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Researchers at Google may have found a way to make large language models learn more like humans.
Their new training method teaches AI systems to update beliefs as new information appears, using a reasoning framework called Bayesian learning. In tests, models began refining their predictions during multi-round interactions.
At the same time Google is pushing AI onto devices through LiteRT, while other companies are building autonomous AI agents that can perform real tasks.
Together these developments point toward AI systems that can reason, adapt, and operate more independently.