Max Tegmark: AI and Physics
Guests: Max Tegmark
Full transcript · Read the full conversation in order, with its original context.
Contents20 chapters
§01Transcript · 00:00–09:40§02Transcript · 09:40–18:26§03Transcript · 18:26–27:42§04Transcript · 27:42–36:45§05Transcript · 36:45–45:52§06Transcript · 45:52–55:06§07Transcript · 55:06–1:04:29§08Transcript · 1:04:29–1:12:49§09Transcript · 1:12:49–1:22:11§10Transcript · 1:22:11–1:31:43§11Transcript · 1:31:43–1:40:22§12Transcript · 1:40:22–1:49:51§13Transcript · 1:49:51–1:58:33§14Transcript · 1:58:33–2:07:40§15Transcript · 2:07:40–2:16:42§16Transcript · 2:16:42–2:25:38§17Transcript · 2:25:38–2:34:54§18Transcript · 2:34:54–2:43:58§19Transcript · 2:43:58–2:53:11§20Transcript · 2:53:11–3:02:46§01Transcript · 00:00–09:40
The following is a conversation with Max Tegmark,
his second time on the podcast.
In fact, the previous conversation
was episode number one of this very podcast.
He is a physicist and artificial intelligence researcher
at MIT, cofounder of the Future of Life Institute,
and author of Life 3.0,
Being Human in the Age of Artificial Intelligence.
He's also the head of a bunch of other huge,
fascinating projects and has written
a lot of different things
that you should definitely check out.
He has been one of the key humans
who has been outspoken about longterm existential risks
of AI and also its exciting possibilities
and solutions to real world problems.
Most recently at the intersection of AI and physics,
and also in reengineering the algorithms
that divide us by controlling the information we see
and thereby creating bubbles and all other kinds
of complex social phenomena that we see today.
In general, he's one of the most passionate
and brilliant people I have the fortune of knowing.
I hope to talk to him many more times
on this podcast in the future.
Quick mention of our sponsors,
The Jordan Harbinger Show,
Four Sigmatic Mushroom Coffee,
BetterHelp Online Therapy, and ExpressVPN.
So the choices, wisdom, caffeine, sanity, or privacy.
Choose wisely, my friends, and if you wish,
click the sponsor links below to get a discount
and to support this podcast.
As a side note, let me say that much of the researchers
in the machine learning
and artificial intelligence communities
do not spend much time thinking deeply
about existential risks of AI.
Because our current algorithms are seen as useful but dumb,
it's difficult to imagine how they may become destructive
to the fabric of human civilization
in the foreseeable future.
I understand this mindset, but it's very troublesome.
To me, this is both a dangerous and uninspiring perspective,
reminiscent of a lobster sitting in a pot of lukewarm water
that a minute ago was cold.
I feel a kinship with this lobster.
I believe that already the algorithms
that drive our interaction on social media
have an intelligence and power
that far outstrip the intelligence and power
of any one human being.
Now really is the time to think about this,
to define the trajectory of the interplay
of technology and human beings in our society.
I think that the future of human civilization
very well may be at stake over this very question
of the role of artificial intelligence in our society.
If you enjoy this thing, subscribe on YouTube,
review it on Apple Podcasts, follow on Spotify,
support on Patreon, or connect with me on Twitter
at Lex Friedman.
And now, here's my conversation with Max Tegmark.
So people might not know this,
but you were actually episode number one of this podcast
just a couple of years ago, and now we're back.
And it so happens that a lot of exciting things happened
in both physics and artificial intelligence,
both fields that you're super passionate about.
Can we try to catch up to some of the exciting things
happening in artificial intelligence,
especially in the context of the way it's cracking,
open the different problems of the sciences?
Yeah, I'd love to, especially now as we start 2021 here,
it's a really fun time to think about
what were the biggest breakthroughs in AI,
not the ones necessarily that media wrote about,
but that really matter, and what does that mean
for our ability to do better science?
What does it mean for our ability
to help people around the world?
And what does it mean for new problems
that they could cause if we're not smart enough
to avoid them, so what do we learn basically from this?
Yes, absolutely.
So one of the amazing things you're a part of
is the AI Institute for Artificial Intelligence
and Fundamental Interactions.
What's up with this institute?
What are you working on?
What are you thinking about?
The idea is something I'm very on fire with,
which is basically AI meets physics.
And it's been almost five years now
since I shifted my own MIT research
from physics to machine learning.
And in the beginning, I noticed that a lot of my colleagues,
even though they were polite about it,
were like kind of, what is Max doing?
What is this weird stuff?
He's lost his mind.
But then gradually, I, together with some colleagues,
were able to persuade more and more of the other professors
in our physics department to get interested in this.
And now we've got this amazing NSF Center,
so 20 million bucks for the next five years, MIT,
and a bunch of neighboring universities here also.
And I noticed now those colleagues
who were looking at me funny have stopped
asking what the point is of this,
because it's becoming more clear.
And I really believe that, of course,
AI can help physics a lot to do better physics.
But physics can also help AI a lot,
both by building better hardware.
My colleague, Marin Soljacic, for example,
is working on an optical chip for much faster machine
learning, where the computation is done
not by moving electrons around, but by moving photons around,
dramatically less energy use, faster, better.
We can also help AI a lot, I think,
by having a different set of tools
and a different, maybe more audacious attitude.
AI has, to a significant extent, been an engineering discipline
where you're just trying to make things that work
and being more interested in maybe selling them
than in figuring out exactly how they work
and proving theorems about that they will always work.
Contrast that with physics.
When Elon Musk sends a rocket to the International Space
Station, they didn't just train with machine learning.
Oh, let's fire it a little bit more to the left,
a bit more to the right.
Oh, that also missed.
Let's try here.
No, we figured out Newton's laws of gravitation and other things
and got a really deep fundamental understanding.
And that's what gives us such confidence in rockets.
And my vision is that in the future,
all machine learning systems that actually have impact
on people's lives will be understood
at a really, really deep level.
So we trust them, not because some sales rep told us to,
but because they've earned our trust.
And really safety critical things
even prove that they will always do what we expect them to do.
That's very much the physics mindset.
So it's interesting, if you look at big breakthroughs
that have happened in machine learning this year,
from dancing robots, it's pretty fantastic.
Not just because it's cool, but if you just
think about not that many years ago,
this YouTube video at this DARPA challenge with the MIT robot
comes out of the car and face plants.
How far we've come in just a few years.
Similarly, Alpha Fold 2, crushing the protein folding
problem.
We can talk more about implications
for medical research and stuff.
But hey, that's huge progress.
You can look at the GPT3 that can spout off
English text, which sometimes really, really blows you away.
You can look at DeepMind's MuZero,
which doesn't just kick our butt in Go and Chess and Shogi,
but also in all these Atari games.
And you don't even have to teach it the rules now.
What all of those have in common is, besides being powerful,
is we don't fully understand how they work.
And that's fine if it's just some dancing robots.
And the worst thing that can happen is they face plant.
Or if they're playing Go, and the worst thing that can happen
is that they make a bad move and lose the game.
It's less fine if that's what's controlling
your self driving car or your nuclear power plant.
And we've seen already that even though Hollywood
had all these movies where they try
to make us worry about the wrong things,
like machines turning evil, the actual bad things that
have happened with automation have not
been machines turning evil.
They've been caused by overtrust in things
we didn't understand as well as we thought we did.
Even very simple automated systems
like what Boeing put into the 737 MAX killed a lot of people.
Was it that that little simple system was evil?
Of course not.
But we didn't understand it as well as we should have.
And we trusted without understanding.
Exactly.
That's the overtrust.
We didn't even understand that we didn't understand.
The humility is really at the core of being a scientist.
I think step one, if you want to be a scientist,
is don't ever fool yourself into thinking you understand things
when you actually don't.
That's probably good advice for humans in general.
I think humility in general can do us good.
But in science, it's so spectacular.
Why did we have the wrong theory of gravity
ever from Aristotle onward until Galileo's time?
§02Transcript · 09:40–18:26
Why would we believe something so dumb as that if I throw
this water bottle, it's going to go up with constant speed
until it realizes that its natural motion is down?
It changes its mind.
Because people just kind of assumed Aristotle was right.
He's an authority.
We understand that.
Why did we believe things like that the sun is
going around the Earth?
Why did we believe that time flows
at the same rate for everyone until Einstein?
Same exact mistake over and over again.
We just weren't humble enough to acknowledge that we actually
didn't know for sure.
We assumed we knew.
So we didn't discover the truth because we
assumed there was nothing there to be discovered, right?
There was something to be discovered about the 737 Max.
And if you had been a bit more suspicious
and tested it better, we would have found it.
And it's the same thing with most harm
that's been done by automation so far, I would say.
So I don't know if you heard here of a company called
Knight Capital?
So good.
That means you didn't invest in them earlier.
They deployed this automated trading system,
all nice and shiny.
They didn't understand it as well as they thought.
And it went about losing $10 million
per minute for 44 minutes straight
until someone presumably was like, oh, no, shut this up.
Was it evil?
No.
It was, again, misplaced trust, something they didn't fully
understand, right?
And there have been so many, even when people
have been killed by robots, which is quite rare still,
but in factory accidents, it's in every single case
been not malice, just that the robot didn't understand
that a human is different from an auto part or whatever.
So this is why I think there's so much opportunity
for a physics approach, where you just aim for a higher
level of understanding.
And if you look at all these systems
that we talked about from reinforcement learning
systems and dancing robots to all these neural networks
that power GPT3 and go playing software and stuff,
they're all basically black boxes,
not so different from if you teach a human something,
you have no idea how their brain works, right?
Except the human brain, at least,
has been error corrected during many, many centuries
of evolution in a way that some of these systems have not,
right?
And my MIT research is entirely focused
on demystifying this black box, intelligible intelligence
is my slogan.
That's a good line, intelligible intelligence.
Yeah, that we shouldn't settle for something
that seems intelligent, but it should
be intelligible so that we actually trust it
because we understand it, right?
Like, again, Elon trusts his rockets
because he understands Newton's laws and thrust
and how everything works.
And can I tell you why I'm optimistic about this?
Yes.
I think we've made a bit of a mistake
where some people still think that somehow we're never going
to understand neural networks.
We're just going to have to learn to live with this.
It's this very powerful black box.
Basically, for those who haven't spent time
building their own, it's super simple what happens inside.
You send in a long list of numbers,
and then you do a bunch of operations on them,
multiply by matrices, et cetera, et cetera,
and some other numbers come out that's output of it.
And then there are a bunch of knobs you can tune.
And when you change them, it affects the computation,
the input output relation.
And then you just give the computer
some definition of good, and it keeps optimizing these knobs
until it performs as good as possible.
And often, you go like, wow, that's really good.
This robot can dance, or this machine
is beating me at chess now.
And in the end, you have something
which, even though you can look inside it,
you have very little idea of how it works.
You can print out tables of all the millions of parameters
in there.
Is it crystal clear now how it's working?
No, of course not.
Many of my colleagues seem willing to settle for that.
And I'm like, no, that's like the halfway point.
Some have even gone as far as sort of guessing
that the mistrutability of this is
where some of the power comes from,
and some sort of mysticism.
I think that's total nonsense.
I think the real power of neural networks
comes not from inscrutability, but from differentiability.
And what I mean by that is simply
that the output changes only smoothly if you tweak your knobs.
And then you can use all these powerful methods
we have for optimization in science.
We can just tweak them a little bit and see,
did that get better or worse?
That's the fundamental idea of machine learning,
that the machine itself can keep optimizing
until it gets better.
Suppose you wrote this algorithm instead in Python
or some other programming language,
and then what the knobs did was they just changed
random letters in your code.
Now it would just epically fail.
You change one thing, and instead of saying print,
it says, synth, syntax error.
You don't even know, was that for the better
or for the worse, right?
This, to me, is what I believe is
the fundamental power of neural networks.
And just to clarify, the changing
of the different letters in a program
would not be a differentiable process.
It would make it an invalid program, typically.
And then you wouldn't even know if you changed more letters
if it would make it work again, right?
So that's the magic of neural networks, the inscrutability.
The differentiability, that every setting of the parameters
is a program, and you can tell is it better or worse, right?
And so.
So you don't like the poetry of the mystery of neural networks
as the source of its power?
I generally like poetry, but.
Not in this case.
It's so misleading.
And above all, it shortchanges us.
It makes us underestimate the good things
we can accomplish.
So what we've been doing in my group
is basically step one, train the mysterious neural network
to do something well.
And then step two, do some additional AI techniques
to see if we can now transform this black box into something
equally intelligent that you can actually understand.
So for example, I'll give you one example, this AI Feynman
project that we just published, right?
So we took the 100 most famous or complicated equations
from one of my favorite physics textbooks,
in fact, the one that got me into physics
in the first place, the Feynman lectures on physics.
And so you have a formula.
Maybe it has what goes into the formula
as six different variables, and then what comes out as one.
So then you can make a giant Excel spreadsheet
with seven columns.
You put in just random numbers for the six columns
for those six input variables, and then you
calculate with a formula the seventh column, the output.
So maybe it's like the force equals in the last column
some function of the other.
And now the task is, OK, if I don't tell you
what the formula was, can you figure that out
from looking at my spreadsheet I gave you?
This problem is called symbolic regression.
If I tell you that the formula is
what we call a linear formula, so it's just
that the output is sum of all the things, input, the times,
some constants, that's the famous easy problem
we can solve.
We do it all the time in science and engineering.
But the general one, if it's more complicated functions
with logarithms or cosines or other math,
it's a very, very hard one and probably impossible
to do fast in general, just because the number of formulas
with n symbols just grows exponentially,
just like the number of passwords
you can make grow dramatically with length.
But we had this idea that if you first
have a neural network that can actually approximate
the formula, you just trained it,
even if you don't understand how it works,
that can be the first step towards actually understanding
how it works.
So that's what we do first.
And then we study that neural network now
and put in all sorts of other data
that wasn't in the original training data
and use that to discover simplifying
properties of the formula.
And that lets us break it apart, often
into many simpler pieces in a kind of divide
and conquer approach.
So we were able to solve all of those 100 formulas,
discover them automatically, plus a whole bunch
of other ones.
And it's actually kind of humbling
§03Transcript · 18:26–27:42
to see that this code, which anyone who wants now
is listening to this, can type pip install AI Feynman
on the computer and run it.
It can actually do what Johannes Kepler spent four years doing
when he stared at Mars data until he was like,
finally, Eureka, this is an ellipse.
This will do it automatically for you in one hour.
Or Max Planck, he was looking at how much radiation comes out
from different wavelengths from a hot object
and discovered the famous blackbody formula.
This discovers it automatically.
I'm actually excited about seeing
if we can discover not just old formulas again,
but new formulas that no one has seen before.
I do like this process of using kind of a neural network
to find some basic insights and then dissecting
the neural network to then gain the final.
So in that way, you've forcing the explainability issue,
really trying to analyze the neural network for the things
it knows in order to come up with the final beautiful,
simple theory underlying the initial system
that you were looking at.
I love that.
And the reason I'm so optimistic that it
can be generalized to so much more
is because that's exactly what we do as human scientists.
Think of Galileo, whom we mentioned, right?
I bet when he was a little kid, if his dad threw him an apple,
he would catch it.
Why?
Because he had a neural network in his brain
that he had trained to predict the parabolic orbit of apples
that are thrown under gravity.
If you throw a tennis ball to a dog,
it also has this same ability of deep learning
to figure out how the ball is going to move and catch it.
But Galileo went one step further when he got older.
He went back and was like, wait a minute.
I can write down a formula for this.
Y equals x squared, a parabola.
And he helped revolutionize physics as we know it, right?
So there was a basic neural network
in there from childhood that captured the experiences
of observing different kinds of trajectories.
And then he was able to go back in
with another extra little neural network
and analyze all those experiences and be like,
wait a minute.
There's a deeper rule here.
Exactly.
He was able to distill out in symbolic form
what that complicated black box neural network was doing.
Not only did the formula he got ultimately
become more accurate, and similarly, this
is how Newton got Newton's laws, which
is why Elon can send rockets to the space station now, right?
So it's not only more accurate, but it's also simpler,
much simpler.
And it's so simple that we can actually describe it
to our friends and each other, right?
We've talked about it just in the context of physics now.
But hey, isn't this what we're doing when we're
talking to each other also?
We go around with our neural networks,
just like dogs and cats and chipmunks and Blue Jays.
And we experience things in the world.
But then we humans do this additional step
on top of that, where we then distill out
certain high level knowledge that we've extracted from this
in a way that we can communicate it
to each other in a symbolic form in English in this case, right?
So if we can do it and we believe
that we are information processing entities,
then we should be able to make machine learning that
does it also.
Well, do you think the entire thing could be learning?
Because this dissection process, like for AI Feynman,
the secondary stage feels like something like reasoning.
And the initial step feels more like the more basic kind
of differentiable learning.
Do you think the whole thing could be differentiable
learning?
Do you think the whole thing could be basically neural
networks on top of each other?
It's like turtles all the way down.
Could it be neural networks all the way down?
I mean, that's a really interesting question.
We know that in your case, it is neural networks all the way
down because that's all you have in your skull
is a bunch of neurons doing their thing, right?
But if you ask the question more generally,
what algorithms are being used in your brain,
I think it's super interesting to compare.
I think we've gone a little bit backwards historically
because we humans first discovered good old fashioned
AI, the logic based AI that we often call GoFi
for good old fashioned AI.
And then more recently, we did machine learning
because it required bigger computers.
So we had to discover it later.
So we think of machine learning with neural networks
as the modern thing and the logic based AI
as the old fashioned thing.
But if you look at evolution on Earth,
it's actually been the other way around.
I would say that, for example, an eagle
has a better vision system than I have using.
And dogs are just as good at casting tennis balls as I am.
All this stuff which is done by training in neural network
and not interpreting it in words is
something so many of our animal friends can do,
at least as well as us, right?
What is it that we humans can do that the chipmunks
and the eagles cannot?
It's more to do with this logic based stuff, right,
where we can extract out information
in symbols, in language, and now even with equations
if you're a scientist, right?
So basically what happened was first we
built these computers that could multiply numbers real fast
and manipulate symbols.
And we felt they were pretty dumb.
And then we made neural networks that
can see as well as a cat can and do
a lot of this inscrutable black box neural networks.
What we humans can do also is put the two together
in a useful way.
Yes, in our own brain.
Yes, in our own brain.
So if we ever want to get artificial general intelligence
that can do all jobs as well as humans can, right,
then that's what's going to be required
to be able to combine the neural networks with symbolic,
combine the old AI with the new AI in a good way.
We do it in our brains.
And there seems to be basically two strategies
I see in industry now.
One scares the heebie jeebies out of me,
and the other one I find much more encouraging.
OK, which one?
Can we break them apart?
Which of the two?
The one that scares the heebie jeebies out of me
is this attitude that we're just going
to make ever bigger systems that we still
don't understand until they can be as smart as humans.
What could possibly go wrong?
I think it's just such a reckless thing to do.
And unfortunately, if we actually
succeed as a species to build artificial general intelligence,
then we still have no clue how it works.
I think at least 50% chance we're
going to be extinct before too long.
It's just going to be an utter epic own goal.
So it's that 44 minute losing money problem or the paper clip
problem where we don't understand how it works,
and it just in a matter of seconds
runs away in some kind of direction
that's going to be very problematic.
Even long before, you have to worry about the machines
themselves somehow deciding to do things.
And to us, we have to worry about people using machines
that are short of AGI and power to do bad things.
I mean, just take a moment.
And if anyone is not worried particularly about advanced AI,
just take 10 seconds and just think
about your least favorite leader on the planet right now.
Don't tell me who it is.
I want to keep this apolitical.
But just see the face in front of you,
that person, for 10 seconds.
Now imagine that that person has this incredibly powerful AI
under their control and can use it
to impose their will on the whole planet.
How does that make you feel?
Yeah.
So can we break that apart just briefly?
For the 50% chance that we'll run
to trouble with this approach, do you
see the bigger worry in that leader or humans
using the system to do damage?
Or are you more worried, and I think I'm in this camp,
more worried about accidental, unintentional destruction
of everything?
So humans trying to do good, and in a way
where everyone agrees it's kind of good,
it's just they're trying to do good without understanding.
Because I think every evil leader in history
thought they're, to some degree, thought
they're trying to do good.
Oh, yeah.
I'm sure Hitler thought he was doing good.
Yeah.
I've been reading a lot about Stalin.
I'm sure Stalin is from, he legitimately
thought that communism was good for the world,
and that he was doing good.
I think Mao Zedong thought what he was doing with the Great
Leap Forward was good too.
Yeah.
§04Transcript · 27:42–36:45
I'm actually concerned about both of those.
Before, I promised to answer this in detail,
but before we do that, let me finish
answering the first question.
Because I told you that there were two different routes we
could get to artificial general intelligence,
and one scares the hell out of me,
which is this one where we build something,
we just say bigger neural networks, ever more hardware,
and just train the heck out of more data,
and poof, now it's very powerful.
That, I think, is the most unsafe and reckless approach.
The alternative to that is the intelligible intelligence
approach instead, where we say neural networks is just
a tool for the first step to get the intuition,
but then we're going to spend also
serious resources on other AI techniques
for demystifying this black box and figuring out
what it's actually doing so we can convert it
into something that's equally intelligent,
but that we actually understand what it's doing.
Maybe we can even prove theorems about it,
that this car here will never be hacked when it's driving,
because here is the proof.
There is a whole science of this.
It doesn't work for neural networks
that are big black boxes, but it works well
and works with certain other kinds of codes, right?
That approach, I think, is much more promising.
That's exactly why I'm working on it, frankly,
not just because I think it's cool for science,
but because I think the more we understand these systems,
the better the chances that we can
make them do the things that are good for us
that are actually intended, not unintended.
So you think it's possible to prove things
about something as complicated as a neural network?
That's the hope?
Well, ideally, there's no reason it
has to be a neural network in the end either, right?
We discovered Newton's laws of gravity
with neural network in Newton's head.
But that's not the way it's programmed into the navigation
system of Elon Musk's rocket anymore.
It's written in C++, or I don't know
what language he uses exactly.
And then there are software tools called symbolic
verification.
DARPA and the US military has done a lot of really great
research on this, because they really
want to understand that when they build weapon systems,
they don't just go fire at random or malfunction, right?
And there is even a whole operating system
called Cell 3 that's been developed by a DARPA grant,
where you can actually mathematically prove
that this thing can never be hacked.
Wow.
One day, I hope that will be something
you can say about the OS that's running on our laptops too.
As you know, we're not there.
But I think we should be ambitious, frankly.
And if we can use machine learning
to help do the proofs and so on as well,
then it's much easier to verify that a proof is correct
than to come up with a proof in the first place.
That's really the core idea here.
If someone comes on your podcast and says
they proved the Riemann hypothesis
or some sensational new theorem, it's
much easier for someone else, take some smart grad,
math grad students to check, oh, there's an error here
on equation five, or this really checks out,
than it was to discover the proof.
Yeah, although some of those proofs are pretty complicated.
But yes, it's still nevertheless much easier
to verify the proof.
I love the optimism.
We kind of, even with the security of systems,
there's a kind of cynicism that pervades people
who think about this, which is like, oh, it's hopeless.
I mean, in the same sense, exactly like you're saying
when you own networks, oh, it's hopeless to understand
what's happening.
With security, people are just like, well,
it's always going, there's always going to be
attack vectors, like ways to attack the system.
But you're right, we're just very new
with these computational systems.
We're new with these intelligent systems.
And it's not out of the realm of possibly,
just like people that understand the movement
of the stars and the planets and so on.
It's entirely possible that within, hopefully soon,
but it could be within 100 years,
we start to have an obvious laws of gravity
about intelligence and God forbid about consciousness too.
That one is...
Agreed.
I think, of course, if you're selling computers
that get hacked a lot, that's in your interest
as a company that people think it's impossible
to make it safe, but he's going to get the idea
of suing you.
I want to really inject optimism here.
It's absolutely possible to do much better
than we're doing now.
And your laptop does so much stuff.
You don't need the music player to be super safe
in your future self driving car, right?
If someone hacks it and starts playing music
you don't like, the world won't end.
But what you can do is you can break out
and say that your drive computer that controls your safety
must be completely physically decoupled entirely
from the entertainment system.
And it must physically be such that it can't take on
over the air updates while you're driving.
And it can have ultimately some operating system on it
which is symbolically verified and proven
that it's always going to do what it's supposed to do, right?
We can basically have, and companies should take
that attitude too.
They should look at everything they do and say
what are the few systems in our company
that threaten the whole life of the company
if they get hacked and have the highest standards for them.
And then they can save money by going for the el cheapo
poorly understood stuff for the rest.
This is very feasible, I think.
And coming back to the bigger question
that you worried about that there'll be unintentional
failures, I think there are two quite separate risks here.
Right?
We talked a lot about one of them
which is that the goals are noble of the human.
The human says, I want this airplane to not crash
because this is not Muhammad Atta
now flying the airplane, right?
And now there's this technical challenge
of making sure that the autopilot is actually
gonna behave as the pilot wants.
If you set that aside, there's also the separate question.
How do you make sure that the goals of the pilot
are actually aligned with the goals of the passenger?
How do you make sure very much more broadly
that if we can all agree as a species
that we would like things to kind of go well
for humanity as a whole, that the goals are aligned here.
The alignment problem.
And yeah, there's been a lot of progress
in the sense that there's suddenly huge amounts
of research going on on it about it.
I'm very grateful to Elon Musk
for giving us that money five years ago
so we could launch the first research program
on technical AI safety and alignment.
There's a lot of stuff happening.
But I think we need to do more than just make sure
little machines do always what their owners do.
That wouldn't have prevented September 11th
if Muhammad Atta said, okay, autopilot,
please fly into World Trade Center.
And it's like, okay.
That even happened in a different situation.
There was this depressed pilot named Andreas Lubitz, right?
Who told his German wings passenger jet
to fly into the Alps.
He just told the computer to change the altitude
to a hundred meters or something like that.
And you know what the computer said?
Okay.
And it had the freaking topographical map of the Alps
in there, it had GPS, everything.
No one had bothered teaching it
even the basic kindergarten ethics of like,
no, we never want airplanes to fly into mountains
under any circumstances.
And so we have to think beyond just the technical issues
and think about how do we align in general incentives
on this planet for the greater good?
So starting with simple stuff like that,
every airplane that has a computer in it
should be taught whatever kindergarten ethics
that's smart enough to understand.
Like, no, don't fly into fixed objects
if the pilot tells you to do so.
Then go on autopilot mode.
Send an email to the cops and land at the latest airport,
nearest airport, you know.
Any car with a forward facing camera
should just be programmed by the manufacturer
so that it will never accelerate into a human ever.
That would avoid things like the NIS attack
and many horrible terrorist vehicle attacks
where they deliberately did that, right?
This was not some sort of thing,
oh, you know, US and China, different views on,
no, there was not a single car manufacturer
in the world, right, who wanted the cars to do this.
They just hadn't thought to do the alignment.
§05Transcript · 36:45–45:52
And if you look at more broadly problems
that happen on this planet,
the vast majority have to do a poor alignment.
I mean, think about, let's go back really big
because I know you're so good at that.
Let's go big, yeah.
Yeah, so long ago in evolution, we had these genes.
And they wanted to make copies of themselves.
That's really all they cared about.
So some genes said, hey, I'm gonna build a brain
on this body I'm in so that I can get better
at making copies of myself.
And then they decided for their benefit
to get copied more, to align your brain's incentives
with their incentives.
So it didn't want you to starve to death.
So it gave you an incentive to eat
and it wanted you to make copies of the genes.
So it gave you incentive to fall in love
and do all sorts of naughty things
to make copies of itself, right?
So that was successful value alignment done on the genes.
They created something more intelligent than themselves,
but they made sure to try to align the values.
But then something went a little bit wrong
against the idea of what the genes wanted
because a lot of humans discovered,
hey, you know, yeah, we really like this business
about sex that the genes have made us enjoy,
but we don't wanna have babies right now.
So we're gonna hack the genes and use birth control.
And I really feel like drinking a Coca Cola right now,
but I don't wanna get a potbelly,
so I'm gonna drink Diet Coke.
We have all these things we've figured out
because we're smarter than the genes,
how we can actually subvert their intentions.
So it's not surprising that we humans now,
when we are in the role of these genes,
creating other nonhuman entities with a lot of power,
have to face the same exact challenge.
How do we make other powerful entities
have incentives that are aligned with ours?
And so they won't hack them.
Corporations, for example, right?
We humans decided to create corporations
because it can benefit us greatly.
Now all of a sudden there's a supermarket.
I can go buy food there.
I don't have to hunt.
Awesome, and then to make sure that this corporation
would do things that were good for us and not bad for us,
we created institutions to keep them in check.
Like if the local supermarket sells poisonous food,
then the owners of the supermarket
have to spend some years reflecting behind bars, right?
So we created incentives to align them.
But of course, just like we were able to see
through this thing and you develop birth control,
if you're a powerful corporation,
you also have an incentive to try to hack the institutions
that are supposed to govern you.
Because you ultimately, as a corporation,
have an incentive to maximize your profit.
Just like you have an incentive
to maximize the enjoyment your brain has,
not for your genes.
So if they can figure out a way of bribing regulators,
then they're gonna do that.
In the US, we kind of caught onto that
and made laws against corruption and bribery.
Then in the late 1800s, Teddy Roosevelt realized that,
no, we were still being kind of hacked
because the Massachusetts Railroad companies
had like a bigger budget than the state of Massachusetts
and they were doing a lot of very corrupt stuff.
So he did the whole trust busting thing
to try to align these other nonhuman entities,
the companies, again,
more with the incentives of Americans as a whole.
It's not surprising, though,
that this is a battle you have to keep fighting.
Now we have even larger companies than we ever had before.
And of course, they're gonna try to, again,
subvert the institutions.
Not because, I think people make a mistake
of getting all too,
thinking about things in terms of good and evil.
Like arguing about whether corporations are good or evil,
or whether robots are good or evil.
A robot isn't good or evil, it's a tool.
And you can use it for great things
like robotic surgery or for bad things.
And a corporation also is a tool, of course.
And if you have good incentives to the corporation,
it'll do great things,
like start a hospital or a grocery store.
If you have any bad incentives,
then it's gonna start maybe marketing addictive drugs
to people and you'll have an opioid epidemic, right?
It's all about,
we should not make the mistake of getting into
some sort of fairytale, good, evil thing
about corporations or robots.
We should focus on putting the right incentives in place.
My optimistic vision is that if we can do that,
then we can really get good things.
We're not doing so great with that right now,
either on AI, I think,
or on other intelligent nonhuman entities,
like big companies, right?
We just have a new second generation of AI
and a secretary of defense who's gonna start up now
in the Biden administration,
who was an active member of the board of Raytheon,
for example.
So, I have nothing against Raytheon.
I'm not a pacifist,
but there's an obvious conflict of interest
if someone is in the job where they decide
who they're gonna contract with.
And I think somehow we have,
maybe we need another Teddy Roosevelt to come along again
and say, hey, you know,
we want what's good for all Americans,
and we need to go do some serious realigning again
of the incentives that we're giving to these big companies.
And then we're gonna be better off.
It seems that naturally with human beings,
just like you beautifully described the history
of this whole thing,
of it all started with the genes
and they're probably pretty upset
by all the unintended consequences that happened since.
But it seems that it kind of works out,
like it's in this collective intelligence
that emerges at the different levels.
It seems to find sometimes last minute
a way to realign the values or keep the values aligned.
It's almost, it finds a way,
like different leaders, different humans pop up
all over the place that reset the system.
Do you want, I mean, do you have an explanation why that is?
Or is that just survivor bias?
And also is that different,
somehow fundamentally different than with AI systems
where you're no longer dealing with something
that was a direct, maybe companies are the same,
a direct byproduct of the evolutionary process?
I think there is one thing which has changed.
That's why I'm not all optimistic.
That's why I think there's about a 50% chance
if we take the dumb route with artificial intelligence
that humanity will be extinct in this century.
First, just the big picture.
Yeah, companies need to have the right incentives.
Even governments, right?
We used to have governments,
usually there were just some king,
who was the king because his dad was the king.
And then there were some benefits
of having this powerful kingdom or empire of any sort
because then it could prevent a lot of local squabbles.
So at least everybody in that region
would stop warring against each other.
And their incentives of different cities in the kingdom
became more aligned, right?
That was the whole selling point.
Harare, Noel Harare has a beautiful piece
on how empires were collaboration enablers.
And then we also, Harare says,
invented money for that reason
so we could have better alignment
and we could do trade even with people we didn't know.
So this sort of stuff has been playing out
since time immemorial, right?
What's changed is that it happens on ever larger scales,
right?
The technology keeps getting better
because science gets better.
So now we can communicate over larger distances,
transport things fast over larger distances.
And so the entities get ever bigger,
but our planet is not getting bigger anymore.
So in the past, you could have one experiment
that just totally screwed up like Easter Island,
where they actually managed to have such poor alignment
that when they went extinct, people there,
there was no one else to come back and replace them, right?
If Elon Musk doesn't get us to Mars
and then we go extinct on a global scale,
then we're not coming back.
That's the fundamental difference.
And that's a mistake we don't make for that reason.
In the past, of course, history is full of fiascos, right?
But it was never the whole planet.
And then, okay, now there's this nice uninhabited land here.
Some other people could move in and organize things better.
This is different.
The second thing, which is also different
§06Transcript · 45:52–55:06
is that technology gives us so much more empowerment, right?
Both to do good things and also to screw up.
In the stone age, even if you had someone
whose goals were really poorly aligned,
like maybe he was really pissed off
because his stone age girlfriend dumped him
and he just wanted to,
if he wanted to kill as many people as he could,
how many could he really take out with a rock and a stick
before he was overpowered, right?
Just handful, right?
Now, with today's technology,
if we have an accidental nuclear war
between Russia and the US,
which we almost have about a dozen times,
and then we have a nuclear winter,
it could take out seven billion people
or six billion people, we don't know.
So the scale of the damage is bigger that we can do.
And there's obviously no law of physics
that says that technology will never get powerful enough
that we could wipe out our species entirely.
That would just be fantasy to think
that science is somehow doomed
to not get more powerful than that, right?
And it's not at all unfeasible in our lifetime
that someone could design a designer pandemic
which spreads as easily as COVID,
but just basically kills everybody.
We already had smallpox.
It killed one third of everybody who got it.
What do you think of the, here's an intuition,
maybe it's completely naive
and this optimistic intuition I have,
which it seems, and maybe it's a biased experience
that I have, but it seems like the most brilliant people
I've met in my life all are really like
fundamentally good human beings.
And not like naive good, like they really wanna do good
for the world in a way that, well, maybe is aligned
to my sense of what good means.
And so I have a sense that the people
that will be defining the very cutting edge of technology,
there'll be much more of the ones that are doing good
versus the ones that are doing evil.
So the race, I'm optimistic on the,
us always like last minute coming up with a solution.
So if there's an engineered pandemic
that has the capability to destroy
most of the human civilization,
it feels like to me either leading up to that before
or as it's going on, there will be,
we're able to rally the collective genius
of the human species.
I can tell by your smile that you're
at least some percentage doubtful,
but could that be a fundamental law of human nature?
That evolution only creates, like karma is beneficial,
good is beneficial, and therefore we'll be all right.
I hope you're right.
I would really love it if you're right,
if there's some sort of law of nature that says
that we always get lucky in the last second
with karma, but I prefer not playing it so close
and gambling on that.
And I think, in fact, I think it can be dangerous
to have too strong faith in that
because it makes us complacent.
Like if someone tells you, you never have to worry
about your house burning down,
then you're not gonna put in a smoke detector
because why would you need to?
Even if it's sometimes very simple precautions,
we don't take them.
If you're like, oh, the government is gonna take care
of everything for us, I can always trust my politicians.
I can always, we advocate our own responsibility.
I think it's a healthier attitude to say,
yeah, maybe things will work out.
Maybe I'm actually gonna have to myself step up
and take responsibility.
And the stakes are so huge.
I mean, if we do this right, we can develop
all this ever more powerful technology
and cure all diseases and create a future
where humanity is healthy and wealthy
for not just the next election cycle,
but like billions of years throughout our universe.
That's really worth working hard for
and not just sitting and hoping
for some sort of fairytale karma.
Well, I just mean, so you're absolutely right.
From the perspective of the individual,
like for me, the primary thing should be
to take responsibility and to build the solutions
that your skillset allows.
Yeah, which is a lot.
I think we underestimate often very much
how much good we can do.
If you or anyone listening to this
is completely confident that our government
would do a perfect job on handling any future crisis
with engineered pandemics or future AI,
I actually reflect a bit on what actually happened in 2020.
Do you feel that the government by and large
around the world has handled this flawlessly?
That's a really sad and disappointing reality
that hopefully is a wake up call for everybody.
For the scientists, for the engineers,
for the researchers in AI especially,
it was disappointing to see how inefficient we were
at collecting the right amount of data
in a privacy preserving way and spreading that data
and utilizing that data to make decisions,
all that kind of stuff.
Yeah, I think when something bad happens to me,
I made myself a promise many years ago
that I would not be a whiner.
So when something bad happens to me,
of course it's a process of disappointment,
but then I try to focus on what did I learn from this
that can make me a better person in the future.
And there's usually something to be learned when I fail.
And I think we should all ask ourselves,
what can we learn from the pandemic
about how we can do better in the future?
And you mentioned there a really good lesson.
We were not as resilient as we thought we were
and we were not as prepared maybe as we wish we were.
You can even see very stark contrast around the planet.
South Korea, they have over 50 million people.
Do you know how many deaths they have from COVID
last time I checked?
No.
It's about 500.
Why is that?
Well, the short answer is that they had prepared.
They were incredibly quick,
incredibly quick to get on it
with very rapid testing and contact tracing and so on,
which is why they never had more cases
than they could contract trace effectively, right?
They never even had to have the kind of big lockdowns
we had in the West.
But the deeper answer to,
it's not just the Koreans are just somehow better people.
The reason I think they were better prepared
was because they had already had a pretty bad hit
from the SARS pandemic,
or which never became a pandemic,
something like 17 years ago, I think.
So it was kind of fresh memory
that we need to be prepared for pandemics.
So they were, right?
So maybe this is a lesson here
for all of us to draw from COVID
that rather than just wait for the next pandemic
or the next problem with AI getting out of control
or anything else,
maybe we should just actually set aside
a tiny fraction of our GDP
to have people very systematically
do some horizon scanning and say,
okay, what are the things that could go wrong?
And let's duke it out and see
which are the more likely ones
and which are the ones that are actually actionable
and then be prepared.
So one of the observations as one little ant slash human
that I am of disappointment
is the political division over information
that has been observed, that I observed this year,
that it seemed the discussion was less about
sort of what happened and understanding
what happened deeply and more about
there's different truths out there.
And it's like an argument,
my truth is better than your truth.
And it's like red versus blue or different.
It was like this ridiculous discourse
that doesn't seem to get at any kind of notion of the truth.
It's not like some kind of scientific process.
Even science got politicized in ways
that's very heartbreaking to me.
You have an exciting project on the AI front
of trying to rethink one of the,
you mentioned corporations.
There's one of the other collective intelligence systems
that have emerged through all of this is social networks.
And just the spread, the internet is the spread
of information on the internet,
our ability to share that information.
There's all different kinds of news sources and so on.
And so you said like that's from first principles,
let's rethink how we think about the news,
how we think about information.
Can you talk about this amazing effort
that you're undertaking?
Oh, I'd love to.
This has been my big COVID project
§07Transcript · 55:06–1:04:29
and nights and weekends on ever since the lockdown.
To segue into this actually,
let me come back to what you said earlier
that you had this hope that in your experience,
people who you felt were very talented
were often idealistic and wanted to do good.
Frankly, I feel the same about all people by and large,
there are always exceptions,
but I think the vast majority of everybody,
regardless of education and whatnot,
really are fundamentally good, right?
So how can it be that people still do so much nasty stuff?
I think it has everything to do with this,
with the information that we're given.
Yes.
If you go into Sweden 500 years ago
and you start telling all the farmers
that those Danes in Denmark,
they're so terrible people, and we have to invade them
because they've done all these terrible things
that you can't fact check yourself.
A lot of people, Swedes did that, right?
And we're seeing so much of this today in the world,
both geopolitically, where we are told that China is bad
and Russia is bad and Venezuela is bad,
and people in those countries are often told
that we are bad.
And we also see it at a micro level where people are told
that, oh, those who voted for the other party are bad people.
It's not just an intellectual disagreement,
but they're bad people and we're getting ever more divided.
So how do you reconcile this with this intrinsic goodness
in people?
I think it's pretty obvious that it has, again,
to do with the information that we're fed and given, right?
We evolved to live in small groups
where you might know 30 people in total, right?
So you then had a system that was quite good
for assessing who you could trust and who you could not.
And if someone told you that Joe there is a jerk,
but you had interacted with him yourself
and seen him in action,
and you would quickly realize maybe
that that's actually not quite accurate, right?
But now that the most people on the planet
are people we've never met,
it's very important that we have a way
of trusting the information we're given.
And so, okay, so where does the news project come in?
Well, throughout history, you can go read Machiavelli,
from the 1400s, and you'll see how already then
they were busy manipulating people
with propaganda and stuff.
Propaganda is not new at all.
And the incentives to manipulate people
is just not new at all.
What is it that's new?
What's new is machine learning meets propaganda.
That's what's new.
That's why this has gotten so much worse.
Some people like to blame certain individuals,
like in my liberal university bubble,
many people blame Donald Trump and say it was his fault.
I see it differently.
I think Donald Trump just had this extreme skill
at playing this game in the machine learning algorithm age.
A game he couldn't have played 10 years ago.
So what's changed?
What's changed is, well, Facebook and Google
and other companies, and I'm not badmouthing them,
I have a lot of friends who work for these companies,
good people, they deployed machine learning algorithms
just to increase their profit a little bit,
to just maximize the time people spent watching ads.
And they had totally underestimated
how effective they were gonna be.
This was, again, the black box, non intelligible intelligence.
They just noticed, oh, we're getting more ad revenue.
Great.
It took a long time until they even realized why and how
and how damaging this was for society.
Because of course, what the machine learning figured out
was that the by far most effective way of gluing you
to your little rectangle was to show you things
that triggered strong emotions, anger, et cetera, resentment,
and if it was true or not, it didn't really matter.
It was also easier to find stories that weren't true.
If you weren't limited, that's just the limitation,
is to show people.
That's a very limiting fact.
And before long, we got these amazing filter bubbles
on a scale we had never seen before.
A couple of days to the fact that also the online news media
were so effective that they killed a lot of people
that were so effective that they killed a lot of print
journalism.
There's less than half as many journalists
now in America, I believe, as there was a generation ago.
You just couldn't compete with the online advertising.
So all of a sudden, most people are not
getting even reading newspapers.
They get their news from social media.
And most people only get news in their little bubble.
So along comes now some people like Donald Trump,
who figured out, among the first successful politicians,
to figure out how to really play this new game
and become very, very influential.
But I think Donald Trump was as simple.
He took advantage of it.
He didn't create the fundamental conditions
were created by machine learning taking over the news media.
So this is what motivated my little COVID project here.
So I said before, machine learning and tech in general
is not evil, but it's also not good.
It's just a tool that you can use
for good things or bad things.
And as it happens, machine learning and news
was mainly used by the big players, big tech,
to manipulate people and to watch as many ads as possible,
which had this unintended consequence of really screwing
up our democracy and fragmenting it into filter bubbles.
So I thought, well, machine learning algorithms
are basically free.
They can run on your smartphone for free also
if someone gives them away to you, right?
There's no reason why they only have to help the big guy
to manipulate the little guy.
They can just as well help the little guy
to see through all the manipulation attempts
from the big guy.
So this project is called,
you can go to improvethenews.org.
The first thing we've built is this little news aggregator.
Looks a bit like Google News,
except it has these sliders on it to help you break out
of your filter bubble.
So if you're reading, you can click, click
and go to your favorite topic.
And then if you just slide the left, right slider
away all the way over to the left.
There's two sliders, right?
Yeah, there's the one, the most obvious one
is the one that has left, right labeled on it.
You go to the left, you get one set of articles,
you go to the right, you see a very different truth
appearing.
Oh, that's literally left and right on the political spectrum.
On the political spectrum.
So if you're reading about immigration, for example,
it's very, very noticeable.
And I think step one always,
if you wanna not get manipulated is just to be able
to recognize the techniques people use.
So it's very helpful to just see how they spin things
on the two sides.
I think many people are under the misconception
that the main problem is fake news.
It's not.
I had an amazing team of MIT students
where we did an academic project to use machine learning
to detect the main kinds of bias over the summer.
And yes, of course, sometimes there's fake news
where someone just claims something that's false, right?
Like, oh, Hillary Clinton just got divorced or something.
But what we see much more of is actually just omissions.
If you go to, there's some stories which just won't be
mentioned by the left or the right, because it doesn't suit
their agenda.
And then they'll mention other ones very, very, very much.
So for example, we've had a number of stories
about the Trump family's financial dealings.
And then there's been a bunch of stories
about the Biden family's, Hunter Biden's financial dealings.
Surprise, surprise, they don't get equal coverage
on the left and the right.
One side loves to cover the Biden, Hunter Biden's stuff,
and one side loves to cover the Trump.
You can never guess which is which, right?
But the great news is if you're a normal American citizen
and you dislike corruption in all its forms,
then slide, slide, you can just look at both sides
and you'll see all those political corruption stories.
It's really liberating to just take in the both sides,
the spin on both sides.
It somehow unlocks your mind to think on your own,
to realize that, I don't know, it's the same thing
that was useful, right, in the Soviet Union times
for when everybody was much more aware
that they're surrounded by propaganda, right?
That is so interesting what you're saying, actually.
So Noam Chomsky, used to be our MIT colleague,
once said that propaganda is to democracy
what violence is to totalitarianism.
And what he means by that is if you have
a really totalitarian government,
you don't need propaganda.
People will do what you want them to do anyway,
but out of fear, right?
But otherwise, you need propaganda.
So I would say actually that the propaganda
§08Transcript · 1:04:29–1:12:49
is much higher quality in democracies,
much more believable.
And it's really, it's really striking.
When I talk to colleagues, science colleagues
like from Russia and China and so on,
I notice they are actually much more aware
of the propaganda in their own media
than many of my American colleagues are
about the propaganda in Western media.
That's brilliant.
That means the propaganda in the Western media
is just better.
Yes.
That's so brilliant.
Everything's better in the West, even the propaganda.
But once you realize that,
you realize there's also something very optimistic there
that you can do about it, right?
Because first of all, omissions,
as long as there's no outright censorship,
you can just look at both sides
and pretty quickly piece together
a much more accurate idea of what's actually going on, right?
And develop a natural skepticism too.
Yeah.
Just an analytical scientific mind
about the way you're taking the information.
Yeah.
And I think, I have to say,
sometimes I feel that some of us in the academic bubble
are too arrogant about this and somehow think,
oh, it's just people who aren't as educated
as the dots are pulled.
When we are often just as gullible also,
we read only our media and don't see through things.
Anyone who looks at both sides like this
and compares a little will immediately start noticing
the shenanigans being pulled.
And I think what I tried to do with this app
is that the big tech has to some extent
tried to blame the individual for being manipulated,
much like big tobacco tried to blame the individuals
entirely for smoking.
And then later on, our government stepped up and say,
actually, you can't just blame little kids
for starting to smoke.
We have to have more responsible advertising
and this and that.
I think it's a bit the same here.
It's very convenient for a big tech to blame.
So it's just people who are so dumb and get fooled.
The blame usually comes in saying,
oh, it's just human psychology.
People just wanna hear what they already believe.
But professor David Rand at MIT actually partly debunked that
with a really nice study showing that people
tend to be interested in hearing things
that go against what they believe,
if it's presented in a respectful way.
Suppose, for example, that you have a company
and you're just about to launch this project
and you're convinced it's gonna work.
And someone says, you know, Lex,
I hate to tell you this, but this is gonna fail.
And here's why.
Would you be like, shut up, I don't wanna hear it.
La, la, la, la, la, la, la, la, la.
Would you?
You would be interested, right?
And also if you're on an airplane,
back in the pre COVID times,
and the guy next to you
is clearly from the opposite side of the political spectrum,
but is very respectful and polite to you.
Wouldn't you be kind of interested to hear a bit about
how he or she thinks about things?
Of course.
But it's not so easy to find out
respectful disagreement now,
because like, for example, if you are a Democrat
and you're like, oh, I wanna see something
on the other side,
so you just go Breitbart.com.
And then after the first 10 seconds,
you feel deeply insulted by something.
And they, it's not gonna work.
Or if you take someone who votes Republican
and they go to something on the left,
then they just get very offended very quickly
by them having put a deliberately ugly picture
of Donald Trump on the front page or something.
It doesn't really work.
So this news aggregator also has this nuance slider,
which you can pull to the right
and then sort of make it easier to get exposed
to actually more sort of academic style
or more respectful,
portrayals of different views.
And finally, the one kind of bias
I think people are mostly aware of is the left, right,
because it's so obvious,
because both left and right are very powerful here, right?
Both of them have well funded TV stations and newspapers,
and it's kind of hard to miss.
But there's another one, the establishment slider,
which is also really fun.
I love to play with it.
And that's more about corruption.
Yeah, yeah.
I love that one. Yes.
Because if you have a society
where almost all the powerful entities
want you to believe a certain thing,
that's what you're gonna read in both the big media,
mainstream media on the left and on the right, of course.
And the powerful companies can push back very hard,
like tobacco companies push back very hard
back in the day when some newspapers
started writing articles about tobacco being dangerous,
so that it was hard to get a lot of coverage
about it initially.
And also if you look geopolitically, right,
of course, in any country, when you read their media,
you're mainly gonna be reading a lot of articles
about how our country is the good guy
and the other countries are the bad guys, right?
So if you wanna have a really more nuanced understanding,
like the Germans used to be told that the British
used to be told that the French were the bad guys
and the French used to be told
that the British were the bad guys.
Now they visit each other's countries a lot
and have a much more nuanced understanding.
I don't think there's gonna be any more wars
between France and Germany.
But on the geopolitical scale,
it's just as much as ever, you know,
big Cold War, now US, China, and so on.
And if you wanna get a more nuanced understanding
of what's happening geopolitically,
then it's really fun to look at this establishment slider
because it turns out there are tons of little newspapers,
both on the left and on the right,
who sometimes challenge establishment and say,
you know, maybe we shouldn't actually invade Iraq right now.
Maybe this weapons of mass destruction thing is BS.
If you look at the journalism research afterwards,
you can actually see that quite clearly.
Both CNN and Fox were very pro.
Let's get rid of Saddam.
There are weapons of mass destruction.
Then there were a lot of smaller newspapers.
They were like, wait a minute,
this evidence seems a bit sketchy and maybe we...
But of course they were so hard to find.
Most people didn't even know they existed, right?
Yet it would have been better for American national security
if those voices had also come up.
I think it harmed America's national security actually
that we invaded Iraq.
And arguably there's a lot more interest
in that kind of thinking too, from those small sources.
So like when you say big,
it's more about kind of the reach of the broadcast,
but it's not big in terms of the interest.
I think there's a lot of interest
in that kind of anti establishment
or like skepticism towards, you know,
out of the box thinking.
There's a lot of interest in that kind of thing.
Do you see this news project or something like it
being basically taken over the world
as the main way we consume information?
Like how do we get there?
Like how do we, you know?
So, okay, the idea is brilliant.
It's a, you're calling it your little project in 2020,
but how does that become the new way we consume information?
I hope, first of all, just to plant a little seed there
because normally the big barrier of doing anything in media
is you need a ton of money, but this costs no money at all.
I've just been paying myself.
You pay a tiny amount of money each month to Amazon
to run the thing in their cloud.
We're not, there never will never be any ads.
The point is not to make any money off of it.
And we just train machine learning algorithms
to classify the articles and stuff.
So it just kind of runs by itself.
So if it actually gets good enough at some point
that it starts catching on, it could scale.
And if other people carbon copy it
and make other versions that are better,
that's the more the merrier.
I think there's a real opportunity for machine learning
to empower the individual against the powerful players.
As I said in the beginning here, it's
been mostly the other way around so far,
that the big players have the AI and then they tell people,
this is the truth, this is how it is.
§09Transcript · 1:12:49–1:22:11
But it can just as well go the other way around.
And when the internet was born, actually, a lot of people
had this hope that maybe this will be
a great thing for democracy, make it easier
to find out about things.
And maybe machine learning and things like this
can actually help again.
And I have to say, I think it's more important than ever now
because this is very linked also to the whole future of life
as we discussed earlier.
We're getting this ever more powerful tech.
Frank, it's pretty clear if you look
on the one or two generation, three generation timescale
that there are only two ways this can end geopolitically.
Either it ends great for all humanity
or it ends terribly for all of us.
There's really no in between.
And we're so stuck in that because technology
knows no borders.
And you can't have people fighting
when the weapons just keep getting ever more
powerful indefinitely.
Eventually, the luck runs out.
And right now we have, I love America,
but the fact of the matter is what's good for America
is not opposite in the long term to what's
good for other countries.
It would be if this was some sort of zero sum game
like it was thousands of years ago when the only way one
country could get more resources was
to take land from other countries
because that was basically the resource.
Look at the map of Europe.
Some countries kept getting bigger and smaller,
endless wars.
But then since 1945, there hasn't been any war
in Western Europe.
And they all got way richer because of tech.
So the optimistic outcome is that the big winner
in this century is going to be America and China and Russia
and everybody else because technology just makes
us all healthier and wealthier.
And we just find some way of keeping the peace
on this planet.
But I think, unfortunately, there
are some pretty powerful forces right now
that are pushing in exactly the opposite direction
and trying to demonize other countries, which just makes
it more likely that this ever more powerful tech we're
building is going to be used in disastrous ways.
Yeah, for aggression versus cooperation,
that kind of thing.
Yeah, even look at just military AI now.
It was so awesome to see these dancing robots.
I loved it.
But one of the biggest growth areas in robotics
now is, of course, autonomous weapons.
And 2020 was like the best marketing year
ever for autonomous weapons.
Because in both Libya, it's a civil war,
and in Nagorno Karabakh, they made the decisive difference.
And everybody else is watching this.
Oh, yeah, we want to build autonomous weapons, too.
In Libya, you had, on one hand, our ally,
the United Arab Emirates that were flying
their autonomous weapons that they bought from China,
bombing Libyans.
And on the other side, you had our other ally, Turkey,
flying their drones.
And they had no skin in the game,
any of these other countries.
And of course, it was the Libyans who really got screwed.
In Nagorno Karabakh, you had actually, again,
Turkey is sending drones built by this company that
was actually founded by a guy who went to MIT AeroAstro.
Do you know that?
No.
Bacratyar.
Yeah.
So MIT has a direct responsibility
for ultimately this.
And a lot of civilians were killed there.
So because it was militarily so effective,
now suddenly there's a huge push.
Oh, yeah, yeah, let's go build ever more autonomy
into these weapons, and it's going to be great.
And I think, actually, people who
are obsessed about some sort of future Terminator scenario
right now should start focusing on the fact
that we have two much more urgent threats happening
from machine learning.
One of them is the whole destruction of democracy
that we've talked about now, where
our flow of information is being manipulated
by machine learning.
And the other one is that right now,
this is the year when the big arms race and out of control
arms race in at least Thomas Weapons is going to start,
or it's going to stop.
So you have a sense that there is like 2020
was an instrumental catalyst for the autonomous weapons race.
Yeah, because it was the first year when they proved
decisive in the battlefield.
And these ones are still not fully autonomous, mostly.
They're remote controlled, right?
But we could very quickly make things
about the size and cost of a smartphone, which you just put
in the GPS coordinates or the face of the one
you want to kill, a skin color or whatever,
and it flies away and does it.
And the real good reason why the US and all
the other superpowers should put the kibosh on this
is the same reason we decided to put the kibosh on bioweapons.
So we gave the Future of Life Award
that we can talk more about later to Matthew Messelson
from Harvard before for convincing
Nixon to ban bioweapons.
And I asked him, how did you do it?
And he was like, well, I just said, look,
we don't want there to be a $500 weapon of mass destruction
that all our enemies can afford, even nonstate actors.
And Nixon was like, good point.
It's in America's interest that the powerful weapons are all
really expensive, so only we can afford them,
or maybe some more stable adversaries, right?
Nuclear weapons are like that.
But bioweapons were not like that.
That's why we banned them.
And that's why you never hear about them now.
That's why we love biology.
So you have a sense that it's possible for the big power
houses in terms of the big nations in the world
to agree that autonomous weapons is not a race we want to be on,
that it doesn't end well.
Yeah, because we know it's just going
to end in mass proliferation.
And every terrorist everywhere is
going to have these super cheap weapons
that they will use against us.
And our politicians have to constantly worry
about being assassinated every time they go outdoors
by some anonymous little mini drone.
We don't want that.
And even if the US and China and everyone else
could just agree that you can only
build these weapons if they cost at least $10 million,
that would be a huge win for the superpowers
and, frankly, for everybody.
And people often push back and say, well, it's
so hard to prevent cheating.
But hey, you could say the same about bioweapons.
Take any of your MIT colleagues in biology.
Of course, they could build some nasty bioweapon
if they really wanted to.
But first of all, they don't want to
because they think it's disgusting because of the stigma.
And second, even if there's some sort of nutcase and want to,
it's very likely that some of their grad students
or someone would rat them out because everyone else thinks
it's so disgusting.
And in fact, we now know there was even a fair bit of cheating
on the bioweapons ban.
But no countries used them because it was so stigmatized
that it just wasn't worth revealing that they had cheated.
You talk about drones, but you kind of
think that drones is a remote operation.
Which they are, mostly, still.
But you're not taking the next intellectual step
of where does this go.
You're kind of saying the problem with drones
is that you're removing yourself from direct violence.
Therefore, you're not able to sort of maintain
the common humanity required to make
the proper decisions strategically.
But that's the criticism as opposed to like,
if this is automated, and just exactly as you said,
if you automate it and there's a race,
then the technology's gonna get better and better and better
which means getting cheaper and cheaper and cheaper.
And unlike, perhaps, nuclear weapons
which is connected to resources in a way,
like it's hard to engineer, yeah.
It feels like there's too much overlap
between the tech industry and autonomous weapons
to where you could have smartphone type of cheapness.
If you look at drones, for $1,000,
you can have an incredible system
that's able to maintain flight autonomously for you
and take pictures and stuff.
You could see that going into the autonomous weapons space
that's, but why is that not thought about
or discussed enough in the public, do you think?
You see those dancing Boston Dynamics robots
and everybody has this kind of,
as if this is like a far future.
They have this fear like, oh, this'll be Terminator
in like some, I don't know, unspecified 20, 30, 40 years.
And they don't think about, well, this is like
some much less dramatic version of that
is actually happening now.
§10Transcript · 1:22:11–1:31:43
It's not gonna be legged, it's not gonna be dancing,
but it already has the capability
to use artificial intelligence to kill humans.
Yeah, the Boston Dynamics legged robots,
I think the reason we imagine them holding guns
is just because you've all seen Arnold Schwarzenegger, right?
That's our reference point.
That's pretty useless.
That's not gonna be the main military use of them.
They might be useful in law enforcement in the future
and then there's a whole debate about,
do you want robots showing up at your house with guns
telling you who'll be perfectly obedient
to whatever dictator controls them?
But let's leave that aside for a moment
and look at what's actually relevant now.
So there's a spectrum of things you can do
with AI in the military.
And again, to put my card on the table,
I'm not the pacifist, I think we should have good defense.
So for example, a predator drone is basically
a fancy little remote controlled airplane, right?
There's a human piloting it and the decision ultimately
about whether to kill somebody with it
is made by a human still.
And this is a line I think we should never cross.
There's a current DOD policy.
Again, you have to have a human in the loop.
I think algorithms should never make life
or death decisions, they should be left to humans.
Now, why might we cross that line?
Well, first of all, these are expensive, right?
So for example, when Azerbaijan had all these drones
and Armenia didn't have any, they start trying
to jerry rig little cheap things, fly around.
But then of course, the Armenians would jam them
or the Azeris would jam them.
And remote control things can be jammed,
that makes them inferior.
Also, there's a bit of a time delay between,
if we're piloting something from far away,
speed of light, and the human has a reaction time as well,
it would be nice to eliminate that jamming possibility
in the time that they by having it fully autonomous.
But now you might be, so then if you do,
but now you might be crossing that exact line.
You might program it to just, oh yeah, the air drone,
go hover over this country for a while
and whenever you find someone who is a bad guy,
kill them.
Now the machine is making these sort of decisions
and some people who defend this still say,
well, that's morally fine because we are the good guys
and we will tell it the definition of bad guy
that we think is moral.
But now it would be very naive to think
that if ISIS buys that same drone,
that they're gonna use our definition of bad guy.
Maybe for them, bad guy is someone wearing
a US army uniform or maybe there will be some,
weird ethnic group who decides that someone
of another ethnic group, they are the bad guys, right?
The thing is human soldiers with all our faults,
we still have some basic wiring in us.
Like, no, it's not okay to kill kids and civilians.
And Thomas Weprin has none of that.
It's just gonna do whatever is programmed.
It's like the perfect Adolf Eichmann on steroids.
Like they told him, Adolf Eichmann, you know,
he wanted to do this and this and this
to make the Holocaust more efficient.
And he was like, yeah, and off he went and did it, right?
Do we really wanna make machines that are like that,
like completely amoral and we'll take the user's definition
of who is the bad guy?
And do we then wanna make them so cheap
that all our adversaries can have them?
Like what could possibly go wrong?
That's I think the big ordeal of the whole thing.
I think the big argument for why we wanna,
this year really put the kibosh on this.
And I think you can tell there's a lot
of very active debate even going on within the US military
and undoubtedly in other militaries around the world also
about whether we should have some sort
of international agreement to at least require
that these weapons have to be above a certain size
and cost, you know, so that things just don't totally spiral
out of control.
And finally, just for your question,
but is it possible to stop it?
Because some people tell me, oh, just give up, you know.
But again, so Matthew Messelsen again from Harvard, right,
who the bioweapons hero, he had exactly this criticism
also with bioweapons.
People were like, how can you check for sure
that the Russians aren't cheating?
And he told me this, I think really ingenious insight.
He said, you know, Max, some people
think you have to have inspections and things
and you have to make sure that you can catch the cheaters
with 100% chance.
You don't need 100%, he said.
1% is usually enough.
Because if it's another big state,
suppose China and the US have signed the treaty drawing
a certain line and saying, yeah, these kind of drones are OK,
but these fully autonomous ones are not.
Now suppose you are China and you have cheated and secretly
developed some clandestine little thing
or you're thinking about doing it.
What's your calculation that you do?
Well, you're like, OK, what's the probability
that we're going to get caught?
If the probability is 100%, of course, we're not going to do it.
But if the probability is 5% that we're going to get caught,
then it's going to be like a huge embarrassment for us.
And we still have our nuclear weapons anyway,
so it doesn't really make an enormous difference in terms
of deterring the US.
And that feeds the stigma that you kind of established,
like this fabric, this universal stigma over the thing.
Exactly.
It's very reasonable for them to say, well, we probably
get away with it.
If we don't, then the US will know we cheated,
and then they're going to go full tilt with their program
and say, look, the Chinese are cheaters,
and now we have all these weapons against us,
and that's bad.
So the stigma alone is very, very powerful.
And again, look what happened with bioweapons.
It's been 50 years now.
When was the last time you read about a bioterrorism attack?
The only deaths I really know about with bioweapons
that have happened when we Americans managed
to kill some of our own with anthrax,
or the idiot who sent them to Tom Daschle and others
in letters, right?
And similarly in Sverdlovsk in the Soviet Union,
they had some anthrax in some lab there.
Maybe they were cheating or who knows,
and it leaked out and killed a bunch of Russians.
I'd say that's a pretty good success, right?
50 years, just two own goals by the superpowers,
and then nothing.
And that's why whenever I ask anyone
what they think about biology, they think it's great.
They associate it with new cures, new diseases,
maybe a good vaccine.
This is how I want to think about AI in the future.
And I want others to think about AI too,
as a source of all these great solutions to our problems,
not as, oh, AI, oh yeah, that's the reason
I feel scared going outside these days.
Yeah, it's kind of brilliant that bioweapons
and nuclear weapons, we've figured out,
I mean, of course there's still a huge source of danger,
but we figured out some way of creating rules
and social stigma over these weapons
that then creates a stability to our,
whatever that game theoretic stability that occurs.
And we don't have that with AI,
and you're kind of screaming from the top of the mountain
about this, that we need to find that
because it's very possible with the future of life,
as you point out, Institute Awards pointed out
that with nuclear weapons,
we could have destroyed ourselves quite a few times.
And it's a learning experience that is very costly.
We gave this Future Life Award,
we gave it the first time to this guy, Vasily Arkhipov.
He was on, most people haven't even heard of him.
Yeah, can you say who he is?
Vasily Arkhipov, he has, in my opinion,
made the greatest positive contribution to humanity
of any human in modern history.
And maybe it sounds like hyperbole here,
like I'm just over the top,
but let me tell you the story and I think maybe you'll agree.
So during the Cuban Missile Crisis,
we Americans first didn't know
that the Russians had sent four submarines,
but we caught two of them.
And we didn't know that,
so we dropped practice depth charges
on the one that he was on,
try to force it to the surface.
But we didn't know that this nuclear submarine
actually was a nuclear submarine with a nuclear torpedo.
We also didn't know that they had authorization
to launch it without clearance from Moscow.
And we also didn't know
that they were running out of electricity.
Their batteries were almost dead.
They were running out of oxygen.
Sailors were fainting left and right.
The temperature was about 110, 120 Fahrenheit on board.
It was really hellish conditions,
really just a kind of doomsday.
§11Transcript · 1:31:43–1:40:22
And at that point,
these giant explosions start happening
from the Americans dropping these.
The captain thought World War III had begun.
They decided they were gonna launch the nuclear torpedo.
And one of them shouted,
we're all gonna die,
but we're not gonna disgrace our Navy.
We don't know what would have happened
if there had been a giant mushroom cloud all of a sudden
against the Americans.
But since everybody had their hands on the triggers,
you don't have to be too creative to think
that it could have led to an all out nuclear war,
in which case we wouldn't be having this conversation now.
What actually took place was
they needed three people to approve this.
The captain had said yes.
There was the Communist Party political officer.
He also said, yes, let's do it.
And the third man was this guy, Vasily Arkhipov,
who said, no.
For some reason, he was just more chill than the others
and he was the right man at the right time.
I don't want us as a species rely on the right person
being there at the right time, you know.
We tracked down his family
living in relative poverty outside Moscow.
When he flew his daughter,
he had passed away and flew them to London.
They had never been to the West even.
It was incredibly moving to get to honor them for this.
The next year we gave them a medal.
The next year we gave this Future Life Award
to Stanislav Petrov.
Have you heard of him?
Yes.
So he was in charge of the Soviet early warning station,
which was built with Soviet technology
and honestly not that reliable.
It said that there were five US missiles coming in.
Again, if they had launched at that point,
we probably wouldn't be having this conversation.
He decided based on just mainly gut instinct
to just not escalate this.
And I'm very glad he wasn't replaced by an AI
that was just automatically following orders.
And then we gave the third one to Matthew Messelson.
Last year, we gave this award to these guys
who actually use technology for good,
not avoiding something bad, but for something good.
The guys who eliminated this disease,
it was way worse than COVID that had killed
half a billion people in its final century.
Smallpox, right?
So you mentioned it earlier.
COVID on average kills less than 1% of people who get it.
Smallpox, about 30%.
And they just ultimately, Viktor Zhdanov and Bill Foege,
most of my colleagues have never heard of either of them,
one American, one Russian, they did this amazing effort
not only was Zhdanov able to get the US and the Soviet Union
to team up against smallpox during the Cold War,
but Bill Foege came up with this ingenious strategy
for making it actually go all the way
to defeat the disease without funding
for vaccinating everyone.
And as a result, we haven't had any,
we went from 15 million deaths the year
I was born in smallpox.
So what do we have in COVID now?
A little bit short of 2 million, right?
Yes.
To zero deaths, of course, this year and forever.
There have been 200 million people,
we estimate, who would have died since then by smallpox
had it not been for this.
So isn't science awesome when you use it for good?
The reason we wanna celebrate these sort of people
is to remind them of this.
Science is so awesome when you use it for good.
And those awards actually, the variety there,
it's a very interesting picture.
So the first two are looking at,
it's kind of exciting to think that these average humans
in some sense, they're products of billions
of other humans that came before them, evolution,
and some little, you said gut,
but there's something in there
that stopped the annihilation of the human race.
And that's a magical thing,
but that's like this deeply human thing.
And then there's the other aspect
where that's also very human,
which is to build solution
to the existential crises that we're facing,
like to build it, to take the responsibility
and to come up with different technologies and so on.
And both of those are deeply human,
the gut and the mind, whatever that is that creates.
The best is when they work together.
Arkhipov, I wish I could have met him, of course,
but he had passed away.
He was really a fantastic military officer,
combining all the best traits
that we in America admire in our military.
Because first of all, he was very loyal, of course.
He never even told anyone about this during his whole life,
even though you think he had some bragging rights, right?
But he just was like, this is just business,
just doing my job.
It only came out later after his death.
And second, the reason he did the right thing
was not because he was some sort of liberal
or some sort of, not because he was just,
oh, peace and love.
It was partly because he had been the captain
on another submarine that had a nuclear reactor meltdown.
And it was his heroism that helped contain this.
That's why he died of cancer later also.
But he had seen many of his crew members die.
And I think for him, that gave him this gut feeling
that if there's a nuclear war
between the US and the Soviet Union,
the whole world is gonna go through
what I saw my dear crew members suffer through.
It wasn't just an abstract thing for him.
I think it was real.
And second though, not just the gut, the mind, right?
He was, for some reason, very levelheaded personality
and very smart guy,
which is exactly what we want our best fighter pilots
to be also, right?
I never forget Neil Armstrong when he's landing on the moon
and almost running out of gas.
And he doesn't even change when they say 30 seconds,
he doesn't even change the tone of voice, just keeps going.
Arkhipov, I think was just like that.
So when the explosions start going off
and his captain is screaming and we should nuke them
and all, he's like,
I don't think the Americans are trying to sink us.
I think they're trying to send us a message.
That's pretty bad ass.
Yes.
Coolness, because he said, if they wanted to sink us,
and he said, listen, listen, it's alternating
one loud explosion on the left, one on the right,
one on the left, one on the right.
He was the only one who noticed this pattern.
And he's like, I think this is,
I'm trying to send us a signal
that they want it to surface
and they're not gonna sink us.
And somehow,
this is how he then managed it ultimately
with his combination of gut
and also just cool analytical thinking,
was able to deescalate the whole thing.
And yeah, so this is some of the best in humanity.
I guess coming back to what we talked about earlier,
it's the combination of the neural network,
the instinctive, with, I'm getting teary up here,
getting emotional, but he was just,
he is one of my superheroes,
having both the heart and the mind combined.
And especially in that time, there's something about the,
I mean, this is a very, in America,
people are used to this kind of idea
of being the individual of like on your own thinking.
I think under, in the Soviet Union under communism,
it's actually much harder to do that.
Oh yeah, he didn't even, he even got,
he didn't get any accolades either
when he came back for this, right?
They just wanted to hush the whole thing up.
Yeah, there's echoes of that with Chernobyl,
there's all kinds of,
that's one, that's a really hopeful thing
that amidst big centralized powers,
whether it's companies or states,
there's still the power of the individual
to think on their own, to act.
But I think we need to think of people like this,
not as a panacea we can always count on,
but rather as a wake up call.
So because of them, because of Arkhipov,
we are alive to learn from this lesson,
to learn from the fact that we shouldn't keep playing
Russian roulette and almost have a nuclear war
by mistake now and then,
because relying on luck is not a good longterm strategy.
If you keep playing Russian roulette over and over again,
the probability of surviving just drops exponentially
with time.
Yeah.
And if you have some probability
of having an accidental nuke war every year,
the probability of not having one also drops exponentially.
I think we can do better than that.
§12Transcript · 1:40:22–1:49:51
So I think the message is very clear,
once in a while shit happens,
and there's a lot of very concrete things we can do
to reduce the risk of things like that happening
in the first place.
On the AI front, if we just link on that for a second.
Yeah.
So you're friends with, you often talk with Elon Musk
throughout history, you've did a lot
of interesting things together.
He has a set of fears about the future
of artificial intelligence, AGI.
Do you have a sense, we've already talked about
the things we should be worried about with AI,
do you have a sense of the shape of his fears
in particular about AI,
of which subset of what we've talked about,
whether it's creating, it's that direction
of creating sort of these giant competition systems
that are not explainable,
they're not intelligible intelligence,
or is it the...
And then like as a branch of that,
is it the manipulation by big corporations of that
or individual evil people to use that for destruction
or the unintentional consequences?
Do you have a sense of where his thinking is on this?
From my many conversations with Elon,
yeah, I certainly have a model of how he thinks.
It's actually very much like the way I think also,
I'll elaborate on it a bit.
I just wanna push back on when you said evil people,
I don't think it's a very helpful concept.
Evil people, sometimes people do very, very bad things,
but they usually do it because they think it's a good thing
because somehow other people had told them
that that was a good thing
or given them incorrect information or whatever, right?
I believe in the fundamental goodness of humanity
that if we educate people well
and they find out how things really are,
people generally wanna do good and be good.
Hence the value alignment,
as opposed to it's about information, about knowledge,
and then once we have that,
we'll likely be able to do good
in the way that's aligned with everybody else
who thinks differently.
Yeah, and it's not just the individual people
we have to align.
So we don't just want people to be educated
to know the way things actually are
and to treat each other well,
but we also need to align other nonhuman entities.
We talked about corporations, there has to be institutions
so that what they do is actually good
for the country they're in
and we should align, make sure that what countries do
is actually good for the species as a whole, et cetera.
Coming back to Elon,
yeah, my understanding of how Elon sees this
is really quite similar to my own,
which is one of the reasons I like him so much
and enjoy talking with him so much.
I feel he's quite different from most people
in that he thinks much more than most people
about the really big picture,
not just what's gonna happen in the next election cycle,
but in millennia, millions and billions of years from now.
And when you look in this more cosmic perspective,
it's so obvious that we are gazing out into this universe
that as far as we can tell is mostly dead
with life being almost imperceptibly tiny perturbation,
and he sees this enormous opportunity
for our universe to come alive,
first to become an interplanetary species.
Mars is obviously just first stop on this cosmic journey.
And precisely because he thinks more long term,
it's much more clear to him than to most people
that what we do with this Russian roulette thing
we keep playing with our nukes is a really poor strategy,
really reckless strategy.
And also that we're just building
these ever more powerful AI systems that we don't understand
is also just a really reckless strategy.
I feel Elon is very much a humanist
in the sense that he wants an awesome future for humanity.
He wants it to be us that control the machines
rather than the machines that control us.
And why shouldn't we insist on that?
We're building them after all, right?
Why should we build things that just make us
into some little cog in the machinery
that has no further say in the matter, right?
That's not my idea of an inspiring future either.
Yeah, if you think on the cosmic scale
in terms of both time and space,
so much is put into perspective.
Yeah.
Whenever I have a bad day, that's what I think about.
It immediately makes me feel better.
It makes me sad that for us individual humans,
at least for now, the ride ends too quickly.
That we don't get to experience the cosmic scale.
Yeah, I mean, I think of our universe sometimes
as an organism that has only begun to wake up a tiny bit,
just like the very first little glimmers of consciousness
you have in the morning when you start coming around.
Before the coffee.
Before the coffee, even before you get out of bed,
before you even open your eyes.
You start to wake up a little bit.
There's something here.
That's very much how I think of where we are.
All those galaxies out there,
I think they're really beautiful,
but why are they beautiful?
They're beautiful because conscious entities
are actually observing them,
experiencing them through our telescopes.
I define consciousness as subjective experience,
whether it be colors or emotions or sounds.
So beauty is an experience.
Meaning is an experience.
Purpose is an experience.
If there was no conscious experience,
observing these galaxies, they wouldn't be beautiful.
If we do something dumb with advanced AI in the future here
and Earth originating, life goes extinct.
And that was it for this.
If there is nothing else with telescopes in our universe,
then it's kind of game over for beauty
and meaning and purpose in our whole universe.
And I think that would be just such
an opportunity lost, frankly.
And I think when Elon points this out,
he gets very unfairly maligned in the media
for all the dumb media bias reasons we talked about.
They want to print precisely the things about Elon
out of context that are really click baity.
He has gotten so much flack
for this summoning the demon statement.
I happen to know exactly the context
because I was in the front row when he gave that talk.
It was at MIT, you'll be pleased to know,
it was the AeroAstro anniversary.
They had Buzz Aldrin there from the moon landing,
a whole house, a Kresge auditorium
packed with MIT students.
And he had this amazing Q&A, it might've gone for an hour.
And they talked about rockets and Mars and everything.
At the very end, this one student
who has actually hit my class asked him, what about AI?
Elon makes this one comment
and they take this out of context, print it, goes viral.
What is it like with AI,
we're summoning the demons, something like that.
And try to cast him as some sort of doom and gloom dude.
You know Elon, he's not the doom and gloom dude.
He is such a positive visionary.
And the whole reason he warns about this
is because he realizes more than most
what the opportunity cost is of screwing up.
That there is so much awesomeness in the future
that we can and our descendants can enjoy
if we don't screw up, right?
I get so pissed off when people try to cast him
as some sort of technophobic Luddite.
And at this point, it's kind of ludicrous
when I hear people say that people who worry about
artificial general intelligence are Luddites
because of course, if you look more closely,
you have some of the most outspoken people making warnings
are people like Professor Stuart Russell from Berkeley
who's written the bestselling AI textbook, you know.
So claiming that he's a Luddite who doesn't understand AI
is the joke is really on the people who said it.
But I think more broadly,
this message is really not sunk in at all.
What it is that people worry about,
they think that Elon and Stuart Russell and others
are worried about the dancing robots picking up an AR 15
and going on a rampage, right?
They think they're worried about robots turning evil.
They're not, I'm not.
The risk is not malice, it's competence.
The risk is just that we build some systems
that are incredibly competent,
which means they're always gonna get
their goals accomplished,
even if they clash with our goals.
That's the risk.
Why did we humans drive the West African black rhino extinct?
Is it because we're malicious, evil rhinoceros haters?
No, it's just because our goals didn't align
with the goals of those rhinos
and tough luck for the rhinos, you know.
So the point is just we don't wanna put ourselves
in the position of those rhinos
creating something more powerful than us
§13Transcript · 1:49:51–1:58:33
if we haven't first figured out how to align the goals.
And I am optimistic.
I think we could do it if we worked really hard on it,
because I spent a lot of time
around intelligent entities that were more intelligent
than me, my mom and my dad.
And I was little and that was fine
because their goals were actually aligned
with mine quite well.
But we've seen today many examples of where the goals
of our powerful systems are not so aligned.
So those click through optimization algorithms
that are polarized social media, right?
They were actually pretty poorly aligned
with what was good for democracy, it turned out.
And again, almost all problems we've had
in the machine learning again came so far,
not from malice, but from poor alignment.
And that's exactly why that's why we should be concerned
about it in the future.
Do you think it's possible that with systems
like Neuralink and brain computer interfaces,
you know, again, thinking of the cosmic scale,
Elon's talked about this, but others have as well
throughout history of figuring out how the exact mechanism
of how to achieve that kind of alignment.
So one of them is having a symbiosis with AI,
which is like coming up with clever ways
where we're like stuck together in this weird relationship,
whether it's biological or in some kind of other way.
Do you think that's a possibility
of having that kind of symbiosis?
Or do we wanna instead kind of focus
on this distinct entities of us humans talking
to these intelligible, self doubting AIs,
maybe like Stuart Russell thinks about it,
like we're self doubting and full of uncertainty
and our AI systems are full of uncertainty.
We communicate back and forth
and in that way achieve symbiosis.
I honestly don't know.
I would say that because we don't know for sure
what if any of our, which of any of our ideas will work.
But we do know that if we don't,
I'm pretty convinced that if we don't get any
of these things to work and just barge ahead,
then our species is, you know,
probably gonna go extinct this century.
I think it's...
This century, you think like,
you think we're facing this crisis
is a 21st century crisis.
Like this century will be remembered.
But on a hard drive and a hard drive somewhere
or maybe by future generations is like,
like there'll be future Future of Life Institute awards
for people that have done something about AI.
It could also end even worse,
whether we're not superseded
by leaving any AI behind either.
We just totally wipe out, you know,
like on Easter Island.
Our century is long.
You know, there are still 79 years left of it, right?
Think about how far we've come just in the last 30 years.
So we can talk more about what might go wrong,
but you asked me this really good question
about what's the best strategy.
Is it Neuralink or Russell's approach or whatever?
I think, you know, when we did the Manhattan project,
we didn't know if any of our four ideas
for enriching uranium and getting out the uranium 235
were gonna work.
But we felt this was really important
to get it before Hitler did.
So, you know what we did?
We tried all four of them.
Here, I think it's analogous
where there's the greatest threat
that's ever faced our species.
And of course, US national security by implication.
We don't know if we don't have any method
that's guaranteed to work, but we have a lot of ideas.
So we should invest pretty heavily
in pursuing all of them with an open mind
and hope that one of them at least works.
These are, the good news is the century is long,
and it might take decades
until we have artificial general intelligence.
So we have some time hopefully,
but it takes a long time to solve
these very, very difficult problems.
It's gonna actually be the,
it's the most difficult problem
we were ever trying to solve as a species.
So we have to start now.
So we don't have, rather than begin thinking about it
the night before some people who've had too much Red Bull
switch it on.
And we have to, coming back to your question,
we have to pursue all of these different avenues and see.
If you were my investment advisor
and I was trying to invest in the future,
how do you think the human species
is most likely to destroy itself in the century?
Yeah, so if the crises,
many of the crises we're facing are really before us
within the next hundred years,
how do we make explicit,
make known the unknowns and solve those problems
to avoid the biggest,
starting with the biggest existential crisis?
So as your investment advisor,
how are you planning to make money on us
destroying ourselves?
I have to ask.
I don't know.
It might be the Russian origins.
Somehow it's involved.
At the micro level of detailed strategies,
of course, these are unsolved problems.
For AI alignment,
we can break it into three sub problems
that are all unsolved.
I think you want first to make machines
understand our goals,
then adopt our goals and then retain our goals.
So to hit on all three real quickly.
The problem when Andreas Lubitz told his autopilot
to fly into the Alps was that the computer
didn't even understand anything about his goals.
It was too dumb.
It could have understood actually,
but you would have had to put some effort in
as a systems designer to don't fly into mountains.
So that's the first challenge.
How do you program into computers human values,
human goals?
We can start rather than saying,
oh, it's so hard.
We should start with the simple stuff, as I said,
self driving cars, airplanes,
just put in all the goals that we all agree on already,
and then have a habit of whenever machines get smarter
so they can understand one level higher goals,
put them into.
The second challenge is getting them to adopt the goals.
It's easy for situations like that
where you just program it in,
but when you have self learning systems like children,
you know, any parent knows
that there was a difference between getting our kids
to understand what we want them to do
and to actually adopt our goals, right?
With humans, with children, fortunately,
they go through this phase.
First, they're too dumb to understand
what we want our goals are.
And then they have this period of some years
when they're both smart enough to understand them
and malleable enough that we have a chance
to raise them well.
And then they become teenagers kind of too late.
But we have this window with machines,
the challenges, the intelligence might grow so fast
that that window is pretty short.
So that's a research problem.
The third one is how do you make sure they keep the goals
if they keep learning more and getting smarter?
Many sci fi movies are about how you have something
in which initially was aligned,
but then things kind of go off keel.
And, you know, my kids were very, very excited
about their Legos when they were little.
Now they're just gathering dust in the basement.
If we create machines that are really on board
with the goal of taking care of humanity,
we don't want them to get as bored with us
as my kids got with Legos.
So this is another research challenge.
How can you make some sort of recursively
self improving system retain certain basic goals?
That said, a lot of adult people still play with Legos.
So maybe we succeeded with the Legos.
Maybe, I like your optimism.
But above all.
So not all AI systems have to maintain the goals, right?
Just some fraction.
Yeah, so there's a lot of talented AI researchers now
who have heard of this and want to work on it.
Not so much funding for it yet.
Of the billions that go into building AI more powerful,
it's only a minuscule fraction
so far going into this safety research.
My attitude is generally we should not try to slow down
the technology, but we should greatly accelerate
the investment in this sort of safety research.
And also, this was very embarrassing last year,
but the NSF decided to give out
six of these big institutes.
§14Transcript · 1:58:33–2:07:40
We got one of them for AI and science, you asked me about.
Another one was supposed to be for AI safety research.
And they gave it to people studying oceans
and climate and stuff.
So I'm all for studying oceans and climates,
but we need to actually have some money
that actually goes into AI safety research also
and doesn't just get grabbed by whatever.
That's a fantastic investment.
And then at the higher level, you asked this question,
okay, what can we do?
What are the biggest risks?
I think we cannot just consider this
to be only a technical problem.
Again, because if you solve only the technical problem,
can I play with your robot?
Yes, please.
If we can get our machines to just blindly obey
the orders we give them,
so we can always trust that it will do what we want.
That might be great for the owner of the robot.
That might not be so great for the rest of humanity
if that person is that least favorite world leader
or whatever you imagine, right?
So we have to also take a look at the,
apply alignment, not just to machines,
but to all the other powerful structures.
That's why it's so important
to strengthen our democracy again,
as I said, to have institutions,
make sure that the playing field is not rigged
so that corporations are given the right incentives
to do the things that both make profit
and are good for people,
to make sure that countries have incentives
to do things that are both good for their people
and don't screw up the rest of the world.
And this is not just something for AI nerds to geek out on.
This is an interesting challenge for political scientists,
economists, and so many other thinkers.
So one of the magical things
that perhaps makes this earth quite unique
is that it's home to conscious beings.
So you mentioned consciousness.
Perhaps as a small aside,
because we didn't really get specific
to how we might do the alignment.
Like you said,
is there just a really important research problem,
but do you think engineering consciousness
into AI systems is a possibility,
is something that we might one day do,
or is there something fundamental to consciousness
that is, is there something about consciousness
that is fundamental to humans and humans only?
I think it's possible.
I think both consciousness and intelligence
are information processing.
Certain types of information processing.
And that fundamentally,
it doesn't matter whether the information is processed
by carbon atoms in the neurons and brains
or by silicon atoms and so on in our technology.
Some people disagree.
This is what I think as a physicist.
That consciousness is the same kind of,
you said consciousness is information processing.
So meaning, I think you had a quote of something like
it's information knowing itself, that kind of thing.
I think consciousness is, yeah,
is the way information feels when it's being processed.
One's being put in complex ways.
We don't know exactly what those complex ways are.
It's clear that most of the information processing
in our brains does not create an experience.
We're not even aware of it, right?
Like for example,
you're not aware of your heartbeat regulation right now,
even though it's clearly being done by your body, right?
It's just kind of doing its own thing.
When you go jogging,
there's a lot of complicated stuff
about how you put your foot down and we know it's hard.
That's why robots used to fall over so much,
but you're mostly unaware about it.
Your brain, your CEO consciousness module
just sends an email,
hey, I'm gonna keep jogging along this path.
The rest is on autopilot, right?
So most of it is not conscious,
but somehow there is some of the information processing,
which is we don't know what exactly.
I think this is a science problem
that I hope one day we'll have some equation for
or something so we can be able to build
a consciousness detector and say, yeah,
here there is some consciousness, here there's not.
Oh, don't boil that lobster because it's feeling pain
or it's okay because it's not feeling pain.
Right now we treat this as sort of just metaphysics,
but it would be very useful in emergency rooms
to know if a patient has locked in syndrome
and is conscious or if they are actually just out.
And in the future, if you build a very, very intelligent
helper robot to take care of you,
I think you'd like to know
if you should feel guilty about shutting it down
or if it's just like a zombie going through the motions
like a fancy tape recorder, right?
And once we can make progress
on the science of consciousness
and figure out what is conscious and what isn't,
then assuming we want to create positive experiences
and not suffering, we'll probably choose to build
some machines that are deliberately unconscious
that do incredibly boring, repetitive jobs
in an iron mine somewhere or whatever.
And maybe we'll choose to create helper robots
for the elderly that are conscious
so that people don't just feel creeped out
that the robot is just faking it
when it acts like it's sad or happy.
Like you said, elderly,
I think everybody gets pretty deeply lonely in this world.
And so there's a place I think for everybody
to have a connection with conscious beings,
whether they're human or otherwise.
But I know for sure that I would,
if I had a robot, if I was gonna develop any kind
of personal emotional connection with it,
I would be very creeped out
if I knew it in an intellectual level
that the whole thing was just a fraud.
Now today you can buy a little talking doll for a kid
which will say things and the little child will often think
that this is actually conscious
and even real secrets to it that then go on the internet
and with lots of the creepy repercussions.
I would not wanna be just hacked and tricked like this.
If I was gonna be developing real emotional connections
with the robot, I would wanna know
that this is actually real.
It's acting conscious, acting happy
because it actually feels it.
And I think this is not sci fi.
I think it's possible to measure, to come up with tools.
After we understand the science of consciousness,
you're saying we'll be able to come up with tools
that can measure consciousness
and definitively say like this thing is experiencing
the things it says it's experiencing.
Kind of by definition.
If it is a physical phenomenon, information processing
and we know that some information processing is conscious
and some isn't, well, then there is something there
to be discovered with the methods of science.
Giulio Tononi has stuck his neck out the farthest
and written down some equations for a theory.
Maybe that's right, maybe it's wrong.
We certainly don't know.
But I applaud that kind of efforts to sort of take this,
say this is not just something that philosophers
can have beer and muse about,
but something we can measure and study.
And coming, bringing that back to us,
I think what we would probably choose to do, as I said,
is if we cannot figure this out,
choose to make, to be quite mindful
about what sort of consciousness, if any,
we put in different machines that we have.
And certainly, we wouldn't wanna make,
we should not be making much machines that suffer
without us even knowing it, right?
And if at any point someone decides to upload themselves
like Ray Kurzweil wants to do,
I don't know if you've had him on your show.
We agree, but then COVID happens,
so we're waiting it out a little bit.
Suppose he uploads himself into this robo Ray
and it talks like him and acts like him and laughs like him.
And before he powers off his biological body,
he would probably be pretty disturbed
if he realized that there's no one home.
This robot is not having any subjective experience, right?
If humanity gets replaced by machine descendants,
which do all these cool things and build spaceships
and go to intergalactic rock concerts,
and it turns out that they are all unconscious,
just going through the motions,
wouldn't that be like the ultimate zombie apocalypse, right?
Just a play for empty benches?
Yeah, I have a sense that there's some kind of,
once we understand consciousness better,
we'll understand that there's some kind of continuum
and it would be a greater appreciation.
And we'll probably understand, just like you said,
it'd be unfortunate if it's a trick.
We'll probably definitely understand
that love is indeed a trick that we'll play on each other,
that we humans are, we convince ourselves we're conscious,
§15Transcript · 2:07:40–2:16:42
but we're really, us and trees and dolphins
are all the same kind of consciousness.
Can I try to cheer you up a little bit
with a philosophical thought here about the love part?
Yes, let's do it.
You know, you might say,
okay, yeah, love is just a collaboration enabler.
And then maybe you can go and get depressed about that.
But I think that would be the wrong conclusion, actually.
You know, I know that the only reason I enjoy food
is because my genes hacked me
and they don't want me to starve to death.
Not because they care about me consciously
enjoying succulent delights of pistachio ice cream,
but they just want me to make copies of them.
The whole thing, so in a sense,
the whole enjoyment of food is also a scam like this.
But does that mean I shouldn't take pleasure
in this pistachio ice cream?
I love pistachio ice cream.
And I can tell you, I know this is an experimental fact.
I enjoy pistachio ice cream every bit as much,
even though I scientifically know exactly why,
what kind of scam this was.
Your genes really appreciate
that you like the pistachio ice cream.
Well, but I, my mind appreciates it too, you know?
And I have a conscious experience right now.
Ultimately, all of my brain is also just something
the genes built to copy themselves.
But so what?
You know, I'm grateful that,
yeah, thanks genes for doing this,
but you know, now it's my brain that's in charge here
and I'm gonna enjoy my conscious experience,
thank you very much.
And not just the pistachio ice cream,
but also the love I feel for my amazing wife
and all the other delights of being conscious.
I don't, actually Richard Feynman,
I think said this so well.
He is also the guy, you know, really got me into physics.
Some art friend said that,
oh, science kind of just is the party pooper.
It's kind of ruins the fun, right?
When like you have a beautiful flowers as the artist
and then the scientist is gonna deconstruct that
into just a blob of quarks and electrons.
And Feynman pushed back on that in such a beautiful way,
which I think also can be used to push back
and make you not feel guilty about falling in love.
So here's what Feynman basically said.
He said to his friend, you know,
yeah, I can also as a scientist see
that this is a beautiful flower, thank you very much.
Maybe I can't draw as good a painting as you
because I'm not as talented an artist,
but yeah, I can really see the beauty in it.
And it just, it also looks beautiful to me.
But in addition to that, Feynman said, as a scientist,
I see even more beauty that the artist did not see, right?
Suppose this is a flower on a blossoming apple tree.
You could say this tree has more beauty in it
than just the colors and the fragrance.
This tree is made of air, Feynman wrote.
This is one of my favorite Feynman quotes ever.
And it took the carbon out of the air
and bound it in using the flaming heat of the sun,
you know, to turn the air into a tree.
And when you burn logs in your fireplace,
it's really beautiful to think that this is being reversed.
Now the tree is going, the wood is going back into air.
And in this flaming, beautiful dance of the fire
that the artist can see is the flaming light of the sun
that was bound in to turn the air into tree.
And then the ashes is the little residue
that didn't come from the air
that the tree sucked out of the ground, you know.
Feynman said, these are beautiful things.
And science just adds, it doesn't subtract.
And I feel exactly that way about love
and about pistachio ice cream also.
I can understand that there is even more nuance
to the whole thing, right?
At this very visceral level,
you can fall in love just as much as someone
who knows nothing about neuroscience.
But you can also appreciate this even greater beauty in it.
Just like, isn't it remarkable that it came about
from this completely lifeless universe,
just a bunch of hot blob of plasma expanding.
And then over the eons, you know, gradually,
first the strong nuclear force decided
to combine quarks together into nuclei.
And then the electric force bound in electrons
and made atoms.
And then they clustered from gravity
and you got planets and stars and this and that.
And then natural selection came along
and the genes had their little thing.
And you started getting what went from seeming
like a completely pointless universe
that we're just trying to increase entropy
and approach heat death into something
that looked more goal oriented.
Isn't that kind of beautiful?
And then this goal orientedness through evolution
got ever more sophisticated where you got ever more.
And then you started getting this thing,
which is kind of like DeepMind's mu zero and steroids,
the ultimate self play is not what DeepMind's AI
does against itself to get better at go.
It's what all these little quark blobs did
against each other in the game of survival of the fittest.
Now, when you had really dumb bacteria
living in a simple environment,
there wasn't much incentive to get intelligent,
but then the life made environment more complex.
And then there was more incentive to get even smarter.
And that gave the other organisms more of incentive
to also get smarter.
And then here we are now,
just like mu zero learned to become world master at go
and chess from playing against itself
by just playing against itself.
All the quirks here on our planet,
the electrons have created giraffes and elephants
and humans and love.
I just find that really beautiful.
And to me, that just adds to the enjoyment of love.
It doesn't subtract anything.
Do you feel a little more careful now?
I feel way better, that was incredible.
So this self play of quirks,
taking back to the beginning of our conversation
a little bit, there's so many exciting possibilities
about artificial intelligence understanding
the basic laws of physics.
Do you think AI will help us unlock?
There's been quite a bit of excitement
throughout the history of physics
of coming up with more and more general simple laws
that explain the nature of our reality.
And then the ultimate of that would be a theory
of everything that combines everything together.
Do you think it's possible that one, we humans,
but perhaps AI systems will figure out a theory of physics
that unifies all the laws of physics?
Yeah, I think it's absolutely possible.
I think it's very clear
that we're gonna see a great boost to science.
We're already seeing a boost actually
from machine learning helping science.
Alpha fold was an example,
the decades old protein folding problem.
So, and gradually, yeah, unless we go extinct
by doing something dumb like we discussed,
I think it's very likely
that our understanding of physics will become so good
that our technology will no longer be limited
by human intelligence,
but instead be limited by the laws of physics.
So our tech today is limited
by what we've been able to invent, right?
I think as AI progresses,
it'll just be limited by the speed of light
and other physical limits,
which would mean it's gonna be just dramatically beyond
where we are now.
Do you think it's a fundamentally mathematical pursuit
of trying to understand like the laws
of our universe from a mathematical perspective?
So almost like if it's AI,
it's exploring the space of like theorems
and those kinds of things,
or is there some other more computational ideas,
more sort of empirical ideas?
They're both, I would say.
It's really interesting to look out at the landscape
of everything we call science today.
So here you come now with this big new hammer.
It says machine learning on it
and that's, you know, where are there some nails
that you can help with here that you can hammer?
Ultimately, if machine learning gets the point
that it can do everything better than us,
it will be able to help across the whole space of science.
But maybe we can anchor it by starting a little bit
right now near term and see how we kind of move forward.
So like right now, first of all,
you have a lot of big data science, right?
Where, for example, with telescopes,
we are able to collect way more data every hour
than a grad student can just pour over
like in the old times, right?
And machine learning is already being used very effectively,
even at MIT, to find planets around other stars,
to detect exciting new signatures
of new particle physics in the sky,
to detect the ripples in the fabric of space time
§16Transcript · 2:16:42–2:25:38
that we call gravitational waves
caused by enormous black holes
crashing into each other halfway
across the observable universe.
Machine learning is running and ticking right now,
doing all these things,
and it's really helping all these experimental fields.
There is a separate front of physics,
computational physics,
which is getting an enormous boost also.
So we had to do all our computations by hand, right?
People would have these giant books
with tables of logarithms,
and oh my God, it pains me to even think
how long time it would have taken to do simple stuff.
Then we started to get little calculators and computers
that could do some basic math for us.
Now, what we're starting to see is
kind of a shift from GOFI, computational physics,
to neural network, computational physics.
What I mean by that is most computational physics
would be done by humans programming in
the intelligence of how to do the computation
into the computer.
Just as when Garry Kasparov got his posterior kicked
by IBM's Deep Blue in chess,
humans had programmed in exactly how to play chess.
Intelligence came from the humans.
It wasn't learned, right?
Mu zero can be not only Kasparov in chess,
but also Stockfish,
which is the best sort of GOFI chess program.
By learning, and we're seeing more of that now,
that shift beginning to happen in physics.
So let me give you an example.
So lattice QCD is an area of physics
whose goal is basically to take the periodic table
and just compute the whole thing from first principles.
This is not the search for theory of everything.
We already know the theory
that's supposed to produce as output the periodic table,
which atoms are stable, how heavy they are,
all that good stuff, their spectral lines.
It's a theory, lattice QCD,
you can put it on your tshirt.
Our colleague Frank Wilczek
got the Nobel Prize for working on it.
But the math is just too hard for us to solve.
We have not been able to start with these equations
and solve them to the extent that we can predict, oh yeah.
And then there is carbon,
and this is what the spectrum of the carbon atom looks like.
But awesome people are building
these supercomputer simulations
where you just put in these equations
and you make a big cubic lattice of space,
or actually it's a very small lattice
because you're going down to the subatomic scale,
and you try to solve it.
But it's just so computationally expensive
that we still haven't been able to calculate things
as accurately as we measure them in many cases.
And now machine learning is really revolutionizing this.
So my colleague Fiala Shanahan at MIT, for example,
she's been using this really cool
machine learning technique called normalizing flows,
where she's realized she can actually speed up
the calculation dramatically
by having the AI learn how to do things faster.
Another area like this
where we suck up an enormous amount of supercomputer time
to do physics is black hole collisions.
So now that we've done the sexy stuff
of detecting a bunch of this with LIGO and other experiments,
we want to be able to know what we're seeing.
And so it's a very simple conceptual problem.
It's the two body problem.
Newton solved it for classical gravity hundreds of years ago,
but the two body problem is still not fully solved.
For black holes.
Black holes, yes, and Einstein's gravity
because they won't just orbit in space,
they won't just orbit each other forever anymore,
two things, they give off gravitational waves
and make sure they crash into each other.
And the game, what you want to do is you want to figure out,
okay, what kind of wave comes out
as a function of the masses of the two black holes,
as a function of how they're spinning,
relative to each other, et cetera.
And that is so hard.
It can take months of supercomputer time
and massive numbers of cores to do it.
Now, wouldn't it be great if you can use machine learning
to greatly speed that up, right?
Now you can use the expensive old GoFi calculation
as the truth, and then see if machine learning
can figure out a smarter, faster way
of getting the right answer.
Yet another area, like computational physics.
These are probably the big three
that suck up the most computer time.
Lattice QCD, black hole collisions,
and cosmological simulations,
where you take not a subatomic thing
and try to figure out the mass of the proton,
but you take something enormous
and try to look at how all the galaxies get formed in there.
There again, there are a lot of very cool ideas right now
about how you can use machine learning
to do this sort of stuff better.
The difference between this and the big data
is you kind of make the data yourself, right?
So, and then finally,
we're looking over the physics landscape
and seeing what can we hammer with machine learning, right?
So we talked about experimental data, big data,
discovering cool stuff that we humans
then look more closely at.
Then we talked about taking the expensive computations
we're doing now and figuring out
how to do them much faster and better with AI.
And finally, let's go really theoretical.
So things like discovering equations,
having deep fundamental insights,
this is something closest to what I've been doing
in my group.
We talked earlier about the whole AI Feynman project,
where if you just have some data,
how do you automatically discover equations
that seem to describe this well,
that you can then go back as a human
and then work with and test and explore.
And you asked a really good question also
about if this is sort of a search problem in some sense.
That's very deep actually what you said, because it is.
Suppose I ask you to prove some mathematical theorem.
What is a proof in math?
It's just a long string of steps, logical steps
that you can write out with symbols.
And once you find it, it's very easy to write a program
to check whether it's a valid proof or not.
So why is it so hard to prove it?
Well, because there are ridiculously many possible
candidate proofs you could write down, right?
If the proof contains 10,000 symbols,
even if there were only 10 options
for what each symbol could be,
that's 10 to the power of 1,000 possible proofs,
which is way more than there are atoms in our universe.
So you could say it's trivial to prove these things.
You just write a computer, generate all strings,
and then check, is this a valid proof?
No.
Is this a valid proof?
Is this a valid proof?
No.
And then you just keep doing this forever.
But there are a lot of,
but it is fundamentally a search problem.
You just want to search the space of all those,
all strings of symbols to find one that is the proof, right?
And there's a whole area of machine learning called search.
How do you search through some giant space
to find the needle in the haystack?
And it's easier in cases
where there's a clear measure of good,
like you're not just right or wrong,
but this is better and this is worse,
so you can maybe get some hints
as to which direction to go in.
That's why we talked about neural networks work so well.
I mean, that's such a human thing
of that moment of genius
of figuring out the intuition of good, essentially.
I mean, we thought that that was...
Or is it?
Maybe it's not, right?
We thought that about chess, right?
That the ability to see like 10, 15,
sometimes 20 steps ahead was not a calculation
that humans were performing.
It was some kind of weird intuition
about different patterns, about board positions,
about the relative positions,
somehow stitching stuff together.
And a lot of it is just like intuition,
but then you have like alpha,
I guess zero be the first one that did the self play.
It just came up with this.
It was able to learn through self play mechanism,
this kind of intuition.
Exactly.
But just like you said, it's so fascinating to think,
well, they're in the space of totally new ideas.
Can that be done in developing theorems?
We know it can be done by neural networks
because we did it with the neural networks
in the craniums of the great mathematicians of humanity.
And I'm so glad you brought up alpha zero
§17Transcript · 2:25:38–2:34:54
because that's the counter example.
It turned out we were flattering ourselves
when we said intuition is something different.
Only humans can do it.
It's not information processing.
It used to be that way.
Again, it's really instructive, I think,
to compare the chess computer Deep Blue
that beat Kasparov with alpha zero
that beat Lisa Dahl at Go.
Because for Deep Blue, there was no intuition.
There was some, humans had programmed in some intuition.
After humans had played a lot of games,
they told the computer, count the pawn as one point,
the bishop is three points, rook is five points,
and so on, you add it all up,
and then you add some extra points for past pawns
and subtract if the opponent has it and blah, blah, blah.
And then what Deep Blue did was just search.
Just very brute force and tried many, many moves ahead,
all these combinations and a prune tree search.
And it could think much faster than Kasparov, and it won.
And that, I think, inflated our egos
in a way it shouldn't have,
because people started to say, yeah, yeah,
it's just brute force search, but it has no intuition.
Alpha zero really popped our bubble there,
because what alpha zero does,
yes, it does also do some of that tree search,
but it also has this intuition module,
which in geek speak is called a value function,
where it just looks at the board
and comes up with a number for how good is that position.
The difference was no human told it
how good the position is, it just learned it.
And mu zero is the coolest or scariest of all,
depending on your mood,
because the same basic AI system
will learn what the good board position is,
regardless of whether it's chess or Go or Shogi
or Pacman or Lady Pacman or Breakout or Space Invaders
or any number, a bunch of other games.
You don't tell it anything,
and it gets this intuition after a while for what's good.
So this is very hopeful for science, I think,
because if it can get intuition
for what's a good position there,
maybe it can also get intuition
for what are some good directions to go
if you're trying to prove something.
I often, one of the most fun things in my science career
is when I've been able to prove some theorem about something
and it's very heavily intuition guided, of course.
I don't sit and try all random strings.
I have a hunch that, you know,
this reminds me a little bit of about this other proof
I've seen for this thing.
So maybe I first, what if I try this?
Nah, that didn't work out.
But this reminds me actually,
the way this failed reminds me of that.
So combining the intuition with all these brute force
capabilities, I think it's gonna be able to help physics too.
Do you think there'll be a day when an AI system
being the primary contributor, let's say 90% plus,
wins the Nobel Prize in physics?
Obviously they'll give it to the humans
because we humans don't like to give prizes to machines.
It'll give it to the humans behind the system.
You could argue that AI has already been involved
in some Nobel Prizes, probably,
maybe something with black holes and stuff like that.
Yeah, we don't like giving prizes to other life forms.
If someone wins a horse racing contest,
they don't give the prize to the horse either.
That's true.
But do you think that we might be able to see
something like that in our lifetimes when AI,
so like the first system I would say
that makes us think about a Nobel Prize seriously
is like Alpha Fold is making us think about
in medicine, physiology, a Nobel Prize,
perhaps discoveries that are direct result
of something that's discovered by Alpha Fold.
Do you think in physics we might be able
to see that in our lifetimes?
I think what's probably gonna happen
is more of a blurring of the distinctions.
So today if somebody uses a computer
to do a computation that gives them the Nobel Prize,
nobody's gonna dream of giving the prize to the computer.
They're gonna be like, that was just a tool.
I think for these things also,
people are just gonna for a long time
view the computer as a tool.
But what's gonna change is the ubiquity of machine learning.
I think at some point in my lifetime,
finding a human physicist who knows nothing
about machine learning is gonna be almost as hard
as it is today finding a human physicist
who doesn't says, oh, I don't know anything about computers
or I don't use math.
That would just be a ridiculous concept.
You see, but the thing is there is a magic moment though,
like with Alpha Zero, when the system surprises us
in a way where the best people in the world
truly learn something from the system
in a way where you feel like it's another entity.
Like the way people, the way Magnus Carlsen,
the way certain people are looking at the work of Alpha Zero,
it's like, it truly is no longer a tool
in the sense that it doesn't feel like a tool.
It feels like some other entity.
So there's a magic difference like where you're like,
if an AI system is able to come up with an insight
that surprises everybody in some like major way
that's a phase shift in our understanding
of some particular science
or some particular aspect of physics,
I feel like that is no longer a tool.
And then you can start to say
that like it perhaps deserves the prize.
So for sure, the more important
and the more fundamental transformation
of the 21st century science is exactly what you're saying,
which is probably everybody will be doing machine learning.
It's to some degree.
Like if you want to be successful
at unlocking the mysteries of science,
you should be doing machine learning.
But it's just exciting to think about like,
whether there'll be one that comes along
that's super surprising and they'll make us question
like who the real inventors are in this world.
Yeah.
Yeah, I think the question of,
isn't if it's gonna happen, but when?
And, but it's important.
Honestly, in my mind, the time when that happens
is also more or less the same time
when we get artificial general intelligence.
And then we have a lot bigger things to worry about
than whether we should get the Nobel prize or not, right?
Yeah.
Because when you have machines
that can outperform our best scientists at science,
they can probably outperform us
at a lot of other stuff as well,
which can at a minimum make them
incredibly powerful agents in the world.
And I think it's a mistake to think
we only have to start worrying about loss of control
when machines get to AGI across the board,
where they can do everything, all our jobs.
Long before that, they'll be hugely influential.
We talked at length about how the hacking of our minds
with algorithms trying to get us glued to our screens,
right, has already had a big impact on society.
That was an incredibly dumb algorithm
in the grand scheme of things, right?
The supervised machine learning,
yet that had huge impact.
So I just don't want us to be lulled
into false sense of security
and think there won't be any societal impact
until things reach human level,
because it's happening already.
And I was just thinking the other week,
when I see some scaremonger going,
oh, the robots are coming,
the implication is always that they're coming to kill us.
Yeah.
And maybe you should have worried about that
if you were in Nagorno Karabakh
during the recent war there.
But more seriously, the robots are coming right now,
but they're mainly not coming to kill us.
They're coming to hack us.
They're coming to hack our minds,
into buying things that maybe we didn't need,
to vote for people who may not have
our best interest in mind.
And it's kind of humbling, I think,
actually, as a human being to admit
that it turns out that our minds are actually
much more hackable than we thought.
And the ultimate insult is that we are actually
getting hacked by the machine learning algorithms
that are, in some objective sense,
much dumber than us, you know?
But maybe we shouldn't be so surprised
because, you know, how do you feel about cute puppies?
Love them.
So, you know, you would probably argue
that in some across the board measure,
you're more intelligent than they are,
but boy, are cute puppies good at hacking us, right?
Yeah.
They move into our house, persuade us to feed them
and do all these things.
§18Transcript · 2:34:54–2:43:58
And what do they ever do but for us?
Yeah.
Other than being cute and making us feel good, right?
So if puppies can hack us,
maybe we shouldn't be so surprised
if pretty dumb machine learning algorithms can hack us too.
Not to speak of cats, which is another level.
And I think we should,
to counter your previous point about there,
let us not think about evil creatures in this world.
We can all agree that cats are as close
to objective evil as we can get.
But that's just me saying that.
Okay, so you have.
Have you seen the cartoon?
I think it's maybe the onion
with this incredibly cute kitten.
And it just says, it's underneath something
that thinks about murder all day.
Exactly.
That's accurate.
You've mentioned offline that there might be a link
between post biological AGI and SETI.
So last time we talked,
you've talked about this intuition
that we humans might be quite unique
in our galactic neighborhood.
Perhaps our galaxy,
perhaps the entirety of the observable universe
who might be the only intelligent civilization here,
which is, and you argue pretty well for that thought.
So I have a few little questions around this.
One, the scientific question,
in which way would you be,
if you were wrong in that intuition,
in which way do you think you would be surprised?
Like why were you wrong?
We find out that you ended up being wrong.
Like in which dimension?
So like, is it because we can't see them?
Is it because the nature of their intelligence
or the nature of their life is totally different
than we can possibly imagine?
Is it because the,
I mean, something about the great filters
and surviving them,
or maybe because we're being protected from signals,
all those explanations for why we haven't heard
a big, loud, like red light that says we're here.
So there are actually two separate things there
that I could be wrong about,
two separate claims that I made, right?
One of them is, I made the claim,
I think most civilizations,
when you're going from simple bacteria like things
to space colonizing civilizations,
they spend only a very, very tiny fraction
of their life being where we are.
That I could be wrong about.
The other one I could be wrong about
is the quite different statement that I think that actually
I'm guessing that we are the only civilization
in our observable universe
from which light has reached us so far
that's actually gotten far enough to invent telescopes.
So let's talk about maybe both of them in turn
because they really are different.
The first one, if you look at the N equals one,
the data point we have on this planet, right?
So we spent four and a half billion years
fluxing around on this planet with life, right?
We got, and most of it was pretty lame stuff
from an intelligence perspective,
you know, it was bacteria and then the dinosaurs spent,
then the things gradually accelerated, right?
Then the dinosaurs spent over a hundred million years
stomping around here without even inventing smartphones.
And then very recently, you know,
it's only, we've only spent 400 years
going from Newton to us, right?
In terms of technology.
And look what we've done even, you know,
when I was a little kid, there was no internet even.
So it's, I think it's pretty likely for,
in this case of this planet, right?
That we're either gonna really get our act together
and start spreading life into space, the century,
and doing all sorts of great things,
or we're gonna wipe out.
It's a little hard.
If I, I could be wrong in the sense that maybe
what happened on this earth is very atypical.
And for some reason, what's more common on other planets
is that they spend an enormously long time
futzing around with the ham radio and things,
but they just never really take it to the next level
for reasons I don't, I haven't understood.
I'm humble and open to that.
But I would bet at least 10 to one
that our situation is more typical
because the whole thing with Moore's law
and accelerating technology,
it's pretty obvious why it's happening.
Everything that grows exponentially,
we call it an explosion,
whether it's a population explosion or a nuclear explosion,
it's always caused by the same thing.
It's that the next step triggers a step after that.
So I, we, tomorrow's technology,
today's technology enables tomorrow's technology
and that enables the next level.
And as I think, because the technology is always better,
of course, the steps can come faster and faster.
On the other question that I might be wrong about,
that's the much more controversial one, I think.
But before we close out on this thing about,
if, the first one, if it's true
that most civilizations spend only a very short amount
of their total time in the stage, say,
between inventing
telescopes or mastering electricity
and leaving there and doing space travel,
if that's actually generally true,
then that should apply also elsewhere out there.
So we should be very, very,
we should be very, very surprised
if we find some random civilization
and we happen to catch them exactly
in that very, very short stage.
It's much more likely
that we find a planet full of bacteria.
Or that we find some civilization
that's already post biological
and has done some really cool galactic construction projects
in their galaxy.
Would we be able to recognize them, do you think?
Is it possible that we just can't,
I mean, this post biological world,
could it be just existing in some other dimension?
It could be just all a virtual reality game
for them or something, I don't know,
that it changes completely
where we won't be able to detect.
We have to be honestly very humble about this.
I think I said earlier the number one principle
of being a scientist is you have to be humble
and willing to acknowledge that everything we think,
guess might be totally wrong.
Of course, you could imagine some civilization
where they all decide to become Buddhists
and very inward looking
and just move into their little virtual reality
and not disturb the flora and fauna around them
and we might not notice them.
But this is a numbers game, right?
If you have millions of civilizations out there
or billions of them,
all it takes is one with a more ambitious mentality
that decides, hey, we are gonna go out
and settle a bunch of other solar systems
and maybe galaxies.
And then it doesn't matter
if they're a bunch of quiet Buddhists,
we're still gonna notice that expansionist one, right?
And it seems like quite the stretch to assume that,
now we know even in our own galaxy
that there are probably a billion or more planets
that are pretty Earth like.
And many of them are formed over a billion years
before ours, so had a big head start.
So if you actually assume also
that life happens kind of automatically
on an Earth like planet,
I think it's quite the stretch to then go and say,
okay, so there are another billion civilizations out there
that also have our level of tech
and they all decided to become Buddhists
and not a single one decided to go Hitler on the galaxy
and say, we need to go out and colonize
or not a single one decided for more benevolent reasons
to go out and get more resources.
That seems like a bit of a stretch, frankly.
And this leads into the second thing
you challenged me that I might be wrong about,
how rare or common is life, you know?
So Francis Drake, when he wrote down the Drake equation,
multiplied together a huge number of factors
and then we don't know any of them.
So we know even less about what you get
when you multiply together the whole product.
Since then, a lot of those factors
have become much better known.
One of his big uncertainties was
how common is it that a solar system even has a planet?
Well, now we know it very common.
Earth like planets, we know we have better.
There are a dime a dozen, there are many, many of them,
even in our galaxy.
At the same time, you know, we have thanks to,
I'm a big supporter of the SETI project and its cousins
§19Transcript · 2:43:58–2:53:11
and I think we should keep doing this
and we've learned a lot.
We've learned that so far,
all we have is still unconvincing hints, nothing more, right?
And there are certainly many scenarios
where it would be dead obvious.
If there were a hundred million
other human like civilizations in our galaxy,
it would not be that hard to notice some of them
with today's technology and we haven't, right?
So what we can say is, well, okay,
we can rule out that there is a human level of civilization
on the moon and in fact, the many nearby solar systems
where we cannot rule out, of course,
that there is something like Earth sitting in a galaxy
five billion light years away.
But we've ruled out a lot
and that's already kind of shocking
given that there are all these planets there, you know?
So like, where are they?
Where are they all?
That's the classic Fermi paradox.
And so my argument, which might very well be wrong,
it's very simple really, it just goes like this.
Okay, we have no clue about this.
It could be the probability of getting life
on a random planet, it could be 10 to the minus one
a priori or 10 to the minus five, 10, 10 to the minus 20,
10 to the minus 30, 10 to the minus 40.
Basically every order of magnitude is about equally likely.
When then do the math and ask the question,
how close is our nearest neighbor?
It's again, equally likely that it's 10 to the 10 meters away,
10 to 20 meters away, 10 to the 30 meters away.
We have some nerdy ways of talking about this
with Bayesian statistics and a uniform log prior,
but that's irrelevant.
This is the simple basic argument.
And now comes the data.
So we can say, okay, there are all these orders
of magnitude, 10 to the 26 meters away,
there's the edge of our observable universe.
If it's farther than that, light hasn't even reached us yet.
If it's less than 10 to the 16 meters away,
well, it's within Earth's,
it's no farther away than the sun.
We can definitely rule that out.
So I think about it like this,
a priori before we looked at the telescopes,
it could be 10 to the 10 meters, 10 to the 20,
10 to the 30, 10 to the 40, 10 to the 50, 10 to blah, blah, blah.
Equally likely anywhere here.
And now we've ruled out like this chunk.
And here is the edge of our observable universe already.
So I'm certainly not saying I don't think
there's any life elsewhere in space.
If space is infinite,
then you're basically a hundred percent guaranteed
that there is, but the probability that there is life,
that the nearest neighbor,
it happens to be in this little region
between where we would have seen it already
and where we will never see it.
There's actually significantly less than one, I think.
And I think there's a moral lesson from this,
which is really important,
which is to be good stewards of this planet
and this shot we've had.
It can be very dangerous to say,
oh, it's fine if we nuke our planet or ruin the climate
or mess it up with unaligned AI,
because I know there is this nice Star Trek fleet out there.
They're gonna swoop in and take over where we failed.
Just like it wasn't the big deal
that the Easter Island losers wiped themselves out.
That's a dangerous way of lulling yourself
into false sense of security.
If it's actually the case that it might be up to us
and only us, the whole future of intelligent life
in our observable universe,
then I think it really puts a lot of responsibility
on our shoulders.
It's inspiring, it's a little bit terrifying,
but it's also inspiring.
But it's empowering, I think, most of all,
because the biggest problem today is,
I see this even when I teach,
so many people feel that it doesn't matter what they do
or we do, we feel disempowered.
Oh, it makes no difference.
This is about as far from that as you can come.
But we realize that what we do
on our little spinning ball here in our lifetime
could make the difference for the entire future of life
in our universe.
How empowering is that?
Yeah, survival of consciousness.
I mean, a very similar kind of empowering aspect
of the Drake equation is,
say there is a huge number of intelligent civilizations
that spring up everywhere,
but because of the Drake equation,
which is the lifetime of a civilization,
maybe many of them hit a wall.
And just like you said, it's clear that that,
for us, the great filter,
the one possible great filter seems to be coming
in the next 100 years.
So it's also empowering to say,
okay, well, we have a chance to not,
I mean, the way great filters work,
they just get most of them.
Exactly.
Nick Bostrom has articulated this really beautifully too.
Every time yet another search for life on Mars
comes back negative or something,
I'm like, yes, yes.
Our odds for us surviving is the best.
You already made the argument in broad brush there, right?
But just to unpack it, right?
The point is we already know
there is a crap ton of planets out there
that are Earth like,
and we also know that most of them do not seem
to have anything like our kind of life on them.
So what went wrong?
There's clearly one step along the evolutionary,
at least one filter or roadblock
in going from no life to spacefaring life.
And where is it?
Is it in front of us or is it behind us, right?
If there's no filter behind us,
and we keep finding all sorts of little mice on Mars
or whatever, right?
That's actually very depressing
because that makes it much more likely
that the filter is in front of us.
And that what actually is going on
is like the ultimate dark joke
that whenever a civilization
invents sufficiently powerful tech,
it's just, you just set your clock.
And then after a little while it goes poof
for one reason or other and wipes itself out.
Now wouldn't that be like utterly depressing
if we're actually doomed?
Whereas if it turns out that there is a really,
there is a great filter early on
that for whatever reason seems to be really hard
to get to the stage of sexually reproducing organisms
or even the first ribosome or whatever, right?
Or maybe you have lots of planets with dinosaurs and cows,
but for some reason they tend to get stuck there
and never invent smartphones.
All of those are huge boosts for our own odds
because been there done that, you know?
It doesn't matter how hard or unlikely it was
that we got past that roadblock
because we already did.
And then that makes it likely
that the future is in our own hands, we're not doomed.
So that's why I think the fact
that life is rare in the universe,
it's not just something that there is some evidence for,
but also something we should actually hope for.
So that's the end, the mortality,
the death of human civilization
that we've been discussing in life,
maybe prospering beyond any kind of great filter.
Do you think about your own death?
Does it make you sad that you may not witness some of the,
you know, you lead a research group
on working some of the biggest questions
in the universe actually,
both on the physics and the AI side?
Does it make you sad that you may not be able
to see some of these exciting things come to fruition
that we've been talking about?
Of course, of course it sucks, the fact that I'm gonna die.
I remember once when I was much younger,
my dad made this remark that life is fundamentally tragic.
And I'm like, what are you talking about, daddy?
And then many years later, I felt,
now I feel I totally understand what he means.
You know, we grow up, we're little kids
and everything is infinite and it's so cool.
And then suddenly we find out that actually, you know,
you got to serve only,
this is the, you're gonna get game over at some point.
So of course it's something that's sad.
Are you afraid?
No, not in the sense that I think anything terrible
is gonna happen after I die or anything like that.
No, I think it's really gonna be a game over,
but it's more that it makes me very acutely aware
of what a wonderful gift this is
that I get to be alive right now.
And is a steady reminder to just live life to the fullest
and really enjoy it because it is finite, you know.
And I think actually, and we know we all get
§20Transcript · 2:53:11–3:02:46
the regular reminders when someone near and dear to us dies
that one day it's gonna be our turn.
It adds this kind of focus.
I wonder what it would feel like actually
to be an immortal being if they might even enjoy
some of the wonderful things of life a little bit less
just because there isn't that.
Finiteness?
Yeah.
Do you think that could be a feature, not a bug,
the fact that we beings are finite?
Maybe there's lessons for engineering
in artificial intelligence systems as well
that are conscious.
Like do you think it makes, is it possible
that the reason the pistachio ice cream is delicious
is the fact that you're going to die one day
and you will not have all the pistachio ice cream
that you could eat because of that fact?
Well, let me say two things.
First of all, it's actually quite profound
what you're saying.
I do think I appreciate the pistachio ice cream
a lot more knowing that I will,
there's only a finite number of times I get to enjoy that.
And I can only remember a finite number of times
in the past.
And moreover, my life is not so long
that it just starts to feel like things are repeating
themselves in general.
It's so new and fresh.
I also think though that death is a little bit overrated
in the sense that it comes from a sort of outdated view
of physics and what life actually is.
Because if you ask, okay, what is it that's gonna die
exactly, what am I really?
When I say I feel sad about the idea of myself dying,
am I really sad that this skin cell here is gonna die?
Of course not, because it's gonna die next week anyway
and I'll grow a new one, right?
And it's not any of my cells that I'm associating really
with who I really am.
Nor is it any of my atoms or quarks or electrons.
In fact, basically all of my atoms get replaced
on a regular basis, right?
So what is it that's really me
from a more modern physics perspective?
It's the information in processing me.
That's where my memory, that's my memories,
that's my values, my dreams, my passion, my love.
That's what's really fundamentally me.
And frankly, not all of that will die when my body dies.
Like Richard Feynman, for example, his body died of cancer,
but many of his ideas that he felt made him very him
actually live on.
This is my own little personal tribute to Richard Feynman.
I try to keep a little bit of him alive in myself.
I've even quoted him today, right?
Yeah, he almost came alive for a brief moment
in this conversation, yeah.
Yeah, and this honestly gives me some solace.
When I work as a teacher, I feel,
if I can actually share a bit about myself
that my students feel worthy enough to copy and adopt
as some part of things that they know
or they believe or aspire to,
now I live on also a little bit in them, right?
And so being a teacher is a little bit
of what I, that's something also that contributes
to making me a little teeny bit less mortal, right?
Because I'm not, at least not all gonna die all at once,
right?
And I find that a beautiful tribute to people
we do not respect.
If we can remember them and carry in us
the things that we felt was the most awesome about them,
right, then they live on.
And I'm getting a bit emotional here,
but it's a very beautiful idea you bring up there.
I think we should stop this old fashioned materialism
and just equate who we are with our quirks and electrons.
There's no scientific basis for that really.
And it's also very uninspiring.
Now, if you look a little bit towards the future, right?
One thing which really sucks about humans dying is that even
though some of their teachings and memories and stories
and ethics and so on will be copied by those around them,
hopefully, a lot of it can't be copied
and just dies with them, with their brain.
And that really sucks.
That's the fundamental reason why we find it so tragic
when someone goes from having all this information there
to the more just gone, ruined, right?
With more post biological intelligence,
that's going to shift a lot, right?
The only reason it's so hard to make a backup of your brain
in its entirety is exactly
because it wasn't built for that, right?
If you have a future machine intelligence,
there's no reason for why it has to die at all.
If you want to copy it, whatever it is,
into some other machine intelligence,
whatever it is, into some other quark blob, right?
You can copy not just some of it, but all of it, right?
And so in that sense,
you can get immortality because all the information
can be copied out of any individual entity.
And it's not just mortality that will change
if we get to more post biological life.
It's also with that, very much the whole individualism
we have now, right?
The reason that we make such a big difference
between me and you is exactly because
we're a little bit limited in how much we can copy.
Like I would just love to go like this
and copy your Russian skills, Russian speaking skills.
Wouldn't it be awesome?
But I can't, I have to actually work for years
if I want to get better on it.
But if we were robots.
Just copy and paste freely, then that loses completely.
It washes away the sense of what immortality is.
And also individuality a little bit, right?
We would start feeling much more,
maybe we would feel much more collaborative with each other
if we can just, hey, you know, I'll give you my Russian,
you can give me your Russian
and I'll give you whatever,
and suddenly you can speak Swedish.
Maybe that's less a bad trade for you,
but whatever else you want from my brain, right?
And there've been a lot of sci fi stories
about hive minds and so on,
where people, where experiences
can be more broadly shared.
And I think one, we don't,
I don't pretend to know what it would feel like
to be a super intelligent machine,
but I'm quite confident that however it feels
about mortality and individuality
will be very, very different from how it is for us.
Well, for us, mortality and finiteness
seems to be pretty important at this particular moment.
And so all good things must come to an end.
Just like this conversation, Max.
I saw that coming.
Sorry, this is the world's worst translation.
I could talk to you forever.
It's such a huge honor that you've spent time with me.
The honor is mine.
Thank you so much for getting me essentially
to start this podcast by doing the first conversation,
making me realize falling in love
with conversation in itself.
And thank you so much for inspiring
so many people in the world with your books,
with your research, with your talking,
and with the other, like this ripple effect of friends,
including Elon and everybody else that you inspire.
So thank you so much for talking today.
Thank you, I feel so fortunate
that you're doing this podcast
and getting so many interesting voices out there
into the ether and not just the five second sound bites,
but so many of the interviews I've watched you do.
You really let people go in into depth
in a way which we sorely need in this day and age.
That I got to be number one, I feel super honored.
Yeah, you started it.
Thank you so much, Max.
Thanks for listening to this conversation
with Max Tegmark, and thank you to our sponsors,
the Jordan Harbinger Show, For Sigmatic Mushroom Coffee,
BetterHelp Online Therapy, and ExpressVPN.
So the choice is wisdom, caffeine, sanity, or privacy.
Choose wisely, my friends.
And if you wish, click the sponsor links below
to get a discount and to support this podcast.
And now let me leave you with some words from Max Tegmark.
If consciousness is the way that information feels
when it's processed in certain ways,
then it must be substrate independent.
It's only the structure of information processing
that matters, not the structure of the matter
doing the information processing.
Thank you for listening, and hope to see you next time.