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New research from MIT automates the math for trading off risk and reward, in domains ranging from artificial intelligence to climate to finance, fixing errors made by deep learning systems.
MIT researchers have used probabilistic programming and inverse graphics to develop a framework that enables computer vision systems to more accurately explain 3D scenes observed in 2D images.
Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a