91猫先生

1. Speaker: Prof. Chao-Lin Liu, NCCU College of Informatics 2. Date & Time: Tuesday, November 3, 2026, 13:00-15:00 3. Venue: AI Lecture Hall,11th Floor-Management Building 4. Meeting Link: https://reurl.cc/QNl3rM About the Speaker: Professor Chao-Lin Liu is currently a distinguished professor in the Department of Computer Science, National Chengchi University. His research interests include natural language processing, cognitive processes, automated reasoning, machine learning theory, information retrieval, knowledge management systems, digital humanities, legal informatics, intelligent tutoring systems, intelligent transportation systems, decision support, and planning and diagnostic systems. He has served as Vice Dean of the College of Science, Vice Dean of the College of Informatics, and Chair of the Department of Computer Science at National Chengchi University. He has also held academic service positions, including President of the Taiwan Digital Humanities Society. Professor Liu has been a visiting scholar at the University of Michigan, Harvard University, and the University of California, Santa Barbara. He is committed to advancing academic research and social development through computational technologies and artificial intelligence.  Lecture Abstract： Generic statistical language models and unstructured text embeddings often struggle when applied to specialized Chinese corpora, where meaning is strictly governed by historical conventions or institutional norms. This talk presents an interdisciplinary journey across two distinct applications of computational semantics: discovering latent patterns in historical culture and enabling explainable decision-making in modern jurisprudence. The first half of the presentation focuses on digital humanities, demonstrating how computational methods overcome unpunctuated and irregular classical formats. We examine how linguistic heuristics and structural parallelism resolve classical poetry word segmentation, how large-scale alignment reveals diachronic stylometric patterns and text reuse across 2,000 years of poetic collections, and how language models and Conditional Random Fields extract prosopographical knowledge and demarcate biographical entries from historical local gazetteers (Difangzhi). The second half turns to legal informatics, exploring how domain priors transform unstructured prosecutorial and judicial documents into transparent, interpretable outputs. We demonstrate how statutory article co-citation networks establish objective case similarity in civil disputes; how deconstructing criminal fraud indictments into statutory constitutive elements yields explainable case analogies aligned with professional legal judgment; how structured prompt frameworks guide large language models through multi-cause concurrence and penalty comparisons; and how procedural trajectories can quantify case disputability and litigation risk. Together, these case studies demonstrate that computational semantics achieves its highest reliability not by treating language as generic token distributions, but by formalizing the structural and normative logic of the target domain. 

Speaker: Prof. Chun-Yi Lee, Department of Computer Science and Information Engineering, National Taiwan University Topic: World Models and World-Action Models: Learning to Predict, Plan, and Act Participants: All third-year and senior students, faculty and staff members, and physicians affiliated with Chang Gung Venue: AI Lecture Hall, 11th Floor, Management Building Date and Time: Tuesday, October 6, 2026, 1:00-3:00 PM Online Meeting Link: https://reurl.cc/DYKy9e or scan the QR code on the poster No registration is required. Please join the Teams meeting room at the scheduled time. About the Speaker: Chun-Yi Lee is a Professor in the Department of Computer Science and Information Engineering at National Taiwan University and the Director of Elsa Lab. He received his Ph.D. in Electrical Engineering from Princeton University. His research interests include deep reinforcement learning, intelligent robotics, computer vision, and generative AI. He is a recipient of the 2026 Academia Sinica Scholar Award and the K. T. Li Breakthrough Award. Lecture Abstract: World models learn to predict how an environment evolves and are rapidly becoming a foundation for physical AI. This talk will review recent advances, ranging from model-based reinforcement learning, such as DreamerV3, and generative interactive world models, including Genie and NVIDIA Cosmos, to self-supervised, prediction-based models such as Meta's V-JEPA and V-JEPA 2. The talk will then introduce world-action models, which combine learned world models with actions for planning and control. Through examples such as V-JEPA 2-AC and Cosmos Policy, the speaker will explain how these models enable zero-shot robotic planning. The talk will conclude with a discussion of the major open challenges and future opportunities in this field.

Speaker: Prof. Chun-Yi Lee, National Taiwan University CSIE Date & Time: Tuesday, October 6, 2026, 13:00-15:00 Venue: 11th Floor, Management Building AI Lecture Hall, Meeting Link: https://reurl.cc/DYKy9e  or scan the QR code on the poster About the Speaker： Chun-Yi Lee is a Professor in the Department of Computer Science and Information Engineering at National Taiwan University and the supervisor of Elsa Lab. He holds a Ph.D. in Electrical Engineering from Princeton University. His research spans deep reinforcement learning, intelligent robotics, computer vision, and generative AI. He is a recipient of the 2026 Academia Sinica Scholar Award and the K. T. Li Breakthrough Award. Lecture Abstract： World models learn to predict how an environment evolves and are fast becoming a foundation for physical AI. This talk reviews recent progress, from model-based reinforcement learning (DreamerV3) and generative interactive world models (Genie, NVIDIA Cosmos) to self-supervised, prediction-based models such as Meta's V-JEPA and V-JEPA 2. It then turns to world-action models, which couple a learned world model with action for planning and control. Through examples such as V-JEPA 2-AC and Cosmos Policy, it shows how these models enable zero-shot robot planning, before closing with the main open challenges and opportunities.

Speaker: Prof. Chien-Ju Ho Date & Time: Tuesday, Septermber 22 , 2026, 13:00–15:00 Venue: 11th Floor, Management Building AI Lecture Hall, Meeting Link: https://reurl.cc/5XD8Kn or scan the QR code on the poster About the Speaker: Chien-Ju Ho is an Associate Professor of Computer Science and Engineering at Washington University in St. Louis. He is currently on sabbatical and serving as a Visiting Associate Professor at Chang Gung University. His research lies at the intersection of machine learning, human-AI interaction and collaboration, human computation, and algorithmic and behavioral economics. In particular, his work examines how human behavior shapes the learning and decision making of AI systems, and how AI systems in turn influence people. His research has appeared in venues including PNAS, ICML, NeurIPS, ACL, NAACL, WWW, AAAI, and AAMAS. He also serves as Program Co-Chair of ACM Conference on Human-AI Complementarity and Alignment (HCOMP) 2026. Lecture Abstract： Recent advances in artificial intelligence depend heavily on data, feedback, judgments, and demonstrations provided by humans. Yet humans are not simply passive sources of training data: their behavior can be shaped by AI systems and by the process of training them, while the characteristics of human behavior can in turn affect what AI systems learn. In this talk, I will examine human behavior and mutual influence in AI training, and discuss how taking these factors into account can change the way we collect and use training data. I will present a series of studies examining how people change their decisions when they know their behavior will be used to train AI; how accounting for the learnability and fragility of human behavior can improve learning from human demonstrations; and how humans and large language models can be combined to more efficiently elicit and estimate human judgments. These studies highlight the importance of understanding not only the data used to train AI, but also the human behaviors and interactions through which those data are generated.

Dear Students, You are warmly invited to join our International Student Feedback and Support Event at 12:00 p.m. on Tuesday, September 15. The event will take place on 10th Floor, Management Building. This will be a lunch and conversation event where you can meet faculty members and fellow international students. We would also like to learn about your experiences and needs during the application process, your transition to Taiwan, and your studies, so that the department can provide better support in the future. Lunch will be provided. Please indicate any dietary restrictions in the registration form. To help us estimate attendance and prepare food, please complete the form below by Sep 8(Tue) 10:00a.m : https://forms.cloud.microsoft/r/NRLyFsgPfU or scan the QR code 人工智慧學系將於9/15(二)中午辦理「境外生招生回饋交流暨入學支持活動」，邀請同學一同共進午餐、輕鬆交流。 活動將以聊天交流的方式進行，讓同學認識系上師長及其他國際學生。我們也希望透過交流，了解僑生/國際生在申請、來臺及就學適應過程中的經驗與需求，作為本系未來提供更完善支持的參考。 本活動主要開放給人工智慧學系與智慧運算學院僑生/國際生交流與回饋申請、就讀體驗，同時開放5位臺灣本地生名額協助活動並一同交流、享用午餐。協助活動之臺灣學生可獲得3點英語學習護照點數。(開放名額視報名情況調整) 為利估算出席人數及準備餐點，請於9/8(二)上午10點前填寫以下表單 (臺灣學生不出席免填)：https://forms.cloud.microsoft/r/NRLyFsgPfU 或掃描海報QRcode，若有飲食限制請務必於報名表單中填寫。