The following is a conversation from Stuart Russell. He's a professor of computer science at UC
Berkeley and a coauthor of a book that introduced me and millions of other people to the amazing world
of AI called Artificial Intelligence The 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, you know, 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? Well, are we talking about two moves? So no, I think we've
got we got an eight move, eight, you know, depth eight with alpha beta. And we had some tricks of
our own about move ordering and some pruning of the tree. And we were still able to beat that
program. Yeah, 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, you know, 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 a fellow, 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 form of 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
without 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, it's 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 will 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 and 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, there's these beautiful games of chess with Bobby Fisher, 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 might yield a win down the set. 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 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 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. We see plenty
of mistakes, even in grandmaster games, where they just miss some simple three, four, five move
combination that wasn't particularly apparent 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 meta 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 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 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 I think the thing that's scary 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 the real world 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 timescales 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 that amounts to about a trillion motor control steps that you
will eventually be committed to. Including going up the stairs, opening doors,
a 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 timescale that will eventually reduce to
trillions of motor control actions. And so for all these reasons,
you know, AlphaGo 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 timescales, 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 and 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
is a book, AI Modern Approach, I think it 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 anything, 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
from Deep Blue or even from Arthur Samuel's Jacob playing program from the 1950s.
And in fact, 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
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. So let me
ask you sort of, do 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
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, 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 between $150,000 and $300,000 per
workstation in current prices. 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, over invested. 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
scope problems that expert systems had. 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 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? 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, so suppose you're a taxi driver and you drive every day,
eight hours a day for 10 years, 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.
Now, if your vision system only detects 98.3% of the vehicles, that's sort of one
on a bit nine 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. We follow a white line all the
way across the country. But it's the weird stuff that happens. It's all the edge cases. Yeah.
The edge case, other drivers doing weird things. So if you talk to Google, 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 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, in chess programs, this happens all the time. 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. 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
of humans. Right, 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. 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 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 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 drivers are going
to react to your trajectory and so on. So, you know, they've got the classic merging onto the
freeway problem where you're kind of racing a vehicle that's already on the freeway and you're
are you going to pull ahead of them or are you going to let them go first and pull in behind
and you get this sort of uncertainty about who's going first. So all those kinds of things
mean that you need a decision making architecture that's very different from either a rule based
system or it seems to me a kind of an end to end neural network system. You know, so just as Alpha
Go is pretty good when it doesn't do any look ahead, but it's 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. You know, and we've already seen
multiple deaths caused by poorly designed machine learning algorithms that don't really
understand what they're doing. Yeah, and on several levels, I think it's 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.
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.
It doesn't work that way. I mean, but you have to assert, yet others have to be scared of you,
just 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 created. Or at least just a kind of a resoluteness.
You have to display a certain amount of resoluteness. You can't be too tentative.
Yeah. So the solutions then become pretty complicated. You get into game theoretic
analyses. So at Berkeley now, we're working a lot on this kind of interaction between machines
and humans. And that's exciting. So my colleague, Anka Dragan, actually, if you formulate the problem
game theoretically and 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. It's 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 this, 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 light
hill report, 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. So it's 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
unable to have children would like to create life as a kind of replacement, which I think is
really pretty unfair. But 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 and 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 might 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're also thinking 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 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, 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
this 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. And we can think of this as the king Midas problem. 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. It's a warning, it's a failure mode that
pretty much every culture in history has had some story along the same lines. There's the
genie that gives you three wishes and third wish is always, please undo the first two wishes because
I messed up. And 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
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 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. And this is not restricted to AI, right? This is, 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 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. It's a beautiful way to put
it. I love it. That they're humble. They know that they don't know what it is they're supposed
to be doing. And that those objectives, I mean, they exist, they're 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 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,
it 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 that point, I think, is absolutely central
to solving the control problem. And it's a different kind of AI when 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, mark off 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 in 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, you get into trouble
with humans. And I would say with corporations, in fact, some people argue that 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
are 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 that are 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. So 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. And 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. So someone would say, okay, well,
here's a formula for how we should make decisions. So utilitarianism is roughly each person has a
utility function and then we make decisions to maximize the sum of everybody's utility.
And 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.
And this is called the repugnant conclusion. And another version is that we should maximize
pleasure and that's what we mean by utility. And 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?
Yeah, 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. And so we said, okay,
maybe that was a mistake. Let's try something different. And also, we're very slow and inefficient
about implementing these things and so on. So 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, right, they have this different characteristic
from ordinary human activity in the past. So let's say you were a surgeon. You had some idea
about how to do some operation, right? Well, and let's say you were wrong, right, that that way
of doing the operation would mostly kill the patient. Well, you'd find out pretty quickly,
like after three, maybe three or four tries, right? 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. And so that's why we have the FDA,
right? Because of the scalability of pharmaceutical production. And 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, two, right? And all of a sudden,
you're having an impact on a global scale. And yet we have no FDA, right? There's absolutely no
controls at all over what a bunch of undergraduates with too much caffeine can do to the world.
And, you know, we look at what happened with Facebook, well, social media in general, and
click through optimization. So you have a simple feedback algorithm that's trying to just optimize
click through, right? That sounds reasonable, right? Because you don't want to be feeding people
ads that they don't care about or not interested in. And you might even think of that process as
simply adjusting the the feeding of ads or news articles or whatever it might be to match people's
preferences, right? Which sounds like a good idea. But in fact, that isn't how the algorithm works,
right? You make more money. 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, right? So the way to maximize
click through is actually to modify the people, to make them more predictable. And 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.
Now, and I think there's a reasonable argument to say that this, among other things, is
contributing to the destruction of democracy in the world. And where was the oversight
of this process? Where were the people saying, okay, you would like to apply this algorithm to
five 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.
It's just they just did it. And some way outside the scope of my knowledge,
so economists would argue that the invisible hand, so the capitalist system, it was the
oversight. So 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. But in the meantime, but you might even have broken the political system
that enables capitalism to function. Well, you've changed it. So we should see. Yeah.
Change is often painful. So 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. So do you think it's possible? I mean, I haven't, have you seen government?
So 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. And 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, right? We haven't even thought about what those would be for algorithms.
So I mean, I think there are, 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 data sets, how to debias those algorithms, and even what it's going to cost you
to do that. So I think we could start having some standards on that. I think there are things to do
with impersonation and falsification that we could, we could work on. So I think, yeah.
Or the very simple point. So 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 machine 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 now 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. Yeah. 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, that's...
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. So 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... 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 mean, 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
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 were 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 11, 1933. And he said, you know, 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 doo doo. Because this is 1933, right? Hitler had recently come to power in Germany.
Zillard was in London. He eventually became a refugee and he 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 in a war. But you mentioned in the 20s and 30s, so what are the echoes?
The way you've described the story, I mean, there's clearly echoes. What 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 what are the,
yeah, 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 and come up with some reason why it couldn't be
true. And I collected a long list of regions 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. I mean, 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. The AI community is 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. 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 that'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 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,
you know, 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, 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.
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 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, 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
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. There'd be a complete outrage and dismay. And,
you know, I find this really a strange phenomenon. So,
okay, so if you accept it as 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, or in this case, not make the AI. And I think that's really hard to do.
To, 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.
So the, but it seems to me that there's a, 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,
the strategy that it, 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, you know, misuse, right? So even if we solve the control
problem, we make perfectly safe controllable AI systems. Well, why, you know, why does Dr.
Evil going to use those, right? 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, you know, partly a
policing problem, partly a sort of a 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
Slotabots movie that kind of illustrates that beautifully. Well, I want to talk about that.
That's another, there's another topic I'm happy to talk about the, I 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,
the movie, all right, 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. And, you know, so in effect, right,
this would happen like the slow boiling frog, right, we would gradually turn over
more and more of the management of our civilization to machines as we are already doing.
And this, you know, this, if this process continues, you know, we sort of gradually
switch from sort of being the masters of technology to just being the guests, right?
So, so we become guests on a cruise ship, you know, which is fine for a week, but not,
not for the rest of eternity, right? You know, and it's almost irreversible, right? Once you,
once you lose the incentive to, for example, you know, learn to be an engineer or a doctor
or a sanitation operative or any other of the, the infinitely many ways that we
maintain and propagate our civilization. You know, if you, 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 they add 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, the, the difference is that in terms of the knowledge to,
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, 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, 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 literally, right? 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 a, 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 talk to about this
called the machine stops written in 1909 by Ian 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 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 it's 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, you know, 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. And then I think, okay,
okay, how could that be wrong? Or did someone else already have that idea? So I'll go and
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,
what 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, 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.
Well, Stuart, thank you so much for talking today. Pleasure.