Machines may soon be trying to master just about anything you can do on a computer.
Open AI, a nonprofit dedicated to pursuing big advances in AI and making that progress freely available to anyone, has released Universe, a platform that will let AI programs learn, through experimentation and positive reward, how to do all sorts of things on a computer.
Universe will include more than a thousand games, but also desktop programs such as Web browsers. It will make it possible for AI researchers to train programs to do all sorts of new tricks, including potentially useful tasks like filling out online forms, responding to e-mails, and updating spreadsheets.
But Ilya Sutskevar, cofounder and research director at OpenAI, says the motivation for developing and releasing Universe is a lot bigger. Universe will provide way for AI researchers to develop and test algorithms capable of learning to perform a broad range of tasks—a step towards more general types of artificial intelligence. The hope is that it will lead to artificial agents that can learn a wide range of different tasks, and then take what they’ve learned in one setting and apply it to a different one. Such capabilities, known within the field as transfer learning, promise to increase the power and usefulness of artificial intelligence.
“If an agent does well on Universe tasks, which means it can understand what it needs to do, and do it as a result of applying its prior knowledge, then this agent will be significantly more intelligent than anything that exists today,” Sutskevar says.
AI algorithms can sometimes match or surpass human abilities, but only within very narrow domains, such as image recognition or playing a particular game. Most algorithms cannot learn to do lots of different tasks, and they generally cannot apply what they have learned in one domain to a different one.
The Universe environment lets AI agents take screen pixels as input and provide input in the form of keyboard strokes and mouse clicks. The platform will be compatible with AI agents that use reinforcement learning—that learn through experimentation and positive feedback. In the case of a computer game the feedback might be completing the game or finishing a level.
Sutskevar, lured away from Google by OpenAI last year, believes the platform will produce fundamental advances relevant to many different fields.
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