Artificial Intelligence and the Future of the University

The University of Chicago Center in Beijing

20th floor, Culture Plaza
No. 59A Zhong Guan Cun Street
Haidian District Beijing 100872

James Evans

University of Chicago, Sociology and Data Science

Director, Knowledge Lab, Chicago Center for Computational Social Science (C3S2), and the Novel Intelligence Initiative

Artificial Intelligence now presses on the world's universities from two sides: in the transformation of modern education and research. In education, global universities and the University of Chicago in particular are experimenting teaching with AI, without AI, and about AI. In our own experiments at the University of Chicago Knowledge Lab, in the Computational Social Science program, and as chair of the College Committee on Computational Thinking, we are exploring where each approach sharpens thinking and cognitive agency, and where it dulls them. In research, AI scientists already propose hypotheses, run experiments and draft papers, and their capacities are shifting from tool to assistant to autonomous laboratory. The question is no longer whether machines will do science and mathematics, but how we architect discovery so that human and artificial scientists complement rather than copy one another, keeping science diverse, surprising, accountable, and where possible understandable. Both shifts raise questions for the future of the university: What is its purpose when machines generate and transmit knowledge as well as process it? I will offer provocations grounded in my research on AI and the future of research and education over dinner and then open the floor.

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James Evans's research focuses on the collective system of thinking and knowing, ranging from the distribution of attention and intuition, the origin of ideas and shared habits of reasoning to processes of agreement (and dispute), accumulation of certainty (and doubt), and the texture—novelty, ambiguity, topology—of understanding. He is especially interested in innovation—how new ideas and practices emerge—and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery. Much of his work has focused on areas of modern science and technology, but he is also interested in other domains of knowledge—news, law, religion, gossip, hunches, machine and historical modes of thinking and knowing. He supports the creation of novel observatories for human understanding and action through crowd sourcing, information extraction from text and images, and the use of distributed sensors (e.g., RFID tags, cell phones). He uses machine learning, generative modeling, social and semantic network representations to explore knowledge processes, scale up interpretive and field-methods, and create alternatives to current discovery regimes. His research has been supported by the National Science Foundation, the National Institutes of Health, the Air Force office of Science Research, and many philanthropic sources, and has been published in Nature, Science, Proceedings of the National Academy of Science, American Journal of Sociology, American Sociological Review, Social Studies of Science, Research Policy, Critical Theory, Administrative Science Quarterly, and other outlets. His work has been featured in the Economist, Atlantic Monthly, Wired, NPR, BBC, El País, CNN, Le Monde, and many other outlets.