The group says it has a more practical way to get software to learn tasks, such as steering robots, that require multiple actions.
In 2013 a British artificial-intelligence startup called DeepMind surprised computer scientists by showing off software that could learn to play classic Atari games better than an expert human player. DeepMind was soon acquired by Google , and the technique that beat the Atari games, reinforcement learning, has become a hot topic in the field of AI and robotics. Google used reinforcement learning to create software that beat a champion Go player last year.
Now OpenAI, a nonprofit research institute cofounded and funded by Elon Musk, says it has discovered that an easier-to-use alternative to reinforcement learning can get rival results when it plays games and performs other tasks. At MIT Technology Review’s EmTech Digital conference in San Francisco on Monday, OpenAI’s research director, Ilya Sutskever, said that could allow researchers to make progress in machine learning faster.
“It’s competitive with today’s reinforcement-learning algorithms on standard benchmarks,” said Sutskever. “It is surprising that something so simple actually works.”
Sutskever argues that finding new ways to have software learn to do things like play computer games or steer robots is important to making machine-learning software take on more complex tasks than just recognizing images or transcribing our speech.
