The following is a conversation from Stuart Russell. He's a professor of computer science at UC
Berkeley and a co-author of a book that introduced me and millions of other people to the amazing world
of AI called Artificial Intelligence and Modern Approach. So it was an honor for me to have this
conversation as part of MIT course on artificial journal intelligence and the artificial intelligence
podcast. If you enjoy it, please subscribe on YouTube, iTunes or your podcast provider of choice
or simply connect with me on Twitter at Lex Freedman spelled F-R-I-D. And now here's my
conversation with Stuart Russell. So you've mentioned in 1975 in high school you've created
one of your first AI programs that played chess. Were you ever able to build a program that
beat you at chess or another board game? So my program never beat me at chess.
I actually wrote the program at Imperial College. So I used to take the bus every Wednesday with a
box of cards this big and shove them into the card reader and they gave us eight seconds of CPU time.
It took about five seconds to read the cards in and compile the code. So we had three seconds of
CPU time, which was enough to make one move with a not very deep search. And then we would print
that move out and then we'd have to go to the back of the queue and wait to feed the cards in again.
How deep was the search? Are we talking about one move, two moves?
So no, I think we got an eight move, a depth eight with Alpha Beta and we had some tricks of our own
about move ordering and some pruning of the tree. But you were still able to beat that program?
Yeah. I was a reasonable chess player in my youth. I did an Othello program and a backgammon program.
So when I got to Berkeley, I worked a lot on what we call meta reasoning, which really means
reasoning about reasoning. And in the case of a game playing program, you need to reason about
what parts of the search tree you're actually going to explore because the search tree is
enormous or bigger than the number of atoms in the universe. And the way programs succeed and the
way humans succeed is by only looking at a small fraction of the search tree. And if you look at
the right fraction, you play really well. If you look at the wrong fraction, if you waste your time
thinking about things that are never going to happen, the moves that no one's ever going to make,
then you're going to lose because you won't be able to figure out the right decision.
So that question of how machines can manage their own computation, how they decide what to think about
is the meta reasoning question. We developed some methods for doing that. And very simply,
a machine should think about whatever thoughts are going to improve its decision quality. We were
able to show that both for Othello, which is a standard two-player game, and for backgammon,
which includes dice rolls, so it's a two-player game with uncertainty. For both of those cases,
we could come up with algorithms that were actually much more efficient than the standard alpha beta
search, which chess programs at the time were using. And that those programs could beat me.
And I think you can see the same basic ideas in AlphaGo and AlphaZero today.
The way they explore the tree is using a former meta reasoning to select what to think about
based on how useful it is to think about it.
Is there any insights you can describe with our Greek symbols of how do we select which paths to go down?
There's really two kinds of learning going on. So as you say, AlphaGo learns to evaluate board
to evaluate board position. So it can look at a go board, and it actually has probably a super
human ability to instantly tell how promising that situation is. To me, the amazing thing about
AlphaGo is not that it can be the world champion with its hands tied behind his back, but the fact that
if you stop it from searching altogether, so you say, okay, you're not allowed to do any thinking
ahead. You can just consider each of your legal moves and then look at the resulting situation
and evaluate it. So what we call a depth one search. So just the immediate outcome of your
moves and decide if that's good or bad. That version of AlphaGo can still play at a professional
level. And human professionals are sitting there for five, 10 minutes deciding what to do and AlphaGo
in less than a second can instantly intuit what is the right move to make based on its ability
to evaluate positions. And that is remarkable because we don't have that level of intuition
about go. We actually have to think about the situation. So anyway, that capability that AlphaGo
has is one big part of why it beats humans. The other big part is that it's able to look ahead
40, 50, 60 moves into the future. And if it was considering all possibilities, 40 or 50 or 60
moves into the future, that would be 10 to the 200 possibilities. So way more than atoms in
the universe and so on. So it's very, very selective about what it looks at. So let me try to give
you an intuition about how you decide what to think about. It's a combination of two things.
One is how promising it is. So if you're already convinced that a move is terrible,
there's no point spending a lot more time convincing yourself that it's terrible.
Because it's probably not going to change your mind. So the real reason you think is because
there's some possibility of changing your mind about what to do. And is that changing your mind
that would result then in a better final action in the real world? So that's the purpose of thinking
is to improve the final action in the real world. And so if you think about a move that is guaranteed
to be terrible, you can convince yourself it's terrible, you're still not going to change your
mind. But on the other hand, suppose you had a choice between two moves, one of them you've
already figured out is guaranteed to be a draw, let's say. And then the other one looks a little
bit worse, like it looks fairly likely that if you make that move, you're going to lose.
But there's still some uncertainty about the value of that move. There's still some possibility
that it will turn out to be a win. Then it's worth thinking about that. So even though it's less
promising on average than the other move, which is guaranteed to be a draw, there's still some
purpose in thinking about it because there's a chance that you'll change your mind and discover
that in fact it's a better move. So it's a combination of how good the move appears to be
and how much uncertainty there is about its value. The more uncertainty, the more it's worth thinking
about because there's a higher upside if you want to think of it that way. And of course, in the
beginning, especially in the AlphaGo Zero formulation, everything is shrouded in uncertainty.
So you're really swimming in a sea of uncertainty, so it benefits you to,
I mean, actually falling in the same process as you described, but because you're so uncertain
about everything, you basically have to try a lot of different directions. Yeah, so the early parts
of the search tree are fairly bushy, that it would look at a lot of different possibilities,
but fairly quickly, the degree of certainty about some of the moves, I mean, if a move is really
terrible, you'll pretty quickly find out, right? You'll lose half your pieces or half your territory.
And then you'll say, okay, this is not worth thinking about anymore. And then so further down,
the tree becomes very long and narrow. And you're following various lines of play, 10, 20, 30,
40, 50 moves into the future. And that again is something that human beings have a very hard time
doing, mainly because they just lack the short-term memory. You just can't remember a sequence of
moves that's 50 moves long. And you can't imagine the board correctly for that many moves into
the future. Of course, the top players, I'm much more familiar with chess, but the top players
probably have, they have echoes of the same kind of intuition instinct that in a moment's time,
AlphaGo applies when they see a board. I mean, they've seen those patterns, human beings have
seen those patterns before at the top, at the grandmaster level. It seems that there is some
similarities, or maybe it's our imagination creates a vision of those similarities, but it feels like
this kind of pattern recognition that the AlphaGo approaches are using is similar to what human
beings at the top level are using. I think there's some truth to that. But not entirely. Yeah, I mean,
I think the extent to which a human grandmaster can reliably, instantly recognize the right move,
instantly recognize the values of position, I think that's a little bit overrated. But if you
sacrifice a queen, for example, I mean, there's these beautiful games of chess with Bobby Fischer,
somebody where it's seeming to make a bad move. And I'm not sure there's a perfect degree of
calculation involved, where they've calculated all the possible things that happen. But there's
an instinct there, right, that somehow adds up to. Yeah, so I think what happens is you get a sense
that there's some possibility in the position, even if you make a weird looking move, that it opens up
some lines of calculation that otherwise would be definitely bad. And it's that intuition that
there's something here in this position that might yield a win. And then you follow that,
right? And in some sense, when a chess player is following a line in his or her mind,
they're, they're mentally simulating what the other person is going to do, what the opponent is
going to do. And they can do that as long as the moves are kind of forced, right, as long as there's
a, you know, there's a fort, we call a forcing variation where the opponent doesn't really
have much choice how to respond. And then you see if you can force them into a situation where you
win, you know, we see plenty of mistakes, even even in grandmaster games, where they just miss some
simple three, four, five move combination that, you know, wasn't particularly apparent in in the
position, but was still there. That's the thing that makes us human. Yeah. So when you mentioned
that in Othello, those games were after some matter reasoning improvements and research
was able to beat you. How did that make you feel? Part of the meta reasoning capability that it had
was based on learning. And, and you could sit down the next day and you could just feel that it
had got a lot smarter. You know, and all of a sudden, you really felt like you're sort of pressed
against the wall because it was it was much more aggressive and was totally unforgiving of any
minor mistake that you might make. And, and actually, it seemed understood the game better
than I did. And Gary Kasparov has this quote where during his match against deep blue,
he said he suddenly felt that there was a new kind of intelligence across the board.
Do you think that's a scary or an exciting possibility for Kasparov and for yourself
in, in the context of chess purely sort of in this like that feeling, whatever that is?
I think it's definitely an exciting feeling. You know, this is what made me work on AI in the
first place was as soon as I really understood what a computer was, I wanted to make it smart.
You know, I started out with the first program I wrote was for the Sinclair Programmable
Calculator. And I think you could write a 21 step algorithm that was the biggest
program you could write something like that and do little arithmetic calculations. So I think I
implemented Newton's method for square roots and a few other things like that. But then, you know,
I thought, okay, if I just had more space, I could make this thing intelligent.
And so I started thinking about AI and
and I think the thing that's scary is not, is not the chess program.
Because, you know, chess programs, they're not in the taking over the world business.
But if you extrapolate, you know, there are things about chess that don't resemble the real
world, right? We know, we know the rules of chess. The chess board is completely visible
to the program where, of course, the real world is not most most the real world is
is not visible from wherever you're sitting, so to speak. And to overcome those kinds of problems,
you need qualitatively different algorithms. Another thing about the real world is that,
you know, we we regularly plan ahead on the time scales involving billions or trillions of steps.
Now, we don't plan those in detail. But, you know, when you choose to do a PhD at Berkeley,
that's a five year commitment. And that amounts to about a trillion motor control steps that you
will eventually be committed to, including going up the stairs, opening doors, drinking water,
type, yeah, I mean, every every finger movement while you're typing every character of every
paper and the thesis and everything. So you're not committing in advance to the specific
motor control steps, but you're still reasoning on a time scale that will eventually reduce to
trillions of motor control actions. And so for all these reasons, you know, AlphaGo and and Deep
Blue and so on don't represent any kind of threat to humanity, but they are a step towards it,
right? And progress in AI occurs by essentially removing one by one, these assumptions that
make problems easy, like the assumption of complete observability of the situation, right?
We remove that assumption, you need a much more complicated kind of computing design,
and you need something that actually keeps track of all the things you can't see and tries to
estimate what's going on. And there's inevitable uncertainty in that. So it becomes a much more
complicated problem. But, you know, we are removing those assumptions, we are starting to have
algorithms that can cope with much longer time scales, cope with uncertainty that can cope with
partial observability. And so each of those steps sort of magnifies by a thousand the range of things
that we can do with AI systems. So the way I started in AI, I wanted to be a psychiatrist for
a long time, I wanted to understand the mind in high school, and of course program and so on. And
I showed up University of Illinois to an AI lab and they said, okay, I don't have time for you,
but here's a book, AI, Modern Approach, I think was the first edition at the time. Here, go learn
this. And I remember the lay of the land was, well, it's incredible that we solved chess, but we'll
never solve go. I mean, it was pretty certain that go in the way we thought about systems that reason
wasn't possible to solve. And now we've solved it. So it's a very... Well, I think I would have said
that it's unlikely we could take the kind of algorithm that was used for chess and just get
it to scale up and work well for go. And at the time, what we thought was that in order to solve go,
we would have to do something similar to the way humans manage the complexity of go, which is to
break it down into kind of sub games. So when a human thinks about a go board, they think about
different parts of the board as sort of weakly connected to each other. And they think about,
okay, within this part of the board, here's how things could go. In that part of board,
here's how things could go. And then you try to sort of couple those two analyses together
and deal with the interactions and maybe revise your views of how things are going to go in each
part. And then you've got maybe five, six, seven, 10 parts of the board. And that actually resembles
the real world much more than chess does. Because in the real world, we have work,
we have home life, we have sport, whatever, different kinds of activities, shopping.
These all are connected to each other, but they're weakly connected. So when I'm typing a paper,
I don't simultaneously have to decide which order I'm going to get the milk and the butter.
That doesn't affect the typing. But I do need to realize, okay, better finish this
before the shops close because I don't have any food at home. So there's some weak connection,
but not in the way that chess works, where everything is tied into a single stream of thought.
So the thought was that go to solve go would have to make progress on stuff that would be
useful for the real world. And in a way, AlphaGo is a little bit disappointing, because the program
designed for AlphaGo is actually not that different from Deep Blue or even from Alpha
Samuel's Jekyll playing program from the 1950s. And in fact, so the two things that make AlphaGo
work is one is this amazing ability to evaluate the positions. And the other is the meta reasoning
capability, which allows it to explore some paths in the tree very deeply and to abandon
other paths very quickly. So this word meta reasoning, while technically correct, inspires
perhaps the wrong degree of power that AlphaGo has, for example, the word reasoning is a powerful
word. Let me ask you sort of, you were part of the symbolic AI world for a while, like where AI
was, there's a lot of excellent interesting ideas there that unfortunately met a winter.
And so do you think it reemerges? Oh, so I would say, yeah, it's not quite as simple as that. So the
AI winter, the first winter that was actually named as such was the one in the late 80s.
And that came about because in the mid 80s, there was a really a concerted attempt to push AI
out into the real world using what was called expert system technology. And for the most part,
that technology was just not ready for prime time. They were trying, in many cases, to do a form of
uncertain reasoning, you know, judgment combinations of evidence diagnosis, those kinds of things,
which was simply invalid. And when you try to apply invalid reasoning methods to real problems,
you can fudge it for small versions of the problem. But when it starts to get larger,
the thing just falls apart. So many companies found that the stuff just didn't work, and they
were spending tons of money on consultants to try to make it work. And there were other practical
reasons like they were asking the companies to buy incredibly expensive Lisp machine workstations,
which were literally between $50,000 and $100,000 in 1980s money, which would be like
between $150,000 and $300,000 per workstation in current prices.
And then the bottom line, they weren't seeing a profit from it.
Yeah. In many cases, I think there were some successes, there's no doubt about that. But
people, I would say, overinvested. Every major company was starting an AI department just like now.
And I worry a bit that we might see similar disappointments, not because the current technology
is invalid, but it's limited in its scope. And it's almost the dual of the, you know,
the scope problems that expert systems had. So
what have you learned from that hype cycle? And what can we do to prevent another winter,
for example? Yeah. So when I'm giving talks these days, that's one of the warnings
that I give. So there's two part warning slide. One is that rather than data being the new oil,
data is the new snake oil. That's a good line. And then the other is that we might see
a kind of very visible failure in some of the major application areas. And I think self-driving cars
would be the flagship. And I think when you look at the history, so the first self-driving car was
on the freeway, driving itself, changing lanes, overtaking in 1987. And so it's more than 30 years.
And that kind of looks like where we are today, right? You know, prototypes on the freeway,
changing lanes and overtaking. Now, I think significant progress has been made,
particularly on the perception side. So we worked a lot on autonomous vehicles in the
early, mid 90s at Berkeley. And we had our own big demonstrations. We put congressmen into
self-driving cars and had them zooming along the freeway. And the problem was clearly perception.
At the time, the problem was perception. Yeah. So in simulation, with perfect perception,
you could actually show that you can drive safely for a long time, even if the other
cars are misbehaving and so on. But simultaneously, we worked on machine vision for detecting cars and
tracking pedestrians and so on. And we couldn't get the reliability of detection and tracking
up to a high enough level, particularly in bad weather conditions, nighttime rainfall.
Good enough for demos, but perhaps not good enough to cover the general operation.
Yeah. So the thing about driving is, suppose you're a taxi driver and you drive every day,
eight hours a day for 10 years, right? That's 100 million seconds of driving.
And any one of those seconds, you can make a fatal mistake. So you're talking about
eight nines of reliability, right? Now, if your vision system only detects 98.3% of the vehicles,
right? That's sort of one on a bit nines of reliability. So you have another seven orders
of magnitude to go. And this is what people don't understand. They think, oh, because I had a
successful demo, I'm pretty much done. But you're not even within seven orders of magnitude
of being done. And that's the difficulty. And it's not, can I follow a white line?
That's not the problem, right? We follow a white line all the way across the country.
But it's the weird stuff that happens. It's on the edge cases, yeah. The edge case,
other drivers doing weird things. So if you talk to Google, right, so they had actually a very
classical architecture where you had machine vision, which would detect all the other cars and
pedestrians and the white lines and the road signs. And then basically, that was fed into a
logical database. And then you had a classical 1970s rule based expert system telling you,
okay, if you're in the middle lane, and there's a bicyclist in the right lane, who is signaling
this, then do that, right? And what they found was that every day that go out and there'd be
another situation that the rules didn't cover. So they come to a traffic circle and there's a
little girl riding her bicycle the wrong way around the traffic circle. Okay, what do you do?
We don't have a rule. Oh my God. Okay, stop. And then they come back and add more rules. And they
just found that this was not really converging. And if you think about it, right, how do you deal
with an unexpected situation, meaning one that you've never previously encountered and the sort of
the reasoning required to figure out the solution for that situation has never been done. It doesn't
match any previous situation in terms of the kind of reasoning you have to do. Well,
well, you know, in chess programs, this happens all the time, right? You're constantly coming up
with situations you haven't seen before. And you have to reason about them. And you have to think
about, okay, here are the possible things I could do. Here are the outcomes. Here's how desirable
the outcomes are, and then pick the right one. You know, in the 90s, we were saying, okay,
this is how you're going to have to do automated vehicles, they're going to have to have a look
ahead capability. But the look ahead for driving is more difficult than it is for chess because
there's humans and they're less predictable than chess pieces. Well, then you have an
opponent in chess who's also somewhat unpredictable. But for example, in chess, you always know the
opponent's intention, they're trying to beat you, right? Whereas in driving, you don't know,
is this guy trying to turn left? Or has he just forgotten to turn off his turn signal? Or is he
drunk? Or is he changing the channel on his radio or whatever it might be? You've got to try and
figure out the mental state, the intent of the other drivers to forecast the possible evolutions
of their trajectories. And then you've got to figure out, okay, which is the trajectory for me
that's going to be safest? And those all interact with each other because the other driver is going
to react to your trajectory and so on. So you've got the classic merging onto the freeway problem
where you're racing a vehicle that's already on the freeway and you pull ahead of them,
or you're going to let them go first and pull in behind and you get this uncertainty about who's
going first. So all those kinds of things mean that you need decision-making architecture
that's very different from either a rule-based system or it seems to me kind of an end-to-end
neural network system. So just as AlphaGo is pretty good when it doesn't do any look ahead,
but is way, way, way, way better when it does, I think the same is going to be true for driving.
You can have a driving system that's pretty good when it doesn't do any look ahead,
but that's not good enough. And we've already seen multiple deaths caused by poorly designed
machine learning algorithms that don't really understand what they're doing.
Yeah, on several levels, I think on the perception side, there's mistakes being made
by those algorithms where the perception is very shallow. On the planning side, the look ahead,
like you said, and the thing that we come up against that's really interesting when you
try to deploy systems in the real world is you can't think of an artificial intelligence system
as a thing that responds to the world always. You have to realize that it's an agent that others
will respond to as well. So in order to drive successfully, you can't just try to do obstacle
avoidance. You can't pretend that you're invisible, right? You're the invisible car,
doesn't work that way. I mean, but you have to assert yet others have to be scared of you.
There's this tension, there's this game. So we study a lot of work with pedestrians. If you
approach pedestrians as purely an obstacle avoidance, so you're doing look ahead as in
modeling the intent, they're not going to take advantage of you, they're not going to respect
you at all. There has to be a tension, a fear, some amount of uncertainty. That's how we have
or at least just kind of a resoluteness. You have to display a certain amount of
resoluteness. You can't be too tentative. And yeah, so the solutions then become
pretty complicated, right? You get into game theoretic analyses and
end. So at Berkeley now, we're working a lot on this kind of interaction between machines and
humans. And that's exciting. And so my colleague, Anka Dragan, actually, if you formulate the
problem game theoretically, you just let the system figure out the solution, it does interesting
unexpected things. Like sometimes at a stop sign, if no one is going first, the car will
actually back up a little. And just to indicate to the other cars that they should go. And that's
something it invented entirely by itself. That's interesting. We didn't say this is the language
of communication at stop signs, it figured it out. That's really interesting. So let me one
just step back for a second. Just this beautiful philosophical notion. So Pamela McCordick in 1979
wrote AI began with the ancient wish to forge the gods. So when you think about the history of
our civilization, do you think that there is an inherent desire to create, let's not say gods,
but to create superintelligence? Is it inherent to us? Is it in our genes that the natural arc of
human civilization is to create things that are of greater and greater power and perhaps
echoes of ourselves. So to create the gods, as Pamela said.
It may be. I mean, we're all individuals, but certainly we see over and over again in history,
individuals who thought about this possibility.
Hopefully, I'm not being too philosophical here. But if you look at the arc of where this is going
and we'll talk about AI safety, we'll talk about greater and greater intelligence, do you see that
when you created the Othello program and you felt this excitement, what was that excitement?
Was it the excitement of a tinkerer who created something cool, like a clock? Or was there
a magic, or was it more like a child being born? Yeah. So I mean, I certainly understand that
viewpoint. And if you look at the Lighthill report, which was, so in the 70s, there was a lot of
controversy in the UK about AI and whether it was for real and how much the money the government
should invest. And so it was a long story, but the government commissioned a report by
Lighthill, who was a physicist, and he wrote a very damning report about AI, which I think was
the point. And he said that these are frustrated men who are unable to have children would like
to create and create life as a kind of replacement, which I think is really pretty unfair.
But there is, I mean, there is a kind of magic, I would say, when you build something
and what you're building in is really just you're building in some understanding of the
principles of learning and decision making. And to see those principles actually then
turn into intelligent behavior in specific situations. It's an incredible thing. And
that is naturally going to make you think, okay, where does this end?
And so there's a there's magical, optimistic views of word ends, whatever your view of optimism is,
whatever your view of utopia is, it's probably different for everybody. But you've often talked
about concerns you have of how things may go wrong. So I've talked to Max Tegmark.
There's a lot of interesting ways to think about AI safety. You're one of the
seminal people thinking about this problem amongst sort of being in the weeds of actually
solving specific AI problems, you also think about the big picture of where we're going.
So can you talk about several elements of it? Let's just talk about maybe the control problem.
So this idea of losing ability to control the behavior of our AI system. So how do you see
that? How do you see that coming about? What do you think we can do to manage it?
Well, so it doesn't take a genius to realize that if you make something that's smarter than you,
you might have a problem. Alan Turing wrote about this and gave lectures about this,
1951. He did a lecture on the radio. And he basically says, once the machine
thinking method starts, very quickly they'll outstrip humanity. And if we're lucky, we might be able to,
I think he says, if we may be able to turn off the power at strategic moments, but even so,
our species would be humbled. And actually, I think it was wrong about that. If it's a
sufficiently intelligent machine, it's not going to let you switch it off. It's actually in competition
with you. So what do you think is meant just for a quick tangent if we shut off this super
intelligent machine that our species would be humbled? I think he means that we would realize
that we are inferior, right? That we only survive by the skin of our teeth because we happen to
get to the off switch. Just in time. And if we hadn't, then we would have lost control
over the earth. So are you more worried when you think about the stuff about super intelligent AI
or are you more worried about super powerful AI that's not aligned with our values? So the
paperclip scenarios kind of... I think, so the main problem I'm working on is the control problem,
the problem of machines pursuing objectives that are, as you say, not aligned with human
objectives. And this has been the way we've thought about AI since the beginning.
You build a machine for optimizing and then you put in some objective and it optimizes,
right? And we can think of this as the King Midas problem, right? Because if the King Midas
put in this objective, everything I touch should turn to gold. And the gods, that's like the machine,
they said, okay, done. You now have this power. And of course, his food and his drink and his
family all turned to gold. And then he dies of misery and starvation. And this is, you know,
it's a warning. It's a failure mode that pretty much every culture in history has had some story
along the same lines. You know, there's the genie that gives you three wishes. And, you know,
third wishes always, you know, please undo the first two wishes because I messed up.
And, you know, when Arthur Samuel wrote his checker playing program, which learned to play
checkers considerably better than Arthur Samuel could play and actually reached a pretty decent
standard. Norbert Wiener, who was one of the major mathematicians of the 20th century,
he's sort of the father of modern automation control systems. He saw this and he basically
extrapolated, you know, as Turing did and said, okay, this is how we could lose control. And
specifically, that we have to be certain that the purpose we put into the machine is the
purpose which we really desire. And the problem is, we can't do that.
Right. You mean we're not, it's a very difficult to encode, to put our values on paper is really
difficult or you're just saying it's impossible? The line is great between the two. So theoretically,
it's possible, but in practice, it's extremely unlikely that we could specify correctly in
advance the full range of concerns of humanity. You talked about cultural transmission of values,
I think, is how humans to human transmission of values happens, right?
Well, we learn, yeah, I mean, as we grow up, we learn about the values that matter, how things
how things should go, what is reasonable to pursue and what isn't reasonable to pursue.
I think machines can learn in the same kind of way.
Yeah, so I think that what we need to do is to get away from this idea that you build an
optimizing machine, and then you put the objective into it. Because if it's possible
that you might put in a wrong objective, and we already know this is possible because it's
happened lots of times, right? That means that the machine should never take an objective that's
given as gospel truth. Because once it takes the objective as gospel truth, then it believes that
whatever actions it's taking in pursuit of that objective are the correct things to do. So you
could be jumping up and down and saying, no, no, no, no, you're going to destroy the world,
but the machine knows what the true objective is and is pursuing it and tough luck to you.
You know, and this is not restricted to AI, right? This is, you know, I think many of the 20th
century technologies, right? So in statistics, you minimize a loss function, the loss function
is exogenously specified in control theory, you minimize a cost function in operations research,
you maximize a reward function and so on. So in all these disciplines, this is how we conceive
of the problem. And it's the wrong problem. Because we cannot specify with certainty the correct
objective, right? We need uncertainty, we need the machine to be uncertain about
its objective, what it is that it's supposed to be maximizing.
It's my favorite idea of yours. I've heard you say somewhere, well, I shouldn't pick favorites,
but it just sounds beautiful. We need to teach machines humility. Yeah, I mean, it's a beautiful
way to put it. I love it. That they humble, they know that they don't know what it is they're
supposed to be doing. And that those, those objectives, I mean, they exist. They are within
us, but we may not be able to explicate them. We may not even know how we want our future to go.
So exactly. And the machine, you know, a machine that's uncertain is going to be differential
to us. So if we say, don't do that, well, now the machines learn something a bit more about
our true objectives, because something that it thought was reasonable in pursuit of our objective
turns out not to be so now it's learned something. So it's going to defer because it wants to be
doing what we really want. And, you know, that, that point, I think is absolutely central to
solving the control problem. And it's a different kind of AI when you, when you
you take away this idea that the objective is known, then, in fact, a lot of the theoretical
frameworks that we're so familiar with, you know, Markov decision processes, goal-based planning,
you know, standard game research, all of these techniques actually become
inapplicable. And you get a more complicated problem because, because now the interaction
with the human becomes part of the problem. Because the human by making choices is giving you
more information about the true objective and that information helps you achieve the objective
better. And so that really means that you're mostly dealing with game-theoretic problems
where you've got the machine and the human and they're coupled together rather than a machine
going off by itself with a fixed objective. Which is fascinating on the machine and the human level
that we, when you don't have an objective means you're together coming up with an objective.
I mean, there's a lot of philosophy that, you know, you could argue that life doesn't really have
meaning. We, we together agree on what gives it meaning and we kind of culturally create
things that give why the heck we are on this earth anyway. We together as a society create
that meaning and you have to learn that objective. And one of the biggest, I thought that's where
you were going to go for a second. One of the biggest troubles we run into outside of statistics
and machine learning and AI in just human civilization is when you look at, I came from,
I was born in the Soviet Union and the history of the 20th century, we ran into the most trouble,
us humans, when there was a certainty about the objective and you do whatever it takes to achieve
that objective, whether you're talking about Germany or communist Russia, all, you get into
trouble with humans. And I would say with, you know, corporations, in fact, some people argue
that, you know, we don't have to look forward to a time when AI systems take over the world,
they already have and they call corporations, right? That corporations happen to be
using people as components right now, but they are effectively algorithmic machines and they're
optimizing an objective, which is quarterly profit that isn't aligned with overall well-being of
the human race and they are destroying the world. They are primarily responsible for our inability
to tackle climate change. So I think that's one way of thinking about what's going on with
corporations. But I think the point you're making is valid that there are many systems in the real
world where we've sort of prematurely fixed on the objective and then decoupled the machine
from those that's supposed to be serving. And I think you see this with government,
right? Government is supposed to be a machine that serves people. But instead, it tends to be
taken over by people who have their own objective and use government to optimize that
objective regardless of what people want. Do you find appealing the idea of almost arguing
machines where you have multiple AI systems with a clear fixed objective? We have in government,
the red team and the blue team, they're very fixed on their objectives and they argue and it kind of
maybe would disagree, but it kind of seems to make it work somewhat that the duality of it.
Okay, let's go 100 years back when there was still was going on or at the founding of this
country. There was disagreements and that disagreement is where so it was a balance
between certainty and forced humility because the power was distributed.
Yeah, I think that the nature of debate and disagreement argument takes as a premise the
idea that you could be wrong, which means that you're not necessarily absolutely convinced
that your objective is the correct one. If you were absolutely convinced, there'd be no point
in having any discussion or argument because you would never change your mind and there wouldn't be
any sort of synthesis or anything like that. I think you can think of argumentation as an
implementation of a form of uncertain reasoning. I've been reading recently about
utilitarianism and the history of efforts to define in a sort of clear mathematical way a
if you like a formula for moral or political decision making. It's really interesting that
the parallels between the philosophical discussions going back 200 years and what you see now in
discussions about existential risk because it's almost exactly the same. Someone would say,
okay, well, here's a formula for how we should make decisions. Utilitarianism is roughly each
person has a utility function and then we make decisions to maximize the sum of everybody's
utility. Then people point out, well, in that case, the best policy is one that leads to the
enormously vast population, all of whom are living a life that's barely worth living.
This is called the repugnant conclusion. Another version is that we should maximize
pleasure and that's what we mean by utility. Then you'll get people effectively saying, well,
in that case, we might as well just have everyone hooked up to a heroin drip and they
didn't use those words, but that debate was happening in the 19th century as it is now
about AI that if we get the formula wrong, we're going to have AI systems working towards
an outcome that in retrospect would be exactly wrong. Do you think there's has beautifully
put so the echoes are there, but do you think, I mean, if you look at Sam Harris, our imagination
worries about the AI version of that because of the speed at which the things going wrong in the
utilitarian context could happen? Is that a worry for you? I think that in most cases,
not in all, but if we have a wrong political idea, we see it starting to go wrong and we're
not completely stupid. Maybe that was a mistake. Let's try something different.
Also, we're very slow and inefficient about implementing these things and so on. You have to
worry when you have corporations or political systems that are extremely efficient,
but when we look at AI systems or even just computers in general, they have this
different characteristic from ordinary human activity in the past. Let's say you were a surgeon.
You had some idea about how to do some operation. Well, let's say you were wrong.
That way of doing the operation would mostly kill the patient. Well, you'd find out pretty quickly
after maybe three or four tries, but that isn't true for pharmaceutical companies
because they don't do three or four operations. They manufacture three or four billion pills
and they sell them and then they find out maybe six months or a year later that,
oh, people are dying of heart attacks or getting cancer from this drug.
That's why we have the FDA because of the scalability of pharmaceutical production.
There have been some unbelievably bad episodes in the history of pharmaceuticals and adulteration
of products and so on that have killed tens of thousands or paralyzed hundreds of thousands of
people. Now, with computers, we have that same scalability problem that you can sit there and
type for i equals one to five billion do. All of a sudden, you're having an impact on a global
scale and yet we have no FDA. There's absolutely no controls at all over what a bunch of undergraduates
with too much caffeine can do to the world. We look at what happened with Facebook, well,
social media in general, and click through optimization. You have a simple feedback
algorithm that's trying to just optimize click through. That sounds reasonable because you
don't want to be feeding people ads that they don't care about or not interested in.
You might even think of that process as simply adjusting the feeding of ads or news articles
or whatever it might be to match people's preferences, which sounds like a good idea.
But in fact, that isn't how the algorithm works. The algorithm makes more money if
it can better predict what people are going to click on because then it can feed them exactly
that. The way to maximize click through is actually to modify the people,
to make them more predictable. One way to do that is to feed them information,
which will change their behavior and preferences towards extremes that make them predictable.
Whatever is the nearest extreme or the nearest predictable point, that's where you're going
to end up and the machines will force you there. I think there's a reasonable argument to say
that this, among other things, is contributing to the destruction of democracy in the world.
Where was the oversight of this process? Where were the people saying, okay, you would like
to apply this algorithm to 5 billion people on the face of the earth? Can you show me that it's
safe? Can you show me that it won't have various kinds of negative effects? No, there was no one
asking that question. There was no one placed between the undergrads with too much caffeine
and the human race. They just did it. But some way outside the scope of my knowledge,
so economists would argue that the invisible hand, the capitalist system, it was the oversight.
If you're going to corrupt society with whatever decision you make as a company,
then that's going to be reflected in people not using your product. That's one model of oversight.
We shall see. In the meantime, you might even have broken the political system that enables
capitalism to function. Well, you've changed it. We should see. Change is often painful. My question
is absolutely, it's fascinating. You're absolutely right that there was zero oversight on algorithms
that can have a profound civilization changing effect. Do you think it's possible? Have you seen
government? Do you think it's possible to create regulatory bodies oversight over AI algorithms,
which are inherently such cutting edge set of ideas and technologies?
Yeah, but I think it takes time to figure out what kind of oversight, what kinds of controls.
I mean, it took time to design the FDA regime. Some people still don't like it and they want to fix
it. I think there are clear ways that it could be improved. But the whole notion that you have
stage one, stage two, stage three, and here are the criteria for what you have to do to pass a
stage one trial. We haven't even thought about what those would be for algorithms. I think there
are things we could do right now with regard to bias. For example, we have a pretty good
technical handle on how to detect algorithms that are propagating bias that exists in datasets,
how to debias those algorithms, and even what it's going to cost you to do that.
I think we could start having some standards on that. I think there are things to do with
impersonation and falsification that we could work on.
A very simple point. Impersonation is a machine acting as if it was a person.
I can't see a real justification for why we shouldn't insist that machines self-identify as
machines. Where is the social benefit in fooling people into thinking that this is really a person
when it isn't? I don't mind if it uses a human-like voice that's easy to understand,
that's fine, but it should just say, I'm a machine in some form.
And how many people are speaking to that, I would think relatively obvious facts.
So I think most people... Yeah, I mean, there is actually a law in California
that bans impersonation, but only in certain restricted circumstances. So for the purpose of
engaging in a fraudulent transaction and for the purpose of modifying someone's voting behavior.
So those are the circumstances where machines have to self-identify.
But I think arguably it should be in all circumstances. And then when you talk about
deep fakes, we're just at the beginning, but already it's possible to make a movie of
anybody saying anything in ways that are pretty hard to detect.
Including yourself because you're on camera now and your voice is coming through with high
resolution. So you could take what I'm saying and replace it with pretty much anything else
you wanted me to be saying. And even it would change my lips and facial expressions to fit.
And there's actually not much in the way of real legal protection against that. I think
in the commercial area, you could say, yeah, you're using my brand and so on. There are rules
about that. But in the political sphere, I think at the moment, it's anything goes.
That could be really, really damaging. And let me just try to make not an argument,
but try to look back at history and say something dark, in essence, is while regulation seems to be
the oversight seems to be exactly the right thing to do here. It seems that human beings,
what they naturally do is they wait for something to go wrong. If you're talking about nuclear weapons,
you can't talk about nuclear weapons being dangerous until somebody actually, like the
United States drops the bomb, or Chernobyl melting. Do you think we will have to wait
for things going wrong in a way that's obviously damaging to society, not an existential risk,
but obviously damaging? Or do you have faith that?
I hope not. But I think we do have to look at history. So the two examples you gave,
nuclear weapons and nuclear power are very, very interesting because in nuclear weapons,
we knew in the early years of the 20th century that atoms contained a huge amount of energy.
We had E equals MC squared. We knew the mass differences between the different atoms and
their components. And we knew that you might be able to make an incredibly powerful explosive.
So H.G. Wells wrote science fiction book I think in 1912. Frederick Soddy, who was the guy who
discovered isotopes as a Nobel Prize winner, he gave a speech in 1915 saying that one pound
of this new explosive would be the equivalent of 150 tons of dynamite, which turns out to be about
right. And this was in World War I. So he was imagining how much worse the world war would be
if we were using that kind of explosive. But the physics establishment simply refused to believe
that these things could be made. Including the people who are making it.
Well, so they were doing the nuclear physics. I mean, eventually were the ones who made it.
You talk about Fermi or whoever. Well, so up to the development was mostly theoretical. So it was
people using sort of primitive kinds of particle acceleration and doing experiments at the level
of single particles or collections of particles. They weren't yet thinking about how to actually
make a bomb or anything like that. But they knew the energy was there and they figured if they
understood it better, it might be possible. But the physics establishment, their view, and I think
because they did not want it to be true, their view was that it could not be true.
That this could not provide a way to make a super weapon. And there was this famous
speech given by Rutherford, who was the sort of leader of nuclear physics. And
it was on September 11th, 1933. And he said, anyone who talks about the possibility of obtaining
energy from transformation of atoms is talking complete moonshine. And the next morning, Leo
Zillard read about that speech and then invented the nuclear chain reaction. And so as soon as he
invented, as soon as he had that idea that you could make a chain reaction with neutrons because
neutrons were not repelled by the nucleus so they could enter the nucleus and then continue the
reaction. As soon as he has that idea, he instantly realized that the world was in deep doodoo.
Because this is 1933, right? Hitler had recently come to power in Germany.
Zillard was in London. He eventually became a refugee and came to the US. And in the process
of having the idea about the chain reaction, he figured out basically how to make a bomb and also
how to make a reactor. And he patented the reactor in 1934. But because of the situation,
the great power conflict situation that he could see happening, he kept that a secret.
And so between then and the beginning of World War II, people were working, including the Germans,
on how to actually create neutron sources, what specific fission reactions would produce
neutrons of the right energy to continue the reaction. And that was demonstrated in Germany,
I think, in 1938, if I remember correctly. The first nuclear weapon patent was 1939
by the French. So this was actually going on well before World War II really got going.
And then the British probably had the most advanced capability in this area. But for safety reasons,
among others, and bless just sort of just resources, they moved the program from Britain to the US.
And then that became Manhattan Project. So the reason why we couldn't
have any kind of oversight of nuclear weapons and nuclear technology
was because we were basically already in an arms race and a war.
But you mentioned that in the 20s and 30s. So what are the echoes?
The way you've described the story, I mean, there's clearly echoes. Why do you think most AI
researchers, folks who are really close to the metal, they really are not concerned about AI,
they don't think about it, whether it's they don't want to think about it. But why do you think that
is? What are the echoes of the nuclear situation to the current AI situation? And what can we do
about it? I think there is a kind of motivated cognition, which is a term in psychology means
that you believe what you would like to be true rather than what is true. And it's unsettling
to think that what you're working on might be the end of the human race, obviously.
So you would rather instantly deny it, come up with some reason why it couldn't be true.
And I collected a long list of reasons that extremely intelligent, competent AI scientists
have come up with for why we shouldn't worry about this. For example, calculators are superhuman
at arithmetic and they haven't taken over the world. So there's nothing to worry about.
Well, okay, my five-year-old could have figured out why that was
an unreasonable and really quite weak argument. Another one was, while it's theoretically possible
that you could have superhuman AI destroy the world, it's also theoretically possible that
a black hole could materialize right next to the earth and destroy humanity. Yes,
it's theoretically possible, quantum theoretically, extremely unlikely that it would just materialize
right there. But that's a completely bogus analogy because if the whole physics community on earth
was working to materialize a black hole in near-earth orbit, wouldn't you ask them,
is that a good idea? Is that going to be safe? What if you succeed? And that's the thing.
Right? The AI community has sort of refused to ask itself, what if you succeed?
And initially, I think that was because it was too hard, but Alan Turing asked himself that
and he said, we'd be toast. Right? If we were lucky, we might be able to switch off the power,
but probably we'd be toast. But there's also an aspect that because we're not exactly
sure what the future holds, it's not clear exactly, so technically, what to worry about,
sort of how things go wrong. And so there is something it feels like, maybe you can correct
me if I'm wrong, but there's something paralyzing about worrying about something that logically
is inevitable, but you don't really know what that will look like. Yeah, I think it's a reasonable
point. And it's certainly in terms of existential risks, it's different from
asteroid collides with the Earth, which again is quite possible. It's happened in the past.
It'll probably happen again. We don't know right now, but if we did detect an asteroid that was
going to hit the Earth in 75 years time, we'd certainly be doing something about it.
Well, it's clear there's got big rock and there's we'll probably have a meeting and see what do
we do about the big rock with AI. Right, with AI, I mean, there are very few people who think it's
not going to happen within the next 75 years. I know Rod Brooks doesn't think it's going to happen.
Maybe Andrew Ng doesn't think it's happened, but a lot of the people who work day to day,
as you say, at the rock face, they think it's going to happen. I think the median estimate
from AI researchers is somewhere in 40 to 50 years from now. Or maybe you know, I think
in Asia, they think it's going to be even faster than that. I'm a little bit more conservative.
I think it probably take longer than that. But I think it's, you know, as happened with nuclear
weapons, it can happen overnight that you have these breakthroughs. And we need more than one
breakthrough. But, you know, it's on the order of half a dozen. I mean, this is a very rough scale,
but so half a dozen breakthroughs of that nature would have to happen for us to reach
superhuman AI. But the, you know, the AI research community is vast now, the massive
investments from governments, from corporations, tons of really, really smart people. You just
have to look at the rate of progress in different areas of AI to see that things are moving pretty
fast. So to say, oh, it's just going to be thousands of years. I don't see any basis for that. You
know, I see, you know, for example, the Stanford 100-year AI project, right, which is supposed
to be sort of, you know, the serious establishment view, their most recent report actually said
it's probably not even possible. Oh, wow. Right. Which if you want a perfect example of people in
denial, that's it. Because, you know, for the whole history of AI, we've been saying to philosophers
who said it wasn't possible. Well, you have no idea what you're talking about. Of course,
it's possible. Right. Give me an, give me an argument for why it couldn't happen. And there
isn't one. Right. And now, because people are worried that maybe AI might get a bad name or
I just don't want to think about this, they're saying, okay, well, of course, it's not really
possible. You know, imagine, right? Imagine if, you know, the leaders of the cancer biology community
got up and said, well, you know, of course, curing cancer, it's not really possible.
It would be a complete outrage and dismay. And, you know, I find this really a strange
phenomenon. So, okay, so if you accept that it's possible, and if you accept that it's probably
going to happen, the point that you're making that, you know, how does it go wrong?
A valid question. Without that, without an answer to that question, then you're stuck with what I
call the gorilla problem, which is, you know, the problem that the gorillas face, right? They made
something more intelligent than them, namely us, a few million years ago, and now they're in deep
doo doo. So there's really nothing they can do. They've lost the control. They failed to solve
the control problem of controlling humans. And so they've lost. So we don't want to be in that
situation. And if the gorilla problem is the only formulation you have, there's not a lot you can do,
right? Other than to say, okay, we should try to stop. You know, we should just not make the humans
or in this case, not make the AI. And I think that's really hard to do. And I'm not actually
proposing that that's a feasible course of action. And I also think that, you know, if properly
controlled AI could be incredibly beneficial. But it seems to me that there's a consensus that one
of the major failure modes is this loss of control, that we create AI systems that are pursuing
incorrect objectives. And because the AI system believes it knows what the objective is,
it has no incentive to listen to us anymore, so to speak, right? It's just carrying out
the strategy that it has computed as being the optimal solution.
And, you know, it may be that in the process,
it needs to acquire more resources to increase the possibility of success or prevent various
failure modes by defending itself against interference. And so that collection of problems,
I think, is something we can address. The other problems are, roughly speaking,
being misuse. So even if we solve the control problem, we make perfectly safe, controllable AI
systems. Well, why does Dr. Evil going to use those? He wants to just take over the world
and he'll make unsafe AI systems that then get out of control. So that's one problem which is
sort of a partly a policing problem, partly a sort of cultural problem for the profession of
how we teach people what kinds of AI systems are safe. You talk about autonomous weapon system and
how pretty much everybody agrees that there's too many ways that that can go horribly wrong.
You have this great slaughterbots movie that kind of illustrates that beautifully.
Well, I want to talk about that. There's another topic I'm having to talk about.
Just want to mention that what I see is the third major failure mode, which is overuse,
not so much misuse, but overuse of AI, that we become overly dependent. So I call this the
warly problems. If you've seen the warly movie, all the humans are on the spaceship and the
machines look after everything for them and they just watch TV and drink big gulps and they're all
sort of obese and stupid and they sort of totally lost any notion of human autonomy.
So in effect, this would happen like the slow boiling frog. We would gradually turn over
more and more of the management of our civilization to machines as we are already doing. If this
process continues, we sort of gradually switch from sort of being the masters of technology
to just being the guests. So we become guests on a cruise ship, which is fine for a week,
but not for the rest of eternity. And it's almost irreversible. Once you lose the incentive
to, for example, learn to be an engineer or a doctor or a sanitation operative or any other of
the infinitely many ways that we maintain and propagate our civilization,
you know, if you don't have the incentive to do any of that, you won't. And then it's really hard
to recover. And of course, yeah, just one of the technologies that could that third failure mode
result in that. There's probably other technology in general detaches us from.
It does a bit, but the difference is that in terms of the knowledge to run our civilization,
you know, up to now, we've had no alternative but to put it into people's heads.
Right. And if you software with Google, I mean, so software in general, so computers in general,
but but the, you know, the knowledge of how, you know, how a sanitation system works, you know,
that's an AI has to understand that it's no good putting it into Google. So I mean, we've always
put knowledge in on paper, but paper doesn't run our civilization, it only runs when it goes from
the paper into people's heads again. Right. So we've always propagated civilization through human
minds. And we've spent about a trillion person years doing that. I literally write you, you can
work it out. It's about right, there's about just over a hundred billion people who've ever lived.
And each of them has spent about 10 years learning stuff to keep their civilization going. And so
that's a trillion person years we put into this effort. Beautiful way to describe all of civilization.
And now we're, you know, we're in danger of throwing that away. So this is a problem that AI
can't solve. It's not a technical problem. It's, you know, and if we do our job right,
the AI systems will say, you know, the human race doesn't in the long run want to be passengers
in a cruise ship. The human race wants autonomy. This is part of human preferences. So we, the AI
systems are not going to do this stuff for you. You've got to do it for yourself. Right. I'm not
going to carry you to the top of Everest in an autonomous helicopter. You have to climb it
if you want to get the benefit and so on. So but I'm afraid that because we are short-sighted
and lazy, we're going to override the AI systems. And, and there's an amazing short story that I
recommend to everyone that I talked to about this called the machine stops written in 1909 by EM
Forster, who, you know, wrote novels about the British empire and sort of things that became
costume dramas on the BBC. But he wrote this one science fiction story, which is an amazing
vision of the future. It has, it has basically iPads. It has video conferencing. It has MOOCs.
It has computer and computer-induced obesity. I mean, literally, the whole thing is what people
spend their time doing is giving online courses or listening to online courses and talking about
ideas. But they never get out there in the real world. They don't really have a lot of face-to-face
contact. Everything is done online. You know, so all the things we're worrying about now
were described in the story. And then the human race becomes more and more dependent on the machine,
loses knowledge of how things really run and then becomes vulnerable to collapse. And so it's a,
it's a pretty unbelievably amazing story for someone writing in 1909 to imagine all this.
Plus, yeah. So there's very few people that represent artificial intelligence more than you,
Stuart Russell. If you say so, okay, that's very kind. So it's all my fault. It's all your fault.
No, right. You're often brought up as the person, well, Stuart Russell,
like the AI person is worried about this. That's why you should be worried about it.
Do you feel the burden of that? I don't know if you feel that at all. But when I talk to people,
like from, you talk about people outside of computer science, when they think about this,
Stuart Russell is worried about AI safety, you should be worried too. Do you feel the burden of
that? I mean, in a practical sense, yeah, because I get a dozen, sometimes 25 invitations a day
to talk about it, to give interviews, to write press articles and so on. So
in that very practical sense, I'm seeing that people are concerned and really interested about this.
Are you worried that you could be wrong, as all good scientists are?
Of course, I worry about that all the time. I mean, that's always been the way that I've worked,
you know, is like I have an argument in my head with myself, right? So I have some idea,
I have some idea. And then I think, okay, how could that be wrong? Or did someone else already
have that idea? So I'll go and, you know, search in as much literature as I can to see whether
someone else already thought of that or even refuted it. So, you know, right now, I'm reading
a lot of philosophy because, you know, in the form of the debates over utilitarianism and other
kinds of moral formulas, shall we say, people have already thought through
some of these issues. But, you know, one of the things I'm not seeing in a lot of these debates
is this specific idea about the importance of uncertainty in the objective, that this is the
way we should think about machines that are beneficial to humans. So this idea of provably
beneficial machines based on explicit uncertainty in the objective, you know, it seems to be,
you know, my gut feeling is this is the core of it. It's going to have to be elaborated in a lot
of different directions. And they're a lot of beneficial. Yeah, but they're, I mean, it has
to be, right? We can't afford, you know, hand wavy beneficial, because there are, you know,
whenever we do hand wavy stuff, there are loopholes. And the thing about super intelligent
machines is they find the loopholes, you know, just like, you know, tax evaders, if you don't write
your tax law properly, that people will find the loopholes and end up paying no tax. And so you
should think of it this way. And getting those definitions right, you know, it is really a long
process. You know, so you can you can define mathematical frameworks. And within that framework,
you can prove mathematical theorems that yes, this will, you know, this, this theoretical entity will
be provably beneficial to that theoretical entity. But that framework may not match the real world
in some crucial way. So the long process thinking through it to iterating and so on. Last question.
Yep. You have 10 seconds to answer it. What is your favorite sci fi movie about AI?
I would say interstellar has my favorite robots. Oh, beats space. Yeah, yeah, yeah. So so Tars,
the robots, one of the robots in interstellar is the way robots should behave. And I would say
X Machina is in some ways the one, the one that makes you think in a nervous kind of way about
about where we're going. Thank you so much for talking today. Pleasure.