Abstract
Academic search engines are racing to incorporate the latest advancements brought about by Large Language models (LLMs) in terms of their ability to understand queries, extract information and directly generate answers. The first movers in this space were startup and challengers such as Elicit, Consensus.AI, Scite assistant, Scispace but they have recently been joined by established academic search engine provider like Elsevier’s Scopus and Digital Science’s Dimensions joining the fray with more to come. Using techniques like RAG (Retrieval augmented generation), this first wave of academic search engines hopes to combine search technology with generative AI by grounding the answers generated by LLMs using information context found by search engines, with the hope of reducing hallucinations. But is this enough? Join Aaron as he shares his experience testing and using these tools and his best guess on how these tools might develop in the future and their impact on research writing in the future.

About the Speaker
Mr. Aaron Tay 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. A past contributor to NMC horizon report (library edition), as well as a founding member of the Initiative for Open Abstracts, he has blended his interest in discovery and the evolving Scholarly ecosystem and has given talks on how AI/ML might change Scholarly communication. More recently, he has contributed to panels and given keynotes on the impact of AI and in particular large language models on academic libraries and institutions at conferences like CILIP, IATUL and more. He has been blogging at MusingsAboutLibrarianship.blogpost.com since 2009.  

Abstract
Screening thousands of research papers for a systematic review or meta-analysis can be overwhelming. The reality is that there simply isn't enough time to read every single article. Join Prof. Dr. Rens van de Schoot as he introduces ASReview, a powerful free and open-source software for systematic reviewing, developed by his research team from Utrecht University. Rens will explain how active learning, a machine learning technique, can accelerate the step of manual screening process by saving up to 95% (!) of screening time. ASReview is more than just a tool; it's a vibrant community of researchers, users, and developers worldwide, contributing to its open-source mission, and Rens will explain how you can join the movement towards fast, open, and transparent systematic reviews.

About the Speaker
Prof. Dr. Rens van de Schoot works as a full professor for 'Statistics for Small Data Sets' at Utrecht University in the Netherlands and as an extra-ordinary professor at North-West University in South Africa. He is also the program director of the research master 'Methodology and Statistics for the Behavioural, Biomedical, and Social Sciences'. He is known for his many tutorials, checklists, and online (free) course materials in the areas of SEM and Bayesian statistics. Currently, his main research project is the community-driven and fully open-source project ASReview: AI-aided systematic reviewing using Active Learning.   

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. 

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.