that really, of course, everything come,

nobody creates in a vacuum,

so there must be clues somewhere,

but just a unique way of putting those things together.

I think some of the greatest scientists in history

have displayed that I would say,

although it’s very hard to know going back to their time,

what was exactly known when they came up with those things.

Although you’re making me really think because just a thought

experiment of deeply knowing a hundred Wikipedia pages.

I don’t think I can,

I’ve been really impressed by Wikipedia for technical topics.

So if you know a hundred pages or a thousand pages,

I don’t think we can truly comprehend

what kind of intelligence that is.

That’s a pretty powerful intelligence.

If you know how to use that

and integrate that information correctly,

I think you can go really far.

You can probably construct thought experiments

based on that, like simulate different ideas.

So if this is true, let me run this thought experiment

that maybe this is true.

It’s not really invention.

It’s like just taking literally the knowledge

and using it to construct the very basic simulation

of the world.

I mean, some argue it’s romantic in part,

but Einstein would do the same kind of things

with a thought experiment.

Yeah, one could imagine doing that systematically

across millions of Wikipedia pages,

plus PubMed, all these things.

I think there are many, many things to be discovered

like that that are hugely useful.

You could imagine,

and I want us to do some of these things in material science

like room temperature superconductors

is something on my list one day.

I’d like to have an AI system to help build

better optimized batteries,

all of these sort of mechanical things.

I think a systematic sort of search

could be guided by a model,

could be extremely powerful.

So speaking of which,

you have a paper on nuclear fusion,

magnetic control of tachymic plasmas

through deep reinforcement learning.

So you’re seeking to solve nuclear fusion with deep RL.

So it’s doing control of high temperature plasmas.

Can you explain this work

and can AI eventually solve nuclear fusion?

It’s been very fun last year or two and very productive

because we’ve been taking off a lot of my dream projects,

if you like, of things that I’ve collected

over the years of areas of science

that I would like to,

I think could be very transformative if we helped accelerate

and really interesting problems,

scientific challenges in of themselves.

So this is energy.

So energy, yes, exactly.

So energy and climate.

So we talked about disease and biology

as being one of the biggest places I think AI can help with.

I think energy and climate is another one.

So maybe they would be my top two.

And fusion is one area I think AI can help with.

Now, fusion has many challenges,

mostly physics and material science

and engineering challenges as well

to build these massive fusion reactors

and contain the plasma.

And what we try to do,

and whenever we go into a new field to apply our systems,

is we look for, we talk to domain experts.

We try and find the best people in the world

to collaborate with.

In this case, in fusion,

we collaborated with EPFL in Switzerland,

the Swiss Technical Institute, who are amazing.

They have a test reactor.

They were willing to let us use,

which I double checked with the team

we were gonna use carefully and safely.

I was impressed they managed to persuade them

to let us use it.

And it’s an amazing test reactor they have there.

And they try all sorts of pretty crazy experiments on it.

And what we tend to look at is,

if we go into a new domain like fusion,

what are all the bottleneck problems?

Like thinking from first principles,

what are all the bottleneck problems

that are still stopping fusion working today?

And then we look at, we get a fusion expert to tell us,

and then we look at those bottlenecks

and we look at the ones,

which ones are amenable to our AI methods today, right?

And would be interesting from a research perspective,

from our point of view, from an AI point of view,

and that would address one of their bottlenecks.

And in this case, plasma control was perfect.

So, the plasma, it’s a million degrees Celsius,

something like that, it’s hotter than the sun.

And there’s obviously no material that can contain it.

So, they have to be containing these magnetic,

very powerful and superconducting magnetic fields.

But the problem is plasma,

it’s pretty unstable as you imagine,

you’re kind of holding a mini sun, mini star in a reactor.

So, you kind of want to predict ahead of time,

what the plasma is gonna do.

So, you can move the magnetic field

within a few milliseconds,

to basically contain what it’s gonna do next.

So, it seems like a perfect problem if you think of it

for like a reinforcement learning prediction problem.

So, you got controller, you’re gonna move the magnetic field.

And until we came along, they were doing it

with traditional operational research type of controllers,

which are kind of handcrafted.

And the problem is, of course,

they can’t react in the moment

to something the plasma is doing,

they have to be hard coded.

And again, knowing that that’s normally our go to solution

is we would like to learn that instead.

And they also had a simulator of these plasma.

So, there were lots of criteria

that matched what we like to use.

So, can AI eventually solve nuclear fusion?

Well, so with this problem,

and we published it in a nature paper last year,

we held the fusion, we held the plasma in a specific shapes.

So, actually, it’s almost like carving the plasma

into different shapes and hold it there

for a record amount of time.

So, that’s one of the problems of fusion sort of solved.

So, have a controller that’s able to,

no matter the shape.

Contain it. Contain it.

Yeah, contain it and hold it in structure.

And there’s different shapes that are better

for the energy productions called droplets and so on.

So, that was huge.

And now we’re looking,

we’re talking to lots of fusion startups

to see what’s the next problem we can tackle

in the fusion area.

So, another fascinating place in a paper titled,

Pushing the Frontiers of Density Functionals

by Solving the Fractional Electron Problem.

So, you’re taking on modeling and simulating

the quantum mechanical behavior of electrons.

Yes.

Can you explain this work and can AI model

and simulate arbitrary quantum mechanical systems

in the future?

Yeah, so this is another problem I’ve had my eye on

for a decade or more,

which is sort of simulating the properties of electrons.

If you can do that, you can basically describe

how elements and materials and substances work.

So, it’s kind of like fundamental

if you want to advance material science.

And we have Schrodinger’s equation

and then we have approximations

to that density functional theory.

These things are famous.

And people try and write approximations

to these functionals and kind of come up

with descriptions of the electron clouds,

where they’re going to go,

how they’re going to interact

when you put two elements together.

And what we try to do is learn a simulation,

learn a functional that will describe more chemistry,

types of chemistry.

So, until now, you can run expensive simulations,

but then you can only simulate very small molecules,

very simple molecules.

We would like to simulate large materials.

And so, today there’s no way of doing that.

And we’re building up towards building functionals

that approximate Schrodinger’s equation

and then allow you to describe what the electrons are doing.

And all material sort of science

and material properties are governed by the electrons

and how they interact.

So, have a good summarization of the simulation

through the functional,

but one that is still close

to what the actual simulation would come out with.

So, how difficult is that task?

What’s involved in that task?

Is it running those complicated simulations

and learning the task of mapping

from the initial conditions

and the parameters of the simulation,

learning what the functional would be?

Yeah.

So, it’s pretty tricky.

And we’ve done it with,

the nice thing is we can run a lot of the simulations,

the molecular dynamic simulations on our compute clusters.

And so, that generates a lot of data.

So, in this case, the data is generated.

So, we like those sort of systems and that’s why we use games.

It’s simulated, generated data.

And we can kind of create as much of it as we want, really.

And just let’s leave some,

if any computers are free in the cloud,

we just run, we run some of these calculations, right?

Compute cluster calculation.

I like how the free compute time

is used up on quantum mechanics.

Yeah, quantum mechanics, exactly.

Simulations and protein simulations and other things.

And so, when you’re not searching on YouTube

for free video, cat videos,

we’re using those computers usefully in quantum chemistry.

It’s the idea.

Finally.

And putting them to good use.

And then, yeah, and then all of that computational data

that’s generated,

we can then try and learn the functionals from that,

which of course are way more efficient

once we learn the functional

than running those simulations would be.

Do you think one day AI may allow us

to do something like basically crack open physics?

So, do something like travel faster than the speed of light?

My ultimate aim is always being with AI

is the reason I am personally working on AI

for my whole life, it was to build a tool

to help us understand the universe.

So, I wanted to, and that means physics, really,

and the nature of reality.

So, I don’t think we have systems

that are capable of doing that yet,

but when we get towards AGI,

I think that’s one of the first things

I think we should apply AGI to.

I would like to test the limits of physics

and our knowledge of physics.

There’s so many things we don’t know.

This is one thing I find fascinating about science.

And as a huge proponent of the scientific method

as being one of the greatest ideas humanity has ever had

and allowed us to progress with our knowledge,

but I think as a true scientist,

I think what you find is the more you find out,

the more you realize we don’t know.

And I always think that it’s surprising

that more people aren’t troubled.

Every night I think about all these things

we interact with all the time,

that we have no idea how they work.

Time, consciousness, gravity, life, we can’t,

I mean, these are all the fundamental things of nature.

I think the way we don’t really know what they are.

To live life, we pin certain assumptions on them

and kind of treat our assumptions as if they’re a fact.

That allows us to sort of box them off somehow.

Yeah, box them off somehow.

But the reality is when you think of time,

you should remind yourself,

you should take it off the shelf

and realize like, no, we have a bunch of assumptions.

There’s still a lot of, there’s even now a lot of debate.

There’s a lot of uncertainty about exactly what is time.

Is there an error of time?

You know, there’s a lot of fundamental questions

that you can’t just make assumptions about.

And maybe AI allows you to not put anything on the shelf.

Yeah.

Not make any hard assumptions

and really open it up and see what’s.

Exactly, I think we should be truly open minded about that.

And exactly that, not be dogmatic to a particular theory.

It’ll also allow us to build better tools,

experimental tools eventually,

that can then test certain theories

that may not be testable today.

Things about like what we spoke about at the beginning

about the computational nature of the universe.

How one might, if that was true,

how one might go about testing that, right?

And how much, you know, there are people

who’ve conjectured people like Scott Aaronson and others

about, you know, how much information

can a specific plank unit of space and time

contain, right?

So one might be able to think about testing those ideas

if you had AI helping you build

some new exquisite experimental tools.

This is what I imagine that, you know,

many decades from now we’ll be able to do.

And what kind of questions can be answered

through running a simulation of them?

So there’s a bunch of physics simulations

you can imagine that could be run

in some kind of efficient way,

much like you’re doing in the quantum simulation work.

And perhaps even the origin of life.

So figuring out how going even back

before the work of AlphaFold begins

of how this whole thing emerges from a rock.

Yes.

From a static thing.

What do you think AI will allow us to,

is that something you have your eye on?

It’s trying to understand the origin of life.

First of all, yourself, what do you think,

how the heck did life originate on Earth?

Yeah, well, maybe I’ll come to that in a second,

but I think the ultimate use of AI

is to kind of use it to accelerate science to the maximum.

So I think of it a little bit

like the tree of all knowledge.

If you imagine that’s all the knowledge there is

in the universe to attain.

And we sort of barely scratched the surface of that so far.

And even though we’ve done pretty well

since the enlightenment, right, as humanity.

And I think AI will turbocharge all of that,

like we’ve seen with AlphaFold.

And I want to explore as much of that tree of knowledge

as is possible to do.

And I think that involves AI helping us

with understanding or finding patterns,

but also potentially designing and building new tools,

experimental tools.

So I think that’s all,

and also running simulations and learning simulations,

all of that we’re sort of doing at a baby steps level here.

But I can imagine that in the decades to come

as what’s the full flourishing of that line of thinking.

It’s gonna be truly incredible, I would say.

If I visualized this tree of knowledge,

something tells me that that tree of knowledge for humans

is much smaller in the set of all possible trees

of knowledge, it’s actually quite small

given our cognitive limitations,

limited cognitive capabilities,

that even with the tools we build,

we still won’t be able to understand a lot of things.

And that’s perhaps what nonhuman systems

might be able to reach farther, not just as tools,

but in themselves understanding something

that they can bring back.

Yeah, it could well be.

So, I mean, there’s so many things

that are sort of encapsulated in what you just said there.

I think first of all, there’s two different things.

There’s like, what do we understand today?

What could the human mind understand?

And what is the totality of what is there to be understood?

And so there’s three concentric,

you can think of them as three larger and larger trees

or exploring more branches of that tree.

And I think with AI, we’re gonna explore that whole lot.

Now, the question is, if you think about

what is the totality of what could be understood,

there may be some fundamental physics reasons

why certain things can’t be understood,

like what’s outside a simulation or outside the universe.

Maybe it’s not understandable from within the universe.

So there may be some hard constraints like that.

It could be smaller constraints,

like we think of space time as fundamental.

Our human brains are really used to this idea

of a three dimensional world with time, maybe.

But our tools could go beyond that.

They wouldn’t have that limitation necessarily.

They could think in 11 dimensions, 12 dimensions,

whatever is needed.

But we could still maybe understand that

in several different ways.

The example I always give is,

when I play Garry Kasparov for speed chess,

or we’ve talked about chess and these kinds of things,

you know, if you’re reasonably good at chess,

you can’t come up with the move Garry comes up with

in his move, but he can explain it to you.

And you can understand.

And you can understand post hoc the reasoning.

So I think there’s an even further level of like,

well, maybe you couldn’t have invented that thing,

but going back to using language again,

perhaps you can understand and appreciate that.

Same way that you can appreciate, you know,

Vivaldi or Mozart or something without,

you can appreciate the beauty of that

without being able to construct it yourself, right?

Invent the music yourself.

So I think we see this in all forms of life.

So it will be that times, you know, a million,

but you can imagine also one sign of intelligence

is the ability to explain things clearly and simply, right?

You know, people like Richard Feynman,

another one of my old time heroes used to say that, right?

If you can’t, you know, if you can explain it

something simply, then that’s the best sign,

a complex topic simply,

then that’s one of the best signs of you understanding it.

Yeah.

I can see myself talking trash in the AI system in that way.

Yes.

It gets frustrated how dumb I am

and trying to explain something to me.

I was like, well, that means you’re not intelligent

because if you were intelligent,

you’d be able to explain it simply.

Yeah, of course, you know, there’s also the other option.

Of course, we could enhance ourselves and with our devices,

we are already sort of symbiotic with our compute devices,

right, with our phones and other things.

And, you know, there’s stuff like Neuralink and Xceptra

that could advance that further.

So I think there’s lots of really amazing possibilities

that I could foresee from here.

Well, let me ask you some wild questions.

So out there looking for friends,

do you think there’s a lot of alien civilizations out there?

So I guess this also goes back

to your origin of life question too,

because I think that that’s key.

My personal opinion, looking at all this,

and, you know, it’s one of my hobbies, physics, I guess.

So, you know, it’s something I think about a lot

and talk to a lot of experts on and read a lot of books on.

And I think my feeling currently is that we are alone.

I think that’s the most likely scenario

given what evidence we have.

So, and the reasoning is I think that, you know,

we’ve tried since things like SETI program

and I guess since the dawning of the space age,

we’ve, you know, had telescopes,

open radio telescopes and other things.

And if you think about and try to detect signals,

now, if you think about the evolution of humans on earth,

we could have easily been a million years ahead

of our time now or million years behind,

right, easily with just some slightly different quirk

thing happening hundreds of thousands of years ago.

You know, things could have been slightly different

if the meteor would hit the dinosaurs a million years earlier,

maybe things would have evolved.

We’d be a million years ahead of where we are now.

So what that means is if you imagine where humanity will be

in a few hundred years, let alone a million years,

especially if we hopefully, you know,

solve things like climate change and other things,

and we continue to flourish and we build things like AI

and we do space traveling and all of the stuff

that humans have dreamed of forever, right?

And sci fi is talked about forever.

We will be spreading across the stars, right?

And von Neumann famously calculated, you know,

it would only take about a million years

if you sent out von Neumann probes to the nearest,

you know, the nearest other solar systems.

And then all they did was build two more versions

of themselves and sent those two out

to the next nearest systems.

You know, within a million years,

I think you would have one of these probes

in every system in the galaxy.

So it’s not actually in cosmological time.

That’s actually a very short amount of time.

So, and you know, people like Dyson have thought

about constructing Dyson spheres around stars

to collect all the energy coming out of the star.

You know, there would be constructions like that

would be visible across space,

probably even across a galaxy.

So, and then, you know, if you think about

all of our radio, television emissions

that have gone out since the, you know, 30s and 40s,

imagine a million years of that.

And now hundreds of civilizations doing that.

When we opened our ears at the point

we got technologically sophisticated enough

in the space age,

we should have heard a cacophony of voices.

We should have joined that cacophony of voices.

And what we did, we opened our ears and we heard nothing.

And many people who argue that there are aliens

would say, well, we haven’t really done

exhaustive search yet.

And maybe we’re looking in the wrong bands

and we’ve got the wrong devices

and we wouldn’t notice what an alien form was like

because it’d be so different to what we’re used to.

But, you know, I don’t really buy that,

that it shouldn’t be as difficult as that.

Like, I think we’ve searched enough.

There should be everywhere.

If it was, yeah, it should be everywhere.

We should see Dyson spheres being put up,

sun’s blinking in and out.

You know, there should be a lot of evidence

for those things.

And then there are other people who argue,

well, the sort of safari view of like,

well, we’re a primitive species still

because we’re not space faring yet.

And we’re, you know, there’s some kind of global,

like universal rule not to interfere,

you know, Star Trek rule.

But like, look, we can’t even coordinate humans

to deal with climate change and we’re one species.

What is the chance that of all of these different

human civilization, you know, alien civilizations,

they would have the same priorities

and agree across these kinds of matters.

And even if that was true

and we were in some sort of safari for our own good,

to me, that’s not much different

from the simulation hypothesis

because what does it mean, the simulation hypothesis?

I think in its most fundamental level,

it means what we’re seeing is not quite reality, right?

It’s something, there’s something more deeper underlying it,

maybe computational.

Now, if we were in a sort of safari park

and everything we were seeing was a hologram

and it was projected by the aliens or whatever,

that to me is not much different

than thinking we’re inside of another universe

because we still can’t see true reality, right?

I mean, there’s other explanations.

It could be that the way they’re communicating

is just fundamentally different,

that we’re too dumb to understand the much better methods

of communication they have.

It could be, I mean, it’s silly to say,

but our own thoughts could be the methods

by which they’re communicating.

Like the place from which our ideas,

writers talk about this, like the muse.

Yeah.

I mean, it sounds like very kind of wild,

but it could be thoughts.

It could be some interactions with our mind

that we think are originating from us

is actually something that is coming

from other life forms elsewhere.

Consciousness itself might be that.

It could be, but I don’t see any sensible argument

to the why would all of the alien species

behave in this way?

Yeah, some of them will be more primitive.

They will be close to our level.

There should be a whole sort of normal distribution

of these things, right?

Some would be aggressive.

Some would be curious.

Others would be very historical and philosophical

because maybe they’re a million years older than us,

but it’s not, it shouldn’t be like,

I mean, one alien civilization might be like that,

communicating thoughts and others,

but I don’t see why potentially the hundreds there should be

would be uniform in this way, right?

It could be a violent dictatorship that the people,

the alien civilizations that become successful

gain the ability to be destructive,

an order of magnitude more destructive,

but of course the sad thought,

well, either humans are very special.

We took a lot of leaps that arrived

at what it means to be human.

There’s a question there, which was the hardest,

which was the most special,

but also if others have reached this level

and maybe many others have reached this level,

the great filter that prevented them from going farther

to becoming a multi planetary species

or reaching out into the stars.

And those are really important questions for us,

whether there’s other alien civilizations out there or not,

this is very useful for us to think about.

If we destroy ourselves, how will we do it?

And how easy is it to do?

Yeah, well, these are big questions

and I’ve thought about these a lot,

but the interesting thing is that if we’re alone,

that’s somewhat comforting from the great filter perspective

because it probably means the great filters were passed us.

And I’m pretty sure they are.

So going back to your origin of life question,

there are some incredible things

that no one knows how happened,

like obviously the first life form from chemical soup,

that seems pretty hard,

but I would guess the multicellular,

I wouldn’t be that surprised if we saw single cell

sort of life forms elsewhere, bacteria type things,

but multicellular life seems incredibly hard,

that step of capturing mitochondria

and then sort of using that as part of yourself,

you know, when you’ve just eaten it.

Would you say that’s the biggest, the most,

like if you had to choose one sort of,

Hitchhiker’s Galaxy, one sentence summary of like,

oh, those clever creatures did this,

that would be the multicellular.

I think that was probably the one that’s the biggest.

I mean, there’s a great book

called The 10 Great Inventions of Evolution by Nick Lane,

and he speculates on 10 of these, you know,

what could be great filters.

I think that’s one.

I think the advent of intelligence

and conscious intelligence and in order, you know,

to us to be able to do science and things like that

is huge as well.

I mean, it’s only evolved once as far as, you know,

in Earth history.

So that would be a later candidate,

but there’s certainly for the early candidates,

I think multicellular life forms is huge.

By the way, what it’s interesting to ask you,

if you can hypothesize about

what is the origin of intelligence?

Is it that we started cooking meat over fire?

Is it that we somehow figured out

that we could be very powerful when we started collaborating?

So cooperation between our ancestors

so that we can overthrow the alpha male.

What is it, Richard?

I talked to Richard Ranham,

who thinks we’re all just beta males

who figured out how to collaborate to defeat the one,

the dictator, the authoritarian alpha male

that controlled the tribe.

Is there other explanation?

Was there 2001 Space Odyssey type of monolith

that came down to Earth?

Well, I think all of those things

you suggested are good candidates,

fire and cooking, right?

So that’s clearly important for energy efficiency,

cooking our meat and then being able to be more efficient

about eating it and consuming the energy.

I think that’s huge and then utilizing fire and tools

I think you’re right about the tribal cooperation aspects

and probably language is part of that

because probably that’s what allowed us

to outcompete Neanderthals

and perhaps less cooperative species.

So that may be the case.

Tool making, spears, axes, I think that let us,

I mean, I think it’s pretty clear now

that humans were responsible

for a lot of the extinctions of megafauna,

especially in the Americas when humans arrived.

So you can imagine once you discover tool usage

how powerful that would have been

and how scary for animals.

So I think all of those could have been explanations for it.

The interesting thing is that it’s a bit

like general intelligence too,

is it’s very costly to begin with to have a brain

and especially a general purpose brain

rather than a special purpose one

because the amount of energy our brains use,

I think it’s like 20% of the body’s energy

and it’s massive and even your thinking chest,

one of the funny things that we used to say

is it’s as much as a racing driver uses

for a whole Formula One race,

just playing a game of serious high level chess,

which you wouldn’t think just sitting there

because the brain’s using so much energy.

So in order for an animal, an organism to justify that,

there has to be a huge payoff.

And the problem with half a brain

or half intelligence, say an IQs of like a monkey brain,

it’s not clear you can justify that evolutionary

until you get to the human level brain.

And so, but how do you do that jump?

It’s very difficult,

which is why I think it has only been done once

from the sort of specialized brains that you see in animals

to this sort of general purpose,

chewing powerful brains that humans have

and which allows us to invent the modern world.

And it takes a lot to cross that barrier.

And I think we’ve seen the same with AI systems,

which is that maybe until very recently,

it’s always been easier to craft a specific solution

to a problem like chess than it has been

to build a general learning system

that could potentially do many things.

Cause initially that system will be way worse

than less efficient than the specialized system.

So one of the interesting quirks of the human mind

of this evolved system is that it appears to be conscious.

This thing that we don’t quite understand,

but it seems very special is ability

to have a subjective experience

that it feels like something to eat a cookie,

the deliciousness of it or see a color

and that kind of stuff.

Do you think in order to solve intelligence,

we also need to solve consciousness along the way?

Do you think AGI systems need to have consciousness

in order to be truly intelligent?

Yeah, we thought about this a lot actually.

And I think that my guess is that consciousness

and intelligence are double dissociable.

So you can have one without the other both ways.

And I think you can see that with consciousness

in that I think some animals and pets,

if you have a pet dog or something like that,

you can see some of the higher animals and dolphins,

things like that have self awareness

and are very sociable, seem to dream.

A lot of the traits one would regard

as being kind of conscious and self aware,

but yet they’re not that smart, right?

So they’re not that intelligent

by say IQ standards or something like that.

Yeah, it’s also possible that our understanding

of intelligence is flawed, like putting an IQ to it.

Maybe the thing that a dog can do

is actually gone very far along the path of intelligence

and we humans are just able to play chess

and maybe write poems.

Right, but if we go back to the idea of AGI

and general intelligence, dogs are very specialized, right?

Most animals are pretty specialized.

They can be amazing at what they do,

but they’re like kind of elite sports people or something,

right, so they do one thing extremely well

because their entire brain is optimized.

They have somehow convinced the entirety

of the human population to feed them and service them.

So in some way they’re controlling.

Yes, exactly.

Well, we co evolved to some crazy degree, right?

Including the way the dogs even wag their tails

and twitch their noses, right?

We find inextricably cute.

But I think you can also see intelligence on the other side.

So systems like artificial systems

that are amazingly smart at certain things

like maybe playing go and chess and other things,

but they don’t feel at all in any shape or form conscious

in the way that you do to me or I do to you.

And I think actually building AI

is these intelligent constructs

is one of the best ways to explore

the mystery of consciousness, to break it down

because we’re gonna have devices

that are pretty smart at certain things

or capable at certain things,

but potentially won’t have any semblance

of self awareness or other things.

And in fact, I would advocate if there’s a choice,

building systems in the first place,

AI systems that are not conscious to begin with

are just tools until we understand them better

and the capabilities better.

So on that topic, just not as the CEO of DeepMind,

just as a human being, let me ask you

about this one particular anecdotal evidence

of the Google engineer who made a comment

or believed that there’s some aspect of a language model,

the Lambda language model that exhibited sentience.

So you said you believe there might be a responsibility

to build systems that are not sentient.

And this experience of a particular engineer,

I think I’d love to get your general opinion

on this kind of thing, but I think it will happen

more and more and more, which not when engineers,

but when people out there that don’t have

an engineering background start interacting

with increasingly intelligent systems,

we anthropomorphize them.

They start to have deep, impactful interactions with us

in a way that we miss them when they’re gone.

And we sure as heck feel like they’re living entities,

self aware entities, and maybe even

we project sentience onto them.

So what’s your thought about this particular system?

Have you ever met a language model that’s sentient?

No, no.

What do you make of the case of when you kind of feel

that there’s some elements of sentience to the system?

Yeah, so this is an interesting question

and obviously a very fundamental one.

So the first thing to say is I think that none

of the systems we have today, I would say,

even have one iota of semblance

of consciousness or sentience.

That’s my personal feeling interacting with them every day.

So I think this way premature to be discussing

what that engineer talked about.

I think at the moment it’s more of a projection

of the way our own minds work,

which is to see sort of purpose and direction

in almost anything that we, you know,

our brains are trained to interpret agency,

basically in things, even inanimate things sometimes.

And of course with a language system,

because language is so fundamental to intelligence,

that’s going to be easy for us to anthropomorphize that.

I mean, back in the day, even the first, you know,

the dumbest sort of template chatbots ever,

Eliza and the ilk of the original chatbots

back in the sixties fooled some people

under certain circumstances, right?

It pretended to be a psychologist.

So just basically rabbit back to you

the same question you asked it back to you.

And some people believe that.

So I don’t think we can, this is why I think

the Turing test is a little bit flawed as a formal test

because it depends on the sophistication of the judge,

whether or not they are qualified to make that distinction.

So I think we should talk to, you know,

the top philosophers about this,

people like Daniel Dennett and David Chalmers and others

who’ve obviously thought deeply about consciousness.

Of course, consciousness itself hasn’t been well,

there’s no agreed definition.

If I was to, you know, speculate about that, you know,

I kind of, the working definition I like is

it’s the way information feels when it gets processed.

I think maybe Max Tegmark came up with that.

I like that idea.

I don’t know if it helps us get towards

any more operational thing,

but I think it’s a nice way of viewing it.

I think we can obviously see from neuroscience

certain prerequisites that are required,

like self awareness, I think is necessary,

but not sufficient component.

This idea of a self and other

and set of coherent preferences

that are coherent over time.

You know, these things are maybe memory.

These things are probably needed

for a sentient or conscious being.

But the reason, the difficult thing,

I think for us when we get,

and I think this is a really interesting

philosophical debate is when we get closer to AGI

and, you know, and much more powerful systems

than we have today,

how are we going to make this judgment?

And one way, which is the Turing test

is sort of a behavioral judgment,

is the system exhibiting all the behaviors

that a human sentient or a sentient being would exhibit?

Is it answering the right questions?

Is it saying the right things?

Is it indistinguishable from a human?

And so on.

But I think there’s a second thing

that makes us as humans regard each other as sentient,

right?

Why do we think this?

And I debated this with Daniel Dennett.

And I think there’s a second reason

that’s often overlooked,

which is that we’re running on the same substrate, right?

So if we’re exhibiting the same behavior,

more or less as humans,

and we’re running on the same, you know,

carbon based biological substrate,

the squishy, you know, few pounds of flesh in our skulls,

then the most parsimonious, I think, explanation

is that you’re feeling the same thing as I’m feeling, right?

But we will never have that second part,

the substrate equivalence with a machine, right?

So we will have to only judge based on the behavior.

And I think the substrate equivalence

is a critical part of why we make assumptions

that we’re conscious.

And in fact, even with animals, high level animals,

why we think they might be,

because they’re exhibiting some of the behaviors

we would expect from a sentient animal.

And we know they’re made of the same things,

biological neurons.

So we’re gonna have to come up with explanations

or models of the gap between substrate differences,

between machines and humans

to get anywhere beyond the behavioral.

But to me, sort of the practical question

is very interesting and very important.

When you have millions, perhaps billions of people

believing that you have a sentient AI,

believing what that Google engineer believed,

which I just see as an obvious, very near term future thing,

certainly on the path to AGI,

how does that change the world?

What’s the responsibility of the AI system

to help those millions of people?

And also what’s the ethical thing?

Because you can make a lot of people happy

by creating a meaningful, deep experience

with a system that’s faking it before it makes it.

And I don’t, are we the right,

who is to say what’s the right thing to do?

Should AI always be tools?

Why are we constraining AI to always be tools

as opposed to friends?

Yeah, I think, well, I mean, these are fantastic questions

and also critical ones.

And we’ve been thinking about this

since the start of DeepMind and before that,

because we plan for success

and however remote that looked like back in 2010.

And we’ve always had sort of these ethical considerations

as fundamental at DeepMind.

And my current thinking on the language models

and large models is they’re not ready,

we don’t understand them well enough yet.

And in terms of analysis tools and guard rails,

what they can and can’t do and so on,

to deploy them at scale, because I think,

there are big, still ethical questions

like should an AI system always announce

that it is an AI system to begin with?

Probably yes.

What do you do about answering those philosophical questions

about the feelings people may have about AI systems,

perhaps incorrectly attributed?

So I think there’s a whole bunch of research

that needs to be done first to responsibly,

before you can responsibly deploy these systems at scale.

That will be at least be my current position.

Over time, I’m very confident we’ll have those tools

like interpretability questions and analysis questions.

And then with the ethical quandary,

I think there it’s important to look beyond just science.

That’s why I think philosophy, social sciences,

even theology, other things like that come into it,

where arts and humanities,

what does it mean to be human and the spirit of being human

and to enhance that and the human condition, right?

And allow us to experience things

we could never experience before

and improve the overall human condition

and humanity overall, get radical abundance,

solve many scientific problems, solve disease.

So this is the era I think, this is the amazing era

I think we’re heading into if we do it right.

But we’ve got to be careful.

We’ve already seen with things like social media,

how dual use technologies can be misused by,

firstly, by bad actors or naive actors or crazy actors,

right, so there’s that set of just the common

or garden misuse of existing dual use technology.

And then of course, there’s an additional thing

that has to be overcome with AI

that eventually it may have its own agency.

So it could be good or bad in and of itself.

So I think these questions have to be approached

very carefully using the scientific method, I would say,

in terms of hypothesis generation, careful control testing,

not live A, B testing out in the world,

because with powerful technologies like AI,

if something goes wrong, it may cause a lot of harm

before you can fix it.

It’s not like an imaging app or game app

where if something goes wrong, it’s relatively easy to fix

and the harm is relatively small.

So I think it comes with the usual cliche of,

like with a lot of power comes a lot of responsibility.

And I think that’s the case here with things like AI,

given the enormous opportunity in front of us.

And I think we need a lot of voices

and as many inputs into things like the design

of the systems and the values they should have

and what goals should they be put to.

I think as wide a group of voices as possible

beyond just the technologists is needed to input into that

and to have a say in that,

especially when it comes to deployment of these systems,

which is when the rubber really hits the road,

it really affects the general person in the street

rather than fundamental research.

And that’s why I say, I think as a first step,

it would be better if we have the choice

to build these systems as tools to give,

and I’m not saying that they should never go beyond tools

because of course the potential is there

for it to go way beyond just tools.

But I think that would be a good first step

in order for us to allow us to carefully experiment

and understand what these things can do.

So the leap between tool, the sentient entity being

is one we should take very careful of.

Let me ask a dark personal question.

So you’re one of the most brilliant people

in the AI community, you’re also one of the most kind

and if I may say sort of loved people in the community.

That said, creation of a super intelligent AI system

would be one of the most powerful things in the world,

tools or otherwise.

And again, as the old saying goes, power corrupts

and absolute power corrupts absolutely.

You are likely to be one of the people,

I would say probably the most likely person

to be in the control of such a system.

Do you think about the corrupting nature of power

when you talk about these kinds of systems

that as all dictators and people have caused atrocities

in the past, always think they’re doing good,

but they don’t do good because the power

has polluted their mind about what is good

and what is evil.

Do you think about this stuff

or are we just focused on language model?

No, I think about them all the time

and I think what are the defenses against that?

I think one thing is to remain very grounded

and sort of humble, no matter what you do or achieve.

And I try to do that, my best friends

are still my set of friends

from my undergraduate Cambridge days,

my family’s and friends are very important.

I’ve always, I think trying to be a multidisciplinary person,

it helps to keep you humble

because no matter how good you are at one topic,

someone will be better than you at that.

And always relearning a new topic again from scratch

is a new field is very humbling, right?

So for me, that’s been biology over the last five years,

huge area topic and I just love doing that,

but it helps to keep you grounded

like it keeps you open minded.

And then the other important thing

is to have a really good, amazing set of people around you

at your company or your organization

who are also very ethical and grounded themselves

and help to keep you that way.

And then ultimately just to answer your question,

I hope we’re gonna be a big part of birthing AI

and that being the greatest benefit to humanity

of any tool or technology ever,

and getting us into a world of radical abundance

and curing diseases and solving many of the big challenges

we have in front of us.

And then ultimately help the ultimate flourishing

of humanity to travel the stars

and find those aliens if they are there.

And if they’re not there, find out why they’re not there,

what is going on here in the universe.

This is all to come.

And that’s what I’ve always dreamed about.

But I think AI is too big an idea.

It’s not going to be,

there’ll be a certain set of pioneers who get there first.

I hope we’re in the vanguard

so we can influence how that goes.

And I think it matters who builds,

which cultures they come from and what values they have,

the builders of AI systems.

Cause I think even though the AI system

is gonna learn for itself most of its knowledge,

there’ll be a residue in the system of the culture

and the values of the creators of that system.

And there’s interesting questions

to discuss about that geopolitically.

Different cultures,

we’re in a more fragmented world than ever, unfortunately.

I think in terms of global cooperation,

we see that in things like climate

where we can’t seem to get our act together globally

to cooperate on these pressing matters.

I hope that will change over time.

Perhaps if we get to an era of radical abundance,

we don’t have to be so competitive anymore.

Maybe we can be more cooperative

if resources aren’t so scarce.

It’s true that in terms of power corrupting

and leading to destructive things,

it seems that some of the atrocities of the past happen

when there’s a significant constraint on resources.

I think that’s the first thing.

I don’t think that’s enough.

I think scarcity is one thing that’s led to competition,

sort of zero sum game thinking.

I would like us to all be in a positive sum world.

And I think for that, you have to remove scarcity.

I don’t think that’s enough, unfortunately,

to get world peace

because there’s also other corrupting things

like wanting power over people and this kind of stuff,

which is not necessarily satisfied by just abundance.

But I think it will help.

But I think ultimately, AI is not gonna be run

by any one person or one organization.

I think it should belong to the world, belong to humanity.

And I think there’ll be many ways this will happen.

And ultimately, everybody should have a say in that.

Do you have advice for young people in high school,

in college, maybe if they’re interested in AI

or interested in having a big impact on the world,

what they should do to have a career they can be proud of

or to have a life they can be proud of?

I love giving talks to the next generation.

What I say to them is actually two things.

I think the most important things to learn about

and to find out about when you’re young

is what are your true passions is first of all,

as two things.

One is find your true passions.

And I think you can do that by,

the way to do that is to explore as many things as possible

when you’re young and you have the time

and you can take those risks.

I would also encourage people to look at

finding the connections between things in a unique way.

I think that’s a really great way to find a passion.

Second thing I would say, advise is know yourself.

So spend a lot of time understanding

how you work best.

Like what are the optimal times to work?

What are the optimal ways that you study?

What are your, how do you deal with pressure?

Sort of test yourself in various scenarios

and try and improve your weaknesses,

but also find out what your unique skills and strengths are

and then hone those.

So then that’s what will be your super value

in the world later on.

And if you can then combine those two things

and find passions that you’re genuinely excited about

that intersect with what your unique strong skills are,

then you’re onto something incredible

and I think you can make a huge difference in the world.

So let me ask about know yourself.

This is fun.

Quick questions about day in the life, the perfect day,

the perfect productive day in the life of Demis’s Hub.

Maybe these days you’re, there’s a lot involved.

So maybe a slightly younger Demis’s Hub

where you could focus on a single project maybe.

How early do you wake up?

Are you a night owl?

Do you wake up early in the morning?

What are some interesting habits?

How many dozens of cups of coffees do you drink a day?

What’s the computer that you use?

What’s the setup?

How many screens?

What kind of keyboard?

Are we talking Emacs Vim

or are we talking something more modern?

So there’s a bunch of those questions.

So maybe day in the life, what’s the perfect day involved?

Well, these days it’s quite different

from say 10, 20 years ago.

Back 10, 20 years ago, it would have been

a whole day of research, individual research or programming,

doing some experiment, neuroscience,

computer science experiment,

reading lots of research papers.

And then perhaps at nighttime,

reading science fiction books or playing some games.

But lots of focus, so like deep focused work

on whether it’s programming or reading research papers.

Yes, so that would be lots of deep focus work.

These days for the last sort of, I guess, five to 10 years,

I’ve actually got quite a structure

that works very well for me now,

which is that I’m a complete night owl, always have been.

So I optimize for that.

So I’ll basically do a normal day’s work,

get into work about 11 o clock

and sort of do work to about seven in the office.

And I will arrange back to back meetings

for the entire time of that.

And with as many, meet as many people as possible.

So that’s my collaboration management part of the day.

Then I go home, spend time with the family and friends,

have dinner, relax a little bit.

And then I start a second day of work.

I call it my second day of work around 10 p.m., 11 p.m.

And that’s the time to about the small hours of the morning,

four or five in the morning, where I will do my thinking

and reading and research, writing research papers.

Sadly, I don’t have time to code anymore,

but it’s not efficient to do that these days,

given the amount of time I have.

But that’s when I do, you know,

maybe do the long kind of stretches

of thinking and planning.

And then probably, you know, using email, other things,

I would set, I would fire off a lot of things to my team

to deal with the next morning.

But actually thinking about this overnight,

we should go for this project

or arrange this meeting the next day.

When you’re thinking through a problem,

are you talking about a sheet of paper with a pen?

Is there some structured process?

I still like pencil and paper best for working out things,

but these days it’s just so efficient

to read research papers just on the screen.

I still often print them out, actually.

I still prefer to mark out things.

And I find it goes into the brain better

and sticks in the brain better

when you’re still using physical pen and pencil and paper.

So you take notes with the…

I have lots of notes, electronic ones,

and also whole stacks of notebooks that I use at home, yeah.

On some of these most challenging next steps, for example,

stuff none of us know about that you’re working on,

you’re thinking,

there’s some deep thinking required there, right?

Like what is the right problem?

What is the right approach?

Because you’re gonna have to invest a huge amount of time

for the whole team.

They’re going to have to pursue this thing.

What’s the right way to do it?

Is RL gonna work here or not?

Yes.

What’s the right thing to try?

What’s the right benchmark to use?

Do we need to construct a benchmark from scratch?

All those kinds of things.

Yes.

So I think of all those kinds of things

in the nighttime phase, but also much more,

I find I’ve always found the quiet hours of the morning

when everyone’s asleep, it’s super quiet outside.

I love that time.

It’s the golden hours,

like between one and three in the morning.

Put some music on, some inspiring music on,

and then think these deep thoughts.

So that’s when I would read my philosophy books

and Spinoza’s, my recent favorite can, all these things.

And I read about a great scientist of history,

how they did things, how they thought things.

So that’s when you do all your creative,

that’s when I do all my creative thinking.

And it’s good, I think people recommend

you do your sort of creative thinking in one block.

And the way I organize the day,

that way I don’t get interrupted.

There’s obviously no one else is up at those times.

So I can go, I can sort of get super deep

and super into flow.

The other nice thing about doing it nighttime wise

is if I’m really onto something

or I’ve got really deep into something,

I can choose to extend it

and I’ll go into six in the morning, whatever.

And then I’ll just pay for it the next day.

So I’ll be a bit tired and I won’t be my best,

but that’s fine.

I can decide looking at my schedule the next day

and given where I’m at with this particular thought

or creative idea that I’m gonna pay that cost the next day.

So I think that’s more flexible than morning people

who do that, they get up at four in the morning.

They can also do those golden hours then,

but then their start of their scheduled day

starts at breakfast, 8 a.m.,

whatever they have their first meeting.

And then it’s hard, you have to reschedule a day

if you’re in flow.

So I don’t have to do that.

So that could be a true special thread of thoughts

that you’re too passionate about.

This is where some of the greatest ideas

could potentially come is when you just lose yourself

late into the night.

And for the meetings, I mean, you’re loading in

really hard problems in a very short amount of time.

So you have to do some kind of first principles thinking

here, it’s like, what’s the problem?

What’s the state of things?

What’s the right next steps?

You have to get really good at context switching,

which is one of the hardest things,

because especially as we do so many things,

if you include all the scientific things we do,

scientific fields we’re working in,

these are complex fields in themselves.

And you have to sort of keep abreast of that.

But I enjoy it.

I’ve always been a sort of generalist in a way.

And that’s actually what happened in my games career

after chess.

One of the reasons I stopped playing chess

was because I got into computers,

but also I started realizing there were many other

great games out there to play too.

So I’ve always been that way inclined, multidisciplinary.

And there’s too many interesting things in the world

to spend all your time just on one thing.

So you mentioned Spinoza, gotta ask the big, ridiculously

big question about life.

What do you think is the meaning of this whole thing?

Why are we humans here?

You’ve already mentioned that perhaps the universe

created us.

Is that why you think we’re here?

To understand how the universe works?

Yeah, I think my answer to that would be,

and at least the life I’m living,

is to gain and understand the knowledge,

to gain knowledge and understand the universe.

That’s what I think, I can’t see any higher purpose

than that if you think back to the classical Greeks,

the virtue of gaining knowledge.

It’s, I think it’s one of the few true virtues

is to understand the world around us

and the context and humanity better.

And I think if you do that, you become more compassionate

and more understanding yourself and more tolerant

and all these, I think all these other things

may flow from that.

And to me, understanding the nature of reality,

that is the biggest question.

What is going on here is sometimes the colloquial way I say.

What is really going on here?

It’s so mysterious.

I feel like we’re in some huge puzzle.

And it’s, but the world is also seems to be,

the universe seems to be structured in a way.

You know, why is it structured in a way

that science is even possible?

That, you know, methods, the scientific method works,

things are repeatable.

It feels like it’s almost structured in a way

to be conducive to gaining knowledge.

So I feel like, and you know,

why should computers be even possible?

Wasn’t that amazing that computational electronic devices

can be possible, and they’re made of sand,

our most common element that we have,

you know, silicon on the Earth’s crust.

It could have been made of diamond or something,

then we would have only had one computer.

So a lot of things are kind of slightly suspicious to me.

It sure as heck sounds, this puzzle sure as heck sounds

like something we talked about earlier,

what it takes to design a game that’s really fun to play

for prolonged periods of time.

And it does seem like this puzzle, like you mentioned,

the more you learn about it,

the more you realize how little you know.

So it humbles you, but excites you

by the possibility of learning more.

It’s one heck of a puzzle we got going on here.

So like I mentioned, of all the people in the world,

you’re very likely to be the one who creates the AGI system

that achieves human level intelligence and goes beyond it.

So if you got a chance and very well,

you could be the person that goes into the room

with the system and have a conversation.

Maybe you only get to ask one question.

If you do, what question would you ask her?

I would probably ask, what is the true nature of reality?

I think that’s the question.

I don’t know if I’d understand the answer

because maybe it would be 42 or something like that,

but that’s the question I would ask.

And then there’ll be a deep sigh from the systems,

like, all right, how do I explain to this human?

All right, let me, I don’t have time to explain.

Maybe I’ll draw you a picture that it is.

I mean, how do you even begin to answer that question?

Well, I think it would.

What would you think the answer could possibly look like?

I think it could start looking like

more fundamental explanations of physics

would be the beginning.

More careful specification of that,

taking you, walking us through by the hand

as to what one would do to maybe prove those things out.

Maybe giving you glimpses of what things

you totally miss in the physics of today.

Exactly, exactly.

Just here’s glimpses of, no, like there’s a much,

a much more elaborate world

or a much simpler world or something.

A much deeper, maybe simpler explanation of things,

right, than the standard model of physics,

which we know doesn’t work, but we still keep adding to.

So, and that’s how I think the beginning

of an explanation would look.

And it would start encompassing many of the mysteries

that we have wondered about for thousands of years,

like consciousness, dreaming, life, and gravity,

all of these things.

Yeah, giving us glimpses of explanations for those things.

Well, Damasir, one of the special human beings

in this giant puzzle of ours,

and it’s a huge honor that you would take a pause

from the bigger puzzle to solve this small puzzle

of a conversation with me today.

It’s truly an honor and a pleasure.

Thank you so much.

Thank you, I really enjoyed it.

Thanks, Lex.

Thanks for listening to this conversation

with Damas Ashabis.

To support this podcast,

please check out our sponsors in the description.

And now, let me leave you with some words

from Edgar Dykstra.

Computer science is no more about computers

than astronomy is about telescopes.

Thank you for listening, and hope to see you next time.