> It is simply using perfect information to optimize its probability of winning.

Perfect information would be the knowledge of all possible outcomes of a given move. That's not even possible in chess, and is fantastically less possible in a game like go. That's why, until AlphaGo, there had never been a go computer program that had ever even come close to beating a professional go player.

Let me emphasize that: As of early 2015, nearly 20 years after Deep Blue beat the world champion Kasparov at chess, there had never been a computer go program that had come close to beating a professional go player. The game starts with literally 361 possible moves, and each move thereafter decreases the possible moves by exactly one. The search space is just so massive that it's impossible, by brute force computation, to do a search with any meaning.

What's needed is an intuition about two things. First, for a given a board position, which color is more likely to win? Secondly, given the hundreds of moves you could make, which ones are the best ones to explore? Go players have some rules, but primarily they develop an intuition for these two things by playing and looking at hundreds and hundreds of games.

AlphaGo's primary architecture was the same. It has two neural nets, which spit out board evaluations and move suggestions. These nets don't have explicitly coded rules, but were developed by playing millions of games. How is that any different than our wetware neural networks?

> In fact, players who have played against it have often gotten worse.

I hadn't heard this, but this is probably not unusual. Take my StarCraft example: Suppose someone had gotten to Diamond league mainly by perfecting their early rush strategies, but then hit a wall. Then they watch that video series which says the foundation of a GrandMaster strategy is having a solid economy first. If they decide to take this advice, it will entail a complete re-building of their skillset; they'll almost certainly drop in their rankings before picking back up again.

You could imagine the same thing happening in go: for hundreds of years, certain principles have been believed to be true. AlphaGo regularly violates these principles. As people are exploring this alternate strategies, they will inevitably get the "new" principles wrong while they're learning.

On the other hand, the early versions of AlphaStar clearly dominated mainly by having inhuman micro capabilities (ability to precisely control individual units). A human trying to replicate a strategy that relied on inhuman micro would inevitably fail.

Similarly, it might be that certain moves in go are good moves if you can do the kind of massively deep search that only AlphaGo can do. If that's the case, then of course humans trying to imitate AlphaGo are going to fail. A similar thing happened in chess: computers historically have been amazing at tactics and weak on strategy. A human playing a computer should focus on a good strategy, because they're never going to beat a computer at tactics.

But none of that changes the fact that what AlphaGo is doing, with two neural nets that spit out answers based not on explicit rules but based on millions of games worth of experience, is indistinguishable from human intuition.