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Artificial intelligence has achieved remarkable mastery over games such as Chess, Go, and poker, and even Jeopardy!, but the rich variety of standardized exams has remained a landmark challenge. Even as recently as 2016, the best artificial intelligence system could only achieve 59.3 percent on an eighth-grade science exam (Schoenick et al. 2017). This article reports success on the Grade 8 New York Regents Science Exam, where, for the first time, a system scores more than ninety percent on the exam's nondiagram, multiple-choice questions. In addition, our Aristo system, building upon the success of recent language models, exceeded eighty-three percent on the corresponding Grade 12 Science Exam's non-diagram, multiple-choice questions. The results, on unseen test questions, are robust across different test years and different variations of this kind of test. They demonstrate that modern natural language processing methods can result in mastery on this task. While not a full solution to general question answering (the questions are limited to eighth-grade multiple-choice science), it represents a significant milestone for the field. [ABSTRACT FROM AUTHOR]

We introduce a machine learning approach to demosaicing, the reconstruction of color images from incomplete color filter array samples. There are two challenges to overcome by a demosaicing method: 1) it needs to model and respect the statistics of natural images in order to reconstruct natural looking images and 2) it should be able to perform well in the presence of noise. To facilitate an objective assessment of current methods, we introduce a public ground truth data set of natural images suitable for research in image demosaicing and denoising. We then use this large data set to develop a machine learning approach to demosaicing. Our proposed method addresses both demosaicing challenges by learning a statistical model of images and noise from hundreds of natural images. The resulting model performs simultaneous demosaicing and denoising. We show that the machine learning approach has a number of benefits: 1) the model is trained to directly optimize a user-specified performance measure such as peak signal-to-noise ratio (PSNR) or structural similarity; 2) we can handle novel color filter array layouts by retraining the model on such layouts; and 3) it outperforms the previous state-of-the-art, in some setups by 0.7-dB PSNR, faithfully reconstructing edges, textures, and smooth areas. Our results demonstrate that in demosaicing and related imaging applications, discriminatively trained machine learning models have the potential for peak performance at comparatively low engineering effort. [ABSTRACT FROM PUBLISHER]