- Authors: Matias del Campo, Sandra Manninger, Alexandra Carlson
- Publication: Proceedings of the 2019 ACADIA Conference – Ubiquity and Autonomy (2019)
Artificial Neural networks have become ubiquitous across disciplines due to their high performance in modeling the real world to execute complex tasks in the wild. This paper presents a computational design approach that uses the internal representations of deep vision neural networks to generate and transfer stylistic form edits to both 2D floor plans and building sections. The main aim of this paper is to demonstrate and interrogate a design technique based on deep learning. The discussion includes aspects of machine learning, 2D to 2D style transfers, and gener- ative adversarial processes. The paper examines the meaning of agency in a world where decision-making processes are defined by human/machine collaborations and their relationship to aspects of a Posthuman design ecology. Taking cues from the language used by experts in AI such as Hallucinations, Dreaming, Style Transfer, and Vision, the paper strives to clarify the position and role of Artificial Intelligence in the discipline of Architecture.
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