[knight-of-lambd]

Because a chess engine isn't scanning 400 possibilities, and a Go engine isn't scanning 130k (where'd you even get that number?) possible game states.

For example, a brute force search of a 19x19 Go board to a depth of 20 would yield on the order of 361^20 = 1.4E51 game states. With a reduction in search depth and better algorithms, state-of-the-art engines might cut this down by ten orders of magnitude, but can still be beaten by rank amateurs.

Deepmind's approach to board game engines blows all previous approaches out of the water. The claim that their success is incidental to Moore's law is categorically false.

[romaniv]

Even chess isn't tractable in terms of pure brute force search. And yet a computer won against arguably the best human player in 1996. It was mostly a PR stunt by IBM.

We had 20 years of doubling of computing power before Lee Sedol match. In those 20 years there were many other AI programs that have beaten various world champions at other board games (and no one cared). There were other good Go engines before Alpha Go. They would beat most human players in the world.

Why AlphaGo of all other programs is seen not as increment, but as some giant leap forward? It doesn't solve a new class of problems and it doesn't use any fundamentally new algorithms.