Whether you call it machine learning, deep learning, or AI, a new set of methods at the interface of statistics and computer science are being applied to research across the sciences. A consequence of the excitement about these new methods is that disciplinary researchers eager to use them in their research must both get up to speed quickly and maintain an awareness of a new literature, one which is moving at high volume and velocity. Increased interest in the AI literature, however, comes just as that literature is getting harder to read thanks to a combination of short publish-response cycles and rapidly evolving norms about what should be cited and explained in a given paper. This grant funds a project by computer scientist Marti Hearst to develop interfaces to the AI literature that offer additional context and support for readers not deeply acquainted with the field. Hearst’s lab will develop algorithms and software to help readers see the meanings of symbols and terms anywhere in the text of a given article, regardless of where they are defined, and pull in explanations from papers in the co-citation network of the paper being read where definitions are not present in the text itself. The resulting software, implemented in a lightweight interface that integrates with PDF readers to ensure wide adoption, will be of value to researchers across the sciences who are adopting machine learning methods.