演講主題：Artificial Intelligence: Deep Learning versus Green Learning
講者: Dr. Jay Kuo美國南加州大學電機與計算機工程及計算機科學傑出教授
時間: 2025/10/21 (二) 14:10-15:00
地點: 管理大樓11樓-AI講堂
直播連結: https://reurl.cc/EQlmra 或 掃描海報 QR code
講者簡介:
Dr. C.-C. Jay Kuo received his Ph.D. from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as the Ming Hsieh Chair Professor, a Distinguished Professor of Electrical and Computer Engineering and Computer Science, and the Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is an Academician of Academia Sinica and a Fellow of AAAS, ACM, IEEE, NAI, and SPIE.
Dr. Kuo has received a few awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was the Editor-in-Chief of the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 181 students to their Ph.D. degrees and supervised 31 postdoctoral research fellows.
演講大綱:
The term “Artificial Intelligence (AI)” was coined in 1956. The field evolved slowly in the first 55 years. Yet, we have witnessed rapid advances in AI in the last decade. A recent successful example is the emergence of large language models. In this talk, I will shed light on two issues. First, I will explain the reasons for the advancement of AI in the last decade. Simply speaking, modern AI relies on numerous training samples that contain input/output pairs. An AI system provides a data-fitting solution to capture input and output mapping. Second, I will present two data–fitting methodologies: deep learning (DL) and green learning (GL). Although DL is dominant today, it is neither interpretable nor sustainable. Developing an alternative, interpretable, and sustainable AI methodology is challenging but essential. I have researched this problem since 2015. GL models offer energy-efficient AI solutions in cloud centers and mobile/edge devices. They have been successfully applied to various applications. I will use several medical imaging examples to highlight the differences between DL and GL solutions.
主辦單位：智慧運算學院、人工智慧研究中心
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