学术报告|Neuro and Cognitive Inspired AI

浙江大学网络空间安全学院

学术报告

  摘 要  

The ultimate goal of AI is to build a machine that does cognitive functions that are more associated with humans, such as thinking, learning, problem solving, and reasoning. However, despite the remarkable success of the unfolding AI wave empowered by deep learning, the current AI practices are in stark contrast to human intelligence. Existing AI models mostly have static architectures and static functional capabilities, limiting their application to learning from only stationary batches of training data whose complexity in no way reflects the richness and uncertainty in real-world cognitive tasks. This talk reviews recent developments in neuroAI and its potential in leading next-wave AI evolution.

  报告人简介  

Hairong Qi received the B.S. and M.S. degrees in computer science from Northern JiaoTong University, Beijing, China in 1992 and 1995, respectively, and the Ph.D. degree in computer engineering from North Carolina State University, Raleigh, in 1999. She is currently the Gonzalez Family Professor with the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. Her research interests are in the general areas of computer vision and machine learning. Dr. Qi's research is supported by National Science Foundation, DARPA, IARPA, Office of Naval Research, NASA, Department of Homeland Security, etc. Dr. Qi is the recipient of the NSF CAREER Award. She is awarded the Highest Impact Paper from the IEEE Geoscience and Remote Sensing Society in 2012. Dr. Qi has published over 200 technical papers in archival journals and refereed conference proceedings, including two co-authored books with Dr. Wesley Snyder in Computer Vision. Dr. Qi is an IEEE Fellow.