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 doing preparatory moves for a nasty ko fight, the best option at the time, AlphaGo just took a route that provided almost no compensation.
If anything, it seems to me that AlphaGo's problem here might be the time management: Seeing a really scary situation, Lee Seido just sank many minutes into reading the problem, going pretty much all the way to byoyomi time. A human, after seeing something like that, would figure out that their assessment of the situation and their opponent's is very different, and spend a lot of budget trying to figure out what was wrong. AlphaGo just didn't see the problem, and didn't just budgets its time to analyze the position to death. It moved slower than before, but not really that much, and ended up making moves a kyu player could see as terrible.
Either way, I'd love to see Deepmind giving us all a good postmortem of the 70-100 range of moves.
I concur that better time management may have made a difference.
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.
I got something different from the AGA commentary. The pros did consider 78, but came to the conclusion that it didn't work! I'm just a 1kyu player but I too can't see any way to make 78 work after Black plays 79 as an atari at L10, as suggested by the pros. So I'd be very interested to see some analysis that shows how White could get a fair result after Black 79 at L10.
It would be interesting to see a neural net evolve to take into account player state, not just game state. I play chess, and no where near professional levels, so I don't know if this anecdote is valuable, but if I see my opponent looking at a particular area of the board, I tend to take a second look.
I suppose beating humans isn't AlphaGo's primary motive though - learning to play a perfect game of Go in general is probably more difficult than playing the perfect game against a particular person.
Having the AI use eye-tracking to predict an opponent's moves is a truly terrifying thought. Just by tracking the eye with millisecond precision you could probably work out the strategy they were reading. It's so game-breaking that it shouldn't be allowed as an input, and indeed, is easily defeated with reflective sunglasses anyway (like a lot of pro Poker players wear).
The AI player can't give up information in this manner because it lacks eyes, so I'd say that it should not be able to use this information from the human player.
This actually came up in the commentary. Michael Redmond actually mentioned that some amateurs watch to see where their opponent is looking, but he called it merely a "trick", and he said it is not useful in professional play.
That type of metagame seems useful though, in the sense that you can start to sense the state of mind of your opponent. If they are working harder than you think they need to, you might get away with a riskier move for instance.
If anything, it seems to me that AlphaGo's problem here might be the time management: Seeing a really scary situation, Lee Seido just sank many minutes into reading the problem, going pretty much all the way to byoyomi time. A human, after seeing something like that, would figure out that their assessment of the situation and their opponent's is very different, and spend a lot of budget trying to figure out what was wrong. AlphaGo just didn't see the problem, and didn't just budgets its time to analyze the position to death. It moved slower than before, but not really that much, and ended up making moves a kyu player could see as terrible.
Either way, I'd love to see Deepmind giving us all a good postmortem of the 70-100 range of moves.