My work centers on the dynamic relationship between the creative practice of design and computational design methods.
While one of these is often characterized as a direct determinant of the other, my work seeks to demonstrate that...
new technologies of design
do not directly determine social relationships,
but are among the network of actors -
designers and specialists,
software and users,
data and drawings -
that compete to shape
the diffusion of design authorship
the social distribution of design work.
The interplay between new technologies of design and the culture of design practice comes into sharp contrast at intense moments of technological or social change. In my career as a student and a scholar of architectural design, I have witnessed two such intense moments.
The first was in the mid-1990s, when, as an undergraduate student of architecture, I was a part of a transitional generation that saw the shift from analog to digital representation.
The second was in the early-2000s, when, as a graduate student and young professional, I saw the adoption of computational techniques in design, such as scripting and parametric modeling.
These moments are what the historian Mario Carpo has called the "two digital turns". Based on my experience, It seems to me that we are at the cusp of a third.
I think so based on what has been happening across a range of creative fields.
Catalyzed by new advances in machine learning, and the development of methods for making these advances visible and accessible to a wider audience, the past five years has seen a burst of renewed interest in generative practices across the domains of fine art, music, and graphic design. The motivation of this studio is to better understand what ramifications might these methods might hold for architectural design.
Background of Creative AI
I'll offer here a quick overview of the short history of these tools in creative practice, and will highlight three precedent projects that I find particularly relevant.
From Classification To Hallucination Models
Tom White is an artist who is interested in representing "the way machines see the world". He uses image classification models to produce abstract ink prints that reveal visual concepts.
Perhaps the most ambitious of the precedents I'll show today comes from the tech-forward band YACHT, who produced a concept album titled "Chain Tripping" in which AI played a role at every step. This included:
The machine-generation of melodies and beats based on the band's previous recordings
The machine-generation of lyrics and song titles from the band's previous albums
The collaboration with AI artists in the design of the album cover and promotional materials.
In a sentiment we hope to emulate in this studio, Claire Evans states: "We wanted to understand it. We knew that the best way to do that is to make something."
Every song on the album was composed using Magenta's MusicVAE model, all lyrics were generated by an LSTM trained by Ross Goodwin, and parts were performed on Magenta and Creative Lab's NSynth Super instrument. Visual aspects of the album were also made using generative neural networks, including the album cover by Tom White's adversarial perception engines and GAN-generated promotional images by Mario Klingemann. The videos include sequences generated with pix2pix and fonts from SVG-VAE using implementations that are available in Magenta's GitHub repo.
"We wanted to find a way to interrogate technology more deeply," explains Claire L. Evans, one-third of the pop group YACHT. "From the ground up," adds her partner and YACHT founder Jona Bechtolt. The group--rounded out by longtime collaborator Rob Kieswetter--would know: their seventeen-year career has been marked by a series of conceptual stunts, experiments, and attempts to use technology "sideways." Even the band's name speaks to this: YACHT is an acronym for Young Americans Challenging High Technology.
Chain Tripping is their seventh album and third with DFA Records. Recorded between the band's home in Los Angeles and Marfa, Texas, the ten-song collection marks a shift in the group's relationship with technology. Rather than trying to comment on existing platforms from within their own filter bubble, the band stripped their process down and rebuilt it using a technology entirely new to them--Artificial Intelligence, and more specifically, machine learning.
In order to compose Chain Tripping, YACHT invented their own AI songwriting process, a journey of nearly three years. They first tried to discover any existing YACHT formulas by collaborating with engineers and creative technologists to explore their own back catalogue of 82 songs using machine learning tools. Eventually they created their own working method, painstakingly stitching meaningful fragments of plausible nonsense together from extensive, seemingly endless fields of machine-generated music and lyrics, themselves emerging from custom models created with the help of generous experts in neural networks, deep learning, and AI.
What Does This Mean For Architecture?
I DON'T KNOW!
(let's find out)
AI in the Design Studio
This undergraduate studio offered in the Spring of 2020 proceeds through a series of lightly-connected "propositions" that explore the potential role of an AI tool in design. These are collected and shown in an exhibit of work. While the role of each AI tool we encounter differs in each proposition - at times acting as an assistant, a critic, or a provocateur - each proposition will offer the studio a chance to better know the underlying technology and how it might figure in a larger process of design.
What is an appropriate test bed for these technologies of the artificial?
Thematically we will focus on the Northern California Landscape, and on the interface between the built environment and the natural environment. Or, rather, on the interface between the artificial built environment and the artificial natural environment.
Text / Image Generation Models
Work from the Studio
What I Teach in General
I primarily teach two types of courses in the Department of Architecture: core courses in design and architectural representation, and topical research studios and seminars in Design Computation. In addition to formal course instruction, I often serve as advisor of theses and dissertations for students both within the Department and in the broader University.