[simonh]

Those vectors are only used to generate the move trees though. That part of the architecture is common to pretty much all MCTS board game AIs ever. The value in AlphaZero is in the neural nets used for the expert policy and the value functions and those don’t have anything about the game rules encoded into them at all.

I agree it’s probably quite constrained in the range of possible applications. Everyone was expecting Deep Blue to revolutionise AI applications too. I know the tech is different, but the fact it seems optimised for a highly constrained problem domain isn’t, and in fact arguably the problem domain addressed by deep blue seemed for a long time to be much more general.

How adaptable is AlphaZero to arbitrarily multidimensional grids though?

[abecedarius]

> Everyone was expecting Deep Blue to revolutionise AI applications too.

That doesn't match my memory at all. The reaction then was dominated by the likes of "this is super-narrow, not real intelligence". (The 80s did have a lot of hyped expectations of related tech, it's true, but that was around 10-15 years earlier.)

[sgt101]

I thought Watson would; what's happened to that project is a salient lesson.

[YeGoblynQueenne]

>> The value in AlphaZero is in the neural nets used for the expert policy and the value functions and those don’t have anything about the game rules encoded into them at all.

They do, in the form of their inputs that are basically vector representations of a checkerboard. It's obvious that the two (types of) networks can learn something useful from that particular representation of a problem. But- other representations, of different problems? That is not obvious.