Drawn, Together (2020)
article⁄Drawn, Together (2020)
abstract⁄Changes in the media through which design proceeds are often associated with the emergence of novel design practices and new subjectivities. While the dynamic between design tools and design practices is complex and nondeterministic, there are moments when rapid development in one of these areas catalyzes changes in the other. The nascent integration of machine learning ML processes into computeraided design suggests that we are in just such a moment. It is in this context that an undergraduate research studio was conducted at UC Berkeley in the spring of 2020. By introducing novice students to a set of experimental tools Steinfeld 2020 and processes based on ML techniques, this studio seeks to uncover those original practices or new subjectivities that might thereby arise. We describe here a series of small design projects that examine the applicability of such tools to earlystage architectural design. Specifically, we document the integration of several conditional textgeneration models and conditional imagegeneration models into undergraduate architectural design pedagogy, and evaluate their use as ‘creative provocateurs’ at the start of a design. After surveying the resulting student work and documenting the studio experience, we conclude that the approach taken here suggests promising new modalities of design authorship, and we offer reflections that may serve as a useful guide for the more widespread adoption of machineaugmented design tools in architectural practice.
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| Year |
2020 |
| Authors |
Steinfeld, Kyle. |
| Issue |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers |
| Pages |
282-289. |
| Library link |
N/A |
| Entry filename |
drawn-together |
