Not much. Go is a fundamentally harder game to play than Chess, by several orders of magnitude. Unlike chess, where the next move set is large but fairly constrained, every turn in go can let you move (nearly) anywhere that hasn't already been moved to.

What's more, go players frequently make moves that to the uninitiated seem like "running away", which is to say not playing near areas of contention. These are placed in moments of low vulnerability so that eventually the player can build towards them. Seldom do even experienced go players know every move that will lead up to connecting with these distant stones, but they know they lend strength to that area and thats a place where they see potential for future moves.

As such, traditional AI game techniques really haven't scaled well to Go. It's only recently that we've began to use huge sums of hardware just to make it play go at a level that most would consider to be an experienced amateur.

It seems like Go is a more recognition-based problem, so we'll have to see if Jeff Hawkin's work might show some progress there once they get their work off the ground.

It's more that the MoGo folks have finally developed an algorithm which is appropriate to Go. Their monte-carlo-based strategies scale well in Go, whereas the alpha-beta-style chess-like algorithms do not scale well. I don't think there are any people involved who used to work on chess who now work on Go.