Singapore Alliance of University Libraries (SAUL)-Research Services Task Force (RSTF) Webinar 2023

Lucy Lu Wang

Speaker Biography:

Lucy Lu Wang is an Assistant Professor at the University of Washington Information School. Her research focuses on how to build better AI and Natural Language Processing 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. Prior to joining the University of Washington, 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.

Synopsis of Presentation:

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.

Deborah Fitchett

Speaker Biography:

Deborah works at Lincoln University, New Zealand as Associate University Librarian - Digital Scholarship. In this wide-ranging role her responsibilities include implementing new library systems and integrations; supporting the university's open access policy and projects; and advising on copyright for teaching and research. She is a member of the ANZREG (Australia New Zealand Ex Libris User Group) committee, and has recently coordinated a national initiative to upload thesis metadata to Wikidata. In her spare time she reads and sometimes writes science fiction, learns ice skating, helps moderate a Mastodon server, and dabbles in crochet and computer programming.

Synopsis of Presentation:

Despite the dreams of science-fiction fans worldwide, the thing being marketed as "artificial intelligence" is no more than high-powered predictive text. What it gets right is thanks to its input data created by billions of humans, and to an invisible and underpaid workforce of content moderators. What it gets wrong threatens privacy, exacerbates sexism, racism and other inequities, and may even be environmentally damaging. There are situations that are well enough defined that machine models can be useful, but scholarly communication by its nature is full of new and unique information, relying on precisely reported data, that algorithms based on probabilities can't deal with. So as a community we need to come with ways to prevent machine-generated fake papers from poisoning the well of science - and we need to be healthily sceptical of vendors selling us machine-based solutions to problems that can still only be addressed by human intelligence.

Andy Tattersall

Speaker Biography:

Andy Tattersall is an Information Specialist at The Division of Population Health (SCHARR) at The University of Sheffield. He writes, teaches and delivers talks and training about research communications (including podcasting, blogging, social media, video/animation, infographics), digital academia, open research, web and information science and altmetrics. In particular, their application for research, teaching, learning, knowledge exchange and collaboration. Andy received a Senate Award from The University of Sheffield for his pioneering work on Massive Online Open Courses (MOOCs) in 2013 and is a Senior Fellow of the Higher Education Academy. In 2017, he was named one of Jisc’s Top 10 Social Media Superstars. Andy co-wrote and edited a book on Altmetrics for Facet Publishing which is aimed at researchers and librarians.

Synopsis of Presentation:

We have long known about AI, largely through science fiction films, but now in 2023 it feels like it has truly arrived. The implications for academia are truly far reaching, from information governance to ethics, from assessment to systematic reviews. It is inevitable that AI will soon become commonplace across the research cycle, including scholarly communications and dissemination. The question is, how useful will it be and can it help broaden the reach and impact of research outputs? Already there are signs that AI could become a useful tool in sharing research and knowledge. This short talk will talk about some of the opportunities as well as pitfalls as we take the next technological step forward.

Aaron Tay

Speaker Biography:

Aaron has been an academic librarian for over 10 years in Singapore and has worked in a variety of areas including library discovery, research support & bibliometrics. He is current Lead Data Services at the Singapore Management University Libraries and has been honoured for his contributions to the profession with a few awards including Library Association of Singapore (LAS) Professional Service Award, Congress of Southeast Asian Libraries (CONSAL) award (Silver) and Pacific Rim Research Library Alliance (PRRLA) Karl Lo award. He enjoys reading and thinking about technology use for academic researchers and regularly blogs his thoughts at the popular Musings about Librarianship since 2009.

Synopsis of Presentation:

The latest generation of Large Language Models (LLM) like ChatGPT seem to be the real deal, demonstrating capabilities that show huge leaps in Natural Language Understanding and Generation from past generations of systems. But like all machine learning applications, training on large amount of data is necessary for success, and it seems obvious that this makes open access even more valuable now. Open Access potentially unlocks value not just from human reading it but also machine ones. But should librarians really push and pay more for Open Access? In this short 25 minute talk, I will cover some arguments both for and against this view. 

Katie Peace

Speaker Biography:

Katie Peace is the Senior Publisher for Taylor & Francis book publishing in Asia. Her team publishes academic research books, professional books and textbooks written by Asia Pacific authors for an international market, under the Routledge (HSS) and CRC Press (STEM) imprints. With more than 20 years’ experience in the publishing industry globally, she has insights into the academic journals and books publishing processes and specialises in advising and guiding authors to produce the best publishing output. She is happy to share her knowledge of publishing with librarians, early career researchers and experienced academics.

Synopsis of Presentation:

With the emergence of ChatGPT into the public consciousness in late 2022, questions around how AI will affect scholarly publishing, particularly in regard to managing its use for authors, how to track AI-generated content, where AI content comes from and the ethics around using generative AI in publications. How much of a help or a hindrance generative AI will be remains a hot topic, and this presentation will explore what we know about how these AI models work, how they use published content, the part authors will continue to play and AI’s place in scholarly book publishing today and possibly in the future.