What about AlphaGo Zero? I'm honestly not sure why a system that plays board games is seen as some kind of giant leap in AI. Seems mostly a result of Google's marketing.
In 1996 IBM made a system that was better than any person at playing chess. Chess has roughly 400 moves possible at any point.
In 2016 Google made a system that was better at any person at playing Go. Go has roughly 130,000 moves possible at any point. That's approximately equal to 400 * 2^9.
Moore's law states that the number of transistors (which is a proxy for computing power) in CPUs doubles every year. 2016 - 1996 = 20 years of computing power growth.
In short, after our computing power has increased roughly by a factor of 2 ^ 20 someone made a system that plays a game that's 2 ^ 9 time more complicated than chess. Why is this seen as some giant, surprising leap forward?
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.
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.
Board game playing programs consider much more than every next possible move from a single state. Any decently skilled amateur could easily beat a program that can only reckon one move into the future. I see the argument you're trying to make here but the numbers you're citing do not support it, in my opinion. DeepMind is considering much more than the 130k moves possible from a single state during each move of play.
It was ML, not AI. It literally just runs enormous amounts of games to determine parameters that guide a search tree. That's nice, but was it truly a breakthrough technologically speaking? Hard to say.
