Brief Report and Materials From AIDR (Artificial Intelligence for Data Discovery and Reuse) 2019 Conference Hosted by Carnegie-Mellon University Libraries
Carnegie Mellon University Libraries recently hosted a conversation on harnessing the power of artificial intelligence for scientific data discovery.
The AIDR (Artificial Intelligence for Data Discovery and Reuse) 2019 conference took place May 13-15 and brought 150 researchers, computer scientists, librarians and industry representatives from 10 countries and 65 institutions and organizations to CMU May 13-15.
With the recent advances in machine learning and AI, it is possible to train computers to find optimal solutions to a problem, such as integrating different datasets and extracting metadata,” said Huajin Wang, a CMU librarian and conference chair. “We created AIDR 2019 because it’s about time that people working in a variety of disciplines come together to benefit from diverse expertise, and address these mutual challenges together, using the power of AI.”
Attendees heard from speakers including Tom Mitchell, the E. Fredkin University Professor of Machine Learning and Computer Science and interim dean of the School of Computer Science; Glen de Vries, a 1994 graduate of the Mellon College of Science and president and co-founder of Medidata Solutions; and Natasha Noy, staff scientist at Google AI and team lead for Google Dataset Search. Discipline-specific presentations and panel discussions rounded out the agenda.
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About Gary Price
Gary Price (email@example.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.