Much about Go/Weiqi is still unknown even to the top players. For example Ke Jie (the current highest ranked player) feels that the current komi (compensation given to white for playing second) is too generous and he prefers white (last year he won almost all his white games). This is why professional players seem very excited about AlphaGo. If AlphaGo can consistently beat top players it may teach us more about the game. It may discover or settle questions about josekis (pattern conventions). It may tell us what komi is fair. It could settle questions about how a particular board position should be valued. On the other hand since it uses patterns learned from human plays, this could also motivate new theories of play that could defeat past strategies. Or if no one can come up with ways to defeat AlphaGo it may indicate the valuation function it produces is approaching ideal.
But AlphaGo is very much about combining existing tools of ConvNet and MCTS, albeit in a highly innovative manner, to solve the search problem of Go. Its success or failure could teach us a lot about how amenable the Go game is to such an approach (and potentially advance theories about the Go game), more than I would say about how in general problems in AI can be solved. That is what is learned here is very specific because Go is a very visual game and/or humans play it in a very visual way to take advantage of the massive parallelism of our vision system to quickly narrow down choices.
Standard komi is 6.5 points under the Japanese and Korean rules; under Chinese, Ing and AGA rules standard komi is 7.5 points
But of course the increase to 6.5 was done for good reasons. Perhaps the strategies for white just suit Ke Jie's style slightly better.
At this point in time, the belief in "incremental" general AI looks like a kind of pseudo-religion emerging in IT. People go way beyond postulating it as a possibility. They fervently defend it as some kind of obvious fact and flaunt this attitude as progressive.
What really disturbs me is the way these bridge the gaping void between existing AI and biological brains. On one hand I see absolutely insane amounts of hype around artificial neural networks, exploding way beyond the optimism warranted by the actual research. On the other hand I see the insistence that biological brains are "nothing special". I wonder how deep that goes. Are these people ultimate sociopath who truly believe that everyone around them is a mere pattern-matching device?
All this bullshit is actually detrimental to the usage of "AI" algorithms as programming techniques aimed at solving real-life problems. For one, many managers look at the hype and mysticism and conclude that AI is something that is too complex for mere mortals to handle. I've seen this on many occasions.
One is whether simple progress on neural nets is enough to close-in on real intelligence and I think those who really understand neural nets generally think not.
The other issue is whether incremental progress in general can achieve AI - there I don't think anyone can be sure, especially the relative vagueness of "incremental" and so I don't one should dismiss incremental progress or uncritically assume it.
I'd agree with the rest of your post but this statement seems to imply there's some "general level" that can't reached by the incremental approaches that yielded the results.
The progress that we see here is incremental progress in a defined area but it's also progress towards more general and easier to implement approach - it's yet "point and solve" but it might be a step toward "point and solve". Given little "general intelligence" is understood, no one can say now for certain we won't arrive at it through a series of these advances.