In particular, the initial input to the neural networks is a 19×19×48 grid, and the layers of this grid include information like:
- How many turns since a move was played
- Number of liberties (empty adjacent points)
- How many opponent stones would be captured
- How many of own stones would be captured
- Number of liberties after this move is played
- Whether a move at this point is a successful ladder capture
- Whether a move at this point is a successful ladder escape
- Whether a move is legal and does not fill its own eyes
Again, before the neural nets even get involved. Some of these layers are repeated 8 times for symmetry. I would say for some of these, AlphaGo got some domain-specific help in a non-general way.
It is of course still groundbreaking academically. The architecture is a state-of-the-art deep learning setup and we learned a ton about how Go and games in general work. The interaction between supervised and reinforcement learning was interesting, especially how the latter behaved worse in practice in selecting most likely moves.
disclaim: Googler, not in anything AI.