Recent progress in machine learning has raised a series of urgent questions: How can we train and debug deep learning models? How can we understand what is going on inside a neural network? And, perhaps most important, how can we design systems that serve people best? We'll show a series of examples from the People+AI Research (PAIR) initiative at Google--ranging from data visualizations for researchers, to tools for medical practitioners, to guidelines for designers--that illustrate how thinking carefully about data can lead to better tools, more effective design, and help humans and AI work together.
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