>> All I know is what the articles and commenters were saying then, as an interesting contrast to this comment now.
I understand, but in such cases (when an opinion of experts is summarised in the popular press, rather than by experts themselves) it may be a good idea to dig a bit further before repeating what may be a misunderstanding on the part of reporters.
For example, my experience is very different than what you report. In an AI course during my data science Master's and in the context of a discussion on game-playing AI, the tutor pointed to Go as the only traditional board game that was not yet conquered by adversarial AI, without offering any predictions or comments about its hardness, other than to say that the difficulty of AI systems with Go is sometimes explained by saying that "intuition" is needed to play well. And I generally don't remember being surprised when I first heard of the AlphaGo result (I have some bakcground in adversarial AI, though I'm not an expert), and in fact thinking that it was bound to happen eventually, one way or another.
A similar discussion can be found in AI: A Modern Approach (3d ed) in the "Bibliographical and Historical Notes" section of chapter 5. Adversarial AI, where recent (at the time) successes are noted, but again no prediction about the timeframe of beating a human master is attempted and no explanation of the hardness of the game is given, other than its great branching factor. In fact, the relevant paragraph notes that "Up to 1997 there were no competent Go programs. Now the best programs play most [sic] of their moves at the master level; the only problem is that over the course of a game they usually make at least one serious blunder that allows a strong opponent to win" - a summary that, given the year is 2010, and to my opinion, strongly contradicts the assumption that most experts considered Go to be out of reach of an AI player. It looks like in 2010 experts understood then-current programs to be quite strong players already.
In general, I would be very surprised to find many actual experts (e.g. authors of Go playing systems) predicting that beating Go would take "at least 10 years", let alone "several decades" (!). Like I say, most AI researchers these days are very conservative with their predictions, precisely because they (and others) have been burned in the past. Stressing "most".
I understand, but in such cases (when an opinion of experts is summarised in the popular press, rather than by experts themselves) it may be a good idea to dig a bit further before repeating what may be a misunderstanding on the part of reporters.
For example, my experience is very different than what you report. In an AI course during my data science Master's and in the context of a discussion on game-playing AI, the tutor pointed to Go as the only traditional board game that was not yet conquered by adversarial AI, without offering any predictions or comments about its hardness, other than to say that the difficulty of AI systems with Go is sometimes explained by saying that "intuition" is needed to play well. And I generally don't remember being surprised when I first heard of the AlphaGo result (I have some bakcground in adversarial AI, though I'm not an expert), and in fact thinking that it was bound to happen eventually, one way or another.
A similar discussion can be found in AI: A Modern Approach (3d ed) in the "Bibliographical and Historical Notes" section of chapter 5. Adversarial AI, where recent (at the time) successes are noted, but again no prediction about the timeframe of beating a human master is attempted and no explanation of the hardness of the game is given, other than its great branching factor. In fact, the relevant paragraph notes that "Up to 1997 there were no competent Go programs. Now the best programs play most [sic] of their moves at the master level; the only problem is that over the course of a game they usually make at least one serious blunder that allows a strong opponent to win" - a summary that, given the year is 2010, and to my opinion, strongly contradicts the assumption that most experts considered Go to be out of reach of an AI player. It looks like in 2010 experts understood then-current programs to be quite strong players already.
In general, I would be very surprised to find many actual experts (e.g. authors of Go playing systems) predicting that beating Go would take "at least 10 years", let alone "several decades" (!). Like I say, most AI researchers these days are very conservative with their predictions, precisely because they (and others) have been burned in the past. Stressing "most".