> For example, if you see a picture of a car, there's very little inherent uncertainty on what the answer is. If you see the world simulator state there's a lot

> For example, if you see a picture of a car, there's very little inherent uncertainty on what the answer is. If you see the world simulator state there's a lot of uncertainty on what happens next. I've been thinking about this a lot. Many ML people work with what is "closed-domain" data -- the data is essentially complete (image, sound, words, or any kind of embeddings) with no unmeasured variables, so the ML algorithm is essentially trying to learn a function that can predict this. Unfortunately a lot of "open-domain" data has tons of unmeasured variables that are contextual. Suppose I were to try to predict how a full a parking lot would be over the course of a week. You can gather lots and lots of data, but still never get to a near-perfect level of accuracy because the co-variates that drive how full a parking lot is (unexpected influencer effects on the demand, competitive forces that happen to shift one day, power outages in the other part of town, other irreducible randomness = "aleatoric uncertainty" in technical parlance) aren't in the data (or at least not completely). Fortunately this isn't a problem in real life because many effects cancel each other out, so we are able to arrive at a good-enough aggregate prediction. But "open-domain" ML problems will never achieve the kind of accuracy that "closed-domain" ML can achieve, even with tons of data. Closed domain ML can assume a degree of regularity that open domain ML can never assume.