Researchers’ Series Special Topics 2023 - Generative AI for Translational Scholarly Communication

Abstract

Many valuable insights embedded in scientific publications are siloed and rarely translated into results that can directly benefit humans. These research-to-practice gaps impede the diffusion of innovation, undermine evidence-based decision making, and contribute to the disconnect between science and the public. Generative AI systems trained on decades of digitized scholarly publications and other human-produced texts are now capable of generating (mostly) high-quality and (sometimes) trustworthy text, images, and media. Applied in the context of scholarly communication, Generative AI can quickly summarize research findings, generate visual diagrams of scientific content, and simplify technical jargon. In essence, Generative AI has the potential to help tailor language, format, tone, and examples to make research more accessible, understandable, engaging, and useful for different audiences.

In this talk, I’ll discuss some uses of Generative AI in these contexts as well as challenges towards realizing the potential of these models, e.g., how to effectively design generated translational science communication artifacts, incorporate human feedback in the process, and mitigate the generation of harmful, misleading, or false information. Scholarly communication is undergoing a major transformation with the emergence of these new tools. By using them safely, we can help bridge the research-to-practice gap and maximize the impacts of scientific discovery. 

Registration: https://lbcube.hkust.edu.hk/ce/event/9594

About the Speaker

Lucy Lu Wang is an Assistant Professor at the University of Washington Information School. Her research focuses on how to build better AI and NLP systems for extracting and understanding information from scientific texts; for example, can we create systems that leverage up-to-date literature to help us make better and more data-driven healthcare decisions, or design document understanding models that can improve the readability of scientific texts for people who are blind and low vision. Lucy’s work on supplement interaction detection, gender trends in academic publishing, COVID-19 datasets, and document understanding has been featured in Geekwire, Boing Boing, Axios, VentureBeat, and the New York Times. Prior to joining the UW, she was a Young Investigator at the Allen Institute for AI, and she received her PhD in Biomedical Informatics and Medical Education from the University of Washington. 

Notes

1) The Zoom meeting ID will be sent to registrants at 5pm, 1 day before the session.

2) 1.5 credit hours will be counted toward the course requirement of PDEV 6770A/C/D/E.