Many and varied, I guess, and there will be lots of fevered speculation. But I liked this summary by Quartz’s Gideon Lichfield:

“It’s not a human move.”

What shocked the grandmasters watching Lee Sedol, one of the world’s top Go players, lose to a computer on Thursday was not that the computer won, but how it won. A pivotal move by AlphaGo, a project of Google AI subsidiary DeepMind, was so unexpected, so at odds with 2,500 years of Go history and wisdom, that some thought it must be a glitch.

Lee’s third game against AlphaGo is this Saturday. Even if man recovers to beat the machine, what we will remember is that moment of bewilderment. Go is much more complex than chess; to play it, as DeepMind’s CEO explained, AlphaGo needs the computer equivalent of intuition. And as Sedol discovered, that intuition is not of the human kind.

A classic fear about AI is that the machines we build to serve us will destroy us instead, not because they become sentient and malicious, but because they devise unforeseen and catastrophic ways to reach the goals we set them. Worse, if they do become sentient and malicious, then—like Ava, the android in the movie Ex Machina—we may not even realize until it’s too late, because the way they think will be unrecognizable to us. What we call common sense and logic will be revealed as small-minded prejudices, baked in by aeons of biological and social evolution, which trap us in a tiny corner of the possible intellectual universe.

But there is a rosier view: that the machines, sentient or not, could help us break our intellectual bonds and see solutions—whether to Go, or to bigger problems—that we couldn’t imagine otherwise. “So beautiful,” as one grandmaster said of AlphaGo’s game. “So beautiful.”

Very good explainer from the Economist:

AlphaGo uses some of the same technologies as those older programs. But its big idea is to combine them with new approaches that try to get the computer to develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot explain. It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.

Deep learning requires two things: plenty of processing grunt and plenty of data to learn from. DeepMind trained its machine on a sample of 30m Go positions culled from online servers where amateurs and professionals gather to play. And by having AlphaGo play against another, slightly tweaked version of itself, more training data can be generated quickly.

Those data are fed into two deep-learning algorithms. One, called the policy network, is trained to imitate human play. After watching millions of games, it has learned to extract features, principles and rules of thumb. Its job during a game is to look at the board’s state and generate a handful of promising-looking moves for the second algorithm to consider.

This algorithm, called the value network, evaluates how strong a move is. The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of possible daughter games those suggestions could give rise to. Because Go is so complex, playing all conceivable games through to the end is impossible. Instead, the value network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before. The idea is to find the board state that looks, statistically speaking, most like the sorts of board states that have led to wins in the past. Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.

As I write this, the score in the best-of-five games between AlphaGo and Lee Sedol, who is generally reckoned to be the world’s best player, stands at 2-nil in favour of AlphaGo.

LATER AlphaGo won the third match. Game over.