Through a Machine Lens: Case Studies of Computer Vision and Machine Learning in Design Methodology

Andrew Witt

doi:10.31261/LC.2022.02.06

Published: 2022-06-28

Vol. 1 No. 2 (2022)

Through a Machine Lens: Case Studies of Computer Vision and Machine Learning in Design Methodology

Andrew Witt

Section: Architecture

https://doi.org/10.31261/LC.2022.02.06

Abstract

Machine vision (MV) and artificial intelligence (AI) offer strange new augmentations and transformations of how architects perceive and conceptualise both the creation of buildings and the analysis of existing architecture. Seeing through machinic eyes allows architects to amplify their intuitions and engage in a flood of digitally sensed imagery in more quantifiable and extensible ways. Through a series of case studies of projects developed through the design office Certain Measures, this article argues for the potential of machine vision and artificial intelligence in the creative practice of design while situating these new developments in the history of mathematical ways of seeing and conceptualising architecture. These case studies, across large and small scales, combine ideas from human and machine perception and mathematical geometry to create new architectural approaches.

Keywords:

architecture , machine vision , artificial intelligence , design , methodology

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References

Penrose, L. S., & Penrose, R. (1958). Impossible objects: A special type of visual illusion. British Journal of Psychology, 49, 31–33.
Google Scholar

Roma, C. (2013). Kintsugi. Ceramic Review, 260, April, 63.
Google Scholar

Ruscha, E. (1966). Every Building on the Sunset Strip. Self-published book.
Google Scholar

Roff, H. (2017). Advancing Human Security Through Artificial Intelligence. In Emerging Technologies and Human Security. Chatham House, 3-10.
Google Scholar

Roff, H. (2017). How understanding animals can help us make the most of artificial intelligence, The Conversatioin (March 30) https://theconversation.com/how-understanding-animals-can-help-us-make-the-most-of-artificial-intelligence-74742
Google Scholar

Sáez, V. P., Osmani, M. (2019). A diagnosis of construction and demolition waste generation and recovery practice in the European Union. Journal of Cleaner Production, 241, 1-2.
Google Scholar

Vol. 1 No. 2 (2022)
Published: 2024-06-21

eISSN: 2720-6122

10.31261/LC

Publisher

Wydawnictwo Uniwersytetu Śląskiego | University of Silesia Press

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