All seminars will take place on Fridays at 11 a.m. in DBH 6011. Check seminar details below.
Information Sciences Institute, USC
November 12, 2021
11:00am - 12:00pm
AI Systems as Authors of Scientific Papers: Implications for the Future of Scientific Research and Discovery
I envision a future where AI systems will be key contributors to science advances, writing papers that are augmented with rich explicit, structured representations of the experiments, software, data, and workflows used to reach new findings. The methods sections of papers will be automatically generated by the AI system from those rich representations. Papers generated by AI systems will culminate the promise of open science and reproducible research. Other AI systems will be able to use those representations to reproduce the results and update the findings of the paper when new data becomes available. In this talk, I will describe our work in developing these rich representations of scientific processes and products, and in designing AI systems that use those representations to automatically generate narrative descriptions of the methods section of papers, reproduce and re-run analyses with new data, and carry out new experiments on their own. I expect that in the next few years AI systems will become paper authors and will radically transform how scientists interact with scientific publications by providing continuously updated findings, customized paper summaries and method narratives for different expertise levels, and interactive on-demand explanations. AI systems will also be able to do creative scientific research, exploring variants of existing methods and questions, and designing innovative methods and approaches to scientific questions leading to new autonomous discoveries.
Dr. Yolanda Gil is Director of New Initiatives in AI and Data Science in USC’s Viterbi School of Engineering, and Research Professor in Computer Science and in Spatial Sciences. She is also Director of Data Science programs and of the USC Center for Knowledge-Powered Interdisciplinary Data Science. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Her research is on intelligent interfaces for knowledge capture and discovery, which she investigates in a variety of projects concerning scientific discovery, knowledge-based planning and problem solving, information analysis and assessment of trust, semantic annotation and metadata, and community-wide development of knowledge bases. Dr. Gil collaborates with scientists in many domains on semantic workflows and metadata capture, social knowledge collection, computer-mediated collaboration, and automated discovery. She is a Fellow of the Association for Computing Machinery (ACM), the Association for the Advancement of Science (AAAS), and the Institute of Electrical and Electronics Engineers (IEEE). She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and served as its 24th President.