[PaulHoule]

I think it's funny how "the old AI" had combinatorical optimization as a major theme, for instance

https://en.wikipedia.org/wiki/Travelling_salesman_problem

which is closely related to the central operation of logic, the canonical NP problem

https://en.wikipedia.org/wiki/Boolean_satisfiability_problem

as well as the playing of games like Chess, Poker, etc.

Modern neural networks also have optimization as a theme even when the output is a classification or something that doesn't look like optimization... That is, the network itself is trained to minimize an error function. People used these kind of algorithms back in the 1980s to layout chips

https://en.wikipedia.org/wiki/A*_search_algorithm

and it's only natural that new techniques of optimization (both direct and through heuristics like the neural network used in AlphaGo) are used today for chips.

[maxwells-daemon]

Yes, the way I see it, one of the major benefits of deep learning is that it lets you define functions (in the R^n -> R^m sense) that would be basically impossible to define with traditional programming techniques. I think this comes up a lot in subroutines of combinatorial optimization, like heuristics for guiding search on subsets of NP-complete problems. The fact that you can automatically evaluate the heuristic and train by RL is also very convenient.