A Collectivist, Economic Perspective on AI

Time: 09:00 23rd November
Speaker Photo
Michael I. Jordan
About the Speaker
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive, biological and social sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Foreign Member of the Royal Society. He is a Fellow of the American Association for the Advancement of Science. He was the inaugural winner of the World Laureates Association (WLA) Prize in 2022. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He gave the Inaugural IMS Grace Wahba Lecture in 2022, the IMS Neyman Lecture in 2011, and an IMS Medallion Lecture in 2004. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018.
Abstract
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word "intelligence" is being used as a North Star for the development of this technology, with human cognition viewed as a baseline. This view neglects the fact that humans are social animals, and that much of our intelligence is social and cultural in origin. Thus, a broader framing is to consider the system level, where the agents in the system, be they computers or humans, are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems. Agents may supply data and other resources to the system only if it is in their interest to do so, and they may be honest and cooperative only if it is in their interest to do so. Critically, intelligence inheres as much in the overall system as it does in individual agents. This is a perspective that is familiar in economics, although without the focus on learning algorithms. A key challenge is thus to bring (micro)economic concepts into contact with foundational issues in the computing and statistical sciences. I'll discuss some concrete examples of problems and solutions at this tripartite interface.