Dwarkesh Patel: commitment

Transcript context

…We talked about U being your final resolution of whether you won or lost. This is a terminal leaf node condition. Humans don’t play all the way to the leaves of the tree. They stop dozens of moves before, maybe even 100 moves before in high-level play. How do they know? You can think about humans as implicitly having a neural network called a value function that takes in a board state and evaluates p(win). The human glances at the board and knows, “I’m probably going to lose.” They’re essentially running a neural network that looks at a board and implicitly amortizes a huge number of possible game playouts. They take that average and decide whether the board is winnable or not, and whether they should concede or keep playing. This is remarkable. If you think about the beauty of something like this, a neural network in a human can somehow do all of this simulation at a glance. They just know within a few seconds, without actually playing every single game logically, based on crystallized knowledge and experience. They can do this. This gives us a hint that in games like Go, there are ways to radically speed up the search process. This is one of the fundamental intuitions behind why AlphaGo works. You can train a value function to look at a board and quickly resolve the game without playing out all of these trees to a very deep search depth. Makes sense. I will say for the audience, for previous episodes when I was prepping and it seemed relevant to understand how AlphaGo works, I would find it very confusing. But it’s the kind of thing where once you understand the problem in this way and then build the next few pieces, it is actually a lot more understandable and it makes a lot of sense. It’s okay to be confused right now, but it’s probably simpler to understand, by the end of this lecture, than you anticipate. I’ll just make that note for the audience. The important intuition at a high level—to step back about where we’re going with all this—is that classically, for games like Go, you could build a tree, but we don’t have computers powerful enough for that. Estimating the value of every action you could possibly take is also hard because you don’t know until the end of the game. You could take averages by playing them to the end, but that’s also hard because you don’t know which actions to take to sample these averages. Conceptually, there are two problems: the breadth of the tree and the depth of the tree. AlphaGo gives us a way to shrink both of those to be tractable. That’s essentially the core idea behind it. We take the idea that humans can glance at a board and instantly predict whether we win. That maybe gives us the opportunity to truncate how deep we search. We also know that humans can intuitively, at a glance, decide what moves might be good on a Go board. These are two things we can use deep neural networks for, to accelerate the search process. Before we talk about neural nets, let’s go back to how this playout works. We’ve only talked about making one move. The AI looks at this encoded Go board. It has a tree. It searches deeply into the tree to find out which of its actions might be the best, takes that action, and goes back to the human. Now the human sees a Go board that looks like this, and they make their move. Maybe they put their stone here. Now we go back to the AI, which now looks at a new encoded board. I’ve used 2 to denote the AI playing as white, 1 to denote the human playing as black, and 0 as empty. On the AI’s turn, it does the MCTS tree search all over again from scratch. It throws away the old tree that it searched last round. Now there’s a new root node and it begins to search anew. You can basically think about MCTS as a search algorithm that decides which moves to play best, aided by neural networks, and it’s done on every move. Let’s talk about the neural network part of this.…

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