Topic: Artificial Intelligence: Deep
Learning versus Green Learning
Speaker: Dr. Jay Kuo, Distinguished
Professor of Electrical and Computer Engineering and Computer Science,
University of Southern California
Time: October 21, 2025 (Tuesday), 2:10 –
3:00 p.m.
Venue: The Management Building, 11F, AI
Lecture Hall
Join Online: https://reurl.cc/EQlmra or
scan QR code on poster
About the Speaker:
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.
Abstract:
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.
Organizers: College of Intelligent
Computing & Artificial Intelligence Research Center
※ No registration needed.