Goat Simulator 3 is a surreal video game in which players take domesticated ungulates on a series of implausible adventures, sometimes involving jetpacks.
That might seem an unlikely venue for the next big leap in artificial intelligence, but Google DeepMind today revealed an AI program capable of learning how to complete tasks in a number of games, including Goat Simulator 3.
Most impressively, when the program encounters a game for the first time, it can reliably perform tasks by adapting what it learned from playing other games. The program is called SIMA, for Scalable Instructable Multiworld Agent, and it builds upon recent AI advances that have seen large language models produce remarkably capable chabots like ChatGPT.
“SIMA is greater than the sum of its parts,” says Frederic Besse, a research engineer at Google DeepMind who was involved with the project. “It is able to take advantage of the shared concepts in the game, to learn better skills and to learn to be better at carrying out instructions.”
As Google, OpenAI, and others jostle to gain an edge in building on the recent generative AI boom, broadening out the kind of data that algorithms can learn from offers a route to more powerful capabilities.
DeepMind’s latest video game project hints at how AI systems like OpenAI’s ChatGPT and Google’s Gemini could soon do more than just chat and generate images or video, by taking control of computers and performing complex commands. That’s a dream being chased by both independent AI enthusiasts and big companies including Google DeepMind, whose CEO, Demis Hassabis, recently told WIRED is “investing heavily in that direction.”
“The paper is an interesting advance for embodied agents across multiple simulations,” says Linxi “Jim” Fan, a senior research scientist at Nvidia who works on AI gameplay and was involved with an early effort to train AI to play by controlling a keyboard and mouse with a 2017 OpenAI project called World of Bits. Fan says the Google DeepMind work reminds him of this project as well as a 2022 effort called VPT that involved agents learning tool use in Minecraft.
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