Project timeline
A chronological overview of all project outputs, important milestones, and the strands of research connecting them.
Carceral Diffusion: From Galton's Criminal Composites to Horse-riding Astronauts and Beyond
Eleanor Dare gives a talk in the frame of the “Eugenics, Race, and Colonialism in the AI Boom” panel at the University of Virginia’s conference “Rethinking the Inevitability of AI, Part 2: Assimilation and Refusal in The History of AI”
Theories of Automation from the Industrial Factory to AI Platforms: An Overview of Political Economy and History of Science and Technology
Matteo Pasquinelli evaluates the state-of-the-art in the debate on automation, advocating for a renewed dialogue between political economy and the history of science and technology (HST)
The Industrial Automation of Aesthetics in Machine Vision
Leonardo Impett demonstrated how current AI visual systems for the recognition and generation of images are grounded on specific and arbitrary measurement of beauty and how old AI visual systems keep on reproducing these standards and conventions of ‘beauty’ through their implementation in many applications.
From Mechanical Thinking to Models of Mind: Notes Towards a Historical Epistemology of Artificial Intelligence
What model of knowledge is the current form of AI (i.e. machine learning) representing? In the history of human civilizations tools have always emerged together with a system of (technical) knowledge associated to them, but this aspect seems very confused in an artefact that is said to directly automate human „intelligence“ and „learning.“ This epistemological dimension, that is the distinction between knowledge and tool of course exist also in machine learning as the distinction, for example, between programming languages and application, but it seems to be continuously removed from the debate on AI that is fixated on an equation unique in the history of epistemology: machine = intelligence. To criticize this assumption this keynote reads the idea of „machine intelligence“ not as a novelty but as the latest stage of the history of algorithmic thinking, and this as the confluence of the longer history of mechanical thinking with statistical thinking. Whereas the epistemology of mechanical thinking and statistical thinking has been flourishing, the epistemology on machine learning is still fragmentary and the keynote attempts an overview of the different epistemological schools and methods that could help consolidate this field.