[grandalf]

It's interesting to consider what it means when the AI can succeed without using brute force.

Suppose at every turn there are n possible future states of the game based on the rules. To avoid "brute force" the AI must be able to ignore many of those states as irrelevant. In effect, the AI is learning what to pay attention to, not just considering what might happen, thereby conserving computational resources.

Chess and Go are interesting for two nearly opposite reasons: 1) because they are too large for humans to consider the reasoning obvious, and 2) because the input to the reasoning is simply a small (and easily perceived by humans) grid of rule-constrained pieces.

But when you think of AI in an information theoretic way, so that given representative training data the system (if large enough) will always "learn" perfectly, it's not really all that remarkable. It's just a different computational way of doing the same transformation from input states to moves. Given a problem (chess, go, etc.) the researchers must simply learn what network structure and training regimen will do the job with the least computational cost.

To see why this is relevant, consider a deep learning model that could continually generate successive digits of pi (or primes) without having the concept baked in already. Would the result be computationally cheaper than highly optimized brute force algorithm? No, because what it would "learn" would be something already known by humans. Perfect chess is simply a function from input states to moves that humans do not already know the definition of. Most humans do know the definition of this function for the game of tic tac toe by the time they reach middle school.

I'd argue that while this is useful it's ultimately not hard. Comparing it with Stockfish mainly demonstrates how chess is hard for humans to reason about and hence hard for humans to write non-brute-force algorithms to solve.

Thus, I think this is an example of "weak AI" even though humans associate chess with high degrees of exceptional human cognition. Chess data contains no noise, so the algorithm is dealing only with signals of varying degrees of utility.

I'm looking forward to AI that can be useful in the midst of lots of noise, such as AI that analyzes peoples' body language to predict interesting things about them, analyzes speech in real time for deception, roulette wheels for biases, and office environments for emotional toxicity.

Chess is interesting because we can't introspect to understand what makes humans good at chess (other than practice). So many human insights and intuitions are similarly opaque yet the data is noisy enough that it will take significantly better AI to be able to do anything that truly seems super-human.