[hajile]

The paper itself repeatedly says that all 48 layers of the policy network are 19x19 matrices. To make the point though, they initially train alphago using actual games. After a hundred thousand or so training games, it's finally ready to start playing and learning. There are less than a couple dozen recorded games on larger boards.

If you haven't played go very much, you may thing that "it's just a bigger board". 19x19 is commonly used because it has an even balance of edge and center influence (in reality, edge influence seems to be slightly higher). With the 13x13, corner plays have overwhelming influence in the center. At 9x9, there is basically no center strategy at all. Normal strategies starting in the corners and expanding influence toward the center don't work as effectively with larger boards (the larger the board, the more this becomes true).

This is a much different issue than image recognition in that strategy doesn't scale in the same way that images do.