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  "documentTitle": "2020 Air Street Capital The State of AI Report 2020",
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      "text": "Convolutional neural networks are designed to learn features from images that are represented as a regular grid of independent pixels in 2D space.",
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      "text": "Most deep learning methods focus on learning from 2D input data (i.e. Euclidean space). Graph neural networks (GNNs) are an emerging family of methods that are designed to process 3D data (i.e. non-Euclidean space).",
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      "text": "Now consider a chemical molecule, which is described as a graph of atoms that are connected to other atoms by bonds. Using a 2D neural network approach would not make use of the information that is explicitly encoded in molecular graph.",
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