Reconsidering otherness: using machine learning to design conceptual architecture

The project Reconsidering otherness is funded by the AI cluster at the Department of Communication and Psyschology.

Using machine learning to generate works of art, design and architecture has been an emerging research field in the last decades, and experiments have been made as early as the 1960, inspired by Alan Turing's question ‘can computers think?’.
In architecture, machine learning allows the exploration of large design spaces and optimization of certain aspects which can be expressed in a numerical format, such as areas, volumes, material use or energy consumption. In the last years, much effort has gone in automating floor plan generation and in generative design tools which optimize material use in concert with structural requirements. Additionally, initial work has shown results from training a generative adversarial neural network to ‘hallucinate’ about architecture (del Campo et al., 2021).

In continuation of previous work, Reconsidering otherness is a design-based research project aiming to create conceptual architecture using machine learning tools for generating texts and images. This might help to uncover implicit biases in training models and will contribute to current discussions on (creative) authorship in a (post?)-digital age (Carpo, 2017). The work builds on Kyle Steinfeld’s (Steinfeld, 2021) categorization of machine learning in art and architecture as (a) actor, (b) as material and (c) as provocateur.


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Carpo, M. (2017). The Second Digital Turn Design Beyond Intelligence. MIT Press.

del Campo, M., Carlson, A., & Manninger, S. (2021). Towards Hallucinating Machines - Designing with Computational Vision. International Journal of Architectural Computing, 19(1). https://doi.org/10.1177/1478077120963366

Steinfeld, K. (2021). Significant others: Machine learning as actor, material and provocateur in art and design. In I. As & P. Basu (Eds.), The Routledge Companion to Artificial Intelligence in Architecture. Routledge. https://doi.org/10.4324/9780367824259