Intelligence and Imitation: Mind, Mechanism, Mimesis

Inaugural Humanities of AI Workshop

Johns Hopkins University, April 24-26, 2026

As a creative aspiration, the Greek notion of mimesis (“imitation”) manifested not only in artistic works imitating reality and philosophical speculations but also in scientific theories and mechanical artifacts. Plato and Aristotle’s nous as a non-bodily principle of intelligibility underwriting cosmic order and thought; Hobbes and LaMettrie’s machine like mind and world; the Jaquet-Droz family’s musical automata; Wolfgang von Kempelen’s chess-playing Turk; Norbert Wiener’s cybernetic analogy between human, animal, and machine; Japanese roboticist Masahiro Mori’s observation of the revulsion to imperfect verisimilitude (Bukimi no Tani: “uncanny valley”); and Soviet semiotician Yuri Lotman’s culture as collective mind, exemplify the broad relevance of “imitations” to science, literature, and culture.

Developments in artificial intelligence (AI) participate in the legacy of mimesis but also complicate and challenge it. In the course of AI’s research history, AIs have variously been claimed to represent, simulate, assist, improve upon, provide a surrogate for, or replace the functioning of human minds. Concepts such as “optimization,” “satisficing,” and “superintelligence” run orthogonal to the classical concept of mimesis.

At the same time, developments in science and society have deeply challenged both mimesis and mindedness as concepts and ideals. Darwinian and embodied cognitive approaches challenge the primacy of abstract reasoning over embodiment; and reflections on human labor’s relation to material (re-)production, social stratification, and human experience from Marx, Wallerstein, Pasquinelli and others call into question the social “value-added” of material imitations as well asthe veracity of accounts of “intelligent” labor’s nature and origins. Deep divisions in the societal uptake of AI – exemplified in anti-AI activism, dueling governance regimes, and popular criticalslang like “AI slop” – exemplify and give opportunity to inform these theoretical challenges.Orientation to these developments requires approaches that scholars in the humanities may beuniquely positioned to provide. We hereby announce a three-day workshop on “Intelligence and Imitation: Mind, Mechanism, Mimesis” for presentation and discussion of new humanities research engaging with this theme.

Our aim is to foster a collective critical engagement with AIs in their history, socioeconomic context, architecture, and other dimensions of significance with the assistance of resources from literature, philosophy, history, or other humanities fields. We invite contributions from both early-career (including graduate students) and established academic researchers, whose work-in-progress projects straddle disciplinary boundaries to illuminate aspects of the diverse mind-machine relations exemplified in AI’s history, current reality, and imagined futures.

In addition to presented papers, some time at the conference will be devoted to reflection on “humanities of AI” as a research domain, including its current state and possible futures, disciplinary articulation, conditions of success, relations with natural and social sciences, and potential impact on sociotechnical systems involving AI.

Featured Speakers 

Yulia Frumer, Bo Jung and Soon Young Kim Professor of East Asian Science, Johns Hopkins University; Author of “Cognition and emotion in Japanese humanoid robots,” History & Technology (2018) and Making Time: Astronomical Time Measurement in Tokugawa Japan (Univ. of Chicago Press, 2018)

N. Katherine Hayles, Distinguished Research Professor at the University of California, Los Angeles, and the James B. Duke Professor Emerita from Duke University; Author of Bacteria to AI: Human Futures with Our Nonhuman Symbionts (Univ. of Chicago Press, 2025), Unthought: The Power of the Cognitive Nonconscious (Univ. of Chicago Press, 2017) and How We Think: Digital Media and Contemporary Technogenesis (Univ. of Chicago Press, 2015)

Matthew L. Jones, Smith Family Professor of History, Princeton University; Author (with Chris Wiggins) of How Data Happened: A History from the Age of Reason to the Age of Algorithms (Norton, 2023)

Matthew Kirschenbaum, Commonwealth Professor of AI and English, University of Virginia; Author of Bitstreams: The Future of Digital Literary Heritage (Univ. of Pennsylvania Press, 2021)

Patrick McCray, Professor of History, University of California, Santa Barbara, Kluge Chair in Technology and Society (2025) at the Library of Congress (2025); Author of README: A Bookish History of Computing from Electronic Brains to Everything Machines (MIT Press, 2025)

Alexander Williams Tolbert, Assistant Professor of Data and Decision Sciences, Emory University; Author of “Why Causal Inference is Necessary for Algorithmic Fairness,” Synthese (2025) and “Causal Agnosticism about Race: Variable Selection Problems in Causal Inference,” Philosophy of Science (2024).

Supporting Institutions

Alexander Grass Humanities Institute, Johns Hopkins

University
(https://krieger.jhu.edu/humanities-institute/)

Center for Equitable AI & Machine Learning Systems (CEAMLS), Morgan State

University
(https://www.morgan.edu/ceamls)

Organizing Committee

Jiantong Liao (Chair)

PhD Student, German Program, Department of Modern Languages and Literatures

[email protected]

Ksenia Tatarchenko (Faculty Sponsor)

Faculty, Medicine, Science & Humanities Program, Johns Hopkins University

[email protected]

Phillip Honenberger (Faculty Sponsor)

AI Ethicist & Researcher, Center for Equitable AI & ML Systems (CEAMLS), Morgan State

University

[email protected]