Earlier quoted context omitted.
Not so coincidentally, "This was when things got weird. From 87 to 101 AlphaGo made a series of very bad moves." [1] The bad moves in the eyes of humans could be risky bets or the horizon effect. [1] [2] AlphaGo's use of deep neural nets (value networks) to evaluate board positions should significantly help counter the horizon effect, but since move 78 by Lee Sedol which turned the situation around was unexpected by…
78 was hard, but not impossible: If you watch the AGA commentary of the game, they had two pros at the time 78 happened, and they found 78 as the best answer a few minutes before Lee did, expecting AlphaGo to go with a stronger, yet still good not good enough 79, that left the game even, instead of basically lost. Then they were elated about how AlphaGo seemed to have failed to read the whole thing, and instead of do…
Beyond that, I think that AlphaGo may still be missing a type of component. From the descriptions of it, the policy network generates possible moves from board positions, and the value network evaluates the probability of desirable outcomes. How this is different than human play is that strategic assessment and planning are implicit in the middle layers rather than a 'conscious' element to searching and decision making. I'm not saying that this is a necessary component as AlphaGo has already done exceptionally well. I do believe this kind of 'middle-out' processing producing and evaluating strategic concepts could make it better handle unusual circumstances. Being trained on high amateur and pro games, it will best respond to the most conventional of those types of games, more unconventional the game becomes, the worse it would fare in terms of efficiency of move generation and choices of which to evaluate.