[frankling_]

Well, the most common ML problems can be expressed as optimization over smooth functions (or reformulated that way manually). We might have to convince the ML world that branches do matter :) On the other hand, there are gradient-free approaches that solve problems with jumps in other ways, like many reinforcement learning algorithms, or metaheuristics such as genetic algorithms in simulation-based optimization. The jury's still out on "killer apps" where gradient descent can outperform these approaches reliably, but we're hoping to add to that body of knowledge...

[YeGoblynQueenne]

>> We might have to convince the ML world that branches do matter :)

Easy: tell them about automata.