I'm a huge proponent of alpha Go and I think it is a revolutionary leap.
The key I think is,
> yet it clearly generalizes rather well to unseen board situations and still evaluates them sucessfully
I'm not sure this has been proven to be meaningful in a general sense, as you seem to also imply. Extrapolation can be a tricky answer subtle business. What about unusual board sizes, for which no training data exists? Or if you changed a rule? I'm sure deepmind would say the adversarial approach would work for these cases, but I'm not sure it would. Would be very interesting to see if humans could 'learn' a new state more quickly than the algorithm.
That might provide a hint that the algorithm is 'just' fitting the data well (with appropriate baked in regularization, of course). Or if it can more generally 'learn' given system rules.
The key I think is,
> yet it clearly generalizes rather well to unseen board situations and still evaluates them sucessfully
I'm not sure this has been proven to be meaningful in a general sense, as you seem to also imply. Extrapolation can be a tricky answer subtle business. What about unusual board sizes, for which no training data exists? Or if you changed a rule? I'm sure deepmind would say the adversarial approach would work for these cases, but I'm not sure it would. Would be very interesting to see if humans could 'learn' a new state more quickly than the algorithm.
That might provide a hint that the algorithm is 'just' fitting the data well (with appropriate baked in regularization, of course). Or if it can more generally 'learn' given system rules.