Also, there's no reason to expect a "naked" neural net to ever hold up against current chess engines. Engines are already far beyond the strongest humans, who can be seen as naked NN players. Except the NN is far beyond anything we could even dream of building today, both in number of parameters and the complexity of a single neuron, not to mention our ability to learn.
NNs are interesting in areas where human performance is not yet attained. Chess is not one of those. Heuristic algorithms are far stronger than humans, and so it seems strange to me to suggest making engines stronger by making them more and more like humans, just with far less compute, and weaker learning methods. The notion seems inherently ill-conceived to me.
human brain while may have larger raw compute power comparing to TPU pod has also many bottlenecks, brain works on much slower frequency per current research than 2GHz TPUs, inputs are very bottle-necked, you can't feed brain with 1TB of text in one day like you can do with neural network.
You put your finger on something though because the bottlenecks of brains are probably the reason why traditional minimax searchers are so good at chess: working memory. Humans just don't have the working memory to search to the depths computers can. frequency is interesting, but chess is inherently "working memory-bound". If you gave Stockfish a normal time control and a grandmaster two days per move, Stockfish would still win. Past about 30 minutes of thought on a position, there's not much more progress a human can make. But if you also gave the GM a second board they were allowed to make moves on, and a notebook, I bet the GM would win, because at that point you're only comparing eval functions, and a GM would still be miles ahead. I wonder if someone's done this experiment.
By making something differentiable, you're hoping relaxations or some smooth proxy doesn't break fundamental structure, allowing you to solve an easier problem than discrete combinatorial search. Sometimes there can be barely any leveragable structure. This has proved very difficult to achieve in general and is a sort of holy grail in some fields.
That said, there are powerful game playing AIs that use either no (DeepNash) or minimal (DORA) search. But in general you can't get something for free, you're always paying something with an approximation.
it could depend on downstream task, is it differentiable or not. Ray tracing of convex textures is likely differentiable, win/loss function for a given chess move maybe not.