Lee Sedol seemed to be doing well before he went into extra time (as far as I could follow from the commentators). How is it ensured that this is a fair game given the time constraints? I'm guessing adding more computing power to the AlphaGo program should definitely help it in this regard.
The human's strength is intuition and insight. They can look at a move and have a good understanding of strengths and weaknesses of positions by "feel" developed by long practice of the game. More time doesn't really help this much.
Another part of the game is "reading" -- playing out scenarios of response and counter-response to evaluate how strong a move is. The computer excels at this, because it can play out as many moves as its computation time allows and remember all the results with complete accuracy. Humans are slower and prone to mistakes when they do lots of reading.
So adding clock time lets the computer increase its advantage over the human in reading depth, but doesn't so much increase the human's advantage of intuition and insight.
I find it interesting that AlphaGo seems to take a long-ish time to play the move every other pro would play instantly. These pauses probably help balance any time oddities. I think Sedol managed his time well, and even AlphaGo went into byoyomi near the end. Also the fact that it's on even time is more than fair, since the standard until now has been "go on, add more computing power, take more time to play a move, you'll still lose to a pro". As for AlphaGo, I kind of remember reading that doubling the computing resources at this point gave an increase in 60 ELO points. (So if they solidly win against all the Sedol matches they may need to double once or twice or find enough software optimizations to take down Ke Jie using standard time control, but it's not out of reach..)
Well, I think the last time computers played the world chess champion (in 2006), they didn't allow the computer to think on the human time!
And you can always give the computer less time than the human, but this just shows that it's stronger than you and you need to handicap it to have a chance.
Increasing the human time is not an option, since no one wants to watch 8 hour games, and fatigue could also come into play, so the only real option for a winning chance is reducing computer time/power
Correspondence go, similarly, would see fewer errors. Holding a world championship would take quite a bit longer than in chess, though (rough guess: 300-ish half-moves per game versus 100-ish half moves, the latter, I guess, with a bit more variation). That could be problematic, as a world championship in chess already takes years (curiously, some championships finished before the one started a year earlier did)
Just as arbitrary. Unless you're a self-sustaining vegan, you're costing a lot of energy even to just get you the few thousands of kcal your metabolism consumes.
Unless you're not counting support systems, in case it becomes very complicated to calculate and decide exactly which energy expenses are for support systems and which are directly integral for function.
We have two computational substrates, human brains, and CPU/GPU clusters. Forget what it takes to support them, just consider what they consume while computing, that is, the energy consumed while they are playing the game.
Lee Sedol is vastly more efficient than the entire AlphaGo cluster. However, while AlphaGo gains a predictable amount of power as its computing power is increased, it's not clear that one could do the same with humans. Our Go players optimize individual play, not multi-brain distributed play. What would the match look like if we trained up a bunch of humans to play Go as a team, and pitted AlphaGo against a team of humans that consume the same number of joules over the course of the match as it does?
Let’s not forget that aside from being vastly more energy efficient as a Go player, Lee Sedol is additionally capable of taking on a virtually unlimited list of other, equally machine-challenging tasks – while AlphaGo can only do one thing. In fact, Lee can lift himself off the chair to a standing position, pace around the table, lift a glass to his mouth, keep it there while emptying some of it, and think about his next move – all at the same time. (And on the same energy budget.) And far beyond all that, he decides whether to do these things – or something else instead.
I admit my first thought on fairness did go in the same direction of limiting energy budgets. But after reflecting on it just long enough to realise the above, I am finding myself surprisingly uninterested. It now seems to me that nothing particularly insightful would be revealed: limiting energy budget is no less arbitrary than limiting time unless the artificial opponent is expected to be capable of a range of things comparable to that expectable of an average human. Or if expectations are much lower, the artificial opponent would need to contend with drastically tighter limits to approach “fairness” – though at this time it would be guesswork how much tighter they ought to be. Either way, it is glaringly obvious that no computer would come within miles of competing.
So ultimately the fact that Go has been “broken” (in a particular sense) at all is far more interesting to me than whether the machine is competitive with the human in any more general sense. “It’s not” as the universal answer is boring.
And to digress a bit from there: From that perspective, this was ultimately a very human achievement. It was humans who chose Go as a problem to attack and it was them who picked MCTS and deep learning as the way to go. (Uh, no pun intended.) That’s not just reassuring. It’s also a framing we should keep in mind as computers become more entangled with the physical world and more autonomous.
Irrelevant in this case, there was the latency of the operator reading the move on the screen and physically picking up a stone and placing it on the board.
