Generative AI improves a wireless vision system that sees through obstructions
With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.
With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
Read MoreAcademia-industry relationship is an early-stage accelerator, supporting professional progress and research.
Read MoreResearchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.
Read MoreMIT computer science students design AI chatbots to help young users become more social, and socially confident.
Read MoreA new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
Read MoreBy leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Read MoreTo help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
Read MoreBy providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.
Read MoreBy minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.
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