Chess engines actually do both now. They have ML models to evaluate positions, essentially a much more advanced version of your positional description, and deep calculations.

That might be the best we can practically achieve with technology, but the point stands. If positional evaluation says one thing but an exhaustive analysis of lines finds a solution 60 moves deep, that one is going to win.

Humans also do search. Also, engines arent doing an exhaustive search when they are 20 moves deep. They heavily prune.

Yes, I understand how chess engines work.

Ignore the existence of engines for a moment. The reason a particular line works, at the end of the day, is simply because it does. Just because we have heuristics that help us skip a lot of evaluation doesn’t mean the heuristics have intrinsic meaning within the game. They don’t.

They’re shortcuts that let us skip having to do the impossible. The heuristics will always lose to concrete analysis to a deep enough depth.

And that’s my point. We come up with models that give us an intuition for “why” things are a certain way. Those models are inarguably helpful toward having a gut feeling. But models aren’t the thing itself, and every model we’ve found breaks down at some deeper point. And maybe at some level things simply “are” some way with no convenient shorthand explanation.

So your point is that we are not omnipotent? Ok.

Because of our limitations, we have to compress reality more in order to reason about it. This means we're blind to some ideas that computers are not. Just like a depth 1 chess engine can't see what's happening at depth 3 but has to make an imperfect guess.

Fine tuned LLMs can play chess at grandmaster levels.

So its clear that there is in fact 'deeper patterns to chess' that allow one to play very well, without any search required (Since LLMs cannot search). Its just that those patterns are probably rather different to human understood ones.

I don't understand why you would draw this conclusion. The deep search you describe is an algorithm that humans can understand perfectly fine. Humans just can't solve it in their heads and need to let a computer handle the number crunching. Just like a scientist may understand the differential equations to describe a system perfectly fine, but require a computer to approximate the solution for an initial value problem.

“Knowing” that some line works to some ridiculous depth is different than understanding how and why.

And at some level the answer is simply “because every possible refutation fails” and there is no simpler pattern to match against nor intuition to be had. That is the how and why of it.

The scientist can understand “how” the model works, how many layers there are, that each neuron has a weight, that some are connected… Parent comment and yours show that “understanding” is a fuzzy concept.