So Garry Kasparov, the world's #1 Chess player, was engaged in yet another match with a computer last week, this one a 4-game match, and it ended in a draw.

The real question is: Does anybody care anymore? They tried to hype this event by dolling up Fritz with voice recognition and having Kasparov put on virtual reality goggles to play on a 3D virtual board. But trying to sex it up just ain't gonna work. Kasparov frames the conflict between human and machine Chess players this way:

"Machines are getting better, but we humans are also learning," said Kasparov, considered by chess experts to be the best player in the history of the ancient game. "Today, I know much more about computers than six years ago."

Well, that's great, Garry. Problem is, most people already consider humanity's ass whipped when it comes to Chess, thanks to Deep Blue's 1997 victory. And since the basic approach it took was brute force ply search, not nearly as many people find Chess to be a very interesting avenue for AI research.

I've said it before, but I'll say it again, when somebody creates an artificial player to beat Cho Chikun at Go, that'll be a time for headlines. Most likely, those headlines will only run in Asian news outlets, but it will be much more significant to AI advances than Deep Blue's 1997 victory.

As far as our project is concerned, we're still trucking along. I've been able to do some test runs on Tic-Tac-Toe, even though our evolutionary algorithm isn't fully implemented. So far our best evolved neural net player beats a random mover about 61% of the time (random players beat each other about 44% of the time, and a perfect player beats a random player about 90% of the time).

In the coming year, we hope to opensource our implementation, begin some real experimentation, and try out some interesting variations on the basic algorithm.