Machine Learning in Architecture, or, The generation of Artificial Rooms
Machine Learning (ML) recognizes, listens, reads, learns and discriminates. It also writes and paints, speaks, and drives. This proliferation of Artificial Intelligence (AI) is fueled by the exhaustive measuring of objects and the environment.
We have learned to produce knowledge from the measurements of autonomous systems and slowly integrate it into the design practice.
This seminar provides a theoretical basis for architects interested in machine learning. The course begins with the historical figures, scientific breakthroughs and failed attempts that shaped AI, as well as the first AI appearances in architecture. We will continue with the position of AI in the philosophy of mind and philosophy of science, where participants will learn about notions like the paradigm shift, the computationalist mind, connectionism, holism, vitalism, data and material extraction. We will explore contemporary arguments pro and contra AI and collectively discuss to what extent data-acquisition can emulate and aid architectural design.
The course addresses, as well, how critical writing and speculative computation can interrogate the transparent lines of techno-politics and culture capitalism. Writings by Lev Manovich, Brian P. McLaughlin, James H. Fetzer, Alan Turing, Mollie Claypool, Critical Computation Bureau, Michel Foucault, Kate Crawford, Yuk Hui will be read and discussed in class. Finally, a selection of artists and architects are invited to present their creative approach on AI to the class.
Throughout the 10 sessions of the course, each participant will work on small writing exercises (200 words) based on the collective readings. We will also play with accessible ML applications shown in class to create images for each abstract. As we progress, these exercises will build the core narrative of an academic paper. The final submission will be the polished collection of these exercises.
Start: Monday, March 14th