>> In 2015, it was commonly thought that it would still be decades before a computer could beat a top human player at Go
This needs a citation and it needs it badly.
It was widely reported in the popular press, to the dismay of many scientists working in game-playing AI, who had very different opinions about how close or far beating a professional human at Go was at the time of AlphaGo. The majority of them in fact did not make predictions- they just pointed out that Go was the last of the traditional board games to remain uncoquered by AI. Not that it would take X years to get there. Most AI researchers are loath to make such predictions, knowing well that they tend to be very inaccurate (on either direction).
All I know is what the articles and commenters were saying then, as an interesting contrast to this comment now. Every article on AlphaGo described a general state of shock at achieving something that (even if at a purely psychological level) seemed at least 10 years away.
> Just a couple of years ago, in fact, most Go players and game programmers believed the game was so complex that it would take several decades before computers might reach the standard of a human expert player.
>> 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".
This needs a citation and it needs it badly.
It was widely reported in the popular press, to the dismay of many scientists working in game-playing AI, who had very different opinions about how close or far beating a professional human at Go was at the time of AlphaGo. The majority of them in fact did not make predictions- they just pointed out that Go was the last of the traditional board games to remain uncoquered by AI. Not that it would take X years to get there. Most AI researchers are loath to make such predictions, knowing well that they tend to be very inaccurate (on either direction).