My dissertation project, “A Literary History of Machine Learning,” offers an alternative history of machine learning as it emerged in the U.S. mid-century. To do so, I show how modernist literature, science literature, and philosophical texts influenced the evolution of machine learning, specifically in the innovations of social scientist Herbert A. Simon (1916–2001). Drawing from a novel digital archive of Simon’s papers totaling ~37,000 documents and comparing them to about 4,100 books from his home and office libraries, the project magnifies models visible in Simon’s technical and scientific writing and translates them back into their literary source texts.

Dissertation CommitteeJeffrey J. Williams (chair), Simon DeDeoJon Klancher, and Annette Vee (University of Pittsburgh).

Funding: This research has been generously supported by the Andrew W. Mellon Foundation, the University of Victoria’s Electronic Textual Cultures Lab, Carnegie Mellon University Libraries, and The Charles Babbage Institute at the University of Minnesota.