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Recent advances in artificial intelligence (AI), driven mainly by deep neural networks, have yielded remarkable progress in fields, such as computer vision, natural language processing, and reinforcement learning. Despite these successes, the inability to predict how AI systems will behave “in...
We present a novel augmented reality (AR) interface to provide effective means to diagnose a robot's erroneous behaviors, endow it with new skills, and patch its knowledge structure represented by an And‐Or‐Graph (AOG). Specifically, an AOG representation of opening medicine bottles is learned...
We study a user‐guided approach for producing global explanations of deep networks for image recognition. The global explanations are produced with respect to a test data set and give the overall frequency of different “recognition reasons” across the data. Each reason corresponds to a small...
In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be interpretable to assist users in both development and...
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