Shawn Wang: belief

Transcript context

…Paint.goodfair.ai. Yeah. Yeah. I think for me, one way in which I think about world models is just like this, like, having this consistent model of the world where everything that you generate operates within the rules of that world. And imagine it would be a bigger deal for science or, like, math or anything that where, like, you have verifiable rules. Whereas, I guess, in natural language, maybe there’s less rules. And so it’s not that important. Yeah. And which makes the debugging of the model’s internal representations or its internal world model, to the extent you can make that legible and explicit and have control over that, I think it makes it all the more important. Because in language, it’s sort of a fuzzy enough domain that if its world model isn’t fully like ours, it can still sort of, like, pass the Turing test, so to speak. But I know there have been papers that have looked at, like, even if you train certain astrophysics models, it does not learn. Like, the same way that you can, you know, have a model do well for modular arithmetic, but it doesn’t really, like, learn how we think of modular arithmetic. It learns some crazy heuristic that is, like, essentially functionally equivalent. But it’s probably not the sort of Grok solution that you would hope for. It’s how an alien would do it. Right. Right. Exactly.…

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