Human and Machine Intelligence in Networks of Early Modern Print

How can artificial intelligence and other computational approaches help us to understand the distant past? Though there is a long history of digital humanities work on the early modern period, studying historical language and print materials with large language models, which are trained mostly on texts from the internet, presents particular technical and ethical challenges. In this talk, John Ladd will share work from two cultural analytics projects that address some of these challenges by combining analog research methods and machine learning to study the print culture of the 16th and 17th centuries: Early Print and Print & Probability. Through a case study using text classification, network analysis, and data visualization to investigate whether early modern printers tend to produce books on the same subjects over their careers, Ladd will demonstrate the value of combining human and machine approaches to investigate early modern research questions.

John Ladd is an assistant professor in the Department of Computing and Information Studies at Washington & Jefferson College. He teaches and researches on the use of data across a wide variety of domains, especially in cultural and humanities contexts, as well as on the histories of information and technology. Building on an English literature background where he studied the intersection between computational methods and early modern print culture, his work includes large-scale digital humanities projects, such as Six Degrees of Francis Bacon and Early Print. He has published essays and web projects on humanities data science and cultural analytics, computational bibliography, the history of data, and network analysis.

Modeling Culture talks
Throughout 2025–26, CDH will host six public talks by leading scholars in Cultural Analytics. Open to all, the series invites audiences to explore the histories, theories, and practices that shape the evolving intersection of AI and the humanities.

Modeling Culture project
Since the release of ChatGPT, conversations about artificial intelligence (AI) have generated both excitement and concern across the humanities. Much of the debate has focused on political and ethical questions—bias, labor, environmental impact, and intellectual property—as well as the effects of large language models (LLMs) on teaching and learning. Yet one question remains underexplored: how might AI contribute directly to humanities scholarship?

Modeling Culture is supported by Princeton Laboratory for Artificial Intelligence, Princeton Humanities Initiative, and the Princeton Humanities Council. Thank you to the Department of English for co-sponsorship of the public talks.