I don’t think the field has converged on “the right abstractions” yet. It’s an active area of programming language research — it feels similar to where AD was

I don’t think the field has converged on “the right abstractions” yet. It’s an active area of programming language research — it feels similar to where AD was at for awhile. I work on this stuff for my research — so I do believe that there is a really good set of abstractions. my lab has had good success at solving problems with these abstractions (which you might not think are amenable or scale well with Bayesian techniques, like pose or trajectory estimation and SLAM, with renderers in a loop). Other PPLs I’ve studied also have a mix of these abstractions, but make other key design distinctions in interface / type design that seem to cause issues when it comes to building modular inference layers (or exposing performance optimization, or extension). I also often have the opinion that the design choices taken by other PPLs feel overspecialized (optimized too early, for specific inference patterns). I’m not blaming the creators! If you setup to design abstractions, you often start with existing problems. On the other hand: if you’re just solving similar problem instances over and over again, in increasingly clever ways — what’s the point? Unless: (a) these problems are massive value drivers for some sector (b) your increasingly clever ways are driving down the cost, by reducing compute, or increasing speed. I think PPLs which overspecialize to existing problems are useful, but have trouble inspiring new paradigms in AI (or e.g. new hardware accelerator design, etc). Partially this is because there’s an upper bound on the inference complexity which you can express with these systems — so it is hard to reach cases where people can ask: what X application would this enable if we could run this inference approximation 1000x faster? (Also note that inference approximations _can_ include neural networks)