Defeated Go world champion beats DeepMind AI in penultimate match

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Machines 3, humans 1… That’s the current score in a five-match series being played out between DeepMind’s AlphaGo and human Go world champion, Lee Sedol.

Last week the Google-owned AI chalked up a landmark victory by winning the opening bout with Sedol — the first time a machine had outplayed a world class professional Go player. The algorithm then went on to cement victory in the five-match series by winning both the next two games, to take a consecutive three-game streak.

However as the final two matches are played out, Sedol has clawed one back for humans by winning the fourth match (via The Verge), and chalking up his first win against the AI.

DeepMind founder Demis Hassabis tweeted that the machine’s loss was due to Sedol’s ingenuity in move 78 pressurizing it into making a fatal mistake…

AlphaGo combines two artificial intelligence techniques in its quest try master the hugely complex game of Go, applying deep learning with Monte Carlo Tree Search — allowing the AI to simulate millions of games, glean the outcomes and learn from those to generalize a (evidently highly successful but not unbeatable) Go game strategy.

Discussing the complexity of the challenge ahead of AlphaGo earlier this year, Google noted in an blog post: “There are 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,


00,000,000,000,000,000,000,000,000,000,000 possible positions [in the game of Go] — that’s more than the number of atoms in the universe, and more than a googol times larger than chess.”

Although AlphaGo’s triumph against Sedol, a professional Go player who is ranked second in the world, is undoubtedly impressive, the artificial intelligence is still very much a narrow AI — in the sense that it has been designed for one very specific task, as with other AIs built to master other games like chess and Jeopardy!

The holy grail of artificial intelligence remains the creation of a general learning AI that can apply multifaceted intelligence to solve problems of all stripes. And machines are clearly very far away from being able to claim victory in the chaotic complexity of the off-boardgame world, as Hassabis himself concedes.

The number of variables involved in even apparently simple human tasks — like tidying a room — quickly makes even the most sophisticated machine intelligence look dumb. So us humans shouldn’t feel too bad about losing at Go…

You can follow the final match in the AlphaGo series, due to take place on March 15, live via YouTube here.