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A computer program, dubbed AlphaGo, has successfully beaten a professional player of Go for the first time.
What is significant however is the method used by that computer program to win:
The IBM chess computer Deep Blue, which famously beat grandmaster Garry Kasparov in 1997, was explicitly programmed to win at the game. But AlphaGo was not preprogrammed to play Go: rather, it learned using a general-purpose algorithm that allowed it to interpret the game’s patterns, in a similar way to how a DeepMind program learned to play 49 different arcade games2.
This means that similar techniques could be applied to other AI domains that require recognition of complex patterns, long-term planning and decision-making, says Hassabis. “A lot of the things we’re trying to do in the world come under that rubric.” Examples are using medical images to make diagnoses or treatment plans, and improving climate-change models.
If computer programs are now successfully able to learn and adapt it means that it will become increasingly difficult to distinguish between those programs and actual humans.