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Abstract: How and why did artificial intelligence--a field that was committed for so long to the explicit, formal representation of human perception, reason, and expertise--become narrowly redefined as a practice of large-scale statistical pattern recognition, one that prioritized effective prediction over explanatory power? One answer lies in the strange and enduring quest to endow machines with the capacity for language.

This talk draws from the new book, Divination Engines: Natural Language Processing, Artificial Intelligence, and the Making of Algorithmic Culture (University of Chicago Press 2026), which traces the long history speech and natural language processing from telephony to text mining and examines the far-reaching consequences of opening language to statistical interpretation.

Bio: Xiaochang Li is assistant professor in the Department of Communication at Stanford University, and affiliate faculty in the Program in Science, Technology, and Society and the Program in Modern Thought and Literature.