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Posted on October 15, 2019 at 7:44 AM

I recently watched the documentary AlphaGo, directed by Greg Kohs. The film tells the story of the refinement of AlphaGo—a computer Go program built by DeepMind—and tracks the match between AlphaGo and 18-time world champion in Go Lee Sedol.

Go is an ancient Chinese board game. It was considered one of the four essential arts of aristocratic Chinese scholars. The goal is to end the game having captured more territory than your opponent. What makes Go a particularly interesting game for AI to master is, first, its complexity. Compared to chess, Go has a larger board, and many more alternatives to consider per move. The number of possible moves in a given position is about 20 in chess; in Go, it’s about 200. The number of possible configurations of the board is more than the number of atoms in the universe. Second, Go is a game in which intuition is believed to play a big role. When professionals get asked why they played a particular move, they will often respond something to the effect that ‘it felt right’. It is this intuitive quality why Go is sometimes considered an art, and Go players artists. For a computer program to beat human Go players, then, it would have to mimic human intuition (or, more precisely, mimic the results of human intuition).

AlphaGo first trained on 100,000 games downloaded from the internet that strong amateurs had played. It then played against itself millions of times, and learned from its mistakes—aided by computer programmers, as well as by at least one professional Go player, European champion Fan Hui.

In 2016, the program was good enough for DeepMind to feel confident in organising a five-game Go match with the world champion, Lee Sedol, in Seoul. If you don’t know how the match ends, and you want to learn about it through the documentary, then stop reading now—spoilers ahead.

It was a historic moment. Go is a big deal in much of Asia. About eight million Koreans play the game. Lee Sedol is a national celebrity. The match was front-page news. There were swarms of cameras capturing the moment. Nervousness was palpable on both sides of the board.

Game one. Lee Sedol loses. Game two. Lee Sedol loses. Game three. Lee Sedol loses. Game four. Lee, using an extreme strategy that forces an ‘all or nothing’ situation, wins. The crowd cheers. Joy and hope could be breathed. Game five. Lee Sedol loses.

I knew the outcome of the match before I watched the documentary. I had read the news at the time of the match, and hadn’t felt very strongly about it, one way or another. I’m not a fan of Go, I had never heard of Lee Sedol before. I recognised the historical importance of the landmark. I was impressed, but the news hadn’t made me feel excited, or fearful—much less sad.

What astounded me about watching the documentary is the deep sadness that permeated Lee Sedol’s defeat and AlphaGo’s victory. With every defeat Lee Sedol looked devastated. But it wasn’t only him. Almost everyone looked sad, or somewhat troubled. A member of the DeepMind team said: ‘I couldn’t celebrate. It was fantastic that we had won. But there was such a big part of me that saw this man trying so hard and being so disappointed…’. Even Demis Hassabis, founder and CEO of DeepMind, confessed feeling ‘ambivalent’.

What is most surprising about the match is that the outcome did not feel like a win for humanity. It did not feel similar to when we conquer a disease, or when the first human being landed on the moon. It felt like we might be losing more than what we might be gaining.

You might think that such sadness simply comes out of sympathy for Lee Sedol. Or perhaps out of nostalgia for the old times; something that we should and will get over. Maybe. But maybe it is a kind of warning. A reminder that not all technological developments lead to a better life. A caution to remember to put human beings first. Let us never forget that technology is a tool, a means, and never an end in itself. Technology is valuable only insofar as it enhances our wellbeing. And AlphaGo and other AI programs still have to prove themselves in that regard.



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