AI for Science Communication: Adapting to Different Stakeholders
1:00pm CDT, Center for Human-Computer Interaction (Frances Searle Building 1-122)
Communicating complex scientific ideas to the public is critical for an equitable, informed society, but doing so without misleading or overwhelming people is challenging. As large language models become more capable at summarizing and simplifying scientific text, we have a unique opportunity to use these models to make science more accessible. In this talk I will share my group’s research developing language tools and systems to help communicate science to more people. I will highlight two key communication strategies—based on our previous work—focused on different levels of language: explaining new findings from scientific papers and defining individual scientific terms. For both, I will discuss novel techniques we developed for adjusting generated language to fit the needs of different audiences and methods for modeling an individual reader’s background. I will close by discussing how these techniques generalize to other knowledge intensive communication tasks (e.g., legal and educational settings) and the opportunities of developing new techniques for these settings.
Tal August is an assistant professor at the University of Illinois at Urbana-Champaign. He studies how to adapt language to different audiences, with a focus on knowledge intensive domains like science, health and legal communication. Tal conducts empirical analyses to study how changes in language will affect different audiences, and he builds intelligent reading and writing systems for augmenting our language in new ways. The long term goal of Tal’s research is to improve our communication with—and understanding of—one another through technology. Tal August previously was a Young Investigator at the Allen Institute for AI. Tal received his PhD at the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Katharina Reinecke and Noah Smith.
Understanding Online Attention: From Items to Markets
4:00pm CDT, Center for Human-Computer Interaction (Frances Searle Building 1-122)
What makes a video popular? What drives collective attention online? What are the similarities and differences between clicks and transactions in a market? This talk aims to address these three questions. First, I will discuss a physics-inspired stochastic time series model that explains and forecasts the seemingly unpredictable patterns of viewership over time. This model provides novel metrics for predicting expected popularity gains per share and assessing sensitivity to promotions. Next, I will describe new measurement studies and machine learning models that analyze how networks of online items influence each other’s attention. Finally, I will introduce a macroscopic view of attention, offering mathematical descriptions of market equilibriums and distributed optimization. These results lay the groundwork for our ongoing research into the computational view of attention markets and potential mechanisms for fostering a healthy online ecosystem. Additionally, I will demonstrate Influence Flower, an interactive web app and arXiv plugin designed for qualitatively visualizing the intellectual influence of academic entities. I posit that processes of academic knowledge creation affords many open questions on the dynamics of attention among crowds.
Lexing Xie is a Professor of Computer Science at the Australian National University (ANU), where she leads the ANU Computational Media Lab and directs the ANU-wide Integrated AI Network. Her research spans machine learning, computational social science, and computational economics, with a particular focus on online optimization, neural networks for sequences and networks, and applied problems such as distributed online markets, decision-making by humans and machines. Lexing received the 2023 ARC Future Fellowship and the 2018 Chris Wallace Award for Outstanding Research. Her research has garnered seven best paper and best student paper awards at ACM and IEEE conferences between 2002 and 2019. Among her editorial roles, she served as the inaugural Editor-in-Chief of the AAAI International Conference on Web and Social Media (ICWSM) and is the Program Co-Chair of ACM Multimedia 2024. Prior to joining ANU, she was a Research Staff Member at the IBM T.J. Watson Research Center in New York. She holds a PhD in Electrical Engineering from Columbia University and a BS in Electrical Engineering from Tsinghua University.
TSB Prospective Student Information Session
12:00pm CDT, Zoom; advanced registration required.
Please join us for Northwestern’s upcoming Technology and Social Behavior (TSB) PhD program information session on November 1 at noon Central Time. We hope to see you there!