I’d expect that if the natural-abstractions theory gets to the point where it’s theoretically applicable to fluid dynamics, then demonstrating said applicability would just be a matter of devoting some amount of raw compute to the task; it wouldn’t be bottlenecked on human cognitive resources. You’d be able to do things like setting up a large-scale fluid simulation, pointing the pragmascope at it, and seeing it derive natural abstractions that match the abstractions human scientists and engineers derived for modeling fluids. And in the case of fluids specifically, I expect you wouldn’t need that much compute.
(Pure mathematical domains might end up a different matter. Roughly speaking, because of the vast gulf of computational complexity between solving some problems approximately (BPP) vs. exactly. “Deriving approximately-correct abstractions for fluids” maps to the former, “deriving exact mathematical abstractions” to the latter.)
I’d expect that if the natural-abstractions theory gets to the point where it’s theoretically applicable to fluid dynamics, then demonstrating said applicability would just be a matter of devoting some amount of raw compute to the task; it wouldn’t be bottlenecked on human cognitive resources. You’d be able to do things like setting up a large-scale fluid simulation, pointing the pragmascope at it, and seeing it derive natural abstractions that match the abstractions human scientists and engineers derived for modeling fluids. And in the case of fluids specifically, I expect you wouldn’t need that much compute.
(Pure mathematical domains might end up a different matter. Roughly speaking, because of the vast gulf of computational complexity between solving some problems approximately (BPP) vs. exactly. “Deriving approximately-correct abstractions for fluids” maps to the former, “deriving exact mathematical abstractions” to the latter.)