Aaron Mendon-Plasek

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Assistant Professor
Purdue University
Department of History
amendonp@purdue.edu

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Intros

I’m a historian of science and technology, focusing on statistics, computing, & machine learning. My work examines how schemes of quantification, including their material, cultural, and institutional instantiations, have been used to imagine, enact, and justify social order.

From curating art exhibitions to studying supermassive black holes, I’m interested in the ways our mediated meaning-making practices inform our contemporary and historical conceptions of science, self, and society. For the past decade, I’ve sought to build critical communities of practice [e.g., see here, here, here] dedicated to producing novel histories of machine learning and artificial intelligence in the 20th and 21st centuries that, among other things, provides insight and perspective on contemporary problems and proposed political, legal, and technical remedies.

Before coming to Purdue I held a postdoc as an associate research scholar in the Information Society Project at Yale University from 2022 to 2024, where I remain an affiliated fellow. I defended my PhD dissertation in History from Columbia University in 2022. I hold seven degrees: a PhD, MPhil, & MA in history from Columbia U.; a MA in humanities & social thought from NYU; an MFA in writing (poetry) from the School of the Art Institute of Chicago; and a BS in physics & astronomy and a BA in English Writing from Drake U.

First Book!

My first book project, tentatively titled The Ill-Defined World: A History of Machine Learning and Novel Political Knowledge, offers a revisionist history of machine learning, from WWII to the present, that demonstrates how and why it became thinkable and subsequently “reasonable” for learning machines and machine learning strategies, rooted in conceptions of creativity and human judgment, to adjudicate social questions in the 21st century. This monograph builds on my dissertation, Genealogies of Machine Learning, 1950-1995, which was supported by a variety of fellowships and grants, including those from the NSF, Columbia University, and the Charles Babbage Institute. [I was particularly excited to win CBI’s Tomash Fellowship, for which I gave this lecture.]

Talking

[not updated!]

“How Machine Learning became a Political and Technical Solution to the Problems of Radical Nominalism, Political Disunity, and Big Science.” Special Interest Group in Computing, Information, and Society (SIGCIS), Society for History of Technology (SHOT), online. 21 September 2023.

“Creativity in an irrational world of inexhaustible meaning: origins of machine learning as subjective decision-making, disunified science, and remedy for what could not be predicted.” History of Technology Working Group, Consortium for History of Science, Technology, & Medicine (CHSTM). Online. 17 October 2023.

“Why Artificial Intelligence Became Machine Learning: a micro-political history of contemporary decision-making, 1978-1995.” Society of History of Technology (SHOT), Long Beach, CA. 25 October 2023.

“How Fear and Promises Shape Technological Governance and Possibility” roundtable panelist. Society of History of Technology (SHOT), Long Beach, CA. 26 October 2023.

“Of learning, creativity, and the ill-defined world: histories of 1950s machine learning as a nominalist strategy for contemporary decision-making, scientific discovery, and epistemological relativism.” History of Science Society (HSS), Portland, OR. 11 November 2023

Doings

[Wildy outdated! Will update soon!]

As the 2020-2021 Tomash Fellow for the Charles Babbage Institute, I discussed how the belief in the efficacy of machine learning is interwoven with a particular notion of creativity held by 1950s – 1970s pattern recognition researchers, and note how this history continues to frame technical and political possibility today.

“Mechanized Significance and Machine Learning: Why It Became Thinkable and Preferable to Teach Machines to Judge the World” was published open-access in Dec 2020.

I was awarded an NSF doctoral dissertation research grant entitled “Recovering the Polyvalent Genealogies of Machine Learning, 1948 – 2017”.

I wrote an early article about the need for a history of machine learning in the IEEE Annals of the History of Computing.

Aaron Mendon-Plasek

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