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As more deep learning projects began to spit out different kinds of data, other teams around the world thought they could make use of them. The Smithsonian Institution created an explanation project for its libraries, cataloging thousands of artifacts, from scrimshaw to Vantablack, with Amazon’s Mechanical Turk service. The National Archives drew up tens of thousands of bar codes so that details about the type of paper, the width of the paper, and the type of image printed on the paper could be cross-referenced. Cultural anthropologists used these kinds of data to study how communities were arranged. The project is part of what is now called the Explanable AI program, which is finding new ways to turn big data into understandable data.

By analyzing user-generated photo stream data, a team at the University of Dundee in Scotland found that effective images are those that appear to capture moments while being modestly sized. A Google research team used Google Street View data to automatically calculate travel times to different places.
A team from the University of Pennsylvania and the University of California, Berkeley, applied deep learning to a set of data about passenger demand in India, to predict the best time for a plane to leave from Delhi to Mumbai.


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