(1) Better timing control. Maybe when the probability of winning reaches below say, 50% but has not hit the losing threshold, spend extra time.

(2) Introducing "anti-fragility". Maybe even train the net asymmetrically to play from losing positions to gain more experience with that.

(3) Debug and find out why it plays what looks like non-sense forcing moves when it thinks it is behind (assuming that is what is actually happening).

There's another interesting thing. Among the Go community, there might have been initially some misplaced pride. But the pros and the community very quickly changed their attitude about AlphaGo (as they have in the past when something that seems to not work, yet proves itself in games). They are seeing an opportunity for the advancement of Go as a game. I think a lot of the pros are very curious, even excited, and might be knocking on Google's doors to try to get access to AlphaGo.

Granted, chess AI is basically at that point right now. But, go AI has a ways to go.

PS: Honestly, it might be a year or a decade, but I suspect there is plenty of headroom to drastically surpass human play.

That's a big difference. Bugs can be identified and fixed. By the time AlphaGo faces another top professional (Ke Jie?) we can safely assume that whatever went wrong in Game 4 won't happen again.

Consider how much stronger the system has become in the few months since the match against Fan Hui. Another advance like that will place it far beyond the reach of anything humans will ever be able to compete with.

I'm not sure this is true. It made the wrong move at move 79 in game 4, but I'm not sure that should be considered an obvious mistake.

My understanding is that the moves that people said were most obviously mistakes later in the game were a result of it being behind (and desperately trying to swing the lead back in its favor), rather than a cause.

Go rankings weren't designed for ML algorithms, which can have high-level deficiencies and behave erratically under certain conditions.