[notahacker]

Any substandard statistical model fitted to by a simple computer program is superior to what an unaided human could achieve with pen and paper, but few of them can claim to practically "solve" the problem because they are better than crude fit heuristics proposed by humans who are not good calculating machines.

An algorithm can't claim to have "solved" Go, when future versions of the algorithm are expected to achieve vastly superior results, never mind any formal mathematical proof of optimality. What it has demonstrated is that humans aren't very good at Go. Given that Go involves estimating Nash equilibrium responses in a perfect information game with a finite, knowable but extremely large range of possible outcomes, it's perhaps not surprising that Go is the sort of problem that humans are not very good at trying to solve and that computers can incrementally improve on our attempted solutions. Perhaps the more interesting finding from AlphaGoZero is that humans were so bad at Go that not training on human games and theory actually improved performance.