Probability theory = combinatorics divided by n
In this vein let me add: Information theory = log(probability theory) practical DSLs
Isn't embedding a "practical DSLs" another toy compiler? what is learnable?
to what is learnable in
poly-time?
(or similar resource constraints). This was pioneered, as far as I am aware, in Valiant's A Theory of the Learnable (please correct me if I'm wrong, I'm not an ML/AI historian). Interestingly, we see a similar evolution of Shannon's thinking about cryptography (what is secure information theoretically, i.e. against computationally unbounded adversaries?) to: what is safe against a poly-time restricted adversary? Co-evolving parasites
This paper strikes me as the earliest manifestation of what would later become GANs (generative adversarial networks). Yes, the mechanism is different (GAs vs NNs), but the spirit (having a competitive mechanism for speeding up local search) is similar.
https://cointelegraph.com/news/neuromorphic-computing-breakt...