Stuart Russell

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1,902 appearances 3 recordings 3 series first heard Dec 2018 last heard 24 Jun

Stuart Russell’s voice in public audio — every appearance, attributed to the second.

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Lex Fridman Podcast · Stuart Russell: Long-Term Future of AI · 9 Dec 2018 podcast

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.

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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