An anonymous reader quotes a report from TechCrunch: AI agents left to explore a virtual island without a specific task began inventing challenges of their own. MIT researchers say the behavior bears the hallmarks of play. The researchers put 13 AI agents called Eko, each running on Claude Opus 4.7, alone on a simulated desert island for 30 hours. They describe the experiment in a paper posted on arXiv, a site where researchers share studies before peer review. Afterward, they gave the agents specific challenges, like getting as many objects as possible onto the island's tallest rock spire. The Eko agents that had spent those 30 hours playing generally beat identical agents that had never set foot on the island.
Today, developers decide in advance what AI should practice and what counts as success. The researchers see this technique -- exploring without a goal -- as a different way for AI agents to learn skills, especially skills that could one day be used to control robots. "We normally develop artificial agents by deciding in advance what they should practice, what they should optimize, and what counts as progress," the researchers write in their report. "Play suggests another regime: give an agent a sufficiently rich and safe world, let it choose what is interesting, and allow useful knowledge to accumulate from the history that follows." UC Berkeley cognitive scientist Eunice Yiu told MIT Technology Review she was impressed with the findings but that more evidence is needed to determine whether AI models are genuinely playing. "It could be the case that the models are genuinely playing, but we don't know based on the paper," says Yiu. "If you only have the behavioral piece, it could be the case that they're just trying to abide by the prompt, trying to imitate humans."
MIT professor and coauthor of the paper, Phillip Isola, added: "It's not like this is a system that just suddenly magically decided to play. It's an artifact that learned from our culture, was trained by engineers at Anthropic, and was then placed on an island by us. It does things that surprise us and that are causally related to its future skillful behavior."
Today, developers decide in advance what AI should practice and what counts as success. The researchers see this technique -- exploring without a goal -- as a different way for AI agents to learn skills, especially skills that could one day be used to control robots. "We normally develop artificial agents by deciding in advance what they should practice, what they should optimize, and what counts as progress," the researchers write in their report. "Play suggests another regime: give an agent a sufficiently rich and safe world, let it choose what is interesting, and allow useful knowledge to accumulate from the history that follows." UC Berkeley cognitive scientist Eunice Yiu told MIT Technology Review she was impressed with the findings but that more evidence is needed to determine whether AI models are genuinely playing. "It could be the case that the models are genuinely playing, but we don't know based on the paper," says Yiu. "If you only have the behavioral piece, it could be the case that they're just trying to abide by the prompt, trying to imitate humans."
MIT professor and coauthor of the paper, Phillip Isola, added: "It's not like this is a system that just suddenly magically decided to play. It's an artifact that learned from our culture, was trained by engineers at Anthropic, and was then placed on an island by us. It does things that surprise us and that are causally related to its future skillful behavior."
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