LFU:online

Erwerb fortgeschrittener individueller Kenntnisse in verschiedenen Fachbereichen der Architektur und Fähigkeit, dieses Wissen reflektiert und strategisch für architektonische Anwendungen einzusetzen. 

Erwerb von fundierten und breit gefächerten Kenntnissen der wissenschaftlichen, kuratorischen und publizistischen Aufarbeitung und Vermittlung von Architektur mit besonderem Schwerpunkt auf Technik, die zu konzeptionellen und kommunikativen Aufgaben befähigen; Vorbereitung auf Tätigkeiten, die über Architektur reflektieren: Architekturtheoretiker, Architekturhistoriker, Architekturjournalisten, Tätigkeiten in Museen, Kultureinrichtungen etc.

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. 

Weekly readings, active participation and discussion, small writing/visual exercises.

At the end of the seminar, participants will be asked to write an academic paper related to the topics discussed in class and oriented to the sample questions below. The paper can be written in English or German. Only participants attending the course regularly will be permitted to deliver the final paper. Weekly attendance and active participation is therefore mandatory throughout the entire 10 sessions.

Sample Questions:
1. Is Architecture Artificial and/or Intelligent?

2. Select an architectural design which can be described as artificial and elaborate.

3. Select an architectural design which can be described as intelligent and elaborate.

4, If AI were a building, what building would it be?

Campolo, Alexander, and Kate Crawford. 2020. ‘Enchanted Determinism: Power without Responsibility in Artificial Intelligence’. Engaging Science, Technology, and Society 6 (January): 1–19. https://doi.org/10.17351/ests2020.277.

Dreyfus, Hubert L. 1986. What Computers Can’t Do: The Limits of Artificial Intelligence. Rev. ed. 7th print. Perennial Library. New York: Harper & Row.

Fetzer, James H. 2004. ‘The Philosophy of AI and Its Critique’. In The Blackwell Guide to the Philosophy of Computing and Information, 119–34. Malden, MA: Blackwell Publishing Ltd.

Grusin, Richard A., ed. 2015. The Nonhuman Turn. 21st Century Studies. Minneapolis: University of Minnesota Press.

Heidegger, Martin. 1977. The Question Concerning Technology, and Other Essays. New York: Garland Pub.

Longo, Giuseppe. 2019. ‘Letter to Turing’. Theory, Culture & Society 36 (6): 73–94. https://doi.org/10.1177/0263276418769733.

Macmillan Education, Limited. 2016. The Radicalisation of Science. London: Macmillan Education, Limited. https://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=5641733.

McLaughlin, Brian P. 2004. ‘Computationalism, Connectionism, and the Philosophy of Mind’. In The Blackwell Guide to the Philosophy of Computing and Information, edited by Luciano Floridi, 135–51. Malden, MA: Blackwell Publishers.

Pasquinelli, Matteo, and Vladan Joler. 2020. ‘The Nooscope Manifested: AI as Instrument of Knowledge Extractivism’. AI & SOCIETY, November. https://doi.org/10.1007/s00146-020-01097-6.

Searle, John R. 1980. ‘Minds, Brains, and Programs’. Behavioral and Brain Sciences 3 (3): 417–24. https://doi.org/10.1017/S0140525X00005756.

Turing, Alan. 1950. ‘Computing Machinery and Intelligence’. Mind: A Quarterly Review of Psychology and Philosophy LIX. (236): 433–60.

Wright Steenson, Molly. 2017. Architectural Intelligence: How Designers and Architects Created the Digital Landscape. Cambridge, MA: The MIT Press. https://mitpress.mit.edu/books/architectural-intelligence.

Start:

Monday, 14.03.2022, 12:00 o‘clock.