When, a year ago, Google bought, for a reported $400M, a small and previously little known British artificial intelligence startup called DeepMind, the acquisition was widely reported, the general press essentially saying; “Gosh golly, these tech type geniuses are, like .. wow! And they get rich too!”(1,2).
Technical news sites concentrated more on the reasons Google would be interested in such a company, connecting it with Google’s buying spree of robotics firms and pointing out that artificial learning could be useful in its core business of selling (more or less) accurately targeted advertising.
But last week’s publication in Nature of a paper by the DeepMind team claiming that their software has been able to learn to successfully play 49 early video games points toward something much more profound than this – because the software they produced has learned to play the games without any previous information about what the games were, or what success consisted of. The only inputs were the screen itself (exactly what a human would see) and the score, and the only outputs from the software were valid actions that could be produced by the game controller. From nothing more than repeated ‘plays’ the software ‘discovered’ strategies for success, and in a quite number of cases (29!), learned to play the game better than humans can.
‘But these are trivial games’, you might say; computers have been able to beat the world champion Chess players for years, and are now making progress even at the game of Go. And you’d be right, but the difference is in the way the software learned to play.