Stuart Russell: Long-Term Future of AI
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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. 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 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 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 positions. So it can look at a Go board and it actually has probably a superhuman ability to instantly tell how promising that situation is. To me, the amazing thing about AlphaGo is not that it can beat 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, right? You can just consider each of your legal moves and then look at the resulting situation and evaluate it.
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