Rapid advances in artificial intelligence (AI) and machine learning (ML) have been attributed to
the wide applications of deep learning (DL) technologies. There are however concerns with this
AI wave. DL solutions are a black box (i.e., not interpretable) and vulnerable to adversarial
attacks (i.e., unreliable). Besides, the high carbon footprint yielded by large DL networks is a
threat to our environment (i.e., not sustainable). It is important to find alternative AI
technologies that are interpretable and sustainable. To this end, I have conducted research on
green AI/ML since 2015. Low carbon footprints, small model sizes, low computational
complexity, and mathematical transparency characterize green AI/ML models. They differ
completely from DL models since they have neither computational neurons nor network
architectures. They are trained efficiently using labels (but no backpropagation). Green AI/ML
models offer energy-effective solutions in cloud centers and mobile/edge devices. They consist
of three main modules: 1) unsupervised representation learning, 2) supervised feature learning,
and 3) decision learning. Green AI/ML has been successfully applied to various applications. I
will use several examples to demonstrate their effectiveness and efficiency.
Speaker’s Biography
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 William M. Hogue Professor, Distinguished Professor of Electrical
and Computer Engineering and Computer Science, and Director of the
Media Communications Laboratory. His research interests are in visual
computing and communication. He is a Fellow of AAAS, ACM, IEEE, NAI, and
SPIE and an Academician of Academia Sinica. 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 Editor-in-Chief for 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 175 students to their Ph.D. degrees and
supervised 31 postdoctoral research fellows.