It seems that AlphaGo needs better time management skills. Not sure how that can be added. Michael Redmond mentioned that if a human player sees an unexpected move, he/she would just take all the time needed to read out the moves. AlphaGo seems to make speedy decision even after unexpected moves.

Yes, that's how modern chess engines manage time. If the score suddenly, drastically changes during search, they give themselves much more time.

In all of these games, AlphaGo used close to a constant amount of time per move, while Lee's varied a lot.

Apparently they only recently added a neural net for time management. Seems it is either not the best approach, or just not yet well trained.

I can't remember where I read this, but one theory was that the move Lee Sedol made was thought to be unlikely by AlphaGo, and so didn't explore down that path.

When Lee Sedol made the move, the AI was in unknown territory as it hadn't explored down that avenue.

David Silver said at the beginning of the broadcast of game 5 that AlphaGo's policy network had given Lee Sedol's move 78 only a 1 in 10,000 chance of occurring.

> When Lee Sedol made the move, the AI was in unknown territory as it hadn't explored down that avenue.

Sounds similar to what a human would do then: you wouldn't spend much time simulating in your head what would happen if your opponent made a very atypical move or a move that would seem very bad at first thought.

That's exactly it. The difference, as far as I have understood it, is that there was a similar move that is typical, but in that particular situation, pretty simple reasoning (of the highly abstract "if this then that so this must lead to that" sense) leads a human to conclude that this version of the move is superior.

So while atypical in the sense of "occurring infrequently", it was not a difficult move to find for a player of that level – all the pro commentators saw it pretty much right away.

This might be the one weakness of AlphaGo, which is interesting.