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.
Hm, well you are no doubt right that it doesn't generalize well to a change of rules. Reminds me of that game DeepZen played. It was trained with a komi of 7.5 and it played too soft and lost when in the actual match komi was 6.5 (or maybe it was the other way around?). A human does not have much trouble adapting to such small rules variations, but at least the version of DeepZen that played that match was hard-coded for that exact komi value, because that's what what used in all of its training examples, and wasn't given as a parameter. It shouldn't be a hard limit of the approach - indeed I think AyaMC was said to have been trained with some flexibility in its komi.
Still, I think AlphaGo does demonstrate amazing positional judgement in unseen board states, and that this is visible in the details of how it plays out particular situations. No two games are exactly alike - difficulty of go for computers is precisely in its extreme combinatorial explosion - and in particular tactical situations every detail of the situation matters. Yet you can see AlphaGo judging the correct sequences of moves, "knowing" how to make a particular group alive for eg, even when a particular other move seems more natural. And probably the most amazing thing about how it plays is how early it becomes completely sure that its got an advantage on the board, and how precisely it judges how much it needs to keep the advantage to the end. Every detail of the board is again relevant here, and basically no human would be so confident so soon. A go bot that couldn't adapt its tactics to unseen situation would be easy to beat; just ensnare it in a large complicated fight, and you're going to kill a big group and guarantee a win. Ofc people tried this in some masterP games, and turns out AlphaGo is tactically just as strong.
So, its basically like with other generalizations you can get from machine learning; a net trained on say ImageNet will generalize to different poses, occlusions, contexts and variations of objects similar to what it was exposed in training etc and still do a superhuman job of classifying such pictures, but will naturally be quite hopeless with completely unseen items. So too AlphaGo seems to know the game of go, generalizing from seen examples to correct judgements in other states, but would be quite hopeless if tested on even a slight variation of the game 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.