So the most likely explanation is that policy/value nets in AlphaGo have learned to extract - with cold logic - the key factors that make up what humans believe to be "good" board positions.
It has little to do with voodoo about neural connections and magic emerging from the weights. AlphaGo has most likely managed to identify the important factors of good board positions (by seeing tons of examples of good and bad moves/positions). It only appears to be magical because these factors are most likely very complex and inter-dependent.
This is supported by the AlphaGo paper - they report that AlphaGo without tree search is about as good as the best tree search programs (amateur pro level). So AlphaGo has taken amateur-pro-level board analysis ability, and combined it with tree search, to achieve top-player performance..
I don't think that's an entirely accurate way of putting it, because a player who reaches that level is also doing a little tree searching. Maybe if you found some human who managed to reach "amateur pro" level by playing purely on snap, instinctual decisions without any logic or exploration of variants at all, yes, then you could say their ability to evaluate positions is as good as AlphaGo's.
But I would guess that you are right anyway that we can deduce that its ability to evaluate a board position really is below that of better professionals, and its huge strength is due to the tree search (which of course involves a second "policy" net to pick moves to explore).
Isn't that pretty much the definition of intuition? Combining a bunch of things in some unknown and nonlinear way to result in a 'feeling' about the situation?
Isn't most human experience an overly mystical interpretation of physics/chemistry?
The point is, the programmers don't quite understand what exactly are those features that the neural net is seeing.
They can see certain stats and high level overview.
But they have no idea what it's thinking.
They know the general pattern of the algorithm. They even explain it.
But the algorithm involves two deep neural networks, and they don't really know what's going on inside them.
One of the developers showed up during the commentary on the second game and talk about this stuff:
Well apparently my question leads to some deep mathematic theories about languages encoded by data. http://cstheory.stackexchange.com/questions/15039/why-can-ma...
The way it picks moves is very similar to how top professionals do.
Intuition is reduced to memories stored vaguely as neural connections.