> I work in AI and, to this day, I don't know what they mean by “world” in “world model”.

But "a physics" can be a model that describes any world, not just the one we live in. For ML models, this should be based on the data they're processing. Ideally we'd want this to be similar to our own, but if it is modeling a "world" where pi=3, then that's still "a physics".

The key points here are that a physics is a counterfactual description of the environment. You have to have language to formalize relationships between objects. In standard physics (and most of science) we use math[0], though we use several languages (different algebras, different groups, different branches, etc) of math to describe different phenomena. But the point is that an equation is designed to be the maximum compression of that description. I don't really care if you use numbers or symbols, what matters is if you have counterfactual, testable, consistent, and concise descriptions of "the world".

Oddly enough, there are a lot of physicists and former physicists that work in ML but it is fairly uncommon for them to be working on "world modeling." I can tell you from my own experience talking to people who research world models that they respond to my concerns as "we just care if it works" as if that is also not my primary concern. Who the fuck isn't concerned with that? Philosophers?

[0] It can be easy to read "The Unreasonable Effectiveness of Mathematics in the Natural Sciences" out of context as we're >60 years past where math has been the lingua Franca of science. But math is a language, we invented it, and it should be surprising that this language is so powerful that we can work out the motion of the stars from a piece of paper. Math is really the closest thing we have to a magical language https://web.archive.org/web/20210212111540/http://www.dartmo...