The application of machine learning (ML) to academic libraries promises to be transformational. A Task Force of the Ontario Council of University Libraries (OCUL) has been exploring this technology and identifying specific ML use cases. OCUL is an association of the 21 university libraries in Ontario who collaborate on many shared services and resources.
This session will review the work of the Task Force with a focus on use cases, and the requirements and processes to implement pilot programs and production services. Particular attention will be placed on the technology infrastructure (compute, software) and the expertise requirements (technology, domain).
Use cases to be discussed include audio to text transcription, metadata creation, virtual reference (chat), and discovery using natural language processing (NLP), semantic search, and summarization. The discovery use case will be applied to some of the extensive data collections maintained by Scholar Portal, the shared resource managed by OCUL, including over 65 million articles from over 27,000 full text scholarly journals and a collection of over 800K digital books and government documents.
Participants will be encouraged to engage with key questions about the adoption and use of machine learning in libraries and to provide feedback on the ongoing evolution of this technology as it benefits library applications.
Level: Introductory
Length: 1.5 Hours
Format: Lecture
Prerequisites: None
- Teacher: Michael Ridley