I, for one, can't wait until abstract nonsense takes over probability theory*.

ML/DS definitely has problems with papers sometimes playing fast and loose with the theory and instead focusing on getting +0.1% on your favorite leader board. "We make model bigger and big model make good predictions." In all seriousness, I think stuff like this is interesting, but I don't think it's very useful as of now. The fact that everything is just an array in Python has always irked me a bit, but maybe there can be some way to leverage type theory in some way to make the ML code more robust. Or perhaps CT can be used to make it easier to compose probabilistic models.

> * From Disintegration and Bayesian Inversion via String Diagrams