HKU Musketeers Foundation Institute of Data Science (HKU-IDS) proudly presents the second event under the “HKU-IDS Scholar Seminar Series” which will run through the year of 2022-23. The seminar series features the HKU-IDS new recruits under HKU-100 Professoriate Recruitment Campaign forming the backbone for the Institute’s cross-disciplinary collaboration. These young talents are co-hosted by IDS and different Departments including Computer Science, Electrical and Electronic Engineering, Architecture, Pharmacy and Pharmacology, Industrial and Manufacturing Systems Engineering and Mathematics.
Recently, we have seen dramatic advances in natural language processing (NLP) driven by huge pre-trained language models such as GPT-3 and DALLE-2. Instead of building many small task-specific models, there is a movement to create and use these more all-purpose huge language models for many NLP applications. The most intriguing finding is that these models employ a new learning paradigm: in-context learning, where they learn to do a downstream task simply by conditioning on a prompt consisting of a few input-output examples without any parameter updates. In this seminar, the speaker will provide a short overview of these language models, discuss their recent progress, why they matter, how they work, and when they fail.
Tao Yu is an Assistant Professor in the Department of Computer Science, The University of Hong Kong. He is also a Postdoctoral Research Fellow in the Department of Computer Science and Engineering at University of Washington and a co-director of the NLP group at The University of Hong Kong. His research interest is in Natural Language Processing and Deep Learning, with a focus on designing and building conversational natural language interfaces that can help humans explore and reason over data in any application (e.g., relational databases and mobile apps) in a robust and trusted manner. He has published and served in the program committee at ACL, EMNLP, ICLR, NAACL, etc. He co-organized the Interactive and Executable Semantic Parsing workshop at EMNLP 2020, and was recently awarded the “Fall 2021 Amazon Research Award”. For more details, please browse this page.
Dr. Yu is currently involved in a research project titled “Democratizing data science via conversational executable natural language understanding: building AI collaborators for everyone including laypeople via a natural language interface to coding, databases, and apps.”
Reynold Cheng is a Professor of the Department of Computer Science in the University of Hong Kong (HKU). His research interests are in data science, big graph analytics and uncertain data management. He was the Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. He received his BEng (Computer Engineering) in 1998, and MPhil (Computer Science and Information Systems) in 2000 from HKU. He then obtained his MSc and PhD degrees from Department of Computer Science of Purdue University in 2003 and 2005.
Prof. Cheng has received numerous academic awards, and he is also the Associate Director of the Musketeers Foundation Institute of Data Science. For full biography of Prof. Cheng, please browse: https://datascience.hku.hk/reynold-cheng/
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