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.
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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