Google develops computer program capable of learning tasks independently

Google scientists have developed the first computer program capable of learning a wide variety of tasks independently, in what has been hailed as a significant step towards true artificial intelligence. The same program, or "agent" as its creators call it, learnt to play 49 different retro computer games, and came up with its own strategies for winning. In the future, the same approach could be used to power self-driving cars, personal assistants in smartphones or conduct scientific research in fields from climate change to cosmology. The research was carried out by DeepMind, the British company bought by Google last year for £400m, whose stated aim is to build "smart machines". Demis Hassabis, the company's founder said: "This is the first significant rung of the ladder towards proving a general learning system can work. It can work on a challenging task that even humans find difficult. The work is seen as a fundamental departure from previous attempts to create AI, such as the program Deep Blue, which famously beat Gary Kasparov at chess in 1997 or IBM's Watson, which won the quiz show Jeopardy! in 2011. In both these cases, computers were pre-programmed with the rules of the game and specific strategies and overcame human performance through sheer number-crunching power. "With Deep Blue, it was team of programmers and grand masters that distilled the knowledge into a program," said Hassabis. "We've built algorithms that learn from the ground up." The DeepMind agent is simply given a raw input, in this case the pixels making up the display on Atari games, and provided with a running score. When the agent begins to play, it simply watches the frames of the game and makes random button presses to see what happens. "A bit like a baby opening their eyes and seeing the world for the first time," said Hassabis. The agent uses a method called "deep learning" to turn the basic visual input into meaningful concepts, mirroring the way the human brain takes raw sensory information and transforms it into a rich understanding of the world. The agent is programmed to work out what is meaningful through "reinforcement learning", the basic notion that scoring points is good and losing them is bad. Tim Behrens, a professor of cognitive neuroscience at University College London, said: "What they've done is really impressive, there's no question.