The Academic Fringe Festival - Heng Ji: SmartBook, AI-Assisted Situation Report Generation

27 maart 2023 16:00 - Locatie: Online | Zet in mijn agenda

by Heng Ji | University of Illinois Urbana-Champaign


Emerging events, such as the COVID pandemic and the Ukraine Crisis, require a time-sensitive comprehensive understanding of the situation to allow for appropriate decision-making and effective action response. Automated generation of situation reports can significantly reduce the time, effort, and cost for domain experts when preparing their official human-curated reports. However, AI research toward this goal has been very limited, and no successful trials have yet been conducted to automate such report generation. Pre-existing natural language processing and information retrieval techniques are insufficient to identify, locate, and summarize important information, and lack detailed, structured, and strategic awareness. We propose SmartBook, a novel task that cannot be solved by ChatGPT, targeting situation report generation which consumes large volumes of news data to produce a structured situation report with multiple hypotheses (claims) summarized and grounded with rich links to factual evidence.

Speaker Biography

Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department and Coordinated Science Laboratory of University of Illinois Urbana-Champaign. She is an Amazon Scholar.

She is the Director of Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE). She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-guided Generation. She was selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. She was named as part of Women Leaders of Conversational AI (Class of 2023) by Project Voice. The awards she received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014, Bosch Research Award in 2014-2018, Amazon AWS Faculty Award in 2021, Best-of-ICDM2013 Paper, Best-of-SDM2013 Paper, ACL2020 Best Demo Paper Award, and NAACL2021 Best Demo Paper Award. She was invited by the Secretary of the Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030. She was elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2021. She has served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJCNLP2022, and she has been the coordinator for the NIST TAC Knowledge Base Population track since 2010.


About the Academic Fringe Festival

The Academic Fringe Festival (TAFF) is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science.This fourth edition is on "Human-Centered AI: Knowledge and Language". The series features prominent researchers and practitioners, whose work has made fundamental contributions in these fields.

Artificial Intelligence is used more and more in society, from healthcare to government decisions and recruitment. Along with the rapid increase of AI adoption comes increased concerns about the inherent shortcomings of such technologies (e.g., robustness) and the social, and ethical implications. To create AI systems that can properly serve humans, it is crucial to put humans at the center of the process such that the outcome system behaves in a way that fits the values and needs of people. This poses new challenges to technological development: how to build AI systems that can be understood by humans and that can align their behaviour with human values? Tackling these challenges requires new ways of looking at AI systems, e.g., machine learning models as knowledge bases and as autonomous agents that people can query, interact with, and influence.

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