[apag]

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.