To clarify: what I am confused about is the high AF score, which probably means that there is something exciting I’m not getting from this paper. Or maybe it’s not a missing insight, but I don’t understand why this kind of work is interesting/important?
Well, I wasn’t interested because AIs were better than humans at go, I was interested because it was evidence of a trend of AIs being better at humans at some tasks, for its future implications on AI capabilities. So from this perspective, I guess this article would be a reminder that adversarial training is an unsolved problem for safety, as Gwern said above. Still doesn’t feel like all there is to it though.
I think it may not be correct to shuffle this off into a box labelled “adversarial example” as if it doesn’t say anything central about the nature of current go AIs.
Go involves intuitive aspects (what moves “look right”), and tree search, and also something that might be seen as “theorem proving”. An example theorem is “a group with two eyes is alive”. Another is “a capture race between two groups, one with 23 liberties, the other with 22 liberties, will be won by the group with more liberties”. Human players don’t search the tree down to a depth of 23 to determine this—they apply the theorem. One might have thought that strong go AIs “know” these theorems, but it seems that they may not—they may just be good at faking it, most of the time.
To clarify: what I am confused about is the high AF score, which probably means that there is something exciting I’m not getting from this paper.
Or maybe it’s not a missing insight, but I don’t understand why this kind of work is interesting/important?
Did you think it was interesting when AIs became better than all humans at go?
If so, shouldn’t you be interested to learn that this is no longer true?
Well, I wasn’t interested because AIs were better than humans at go, I was interested because it was evidence of a trend of AIs being better at humans at some tasks, for its future implications on AI capabilities.
So from this perspective, I guess this article would be a reminder that adversarial training is an unsolved problem for safety, as Gwern said above. Still doesn’t feel like all there is to it though.
I think it may not be correct to shuffle this off into a box labelled “adversarial example” as if it doesn’t say anything central about the nature of current go AIs.
Go involves intuitive aspects (what moves “look right”), and tree search, and also something that might be seen as “theorem proving”. An example theorem is “a group with two eyes is alive”. Another is “a capture race between two groups, one with 23 liberties, the other with 22 liberties, will be won by the group with more liberties”. Human players don’t search the tree down to a depth of 23 to determine this—they apply the theorem. One might have thought that strong go AIs “know” these theorems, but it seems that they may not—they may just be good at faking it, most of the time.