David Runciman

It’s three weeks before Christmas and Los Angeles is in flames, though you wouldn’t know it from inside the bowels of the Long Beach Convention and Entertainment Centre, where all is cool and grey. I am here with eight thousand other attendees of the Neural Information Processing Systems (Nips) conference – the great annual get-together of people who work in machine learning. These are the men and women (mainly men, but we’ll come to that) who are building the artificial systems that may one day, perhaps quite soon, be able to perform many tasks that have traditionally been thought to require human intelligence. The prevailing mood of the conference is one of remorselessly practical problem-solving, mixed with occasional bursts of euphoria at how far machine learning has come in recent years. Meanwhile, about thirty miles to the north, wildfires have reached the Bel Air district, lapping at the edges of the UCLA campus and the Getty Museum. Smoke is drifting across the 405 highway, which carries 400,000 vehicles a day, the busiest stretch of road in the United States. If the people at Nips get their way, it won’t be long before most of these cars drive themselves through the haze, human cargo safely stowed. For now, though, it’s humans doing the driving and strung out firefighters doing battle with the elements. Plenty could still go wrong, though it will take a while for the news to reach Long Beach.

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[*] Paul Taylor wrote about DeepMind and AlphaGo in the LRB of 11 August 2016.