Fostering Collaborative Breakthroughs in Heritage Science through Machine Learning and Data Science

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A quarter century ago, NSF’s investment in the Digital Library Initiative stimulated the digitization of cultural collections, and the development of tools to analyze those collections in ways that were not possible before. This conference brings together experts in heritage and computer science to examine the state of the art in this effort and to identify specific needs and opportunities for novel research on collections of culturally significant artifacts using the whole range of tools and techniques from data science, artificial intelligence, machine learning and imaging. The conference is focused on the how AI systems can be designed and implemented to deal with critical problems for heritage collections such as dealing with damage, extracting useful data without causing additional damage, enabling data from disparate collections to be meaningfully compared and labeling data to support machine learning, and developing standards for representational frameworks to support scholarly advances. The conference organizers will produce and disseminate a consensus report summarizing the pathways to new technical approaches and solutions to long-standing problems within entire classes of cultural heritage collections. The dialog and synergistic activities of the meeting will expand the conventional thinking and the analysis framework around imaging, artificial intelligence, and data science, which will address fundamental challenges with the potential for rapid diffusion into cultural heritage.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.