Jason Griffey is a librarian, technologist, consultant, writer, and speaker. He is the founder and principal at Evenly Distributed, a technology consulting and creation firm for libraries, museums, educational institutions, and other nonprofits. Griffey is an Affiliate researcher at metaLAB at Harvard University, and a former Fellow at the Berkman Klein Center for Internet and Society at Harvard University. He was a winner of the Knight Foundation News Challenge for Libraries in 2015 for the Measure the Future project, an open hardware project designed to provide actionable use metrics for library spaces. Griffey is also the creator and director of the LibraryBox Project, an open-source portable digital file distribution system. He has written and spoken internationally on topics such as the future of technology and libraries, personal electronics in the library, privacy, copyright, and intellectual property.
- Table of Contents
- About the Authors
This issue of Library Technology Reports argues that the near future of library work will be enormously impacted and perhaps forever changed as a result of artificial intelligence (AI) and machine learning systems becoming commonplace. It will do so through both essays on theory and predictions of the future of these systems in libraries and also through essays on current events and systems currently being developed in and by libraries. A variety of librarians will discuss their own AI and machine learning projects, how they implemented AI and to what ends, and what they see as useful for the future of libraries in considering AI systems and services. First up is an essay relating the development and design of a machine learning system developed by a library and deployed to production in a library anywhere in the US. The system is HAMLET (How about Machine Learning Enhanced Theses) by Andromeda Yelton, currently a developer at the Berkman Klein Center for Internet and Society at Harvard. At MIT, she created and developed HAMLET. Next, in chapter three, we have an essay by Bohyun Kim, CTO and associate professor at the University of Rhode Island Libraries, where she discusses the launch of their Artificial Intelligence Lab, which is housed in the library on campus. Then in chapter four, Craig Boman, Discovery Services Librarian and assistant librarian at Miami University Libraries, looks at his attempts to use a type of machine learning to build a system to assign formal subject headings to unclassified, full-text works. This report will conclude with a discussion of possibilities and potentials for using AI in libraries and library science.
Chapter 1 Introduction
Chapter 2 HAMLET: Neural-Net-Powered Prototypes for Library Discovery
Chapter 3 AI and Creating the First Multidisciplinary AI Lab
Chapter 4 An Exploration of Machine Learning in Libraries
Chapter 5 Conclusion
Chapter 6 Sources Consulted