Architectural artificial intelligence: exploring and developing strategies, tools, and pedagogies toward the integration of deep learning in the architectural profession

Abstract

The growing incessance for data collection is a trend born from the basic promise of data: “save everything you can, and someday you’ll be able to figure out some use for it all” (Schneier 2016, p. 40). However, this has manifested as a plague of information overload, where “it would simply be impossible for humans to deal with all of this data” (Davenport 2014, p. 151). Especially within the field of architecture, where designers are tasked with leveraging all available sources of information to compose an informed solution. Too often, “the average designer scans whatever information [they] happen on, […] and introduces this randomly selected information into forms otherwise dreamt up in the artist’s studio of mind” (Alexander 1964, p. 4). As data accumulates— less so the “oil”, and more the “exhaust of the information age” (Schneier 2016, p. 20)—we are rapidly approaching a point where even the programmers enlisted to automate are inadequate. Yet, as the size of data warehouses increases, so too does the available computational power and the invention of clever algorithms to negotiate it. Deep learning is an exemplar. A subset of artificial intelligence, deep learning is a collection of algorithms inspired by the brain, capable of automated self-improvement, or “learning”, through observations of large quantities of data. In recent years, the rise in computational power and the access to these immense databases have fostered the proliferation of deep learning to almost all fields of endeavour. The application of deep learning in architecture not only has the potential to resolve the issue of rising complexity, but introduce a plethora of new tools at the architect’s disposal, such as computer vision, natural language processing, and recommendation systems. Already, we are starting to see its impact on the field of architecture. Which raises the following questions: what is the current state of deep learning adoption in architecture, how can one better facilitate its integration, and what are the implications for doing so? This research aims to answer those questions through an exploration of strategies, tools, and pedagogies for the integration of deep learning in the architectural profession