[erikbern]

Sunfish is really impressive work. From my (brief) understanding of Sunfish, the evaluation function is essentially equivalent to a hardcoded 1 layer network in Deep Pink.

You're right that everything else equal, a better evaluation function should lead to a better chess engine. However in practice I think better evaluation functions means slower evaluation function. So there's some really interesting trade-off there. I doubt humans evaluate more than a few thousand positions, so it seems like a slow but more accurate evaluation function could play chess pretty well

[thomasahle]

One interesting line of research, I think, is using 1 or 2 layered networks to 'simulate' more complex evaluation functions. If you could train such a network to get within a 10% error of Stockfish's evaluation, then you might be able to distil that network as a faster evaluator to plug back into Stockfish for an even stronger engine. As you say, one hard problem is probably finding actually interesting positions to sample for the training.

Anyhow, it's fun to see how engines like these battle it out. It may also be that your approach can yield a more 'fun to play' engine for us mortals.

[erikbern]

I think that's pretty useful approach. It's kind of similar to Hinton's latest work on model compression: http://www.ttic.edu/dl/dark14.pdf

The problem with deep models is when you end up having more than 1 hidden layers, you have a big matrix multiplication to get between the layers. If your hidden layers are a few thousand units, that's still pretty slow. Doing things in minibatches or on the GPU speeds it up significantly, but I'm guessing it's still orders of magnitudes slower than whatever Stockfish uses

[thomasahle]

Sure, the second layer would have to be very sparse. That makes sense since most multi-piece 'chunks' are not really that interesting.