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演讲简介
In this talk, we provide a more systematic and principled view about the practice of artificial intelligence in the past decade from the history of the study of intelligence. We argue that the most fundamental objective of intelligence is to learn a compact and structured representation of the sensed world that maximizes information gain, measurable by coding rates of the learned representation. We contend that optimizing this principled objective provides a unifying white-box explanation for almost all past and current practices of artificial intelligence based on deep networks, including CNNs, ResNets, and Transformers. Hence, mathematically interpretable, practically competitive, and semantically meaningful deep networks are now within our reach, see our latest release: https://ma-lab-berkeley.github.io/CRATE/. Furthermore, our study shows that to learn such representation correctly and automatically, additional computational mechanisms are necessary besides deep networks. For intelligence to become autonomous, one needs to integrate fundamental ideas from coding theory, optimization, feedback control, and game theory. This connects us back to the true origin of the study of intelligence 80 years ago. Probably most importantly, this new framework reveals a much broader and brighter future for developing next-generation autonomous intelligent systems that could truly emulate the computational mechanisms of natural intelligence.
Related papers can be found at:
1. https://ma-lab-berkeley.github.io/CRATE/
2. https://jmlr.org/papers/v23/21-0631.html,
3. https://www.mdpi.com/1099-4300/24/4/456/htm.
关于讲者
Yi Ma is the inaugural director of the Data Science Institute and the new head of the Computer Science Department of the University of Hong Kong. He has been a professor at the EECS Department at the University of California, Berkeley since 2018. His research interests include computer vision, high-dimensional data analysis, and integrated intelligent systems. Yi received his two bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two master’s degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000. He has been on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He joined the faculty of UC Berkeley EECS in 2018. He has published over 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized PCA, and high-dimensional data analysis. He received the NSF Career award in 2004 and the ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM.
