Enlarge / Even hunt-and-fetch quests are better with a little AI help. (credit: Getty Images)
At this point in the progression of machine-learning AI, we’re accustomed to specially trained agents that can utterly dominate everything from Atari games to complex board games like Go. But what if an AI agent could be trained not just to play a specific game but also to interact with any generic 3D environment? And what if that AI was focused not only on brute-force winning but also on responding to natural language commands in that gaming environment?
Those are the kinds of questions animating Google’s DeepMind research group in creating SIMA, a “Scalable, Instructable, Multiworld Agent” that “isn’t trained to win, it’s trained to do what it’s told,” as research engineer Tim Harley put it in a presentation attended by Ars Technica. “And not just in one game, but… across a variety of different games all at once.”
Harley stresses that SIMA is still “very much a research project,” and the results achieved in the project’s initial tech report show there’s a long way to go before SIMA starts to approach human-level listening capabilities. Still, Harley said he hopes that SIMA can eventually provide the basis for AI agents that players can instruct and talk to in cooperative gameplay situations—think less “superhuman opponent” and more “believable partner.”
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