Convivial Machine Learning

June 2026

Ivan Illich wrote of the alphabet and the printing press that they are “almost ideally convivial” because “anybody can learn to use them, and for [their] own purpose. They use cheap materials. People can take them or leave them as they wish. They are not easily controlled by third parties” (Illich, Tools for Conviviality, 1973). Yet, the conviviality pf the alphabet and the printing press, as described by Illich, came after centuries of development of both technical innovations and social practices. 

In this course, we will take a historical and speculative route to interrogate what lessons from manuscript and print cultures we can draw from to design and think machine learning tools that are open and convivial. Machine learning, including large language models, exists on a historical continuum of information systems, from the alphabet (the discretization of speech sounds into letters) and the printing press (the first mechanical means of textual (re)production) to hypertext and natural language processing. Through a series of digital and analog experiments, we will situate machine learning within a history of radical and innovative writing technologies that can serve as models for designing and thinking more convivial machine learning systems.

Specifically, students will learn how to use open-source language models locally, build their own small-scale language models, and learn how to use open-source and powerful transcription models. They will also learn how to document their process digitally and materially with small, hand-made publications. Through this mix of historical inquiry and hands-on experimentation, students will have developed practical skills in working with open machine learning tools and developed a reflection on how to design interactions and technologies that enhance human autonomy and creativity.

This intensive course will take place June 15-19 at the Université de Montréal as part of the 2026 Digital Humanities Summer Institute.

Course full.

Syllabus and material will be shared here after the course.

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