Stuart Russell is a professor of computer science at UC Berkeley and a co-author of the book that introduced me and millions of other people to AI, called Artificial Intelligence: A Modern Approach. Video version is available on YouTube. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations.
Chapter 1: What inspired Stuart Russell to pursue AI?
The following is a conversation with 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, The Modern Approach.
So it was an honor for me to have this conversation as part of MIT course in Artificial General 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 Friedman, 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, three moves?
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, 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 you know bigger than the number of atoms in the universe and
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.
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Chapter 2: How did early AI programs evolve in their capabilities?
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. 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 will 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 following 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.
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 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.
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Chapter 3: What is meta-reasoning and why is it important in AI?
occurs by essentially removing one by one these assumptions that make problems easy, like the assumption of complete observability of the situation. If we remove that assumption, you need a much more complicated kind of computing design. 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 we are removing those assumptions. We are starting to have algorithms that can cope with much longer timescales, that can 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 at the University of Illinois to an AI lab, and they said, okay, I don't have time for you, but here's a book, AI, A Modern Approach. I think it was the first edition at the time. Mm-hmm. 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, was impossible to solve, and now we've solved it.
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 the 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, ten parts of the board. And that actually resembles...
the real world much more than chess does because in the real world you know we have work we have home life we have sport you know whatever different kinds of activities you know 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, I 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.
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Chapter 4: How do AI systems learn and adapt their strategies?
Okay, what do you do? We don't have a rule. Oh my God. Okay, stop. And then they'd come back and add more rules, and they just found that this was not really converging. And if you think about it, how do you deal with an unexpected situation, meaning one that you've never previously encountered, and 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, you know, 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. 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 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, 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 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 going to pull ahead of them or you're 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 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 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, 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
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