“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
Read MoreA new algorithm solves complicated partial differential equations by breaking them down into simpler problems, potentially guiding computer graphics and geometry processing.
Read MoreAI agents could soon become indistinguishable from humans online. Could “personhood credentials” protect people against digital imposters?
Read MoreIn controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.
Read MoreThe approach can detect anomalies in data recorded over time, without the need for any training.
Read MoreA new algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
Read MoreCSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.
Read MoreMore efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.
Read MoreIntroducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
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