A collectivist, economic perspective on AI
The recent focus of artificial intelligence (AI) has been on the exploitation of massive data to mimic and augment the cognitive capabilities of humans. A complementary focus is provided by social and economic aspects of intelligence.
Many new challenges and opportunities can be found in the design of systems that use learning algorithms and economic mechanisms to support intelligent, adaptive behaviour at the level of the collective while also rewarding individual creators.
Meet our speaker and chair
Michael I Jordan is a Director of Research at Inria Paris, and a Distinguished Professor Emeritus at the University of California, Berkeley. He has been a leading researcher in computer science and statistics for over four decades. He is a member of the US National Academy of Sciences, a Foreign Member of the Royal Society, and a Foreign Member of the Chinese Academy of Sciences.
Maximilian Kasy is a Professor of Economics at the University of Oxford. He received his PhD from UC Berkeley and has held appointments at UCLA and Harvard University. His research interests include economic inequality, job guarantee programs, and basic income, as well as the intersection of machine learning and economics. He coordinates the Machine Learning and Economics Group at Oxford and teaches a course on foundations of machine learning.
Milan Vojnovic is Head of Department in the Department of Statistics at LSE. His research focuses on machine learning and optimisation, involving the development of novel algorithms and the analysis of their theoretical guarantees to support the design of efficient intelligent systems. He has made significant contributions to scalable optimisation methods for machine learning, multi-armed bandits, multi-agent systems, algorithms under uncertainty, game theory, and network system control and optimisation.
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A founder department of LSE, the Department of Statistics is home to internationally respected experts with cutting- edge specialisms in AI, data science, probability in finance and insurance, social statistics, time series and statistical learning, and more. We produce cutting-edge research in developing new statistical and data science methods and their theoretical foundations, and apply these to new forms of social, economic and health data, addressing real-world challenges.
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