Cristopher Moore: prediction

Transcript context

…Yeah. What 1 thing I like about the puzzle design community, it's like there are, like, 10,000 people on this Discord sort of built up around this channel, is that they talk a lot, not typically in a formal mathematical way, but they talk a lot about the art and science of designing insights and then finding insights. And so acting not as an adversary, but as a, a challenging but ultimately compassionate teacher who's trying to create fun insights for the solver to have. And then, you know, a lot of talk about like, because I I I think the sensation you want to have as a puzzle solver, whether it's a wooden puzzle or, you know, fitting, little tiles into some tray, or or a sudoku, you want to have this, at the first, this vertiginous sense when you look at it like, oh my god, I'm in this exponentially large search space a priori. The last thing I want to have to do is exhaustive search. It's boring. Humans are bad at it. That's the last thing anyone wants. And so you want to sort of feel that that sort of looking over this vast, forbidding landscape, and then you see an insight, and you start realizing things. I think 1 thing which is really fascinating is that humans are quite good at, designing on the fly different kinds of partial knowledge or partial solution to a problem. So, you know, if you go back to the days of good old fashioned AI where people were doing different branching rules for backtracking search, Davis Putnam search, certainly there was a lot of clever ideas about if you have some big Boolean problem, which variable should you try setting first? And people came up with these sensible heuristics, like if a variable occurs in many different constraints, well, we should set it first because that way, whichever way we set it will satisfy a bunch of constraints, will make a bunch of other constraints more upset, and that will narrow the search space, and that's great. Okay. But humans do something richer than that. So, like, imagine you're solving 1 of these wooden puzzles where you have tiles with different shapes. Pentominos are my favorite. You're trying to fit them together. Humans will very fluidly switch from asking which piece can fit here and where can this piece go, and which are 2 different kinds of variables. In these modern Sudoku variants, powered partly by these really awesome apps, there are you know, traditionally, Sudoku fans had invented these different kinds of pencil marks, 1 of which means the thing here is either a 2 or a 7, which is 1 kind of partial knowledge. And another is the 3 in this box is either here or there or there, which is a different kind of partial knowledge. Now people are inventing new kinds of partial knowledge like, these 2 cells, I don't know what they are, but they have to be the same. So I'll color them both blue and then figure out what their numerical value is later. Or these 3 cells, I don't know what they are, but they all have to be different. And so I don't know. be the same. So I'll color them both blue and then figure out what their numerical value is later. Or these 3 cells, I don't know what they are, but they all have to be different. And so I don't know. This this to me is a really interesting frontier for AI where you take the problem and you invent on the fly what the what kind of variable you should use to address the problem. Right? Be which is very different from being told, here are the variables. Here are the constraints. There's already a lot of interesting questions there, but here it's more like, okay, you know, fit these things in. You formalize the problem. You mathematize the problem, and then make some progress, and maybe even fluidly jump from 1 mathematization to another during the solving process. And it's a lot to me, this is a lot like science. And when you're doing mathematical modeling, right, in many cases, the challenge if you're working with a social scientist or a biologist or whatever, 90% of the work is the mathematization, figuring out what kind of mathematical structure could fit here. And often, once you do that, it's relatively easy to simulate the model or solve the model or prove something about it with whatever kind of work you're trying to do. That formalization process is something that I think is a really interesting kind of task for an AI to do. Yes. Yes. There's always this lingering problem of, know, residuals. What happens when we when we leave things out? But this process of epistemic foraging fascinates me. Right? Also from a Very good phrase. It's 1 of I got it from my friend Carl Friston.…

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