时间：2020年10月31日上午9点
地点：创新港5号巨构新港报告厅
控制论与人工智能简史
A Brief History of Cybernetics and Artificial Intelligence
This talk will present a brief history of the origins of the two disciplines that led to the design of intelligence machines, which we now call globally artificial intelligence. The most influential players’ contributions will be summarized. The talk will also address the requirements that support AI research and how it can be adopted by the industry. A brief list of current challenging problems will also be discussed.
这篇演讲将简要介绍两个学科的起源，这两个学科引发了智能机器的设计，我们现在称之为全球人工智能。最有影响力的参与者的贡献将被总结。会谈还将讨论支持人工智能研究的需求，以及人工智能如何被业界所采用。此外，还将讨论目前具有挑战性的问题。
报告人简介：
Jose Carlos Principe
美国佛罗里达大学
University of Florida
Jose C. Principe (M’83-SM’90-F’00) is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs) modeling. He is the Eckis Endowed Professor and the Founder and Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL) www.cnel.ufl.edu . His primary area of interest is processing of time varying signals with adaptive neural models. The CNEL Lab has been studying signal and pattern recognition principles based on information theoretic criteria (entropy and mutual information).
Dr. Principe is an IEEE Fellow and received the prestigious IEEE Neural Network Pioneer Award in 2012. He was the past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, Past-President of the International Neural Network Society, and Past-Editor in Chief of the IEEE Transactions on Biomedical Engineering. Dr. Principe has more than 900 publications, and an H-index of 82 (Google Scholar). He directed 98 Ph.D. dissertations and 65 Master theses. He wrote in 2000 an interactive electronic book entitled “Neural and Adaptive Systems” published by John Wiley and Sons and more recently co-authored several books on “Brain Machine Interface Engineering” Morgan and Claypool, “Information Theoretic Learning”, Springer, and “Kernel Adaptive Filtering”, Wiley.
