Abstract
Modern AI is built upon a data-driven approach, where AI problems are solved by deep neural networks (e.g., CNNs, Resinets, and Transformers). Do neural networks own human-like intelligence? To answer this, I will relate “modern AI” to “heavily supervised learning” (or weak AI) and “neural networks” to “data-fitting machines,” respectively. This view provides deeper insights into the working principle of neural networks, and we can clearly understand what they can and cannot do. They are fundamentally different from human brains. The next question is “whether neural networks provide a unique data-fitting machinery for huge input-output data pairs.” If not, what is the alternative? Is it a better one? I have researched this topic since 2014, developed alternative data-fitting machinery, and coined this emerging field “green learning (GL).” It is called “green” since it demands low power consumption in training and inference. GL has many attractive characteristics, such as small model sizes, fewer training samples, mathematical transparency, ease of incremental learning, etc. GL adopts signal processing and statistical tools such as filter banks, linear algebra, probability theory, etc. We recently used the wavelet transform in representation learning to handle input images of higher resolutions. Furthermore, we derived ways to give weights to wavelet coefficients. The weighted wavelet (W2) coefficients offer highly discriminant features for decision learning. These new GL developments will be introduced in the second half of my talk.
Bio
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 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 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 (2022-2025). He has guided 180 students to their Ph.D. degrees and supervised 31 postdoctoral research fellows.