Artificial Intelligence and Architecture: From Research to Practice 9783035624045, 9783035624007

Table of contents :
Acknowledgments
About the Author
Table of Contents
Foreword
Artificial Intelligence
The Advent of Architectural AI
AI's Deployment in Architecture
The Outlooks of AI in Architecture
Closing Remarks
Image Credits
Contributors’ Biographies
Index

Citation preview

Artificial Intelligence and Architecture From Research to Practice

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Artificial Intelligence and Architecture From Research to Practice Stanislas Chaillou

Birkhäuser Basel

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Stanislas Chaillou Acquisitions Editor: David Marold, Birkhäuser Verlag, A-Vienna Content & Production Editor: Bettina R. Algieri, Birkhäuser Verlag, A-Vienna Proofreading: Alun Brown Layout: Stanislas Chaillou Cover Design: Floyd Schulze Image editing: Stanislas Chaillou Printing and binding: Beltz, D-Bad Langensalza Paper: Condat matt Périgord 135 g/m2 Typeface: Crimson Text, Neue Haas Grotesk Library of Congress Control Number: 2021937064 Bibliographic information published by the German National Library The German National Library lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in other ways, and storage in databases. For any kind of use, permission of the copyright owner must be obtained. ISBN 978-3-0356-2400-7 e-ISBN (PDF) 978-3-0356-2404-5 © 2022 Birkhäuser Verlag GmbH, Basel P. O. Box 44, 4009 Basel, Switzerland Part of Walter de Gruyter GmbH, Berlin/Boston 9 8 7 6 5 4 3 2 1

www.birkhauser.com

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Acknowledgments Before anything else, I would like to take the opportunity to thank the people who made this book possible; our contributors, for their time and effort: Foster & Partners’ ARD Group, the City Intelligence Lab, Kyle Steinfeld, Andrew Witt, Alexandra Carlson & Matias del Campo, Caitlin Mueller & Renaud Danhaive, Immanuel Koh, and Carl Christensen. David Marold from Birkhäuser for his judicious advice from the very beginning. Last but not least, I would like to dedicate this book to Reinier.

About the Author Stanislas Chaillou is a Paris-based architect and data scientist. He is the co-founder of a software company building cloud-based solutions for the AEC industry. Stanislas received his Bachelor of Science in Architecture from the Swiss Federal Institute of Technology of Lausanne (EPFL, 2015) and his Master's degree in Architecture from Harvard University (GSD, 2019). Since 2018, his work focuses on the theoretical and experimental aspects of Artificial Intelligence in Architecture. Stanislas was the curator of the exhibition “Artificial Intelligence & Architecture”, organized at the Arsenal Pavilion in Paris in 2020. He is also the author of a book entitled “L’Intelligence Artificielle au service de l’Architecture”, published in 2021 by le Moniteur Editions.

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Table of Contents

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8 Foreword 12 ArtificiaI Intelligence, Another Field

16 The Post-War Period 20 Expert Systems & AI Winters 24 The Deep Learning Revolution

32 The Advent of Architectural AI

36 42 48 56

62 AI's Deployment in Architecture

64 82 86 90 94 98 102

106 The Outlooks of AI in Architecture

Modularity Computer-Aided Design Parametricism Artificial Intelligence Artificial Intelligence 101 Urban Scale Floor Plans Facades Perspectives Structures Predictive Simulations

108 The Contribution 110 The Form 118 The Context 126 The Performance

134 The Adoption 136 The Practice 146 The Model 162 The Scale

170 The Prospects

198 Closing Remarks

172 The Style 180 The Ecology 188 The Language

203 Image Credits 205 Contributors’ Biographies 206 Index 7

Foreword

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The presence of Artificial Intelligence (AI) in Architecture may still be in its early days. If current research makes a strong case for its potential adoption, it also reaffirms the importance of the discussion surrounding its inception and its necessary adaptation to support the architectural agenda. From its immediate technical benefits to its longer-term cultural implications, AI’s dialogue with Architecture is unfolding today at multiple levels. To grasp the full magnitude of this technological shift, this book considers three complementary angles, together offering a pedagogical overview. By exploring the historical, experimental, and theoretical facets of AI’s beginnings in Architecture, it provides its readers with the opportunity to contemplate both the tangible and the speculative nature of this encounter. Starting from a historical perspective, the first chapters place AI back into the past century’s conversation between Technology and Architecture. Recent results of AI research then follow and ground the reflection into experimental, yet tangible, applications to Architecture. Finally, this book gives the stage to theorists, researchers and entrepreneurs working today

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Foreword

at the forefront of this revolution. From Harvard to MIT or companies like Spacemaker and Foster & Partners, this book’s final segment presents a wide outlook of current theories and discourses surrounding AI’s presence in the field. More importantly, we hope to help bridge the gap still existing between the state of AI research and architectural practice. As AI’s gradual dissemination into Architecture’s tools and methods is an ongoing reality, this book aims at clarifying the terms and definitions, while providing the necessary explanations needed to explore this fascinating topic. To that end, we hope to lay down the groundwork for a meaningful exchange between both disciplines, and to demystify what too often appears as a blurry technological maze. Finally, our hope is to unveil the diversity and excitement present in the current landscape. The intersection of AI and Architecture can be the source of a new momentum for the discipline, provided our collective work helps frame and develop this technology so as to truly serve architects.

The content of this book echoes the exhibition “Artificial Intelligence & Architecture” that took place in Paris in 2020. Curated by Stanislas Chaillou, and produced by the Arsenal Pavilion, this show originally presented an early overview of AI’s application to Architecture. The exhibit is today available online as a virtual tour, accessible by scanning the QR code. Moreover, this book makes extensive use of digital contents, accessible through a system of QR codes that can be scanned at the end of each chapter. These various references, in the QR Code for the “AI & Architecture” virtual exhibit at the Arsenal Pavilion (image on the opposite page).

form of books (

), articles (

), videos (

), and others offer readers the opportunity to

continue exploring this fascinating topic beyond the sole content of this book.

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Artificial Intelligence Another Field

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“Stories about the creation of machines having human qualities have long been a fascinating province in the realm of science fiction; yet we are about to witness the birth of such a machine – a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control”. 1. F. Rosenblatt, “The Design of an Intelligent Automaton”, ONR Research Reviews, 1958.

These words1 in 1958 by the American psychologist Frank Rosenblatt are a telling testimony to the radical optimism of AI’s early pioneers. However, nearly 70 years later, Rosenblatt’s vision is still under development across the world. In hindsight, such assertions are a striking reminder that the history of computer science is far from being a linear journey. From the early days of AI in the 1940s-50s up until the deep learning revolution, this technology is the result of a slow sedimentation of scientific hypotheses and technological breakthroughs. Far from the siloed research of the 1950s, AI nowadays engages with countless other fields. Architecture is no exception. It is why framing its potential contributions to the discipline requires first an understanding – however rudimentary – of its early developments, of the challenge it faced along the way and a short reminder of its concomitant adoption in other industries.

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Artificial Intelligence, Another Field

1 AI's Historical Timeline, from the post-war period up until the Deep Learning revolution.

Artificial Neuron W. McCulloch & W. Pitts (’43)

Transistor

Bell Labs (’47)

Post-War Period

First Expert AI Winter Systems

1950s

1970s

Dartmouth Workshop

R1 Program

(’56)

J. McDermott (’78)

Perceptron

Lighthill Report

F. Rosenblatt (’57)

ELIZA

J. Weizenbaum (’66)

1980s

SID Program DEC (’82)

J. Lighthill (’73)

“Perceptrons” M. Minsky, S. Pappert (’69)

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CYC Program D. Lenat (’84)

“Some Expert Systems Need Common Sense” J. McCarthy (’84)

Second AI Winter

Deep Learning Revolution

1990s

2010s

DeepBlue vs Kasparov

AlphaGO vs Sedol

(’97)

(’16)

Stanley at DARPA Challenge

(’05)

Generative Adversarial Net I. Goodfellow (’14)

AlexNET

A. Krizhevsky (’12)

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Artificial Intelligence, Another Field

The Post-War Period

The 1940s are considered the crossroads of multiple significant breakthroughs, together providing the building blocks of our contemporary definition of AI. In 1943, American scientists Warren 2. W. McCullock & W. Pitts, “A logical calculus of the ideas immanent in nervous activity”, Bulletin of Mathematical Biophysics 5, pp 115–133 1943. 3. Bell Labs’ Website, “The 1956 Nobel Prize in Physics”, Source: https://www. bell-labs.com/about/ awards/1956-nobel-prizephysics

2 J. Bardeen, W. Shockley, W. Brattain and the transistor at Bell Labs in 1948.

McCulloch and Walter Pitts first laid down an initial mathematical formulation of the biological neuron2. Although theoretical, this model provided the scientific community of the time with an early definition of the “artificial network”. In a nutshell, their model described the computation performed by a neuron to process a flow of information. This achievement would soon be paired with another experiment stemming from the Bell Lab, a research institution ran by American telecom company AT&T. At this lab, in 1947, John Bardeen, Walter Houser Brattain and William Bradford Shockley together came up with a new type of semiconductor device: the transistor3 (Fig. 2). In brief, this machine could modulate an electric signal by dimming or amplifying it. This new hardware generation soon enabled theoretical models like McCulloch and Pitts’ to be materialized by actual functioning prototypes. A few years later, in 1957, the American psychologist Frank Rosenblatt best harvested this potential by successfully running a groundbreaking experiment at the Cornell Aeronautical Laboratory using custom-built hardware:

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The Post-War Period

3 Frank Rosenblatt and the Mark I Perceptron.

the Perceptron (Fig. 3). Designed to classify images, the Perceptron was built upon previous theoretical work and offered a functioning prototype of a “learning” machine. “Learning” here refers to the ability of the Perceptron to self tune its settings when exposed to arrays of images; a process also referred to as “training”. Through this trial-and-error procedure, the network would adjust its values to improve its ability to accurately predict each image’s category. The same year, the New York Times covered Rosenblatt’s experiment,

4. “New Navy Device [that] Learns By Doing”, New York Times, July 8th 1958.

describing it as a “New Navy Device [that] Learns By Doing”4. The Perceptron’s specificity lay precisely in this ability to perform a self-corrective feedback loop. This process would set it apart from previous algorithmic theories while opening new research avenues for the forthcoming decades. Yet another foundational moment in AI’s history took place during the same decade. In 1956, researchers gathered for the Dartmouth Summer Research Project – held at the eponymous university – formulated an initial definition of AI, and set the roadmap for future developments in the field. Among others, Marvin Minsky, John McCarthy, Ray Solomonoff, and Oliver Selfridge took part in the

5. J. McCarthy, M. L. Minsky, N. Rochester, C. E. Shannon,“A Proposal for The Dartmouth Summer Research Project On Artificial Intelligence”, AI Magazine, August 31st 1955.

workshop. Their team put forth both the term “Artificial Intelligence"5 and its meaning: the use of the human brain as a model for machine logic. To them, emulating the human brain’s mode of acquisition, its structure and its functioning principles would represent an alternate way of defining algorithmic logics. In the footsteps of these early experiments, applications started spreading across various domains. Natural language processing (NLP) probably offers one of the most interesting developments of

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Artificial Intelligence, Another Field

the period. With ELIZA (1966), a project developed by the GermanAmerican scientist Joseph Weizenbaum, a computer was able to simulate an exchange with a person through a chat-based 6. J. Weizenbaum, “ELIZA, A Computer Program For the Study of Natural Language Communication Between Man and Machine”, Communications of the ACM, Volume 9, Issue 1, pp 36–45, 1966.

program6. Weizenbaun attempted to formalize some of the underlying patterns of casual conversations, which would then be used by ELIZA in the context of a textual exchange with the user. At the other end of the spectrum, robotics engineering saw in AI the possibility to offer given systems a partial autonomy. With applications in manufacturing as early as the 1950s, AI yielded large-scale results early on. Unimate (1961), a project developed by the American engineers George Devol and Joseph Engelberger for General Motors’ assembly lines, perhaps best embodied this momentum: their robotic arm could perform tasks like transporting manufactured parts and welding. ELIZA and Unimate are iconic examples of the optimism of the period; experiments would gradually spread beyond the realm of research institutions and would be applied to real-world problems. And as AI began to provide tangible results across the board, the scientific community’s confidence was bolstered all the more. The American cognitive psychologist Hebert Simon maybe best captured the period’s zeitgeist: “The simplest way I can summarize is to say that there are now in the world machines that think, that learn, and that create. Moreover, their ability to do these things is going to increase rapidly until […] the range of problems they can handle will

7. H. Simon, “Heuristic Problem Solving: The Next Advance Operations Research”, Operations Research 6(1), pp 1-10, 1958.

be coextensive with the range to which the human mind has been applied”7. However, Herbert’s predictions would face a very different reality, as AI research soon reached a long-lasting plateau, putting a halt to the seemingly positive outlook of the 1960s.

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Artificial Intelligence, Another Field

Expert Systems & AI Winters

Throughout the 1960s-70s, and later in the 1990s, the field would undergo two acute periods of self doubt, today known as the “AI winters”. In both instances, the general mindset in the private sector and among research institutions would sharply contrast with the enthusiasm of the early days. The first AI winter took place in the aftermath of Rosenblatt’s experiments. Among many factors, two specific publications would be symptomatic of the period’s growing skepticism. The first one was a book entitled “Perceptrons” (1969), authored by Marvin Min8. M. Minsky & S. Papert, “Perceptrons: An Introduction to Computational Geometry”, MIT Press, 1969.

sky and Seymour Papert8. The two scientists laid down a critical view of Rosenblatt’s Perceptron and derived research. To them, the Perceptron was limited to simple use cases, and could not address more complex problems. The second publication was the Lighthill Report (1973), directed by the British mathematician James Light-

9. J. Lighthill, “Artificial Intelligence: a General Survey”, Artificial Intelligence: a paper symposium, Science Research Council, 1973.

hill9. The report, initially called “Artificial Intelligence: a General Survey”, assessed AI’s results across the field. In this excerpt from the report, Lighthill established a rather pessimistic diagnostic: “Most workers in Al research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the past

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Expert Systems & AI Winters

twenty-five years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from having been realized in 1972. In no part of the field have the discoveries made so far 10. J. Lighthill, “Artificial Intelligence: a General Survey”, Artificial Intelligence: a paper symposium, Science Research Council, Part 1, p. 8, 1973.

produced the major impact that was then promised”10. For Lighthill and his team, AI’s seemingly negligible impact should call the entire discipline into question. The influence of these two publications was quite significant at the time: both public funding and private investments in R&D programs got momentarily frozen or reassigned to other scientific domains. AI would have to wait a short while before seeing confidence and funding come its way once again. The 1980s would correspond to a revival. The advent of expert systems, as a new generation of AI models fueled by the increasing availability of computing power, prompted this resurgence of confidence. As an immediate consequence, funding soared and flowed back into the field, giving it a sudden second chance. Expert systems were the signature of this period; these models allowed machines to reason based on a set of rules and collections of facts. In other words, from a given knowledge base, an expert system could infer the truth of new statements. The reliability of these models, when applied to specific domains, is what would explain their suc-

11. B. G. Buchanan & E. H. Shortliffe, “Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project”, Addison-Wesley, 1984.

4 Cover of MYCIN expert system's guide book,1972.

cess throughout the 1980s. The MYCIN project (1972), at Stanford University, stands as an essential milestone in the early days of expert systems. Meant to be used in medicine to identify infection-inducing bacteria, this AI model would reason on a knowledge base of roughly 600 rules (Fig. 4)11. If MYCIN was in fact never used for actual cases, it remains a striking demonstration of the potential of expert

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Artificial Intelligence, Another Field

systems’ relevance at that time. John P. McDermott’s R1 program (1978) – also called XCON – is another example, yet this time actually applied to a real-world problem. McDermott’s program was deployed in 1980 to assist DEC, an American computer manufacturer, in automating the ordering of computer components based on customer requirements. Given the specialized nature of the task, this rule-based model proved extremely successful at improving the general reliability of industrial processes. However, one of the most iconic expert systems remains the “Cyc” project, 12. Matuszek et al., “An Introduction to the Syntax and Content of Cyc”, AAAI Spring Symposium, 2006.

developed from 1984 onwards by the American AI researcher Douglas Lenat12. With Cyc, Lenat wanted to model common-sense knowledge, concepts and rules about how the world works. It is to this day one of the most significant examples of the kind of experiment that took shape during this period. This project is in fact still under development today at Cycorp. By the end of the 1980s, however, expert systems reached a plateau, due to certain obvious limitations, prompting the beginning of a second AI winter. John McCarthy maybe best formulated its causes in his

13. J. McCarthy, “Some Expert Systems Need Common Sense”, Annals of the New York Academy of Sciences, Volume 426, pp 129-137, 1984.

article “Some Expert Systems Need Common Sense”13. In this publication, McCarthy reflected on expert systems’ “difficulty to extend beyond the scope originally contemplated by their designers, [and inability to] recognize their own limitations”13. At the same time, Jacob T. Schwarz, then Director of DARPA ISTO – the Information Science & Technology branch of the Defense Advanced Research Projects Agency – came to the same realization, and decided to significantly reduce the funding dedicated to the field. General skepticism and a lack of investment would plague AI research for the decade to come, plunging the entire discipline into a new period of self doubt.

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Artificial Intelligence, Another Field

The Deep Learning Revolution

In the 1990s and 2000s, AI research would gradually pivot to embrace machine learning-based methods. Since expert systems had set aside the principle of “learning”, their limitations gave rise to explorations in new directions: neural networks, Bayesian networks, evolutionary algorithms, etc. All these methods build upon the concept of a gradual acquisition of knowledge, through a trial-and-error learning process. In a seemingly quiet research landscape, investigations into these models would spread. A few events finally shook up the scientific community to revive this stalling field once again. In 1997, Deep Blue, an AI computer conceived at IBM research, eventually beat Garry Kasparov, then chess world champion. This was an initial wake-up call for the en14. M. Newborn, “Deep Blue: An Artificial Intelligence Milestone”, Springer, 2002.

15. Stanford Artificial Intelligence Laboratory, “Stanley: The Robot that Won the DARPA Grand Challenge”, The 2005 DARPA Grand Challenge, pp 1-43, Springer, 2006.

tire community and beyond14. From the abstract world of chess to a real-life application, AI was soon going to benefit from another striking demonstration in 2005, at the DARPA Grand Challenge. This car race was then won by Stanley, an autonomous car created by Stanford University and the Volkswagen Electronics Research Lab15. Through a feedback loop between sensors mounted on the car, and a machine learning model, the vehicle was able to complete the race while securing first place.

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The Deep Learning Revolution

These two events put AI research once again under the spotlight: funding was back. This time, however, this revival was concurrent with a few other realities. First, with the rapid development of the Internet, data collection and curation had significantly improved. Large databases were being aggregated and curated, giving AI research a much broader variety and quantity of information to process. Then, GPUs (“Graphical Processing Unit”) had started to become more accessible: this piece of hardware, used by computers to process images, was diverted from its initial purpose to train AI models. By parallelizing operations – i.e. computing operations in parallel rather than sequentially – GPUs could dramatically speed up computational time. This in turn rendered AI projects considered to be impossible until then feasible. Throughout the 2000s, this hardware progressively became more accessible either natively, on users’ laptops, or on the “cloud” by using servers remotely. Building on these foundations, the term “deep learning” emerged at the turn of the 2010s, to refer to the ongoing shift happening within the AI community. This expression is an acknowledgment that artificial networks were the main focus from now on, as opposed to expert systems or other architectures previously employed in AI research. The concept of “depth” refers to the increasing complexity of AI models by the addition of more artificial neurons to their architecture. In return, this network depth allowed AI systems to tackle more challenging problems, although rendering the training process computationally more expensive and tedious. If AI still remained a quiet field until this point, the relevance of this new generation of models would soon start to be evidenced by the work of certain research institutions. A few events accelerated

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Artificial Intelligence, Another Field

this revolution while broadcasting its importance to the world. In 2009, the project ImageNet started at Stanford. By gathering the largest database of labelled images thus far (more than 14 million), the university organized a yearly classification competition: contestants were invited to test their model’s prediction accuracy 16. Krizhevsky et al., “ImageNet Classification with Deep Convolutional Neural Networks”, Advances in neural information processing systems 25, pp 10971105, 2012.

against the ImageNet database. In 2012, AlexNet16, a brandnew deep learning model, overshot every baseline; the team of scientists behind AlexNet proved by the same token the validity of deep architectures for complex problems, and set the bar much higher than any previous research on the topic. This event was eyeopening for the field, and more importantly, for the general public. In an entirely different domain, in 2016, Lee Sedol, world Go game

17. Silver et al., “Mastering the game of Go with deep neural networks and tree search”, Nature 529, pp 484–489, 2016.

champion, lost to AlphaGo, an AI model developed by DeepMind17. If the Go game can seem to be analogous to chess at first – it is played on a board with black and white ponds – it is in fact far more complicated. Because of its combinatorial complexity, scientists had not believed until this point that AI could compete with human intuition in this game. It is precisely why Sedol’s defeat was both a breakthrough and a signal to the research community at large that deep learning represented a quantum leap. Since ImageNet and AlphaGo, the deep learning era has blossomed into countless new breakthroughs and applications. First, the diversity and complexity of AI models have significantly increased: convolutional neural networks, graph neural networks, generative adversarial networks, variational auto-encoders, and many other new architectures have been developed since then, always further pushing previously set performance baselines and expanding AI’s scope. The variety of input mediums has also considerably widened: from

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The Deep Learning Revolution

simple digits and images in the 50s and 60s, AI can today analyze and generate films, sounds, texts and 3D geometries to name only a few formats. This reality, combined with the democratization of computational power, has allowed a widespread dissemination of AI solutions across industries since the 2010s. A few examples illustrate the striking diversity of AI applications today. In bioengineering, for instance, drug discovery has drastically improved. To either determine the solubility of given molecules or their compatibility, AI can generate a vast quantity of molecular structures while predicting their associated performance and 18. Hwang et al., “Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks”, J. Chem. Inf. Model, 60, 12, pp 5936–5945, 2020.

properties (toxicity, metabolism, etc.)18; by the same token, the time spent on searching for new drugs can be dramatically reduced, while scientists can explore more options than with traditional methods. In an entirely different field, mechanical engineering, the design of parts – given a set of constraints and material properties – has always been a key domain of investigation. The repartition of loads under stress is a complex problem to forecast that diverse optimization technics have been trying to tackle for decades. AI to-

19. Rawat et al., “A Novel Topology Optimization Approach using Conditional Deep Learning”, 2019.

day allows the speeding up of such optimizations19 so as to predict the unsuspected path taken by loads and suggest entirely new patterns of material repartition. Image synthesis – a field concerned with the generation of images by computers – has seen recent developments yield surprising results, that Generative Adversarial Networks (GANs) may best exemplify. If these models are explained in more detail in the following chapters, a simple glance at their results gives an idea of the current performance of such generative AI models. Built on a new

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A living room with two white armchairs and a painting of the Colosseum. The painting is mounted over a modern fireplace.

A loft bedroom with a white bed next to a nightstand. There is a fish tank beside the bed.

A photo of Alamo Square, San Francisco, from a street in the afternoon.

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Artificial Intelligence, Another Field

20. Goodfellow et al., “Generative Adversarial Networks”, Advances in neural information processing systems, 27, 2014. 21. Karras et al., “A Style-Based Generator Architecture for Generative Adversarial Networks”, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 44014410, 2018.

type of architecture, initially theorized by the researcher Ian Goodfellow (2014)20, these models can be trained to synthesize images that are realistic in the extreme. Nvidia Research has evidenced their performance with StyleGAN (2018)21, a model able to generate a vast number of realistic human faces in high definition (Fig. 5). More speculative experiments, at the interface of AI and linguistics, finally convey the magnitude of the latest improvements. OpenAI, an American research laboratory founded in 2015, recently published results of their language models, GPT-322, DALL-E23 and GLIDE24. In

5 Portraits generated using StyleGAN, Nvidia Research, 2018. 22. Brown et al., “Language Models are Few-Shot Learners”, 2020. 23. Ramesh et al., “Zero-Shot Text-to-Image Generation”, 2021. 24. Nichol et al., “GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models”, 2021.

6 Results of DALL-E; each collection of images is generated by the model, based on an input sentence, displayed above in the figure.

essence, these architectures can perform the translation of textual information into potential associated visual representations. In simpler terms, a given sentence, fed to these models, returns a wide variety of images, fitting the description conveyed by the input phrase. Figure 6 displays such results. Beyond the strict depiction of literal terms, OpenAI’s projects tackle challenges such as references, analogies and other complexities found in human language. GPT-3, DALL-E and GLIDE simply illustrate the increasing levels of abstraction that current AI models are able to handle. This non-exhaustive collection of examples only underlines the tangible results of AI’s latest developments. They conclude this 70-year-long chronology and set the stage for a discussion between Architecture and AI. If the following chapters will offer a more thorough introduction to this technology and its conceptual underpinnings, for the time being this genealogy acts as a short reminder of how this discipline, foreign to Architecture, came to be.

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The Deep Learning Revolution

References & Resources

Articial Intelligence: A general survey

The Society of Mind

J. Lighthill, 1973

M. Minsky, Touchstone, 1986

Some Expert Systems Need Common Sense

The Dartmouth Research Project on Artificial Intelligence

J. McCarthy, Stanford University, 1984

J. McCarthy, M. Minksy, N. Rochester, C.E. Shannon, 1955

What is AI ? Basic Questions with John McCarthy

The Perceptron

by Stanford University

The Machine That Changed the World, Documentary, 1992

Artificial Intelligence, a discussion with Marvin Minksy

AI & Creativity: Using Generative Models To Make New Things

Edge Interview, 2002

D. Eck, Google Brain, 2017

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The Advent of

Architectural

AI

A Historical Perspective

32

The ties between Architecture and Technology are neither recent, nor have they been a stable reality. Despite having quite distinct agendas, their respective histories display moments of alignment and mutual enrichment. Either by simply inspiring one another, or by sharing entire frameworks with each other, their discussion has brought significant contributions to both worlds. This back-and-forth takes its roots far into the history of Architecture. From the systematization brought by the modular grid at the turn of the century to the advent of computer-aided design (CAD), and later of parametric modeling, the discipline has benefited from the gradual refinement of its technological means and methods over the past century. Today, AI appears as a potential fourth stage of this chronology. As Architecture’s relationship to technology has matured in parallel to AI’s development, understanding how AI eventually might land in the discipline’s technological landscape is essential. This chapter intends to tie both histories together, while setting the stage for AI’s presence in Architecture.

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The Advent of Architectural AI

Modularity

1 A brief historical timeline of technological developments in Architecture since the 1920s.

1940s

ComputerAided Design 1960s

PRONTO P. Hanratty (’59)

Dymaxion House

“Unité d’habitation” in Marseille

B. Fuller (’30)

Baukasten W. Gropius (’23)

Le Corbusier (’52)

The Modulor Le Corbusier (’46)

UNISURF P. Bézier (’66)

Urban 5

N. Negroponte (’73)

Generator C. Price (’76)

SketchPad I. Sutherland (’63)

AI’s History Artificial Neuron ’43

Dartmouth Perceptron Workshop ’57 ’56

ELIZA ’66

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Lighthill Report ’73

Parametricism

Artificial Intelligence

2000s

2010s

Vectorworks First Release

Revit First Release

Nemetschek (’85)

CATIA First Release

Dassault Systèmes (’82)

AutoCAD First Release Autodesk (’82)

“Some Expert Systems Need Common Sense” ’84

RTC (’00)

Rhinoceros Version 1.0 McNeel (’98)

Grasshopper D. Rutten (’07)

Parametricism’s Manifesto

Pro/ENGINEER S. Geisberg (’88)

DeepBlue vs Kasparov ’97

P. Schumacher (’09)

Stanley at DARPA Challenge ’05

AlexNET Generative AlphaGO ’12 Adversarial vs Sedol Network ’16 ’14

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The Advent of Architectural AI

Modularity

Reflecting on the last century, and to set a salient starting point to this chronology, modularity can be considered as both an important milestone for Architecture and a sudden increase of its systematization. At the turn of the 20th century, modularity’s advent mobilized both academics and practitioners to rapidly reshape some of the discipline’s core constructive principles and methodologies. 1. A. M. Seelow, “The Construction Kit and the Assembly Line, Walter Gropius’ Concepts for Rationalizing Architecture”, In Arts, Vol. 7, No. 4, p 95, Multidisciplinary Digital Publishing Institute, 2018. 2. M. M. Cohen, A. Prosina, “Buckminster Fuller’s Dymaxion House as a Paradigm for a Space Habitat”, In ASCEND, p. 4048, 2020.

2 Sections of Buckminster Fuller’s Dymaxion House, 1933.

Modularity was first theorized at the Bauhaus by the German architect Walter Gropius. His initial aim was twofold: simplifying technically the construction process while significantly reducing its cost. In that spirit, Gropius first introduced, as early as 1923, the concept of “Baukasten”1. With this new methodology, standard modules were meant to be assembled as a kit of parts according to strict assembly rules. As a result, the complexity of detail solving would be mitigated by the rigor of the modular system. With the American architect and designer Buckminster Fuller, modularity then evolved towards a more integrated definition. In Fuller’s Dymaxion House (1930)2, systems such as water pipes, HVAC, and other networks were directly embedded within the very modules (Fig. 2).

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Modularity

2 This attempt pushed modular logic to the extreme. The minute decomposition of the different functions into manufacturable assembly kits established the Dymaxion House as one of the first successful proofs of concept for the rest of the industry. The same year, the Winslow Ames House, designed by the American architect Robert W. McLaughlin, constituted another successful experiment. In his project, McLaughlin put the modular principles under even more acute pressure in an attempt to demonstrate the affordability of modular dwellings. By significantly streamlining the manufacturing process, McLaughlin was able to bring the production cost of a single dwelling down to 7,500 dollars. This demonstration would set a lasting precedent, demonstrating the obvious benefits of the modular approach. This rationalization of Architecture into systems and kits rapidly found a broader echo within the discipline. Besides its strict economical

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The Advent of Architectural AI

relevance, modularity gradually inspired theorists across the field. 3. Le Corbusier, “Le Modulor: essai sur une mesure harmonique à l’échelle humaine applicable universellement à l’architecture et à la mécanique”, Édition de l’Architecture d’Aujourd’hui, 1950.

Le Corbusier’s “Modulor” may best express this reality3. From 1946, Le Corbusier developed and implemented a more complete theory, where the rationalization of dimensions would factor into the architect’s broader agenda. In his work, the dimensions of the building were aligned on key metrics and ratios derived from the human body. Consequently, from the “Unité d'Habitation” in Marseille (1952) to the convent of La Tourette (1960), Le Corbusier systematized dimensions and spans to match the prescriptions of his Modulor. In line with these early experiments, architects would increasingly adapt their work to the requirement of the modular principles. In essence, by transferring part of the design’s technicality to the systematic logic of the grid and the assembly systems, architects discovered a methodology allowing them to conceive affordable designs at scale. Two major benefits of modular construction were going to contribute to its rapid adoption: on the one hand, it drastically reduced both the complexity and the cost of building conception and construction. On the other, it substantially increased the reliability of construction processes. Looking at more contemporary iconic projects, whether realized or speculative, one can still read the lasting influence of the modular principles. To mention only two examples, Moshe Safdie's Habitat 67 and Archigram's “Plugin City” are striking examples of the fascination for modularity that were to continue long after the end of the Second World War.

3 Assembly of Moshe Safdie’s Habitat 67, 1967.

In 1967, the Israeli-Canadian architect and urban planner Moshe Safdie (1895–1983) built the housing complex “Habitat 67” (Fig. 3). This project remains today a masterful modular demonstration, long after

3

38

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The Advent of Architectural AI

Gropius’ seminal work: prefabricated housing units were assembled on site with cranes, while the irregularity of the resulting assembly patterns created a vast array of different conditions across the development. With Habitat 67, Safdie achieved a singular combination, bringing together both the affordability of standardized modules and 4. M. Safdie, “For Everyone a Garden”, MIT Press, 1974.

the richness of countless variations across his design4. The influence of the modular principles would also impact the work of theoreticians at other scales. In the 1960s, Archigram's “Plugin City”

5. S. Sadler, “Architecture Without Architecture”, MIT Press, 2005.

envisioned a modular metropolis5. Through the constant assembling and dismantling of modules installed on a three-dimensional structural matrix, cities could experiment with the possibility of modular growth. These principles, however, would rapidly exhibit obvious limitations. To restrict Architecture to a simple assembly of modules aligned on a rigid grid too often reduced the practice to a narrow definition. In many instances, architects could not resolve themselves to merely act as the assembler of predefined design systems, abiding by stringent rules and processes. Moreover, the modular production too often proved to be quite monotonous, while the early systems of assembly eventually revealed real constructive weaknesses. For these reasons, architects’ fascination for modularity, under its initial definition, would gradually fade away throughout the 20th century. It was, however, to have a profound effect on the architectural discipline, establishing a new rational mindset among practitioners, and a certain eagerness to envision buildings as actual systems. As a lasting testimony of this period, the concepts of grid, module, and assembly still today deeply irrigate some of Architecture’s core principles.

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Modularity

References & Resources

The Modulor I & II

Le Corbusier’s Modulor system

Le Corbusier, Harvard University Press, 1954

R. Meier’s Interview, 2017

Towards A New Architecture

The New Architecture and the Bauhaus

Le Corbusier, J. Rodker Publisher, 1931

W. Gropius, MIT Press, 1965

Archigram’s Plug-In City

Buckminster Fuller’s Dymaxion House

VDF, Dezeen, 2020

1940s Futuristic Architecture, 1946

The Dymaxion World of Buckminster Fuller R. W. Marks, S.I. University Press, 1960

Gropius & The Dessau Bauhaus Architecture Collection, ARTE

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The Advent of Architectural AI

Computer-Aided Design

At the turn of the 1980s, the rapid increase of computing power and the availability of new hardware (microprocessors, memories, computer networks, etc.) triggered the advent of multiple design software programs relevant to architectural design. “ComputerAided Design” (or “CAD”) — as this generation of software will later be named — was to significantly impact the practice of Architecture.

6. W. E. Carlson, “A Critical History of Computer Graphics and Animation”, The Ohio State University, 2005.

7. I. Sutherland, “Sketchpad: A manmachine graphical communication system”, Simulation, 2(5), pp R-3. 1964.

4 Ivan Sutherland and SketchPad,1963.

In reality, reflections on the potential of CAD began as early as the mid-1950s within certain engineering firms. In 1959, the American computer scientist and businessman Patrick Hanratty released PRONTO6, the first CAD prototype, developed for designing engineering parts. The possibilities offered by this software marked the beginning of significant research efforts on the topic. Shortly thereafter, in 1963, the American computer scientist Ivan Sutherland created SketchPad7 (Fig. 4), one of the first truly accessible, ergonomic, and simple CAD programs. Working at the Lincoln Laboratory of the Massachusetts Institute of Technology (MIT), Sutherland designed a software that not only allowed for the precise 2D drafting of technical elements, but also offered a streamlined

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Computer-Aided Design

4 and intuitive interface for designers. With the use of a pencil and extremely simplified controls, SketchPad gave drafters an unprece8. P. Bézier, “Essai de définition numérique des courbes et des surfaces experimentales”, PhD diss., these Doctoral d’Etat es Sciences Physiques, 1977. 9. P. Bézier, “Example of an existing system in the motor industry: the Unisurf system”, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 321(1545), pp 207-218, 1971.

dented level of comfort and flexibility. From 2D drafting to 3D modeling, CAD made a leap forward in France, thanks to the work of mathematician and computer scientist Pierre Bézier. Bézier’s work on complex curvatures8 enables drafters to draw increasingly challenging 3D shapes using computers, offering a new momentum to CAD software. Released in 1966, Bezier’s UNISURF9 software was used by the car manufacturer Renault to model the shape of certain prototypes. This sudden leap forward did not limit itself to automotive design, but would have a lasting influence on design software across many other fields.

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The Advent of Architectural AI

Thanks to Sutherland, Hanratty, Bézier, and many others, CAD increasingly stood as a new field of research in its own right. At the same time, CAD was being massively deployed across industries, and Architecture was no exception. At its core, this generation of software allowed the creation and edition of primitives — simple geometrical shapes —, their aggregation and sorting using concepts such as blocks, groups, and layers, to finally output the results under various formats, digital or printed. Besides their obvious contribution in speeding drafting tasks, CAD programs imposed a specific structure to the design process. Drawings were systematically organized across layers, blocks allowed for a certain replicability of module-like groups of shapes, and geometries would be tagged with consistent properties. Through its various conventions, CAD propelled in Architecture a new way to rationalize and systematize the drafting process. In parallel to this gradual dissemination, the work of the CAD pioneers would inspire a generation of computer scientists and architects to 10. N. Negroponte, “The Architecture Machine”, MIT Press, 1970. 11. N. Negroponte, “Toward a Theory of Architecture Machines”, Journal of Architectural Education, Vol. 23, No. 2, pp 9-12, 1969. 12. A reconstituted demo of Urban 5, created by Erik Ulberg, can be accessed at the following URL: https:// c0delab.github.io/ URBAN5/

take more speculative and experimental directions. The Architecture Machine Group (AMG) at MIT, led by Greek-American computer scientist and professor Nicholas Negroponte, is perhaps one of the most significant examples of this period. Negroponte's book, “The Architecture Machine” (1970)10, encapsulates the essence of the AMG's mission: to investigate how computers might improve architectural design in the decades to come. The Urban 2, and later the Urban 511, projects allowed him to demonstrate the potential of CAD specifically in its application to Architecture12, even before the industry had taken this path. Throughout AMG’s projects, researchers investigated the potential interface between computers and designers, as well as the organization of future CAD programs.

5

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The Advent of Architectural AI

From now on, architects and industrialists would increasingly embrace CAD software in its various forms, and sometimes even innovate themselves. In this respect, the initiative of the American-Canadian architect Frank Gehry would pave the way for the following decades. For Gehry, the use of computers applied to architectural design should considerably relax the limits of assembly systems and allow for new forms and geometries in his designs. In 1989, businessman Jim Glymph teamed up with Gehry to initiate the use 13. D. Narayanan, “Gehry Technologies, a Case Study”, 2006.

5

of Dassault Systemes' main computer-aided design and manufacturing (CAD/CAM) software, CATIA, to solve the extreme geometric complexity of some of their projects13. Among many designs, the Walt Disney Concert Hall in Los Angeles (Fig. 5) remains an

The Walt Disney Concert Hall, Frank Gehry, 2003.

iconic example of their success that would set a lasting precedent

14. Haymaker & Fischer, “Challenges and Benefits of 4D Modeling on the Walt Disney Concert Hall Project”, 2001.

Between the 1980s and 2010, the growth of data storage and

demonstrating the value of 3D CAD to architects14.

computing capabilities, combined with their drastic cost decrease, facilitated the development and adoption of CAD software, such as CATIA (1982), AutoCAD (1982), Vectorworks (1985), and many others. Architects widely adopted this new design method as it allowed for the rigorous control of complex geometrical shapes, facilitated collaboration among designers, enabled more iterations than traditional hand-sketching, and limited resulting costs. For all these reasons, CAD gradually became an industry standard. However, as architects embraced this software, obvious limitations arose. The repetitiveness of drafting tasks, the lack of control over certain shapes and the difficulty in specifying complex design rules prompted the industry to start looking elsewhere for complementary technologies.

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Computer-Aided Design

References & Resources

SOFTWARE Show Catalog The Jewish Museum, New York, 1970

The Unisurf System

P. Bézier, 1971

Ivan Sutherland & Sketchpad

Sketchpad, a Thesis at MIT Lincoln Lab

MIT Science Report, 1963

I. Sutherland, 1963

CAD systems

CAD Lab at MIT

MIT, Architecture Machine Group, 1976

MIT Department of Mechanical Enginerring, 1982

Frank Gehry uses CATIA

The Case for Process Change by Digital Means

2011

D. R. Shelden, AD, 2006

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The Advent of Architectural AI

Parametricism

“Parametric modeling” refers to a design methodology that would gradually be integrated within mainstream architecture software (Rhino, Revit, etc.). Besides the manipulation of sketches using standard geometric editing tools, this methodology lets designers specify explicit rules as an alternate way of designing buildings. In reality, the use of such rules long predates the arrival of parametric tools in Architecture; either through the early work of certain architects or because of experiments realized within specific software in the second half of the 20th century. Already, in the early 1960s, the emergence of parametric architecture had been announced by the Italian architect Luigi Moretti. His project, the Stadium N, constituted an early demonstration of 15. L. Moretti, “Parametrica Architettura”, Dizionario Enciclopedico Di Architettura e Urbanistica. Istituto Editoriale Romano, 1968.

parametric modeling’s potential15. By defining nineteen parameters, Moretti formulated a precise procedure, as a set of mathematical equations, directly responsible for the final shape of the structure. Each variation of this parameter set could yield a new shape for the stadium. Moretti’s resulting design not only offered a convincing proof of concept at the time, it also anticipated parametric modeling’s upcoming aesthetics.

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Parametricism

In the meantime, the development of experimental design software will facilitate early experiments on the same topic. Besides its CAD interface, Ivan Sutherland’s SketchPad (1963), mentioned earlier, already formulated certain parametric features. At the heart of this tool, 16. I. Sutherland, “Sketchpad: A manmachine graphical communication system”, Simulation, 2(5), pp R-3. 1964.

the notion of “atomic constraint”16 represented a translation of Moretti's idea of parameter. For any sketch made in SketchPad, each geometry would be translated for the machine into a set of atomic constraints or, in other words, variables accessible to the user. Not only could the designer modify these parameters, but the underlying set of relationships could be changed, giving to the end-user the ability to set both the design rules and their different inputs. In retrospect, SketchPad appeared as a precursor to most parametric design tools later invented throughout the industry. Twenty-five years after Sutherland’s thesis, Samuel Geisberg, founder of Parametric Technology Corporation (PTC), launched Pro/ENGINEER (1988), the first software program to provide users with complete access to geometric parameters. As the software was released, Geisberg perfectly summed up the parametric ideal: “The goal is to create a system that is flexible enough to encourage the engineer to easily consider a variety of designs. And

17. J. Teresko, Industry Week, December 20, 1993.

the cost of design changes should be as close to zero as possible”17. Geisberg’s assertion corresponded to a key concern addressed by parametric modeling: the ability to rationalize shapes into strict rules, to allow for fast and reliable design explorations. This very characteristic would in fact explain parametric modeling’s success and dissemination across the industry over the following decades. These early experiments demonstrated to the discipline the potential relationship between architectural design and its parameterization.

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The Advent of Architectural AI

In the footsteps of Sutherland and Geisberg, a new generation of “parametric” architects could finally flourish. Among many efforts to digest parametric modeling’s implications for Architecture, Patrik Schumacher, a German architect and collaborator at Zaha Hadid Architects (ZHA), attempted to provide a unified theory. For him, the discipline was gradually “converging” towards what he called “Parametricism”, understood as a design technique, but also as a distinct architectural style. In his manifesto, “Parametricism, A 18. P. Schumacher, “Parametricism: A New Global Style for Architecture and Urban Design”, AD Architectural Design - Digital Cities, Vol 79, No 4, 2009.

New Global Style for Architecture and Urban Design” (2009)18, Schumacher laid down the core principles of this new movement. Besides the discussion on its theoretical framework, parametric modeling would rapidly find a more visible manifestation through the work of key architecture offices, such as ZHA. Zaha Hadid, an Iraqi-British architect and urban planner, who trained as a mathematician, grounded her practice early on in the intersection of Mathematics and Architecture. Her work, such as the master plan for the Kartal

6 Master Plan for Kartal Pendik, Zaha Hadid Architects, 2006.

Pendik neighborhood in Istanbul (Fig. 6), would often be the result of rules encoded directly into the program, allowing an unprecedented level of control over building geometry. Throughout her work, many architectural decisions were formulated into parametric procedures whereby key variables drove the resulting design. The distinct organicity of Hadid’s work was in part due to this encoding methodology. Her project’s organic appearance remains to this day the signature of both her own style and, more generally, Parametricism’s. Parametric modeling’s adoption would in fact accelerate as the development of visual programming platforms took off. Behind ZHA's work for instance, Grasshopper, a program developed by computer

50

6

Parametricism

scientist David Rutten in the 2000s, significantly enabled Hadid’s design process. By using a simple graph-like interface, Grasshopper allows for the encoding of design rules. In the software, geometrical objects, functions and their associated parameters are woven together into sequential procedures. Thanks to this tool, architects get simplified access to programming logics, without the complication of learning

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The Advent of Architectural AI

any specific programming language or engaging with the hassle of code development. Nowadays, the simplicity of the interface, combined with the relevance of its multiple features and built-in components, allow Grasshopper to be an essential tool for an entire

7 Grasshopper, Visual Progamming Software, 2018. 19. D. Rutten, “Computing Architectural Concepts: Grasshopper Stories”, Lecture at the AA School of Architecture, 2010.

generation of designers. Evidently, Grasshopper (Fig. 7) built upon Sutherland’s and Geisberg’s intuitions, while opening even more the back door of design software19. Using Grasshopper, the design process could effectively reach an entirely new level of systematization: it could now be conceived more programmatically, as designers invest part of their design time in the formulation of Architecture’s underlying rules, their replicability and applicability at scale. As visual programming interfaces quickly spread across the industry, such a mindset shift was to accompany their deployment. Beyond Grasshopper and its contributions to the profession, another revolution, initiated in the early 2000s, would be timely deployed to leverage the concept of parameter: building information

20. Autodesk, “Building Information Modeling, White Paper”, 2002.

modeling (BIM)20. BIM’s intent is to document and manage the vast quantity of meta information tied to building forms (quantities, materials, specifications, properties, etc.). At the same time, within any BIM software, such as Revit or ArchiCAD, architects can manipulate objects rather than simple geometric primitives. Besides their respective shapes, objects carry their own set of properties and behaviors relative to other objects. While CAD drawings are

21. C. Eastman, P. Teicholz, R. Sacks, K. Liston, “BIM handbook : a guide to building information modeling for owners, managers, designers, engineers and contractors”, Wiley, 2011.

representations of the building, BIM models aspire to offer actual digital replicas of buildings and their respective systems21. This semantic enrichment heavily relies on the management of parameters and on the existence of underlying rules for each element, each family of objects, etc. From Revit to Sutherland’s SketchPad,

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7

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The Advent of Architectural AI

these practices in their diversity followed a common thread: the explicit use of parameters as design drivers. However, since the 2010s, the parametrization of architectural design seems to have been running out of steam, both technically and conceptually. Several factors contribute to this situation. First, with these techniques, concerns about strict efficiency too often take precedence over the imperative of space organization, style and more implicit considerations vital to the discipline. Then, Architecture requires the exploration of broad design spaces. Unfortunately, parametric modeling often fails to capture this reality. Although an improvement over previous methodologies, the variety of generated design options often remains too narrow. Finally, finding the right balance among parameters can be a complex and computationally expensive exercise that often defeats the initial purpose of parametric design. Moreover, and independently of these technical shortcomings, parametric modeling is based on a theoretical premise questioned by some: the important properties of a building could be described using a fixed set of explicit parameters, directly encoded using rigid design rules. In reality, certain essential architectural concerns (sociological, cultural, stylistic, etc.) cannot be as explicitly formulated, putting parametric modeling at odds with certain key aspects of Architecture. AI, the fourth stage of this chronology, is eventually poised to improve over certain limitations of parametric modeling. Its encounter with the architectural profession is a decisive turning point, which the last sixty years have been gradually preparing for.

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Parametricism

References & Resources

An “Other” Aesthetic: Moretti’s Parametric Architecture

Parametricism, A New Global Style for Architecture and Urban Design

A. Imperiale, Log, 2018

P. Schumacher, 2008

Parametricism as Style

The Kartal Pendik Masterplan

P. Schumacher, 2008

Z. Hadid Architects, 2015

A History of Parametric

The Challenges of Parametric Modelling

D. Davis, 2013

D. Davis, 2013

The Future of Making Buildings

Digital Culture in Architecture

P. Bernstein, TEDxYale, 2015

A Lecture by A. Picon, 2013

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The Advent of Architectural AI

Artificial Intelligence

AI’s relevance to Architecture was initially anticipated by a few theorists, who foresaw its potential early on. These precursors would initiate a discussion within the discipline on various aspects of AI’s future contributions. A short glance at some of these milestones can help better grasp the direction taken by current developments. Nicholas Negroponte was to initiate the reflection. His work in the 1970s focused specifically on the notion of interaction with “intelligent” machines. He first introduced the concept of “machine assistant” in his work at the MIT Media Lab’s Architecture Machine Group (AMG) with the aforementioned Urban 2 and 5. These programs were initially designed to help architects draw floor plans by adapting room layouts in order to optimize adjacencies and lighting conditions, while constraining the sketch to fit onto a modular grid. Besides providing an early expression of CAD, Urban 5 investigated the very notion of complementarity between the 22. N. Negroponte, “The Architecture Machine”, MIT Press, 1970.

designer and an “intelligent” agent22. To that effect, the software played off the interaction between two distinct layers of information: the machine handled an array of implicit rules, while the user was in charge of specifying given explicit parameters. Urban 5’s repartition

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Artificial Intelligence

of tasks translated the “machine-human” complementarity desired by Negroponte. With this project, Negroponte put forward a new distribution of contributions between computers and architects. For instance, when users would place elements on the canvas, Urban 5 would issue warnings if clashes were detected. “TED, TOO MANY CONFLICTS ARE HAPPENING” would get flagged if blocks did not coincide. The machine could also suggest rough layouts, letting users tune and adapt them later. Negroponte’s work assigned to computers a more active role in the conception process, beyond the simplicity of other CAD research of the time. His work helped clarify and demonstrate the type of interaction architects could expect from “intelligent” design programs for the foreseeable future. Around the same period, Negroponte’s British counterpart, Cedric Price, investigated another facet of AI: the principle of autonomy.

8 Detail view of the working electronic model of the Generator project, between 1976 and 1979.

To that effect, in 1976, Price – then Professor of Architecture at Cambridge University – invented the Generator (Fig. 8)23. With this project, initially conceived as a proposal for the Gillman Corporation, Price explored the concept of the self-adapting building. In the project, a floor plan, organized as an orthogonal grid, allowed for a

23. S. Hardingham, “Cedric Price Works 19522003, A Forward-minded Retrospective”, pp. 447-470, AA Publications, 2016.

system of partitions to be constantly modified. A computer was re-

24. Furtado et al., “Cedric Price’s Generator and the Frazers’ systems research”, Technoetic Arts: A Journal of Speculative Research 6, no. 1, 2008.

machines as autonomous design agents24. The Generator forecast-

sponsible for offering new partitioning layouts, either to adapt the plan to the users’ behaviors, or spontaneously, as a way to trigger new conditions. At its core, Price’s work addressed the potential of ed, very early on, how AI could find its place within architectural software, while playing a specific role in the design process. Both Price’s work and Negroponte's research have shaped the discussion in Architecture around the topic of AI. As explained in the first chapter

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The Advent of Architectural AI

of this book, AI itself has significantly improved since these early experiments. Price’s and Negroponte’s intuitions find today a new echo: no longer are these convictions limited to a handful of isolated research projects. On the contrary, the increasing affordability and accessibility of AI bring these considerations back to the center of the discussions in Architecture. The last decade has indeed seen a sharp increase in AI’s dissemination across the architectural field. At this point, estimating its current presence remains a challenging exercise, since the AI scene in Architecture seems as diverse as it is recent. To mention only a few manifestations, we first notice a significant increase in publications and applied research projects on the topic across the field. Only browsing through the wealth of published papers and conference proceedings produced over the past decade is sufficient to signal the importance this subject has taken in academia; an importance even echoed by the 2021 Architecture Biennale through the many talks, panels and keynotes engaging with this topic. Then, turning to the state of mainstream software, the gradual inception of AI capability has brought these technologies closer to architects. The addition of generative design features to Revit for instance, or the multiplication of Machine Learning libraries for Grasshopper, represent as many opportunities for practitioners to engage with this technology. In addition, a new generation of lighter design tools has recently surfaced. Mostly browser based, they offer cheap and simple access to AI-based design tools. Spacemaker, Archistar, Delve, XKool, CoveTool are only a few examples of this recent web app ecosystem. More interestingly, a growing number of architects are today being trained to understand, craft

8

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The Advent of Architectural AI

and use this new technology. Throughout colleges and universities, a growing number of workshops, classes, or even degrees offer to prepare architects to engage with AI. Finally, the influence of AI’s momentum in other fields (engineering, computer science, etc.) is to be taken under consideration. As this technology yields promising results across these industries, certain AI applications are being transposed and repurposed to match the architectural agenda. This cross-pollination comes today from fields as varied as self-driving cars or image recognition, where state-of-the-art AI research is being conducted and is often open sourced. In this sense, Architecture benefits today from a broader cross-disciplinary research effort, providing the discipline with many off-theshelf technological solutions. As a matter of fact, AI’s emergence in Architecture leaves us with a brand new fragmented landscape of applications, theories and actors that has not yet crystallized into any single definition. This chronology, therefore, remains as open-ended as the spectrum of potential scenarios we are today facing. To reflect this reality, the following chapters offer to present some of the most important facets of this emerging phenomenon. A first segment will lay down various concepts and definitions to understand some of AI’s most essential technical underpinnings. A landscape of existing experiments and applications will then present AI’s tangible applications to various architectural scales. A collection of articles will finally provide a snapshot of the diversity of current discussions. This dual lens, halfway between application and theory, hopes to convey and reconcile the heterogeneity of AI’s manifestations in Architecture into a comprehensive corpus.

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References & Resources

The Architecture Machine

Being Digital

N. Negroponte, MIT Press, 1970

N. Negroponte, Alfred A. Knopf, 1995

Houses that know the people who live in them

Cedric Price Archive

N. Negroponte, 1975

Canadian Center for Architecture, 1959-95

Information Archaeologies

Soft Architecture Machines

Molly Wright Steenson on Cedric Price’s Generator Project, CCA

N. Negroponte, MIT Press, 1975

Architectural Intelligence

The Creativity Code

M. W. Steenson, Talks at Google, 2018

A Lecture by M. du Sautoy, 2020

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AI’s Deployment in Architecture

An Experimental Perspective

62

Looking back at History, AI’s presence in Architecture appears as the result of a slow maturation. However, the past decade has witnessed the sharp acceleration of this momentum. The recent results of research and the wealth of current applications across Architecture’s different scales together provide tangible signs of AI’s gradual dissemination in the field. This chapter attempts to contemplate the landscape of ongoing applications. It first begins by laying down a simplified definition of AI’s various facets. Rather than diving into any technical depth, the following pages intend to set the stage in accessible terms. The following segment, then, showcases some of AI’s recent contributions to Architecture. Either at different scales, or for various tasks, current projects developed at the intersection of both fields already bridge the gap between research and practice. Although these results provide a snapshot of the state of current investigations, meant to evolve and mature, they prefigure a promising future for AI in Architecture.

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AI's Deployment in Architecture

Artificial Intelligence 101

Since its early formulation in 1956 at the Dartmouth Workshop, AI has taken different forms and matured in its definition. The past 60 years have seen a vast diversity of approaches, all aiming at translating the initial vision into functioning technologies; consequently, AI has today blossomed into multiple distinct categories. As the rapid development of research projects often outpaces the effort to exhaustively map out AI’s ecosystem, this categorization is in fact at the center of intense

1 A brief categorization of AI’s diverse fields of investigation.

debate. Therefore, if the following figure (Fig. 1) offers a simplified classification, it will certainly evolve within the coming decades.

Artificial Intelligence

Machine Learning

Supervised Learning

Expert Systems

Robotics

Unsupervised Learning

Computer Vision

Natural Language Processing

Reinforcement Learning

1

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Artificial Intelligence 101

At this stage, however, the sole purpose of this classification is to position AI’s latest developments, gathered under the flagship name of “machine learning”, within AI’s broader ecosystem. Machine learning encapsulates different models that share a few commonalities, setting them apart from other computational paradigms. On the one hand, machine learning describes the bottom-up acquisition of features through repeated observations. In simpler terms, machine learning models can approximate a phenomenon through an iterative exposure to vast quantities of data. This process, called “learning” — or “training” — corresponds to a tuning phase, during which the model will either succeed or fail at capturing some of the observed phenomenon’s complexity. Once trained, the model can be used to predict or mimic the same phenomenon under new settings or different parameters. On the other hand, machine learning operates a pivot: by embracing an observational approach, this methodology takes its distance with descriptive technics. A canonical example will both illustrate and clarify this reality: the mathematical modeling of water’s boiling point. It is common knowledge that the physical state of water depends on its temperature and ambient pressure. Rather than taking a descriptive approach by formulating the equation tying together temperature and pressure, AI would instead browse through collections of data points called “observations”, to approximate the same phenomenon. Such observations can be obtained by repeatedly recording pairs of temperature and associated pressure values, at different moments of water’s heating process. During the learning phase, an AI model will try to improve its ability to predict

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AI's Deployment in Architecture

the temperature of the boiling point given the ambient pressure, using a feedback loop mechanism. To that effect, all along its training, the model compares its estimates to the actual expected values present in the data. Faced with a residual difference between both, the model tries to recalibrate itself until it reduces this gap as much as possible. Training finishes when the user believes the machine has sufficiently well acquired the “mapping” between a variable and an “objective value”. In other words, this learned mapping can be conceived and visualized as the model’s gradual attempt at fitting a curve best describing the distribution of observations (curves in Fig. 2). If the above example remains fairly simple, it illustrates the

2

Trai ning 30%

Train ing 7 0%

tart

Train ing S Pressure

Train ing En d

broader idea behind machine learning-based technics: iteratively

Example of a machine learning model’s gradual attempt at matching the distribution of water's temperature/ pressure data points.

2

Temperature

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Artificial Intelligence 101

inducing certain characteristics found among the observed data while encapsulating these approximations into a trained and accurate-enough model. Finally, it is worth noticing yet another specificity of machine learning: the user’s control over the computation. With descriptive technics, like the ones Architecture is used to with parametric modeling, the user is entirely responsible for formulating the steps taken by the computation and its associated parameters. With machine learning, however, if the model’s architecture is at the user’s initiative, the tuning of the parameters – and even, for certain models, the very definition of these parameters – happens within the model itself. Users retain control through a handful of high-level settings, also called “hyperparameters”, allowing them to guide the general direction of the learning process. Neither a “white box” – a fully controllable algorithm –, nor a “black box” – an airtight model leaving no control to the end-user –, ma1. A. Witt, “Grayboxing”, pp 69-77, Log #43, 2018.

chine learning stands as a “gray box”1 in the computation landscape. This expression, coined by Andrew Witt, rounds up the description of the balance that machine learning strikes between control and computational complexity; with this technology, when the growing intricacy of the models enables the approximation of ever more challenging problems, the legibility and the interpretability of its deeper computation can sometimes fade away. In machine learning, therefore, users constantly work along a threshold between interpretability and complexity, striving to keep an adequate level of transparency of their models, while leveraging the power of their complex architectures.

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The Mosaic of Machine Learning Machine learning is a field rich in many recent breakthroughs and initiatives. These applications span across industries, from speech recognition to image synthesis, all the way to robotics. Consequently, the variety of existing approaches that populate this domain is both a chance and a source of confusion for the general public. If a comprehensive categorization of machine learning remains a tedious task, a few concepts can help organize a mental map of ongoing investigations; learning strategy, model architecture and performed task are three different lenses, enabling us to draw a somewhat simplified classification. Machine learning is commonly divided into three subcategories, depending on their respective training strategy: supervised, unsupervised, and reinforced. Supervised learning investigates the application of machine learning to the mapping of known input-output data pairs. The boiling water example explained earlier typically illustrates such a learning process: the emulation of a mapping, using labeled data. Unsupervised learning, on the other hand, attempts to model patterns found among observed data, without having any specified output values. In other words, the data presented to the algorithm is not labeled; it is then up to the model to discover trends happening within this raw stream of information. Reinforced learning, finally, refers to a third way of conceiving the training process: this time, rather than consuming preexisting data pairs, the model simulates a sequence of steps, from which it collects rewards; on this ground, it recalibrates its behavior to improve its score further. Over time, the

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model “reinforces” its ability to perform, as a reaction to its quantified accuracy across a broad sequence of steps. Beyond these three distinct training strategies, families of models can also be sorted according to their respective architecture. This reality corresponds to the internal structure of algorithms themselves. From this standpoint, a different and more granular categorization can be achieved: deep neural networks, support vector machines, Bayesian networks, etc. Each family, through its architecture, performs learning differently. However, all families build upon the same concept of artificial neural networks and therefore share basic common principles worth detailing. Artificial neural networks (ANN), as used in most models today, directly stem from the early architectures detailed by McCulloch &

3 Simplified schema of an ANN's architecture.

Pitts, Rosenblatt and others. With ANNs (Fig. 3), computation is conceived as the byproduct of a distributed and diffuse process, as artificial networks aim at mimicking the human brain’s processing of information. Artificial neurons, containing their own parameters – also called weights – are nested into layered architectures to form entire

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networks. The architecture of these networks can vary greatly, as users can modify the number of neurons, layers, training parameters, and other settings to adapt the model to specific tasks. Layers can also be specialized to perform specific tasks such as filtering, activating, normalizing, or pooling information: as many possibilities that express how much ANNs can display a vast diversity of potential architectures. During the training of an ANN, data flows through its network, while the neurons’ weights are gradually tuned, using a feedback loop mechanism. Learning proceeds in fact as a simple repetitive back-

4 Training an ANN: feedforward and backpropagation.

and-forth (Fig. 4): first, the computation flows from input to output, in a process called “feedforward”. Then, as this computation reaches the end of the network to produce a prediction, the result’s accuracy is assessed, triggering a corrective feedback loop also called “backpropagation”. This time, the information flows in the opposite

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direction through the network, while assigning a correction to certain neurons. With ANNs, feedforward and backpropagation get repeated multiple times so as to gradually tune the network and increase its general accuracy. This simple mechanism today powers ANNs used across countless research projects, from basic investigations to larger deep learning experiments. Considering the end task performed by a model can finally provide an alternate way to sort existing machine learning-based technologies. To name only a few, this book will illustrate in upcoming chapters: convolutional neural networks (CNN), graph neural networks (GNN), generative adversarial networks (GAN) and variational auto-encoders (VAE) represent a non-exhaustive list of machine learning architectures, tailor-made for specific applications and data formats. Convolutional neural networks (CNNs) are an essential category, whose recent developments have profoundly changed the course of machine learning. These architectures have been crafted for the treatment of visual imagery. The notion of “convolution” is key to their success; using a 2D patch of parameters called “kernel”, slid across input images, convolutions are a better fit to process visual data than standard neurons found in ANN architectures. Convolutions are at the core of image recognition technologies, video feed analysis, and numerous other applications; ImageNet, a groundbreaking research project from the 2010s presented in a previous chapter, is in fact built on this specific technology. Graph neural networks (GNNs) are another avenue of research within machine learning. Their purpose is to allow the processing

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of graph data. Various topics necessitate working with topological information; that is, data formatted as graphs, collections of nodes and connections, where the 2D or even 3D layout in space of these architectures is key. Molecules, structures, even architectural programs can be represented using graphs, and GNNs have been developed to better parse such complex topological information. Other architectures use a combination of multiple models to perform even more challenging tasks. In this respect, generative adversarial networks (GANs) are a recent revolution that still brings meaningful results today. GANs focus on the generation of data across multiple formats (images, graphs, etc). Their architecture is first theorized by Ian Goodfellow in 2014: in order to synthesize images, GANs use two competing models, a “generator” and a “discriminator”, to steer the

5 Architecture of a standard GAN model.

learning process (Fig. 5). Given a database of images, for instance, the discriminator works on improving its ability to recognize the data, while the generator works on creating synthetic images. At the same time, the discriminator is used to provide feedback to the generator on the quality of its output images.

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This back-and-forth between the generator and the discriminator allows for the progressive improvement of image generation throughout the training phase of a GAN model. This technology represents one of this decade’s most significant breakthroughs: it is a drastically different approach to the very concept of learning, building upon the feedback between two agents, rather than the selfcorrecting loop of a single model. It is also a leap forward regarding the quality of the results. Another technique, finally, tackles a similar task: variational autoencoders (VAEs). They offer an alternative way of using AI for generating information in various formats. This model approaches learning as a process of synthesizing information: with VAEs, learning is conceived as a task of condensing information so as to extract the essential features, before decompressing it back into its initial form. To that effect, VAEs combine two distinct models: an “encoder” and

6 Typical architecture of a VAE model.

a “decoder” (Fig. 6). The first one abstracts the data by compressing it while keeping some of its essential dimensions. The decoder then unpacks the information by bringing it back to its initial format. As it performs this decompression, the decoder can generate variations of the modeled phenomenon. In other words, VAEs can emulate

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a given phenomenon by generating multiple different versions of it. This ability of VAEs to model and render diversity found in the data constitutes their “generative” potential. Over the past few years, VAEs have found application for instance in certain creative fields, such as furniture design, fashion, photography, architecture, and others, providing in each domain large quantities of design options.

The Latent Space To complete this short introduction to some of AI’s most fundamental concepts, touching upon the notion of latent space, even briefly, remains essential. In short, the latent space is a continuous domain, sitting at the heart of most AI models today. It encapsulates a compressed and simplified representation of the data presented to the model during training. At this point, it is worth mentioning a few of its most important characteristics. First of all, if an AI model is properly trained, then each dimension of its latent space will correspond to a feature of importance of the observed data. These dimensions will ideally also be independent of one another. Then, it is crucial to note that the feature each dimension respectively encodes its not directly at the user’s discretion, but rather gradually defined by the model during training. Finally, in latent space, similarity is translated into proximity: in other words, things that look the same are close to one another in this n-dimensional domain. Looking at an example will clarify the latent space’s behavior and relevance. If a model were trained to generate images of characters from various fonts, its latent space would capture the different features of font making. Among many

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of them, italic, size and thickness are criteria that the model could pick up and assign to specific dimensions  (Fig. 7). Fonts would be

7 Diagram of a “walk” in latent space, and the sampling of three distinct font styles.

placed in latent space with respect to these dimensions. A “walk” in latent space, meaning the fact of choosing points along a path in latent space, would yield different fonts as the model’s output (here [1], [2], and [3] in Fig. 7). More interestingly, the balance of features in the generated fonts would be consistent: [2], selected between [1] and [3] would return a font blending together the properties of [1] and [3]. The richness of the features that the latent space can capture, and the legibility of its structure, providing an easy-to-navigate n-dimensional map, makes it both a powerful tool to control the generation of complex designs and a domain of investigation in itself. The following chapters and articles will illustrate and explore these characteristics in more depth.

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8

For designers, the latent space can serve two complementary yet distinct purposes; namely, imitation and exploration. Expanding on the above example, Figure 8 and 9 display results of letters generated using the latent space of a model trained to that end. The precision of

8 Imitation: letters with somewhat regular font styles, generated by sampling the latent space of a trained model.

the images in Figure 8 shows how realistic this “imitation game” can be. Rather than offering unique designs, this replication can serve designers punctually, by providing adequate proposals across various contexts. However, another reality, maybe more immediately relevant to designers, is the possibility of exploring the formal richness lying in the “margins”. In between the rigid categories of fonts, for instance,

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9

a wealth of hybrid styles can be harvested. Figure 9 presents results

9 Exploration: letters displaying hybrid font styles, generated by sampling the latent space of the same model.

selected across the same latent space and leaves us with a vastly different impression: by sampling specific moments of the latent space, the collection of generated types challenges expected classifications and typologies. The letters obtained blend together features picked up from different fonts while merging them into new designs. Far from merely replicating fonts, this exploration unveils alternative font styles and letters, derived from the initial training set. In that way, AI at times can become a source of inspiration, and a tangible tool set, assisting practitioners in their search for new designs. 

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From AI to Architecture The architectural discipline has had the opportunity for the past few years to benefit from the accelerated development of AI. Models, conceived in other fields, for different applications, have been used and repurposed by architects and researchers across various use-

10 Typical pair of input-output images, taken from a training set. Footprint

cases. The complexity of certain concepts and tasks in Architecture offers multiple potential avenues of exploration for the different technologies presented earlier. With the aim to evidence this reality, and in order to bridge the gap between a high-level understanding

Entrance

of AI and the tangible reality of Architecture, the following example

Window

will provide a didactic demonstration. In this experiment, an AI model

Corridor

is taught to arrange rooms within a predefined apartment footprint,

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while respecting the position of the entrance door and that of the

Bathroom Closet

facade windows. Using a database of image pairs (Fig. 10), the model

Living room

progressively learns the mapping from one situation to the other,

Kitchen

from an empty footprint to a fully programmed apartment floor plan. To evidence the gradual acquisition of this task by the model, Figure

11 Typical training sequence.

11 displays results obtained all along the training phase. Each image corresponds to an attempt by the model at organizing the space for

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an input footprint, given its current learning stage. From the first steps of the learning process (top left corner of the figure) all the way to the last hours (bottom right corner), the synthesized image quality gradually increases. Figure 12 displays four snapshots, taken at four distinct moments of

12 Four snapshots, sampled at various moments of the training process displaying the model’s gradual improvement over time.

the training. They provide a clearer overview of the model’s gradual improvement over time. From Image [A] to Image [D], a progressive improvement of the space layout can be noticed. The first attempt ([A]) only emulates the footprint of an apartment. Then the notions of facade and program slowly emerge ([B]), without any spatial coherence yet. Later ([C]), the model acquires the principle of space enclosure, as partitions between rooms are almost systematically added, and the adjacencies between them become clearer.

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12 Finally, once the training is completed ([D]), the model offers a floor plan that seems to take into account basic space layout rules: facade openings, almost valid adjacencies between rooms, initial space partitioning, etc. Although it represents a major improvement over previous generative methods, this process

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is not free of obvious limitations. First of all, the generated plans do not qualify as valid architectural floor plans as such: obvious flaws are still noticeable (aspect ratio of given rooms, relevance of certain adjacencies, etc), while these solutions only answer to formal criteria; matters of contextual relevance are set aside in this particular example. These generated floor plans however can constitute a first draft, an initial proposal, to be corrected and augmented by the architect. These images can act as initial options, meant to nourish the design process early on. Additionally, the resulting relevance of the generated forms also greatly depends on the quality of the data provided to the machine during its training. This consideration is yet another reminder of the importance of our expertise to both train and feed such models. Finally, AI is not free of its own bias, inherent to its learning strategy. In simpler terms, a trained model might have captured certain assumptions found among the data. Since the learning process is not entirely transparent to the end user, such bias can go unnoticed. Part of the difficulty of training AI models lies in our ability to detect these biases and correct training accordingly. However, for all these challenges, the above example still sets the stage for AI’s contribution to Architecture. Beyond its didactic purpose, it shows the tangible results that this technology can bring when applied to problems specific to Architecture. In the following pages, this chapter will cover other use cases and experiments, at many different scales, and present a curated landscape of AI’s potential for the discipline.

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Urban Scale

The study of the urban condition today is a thriving area of research and experimentation. Yet the layered intricacy of urbanization patterns confers a deep complexity to this topic. Although a wide diversity of frameworks have already offered comprehensive methodologies to describe the ramifications of the urban fabric, significant improvements are still awaited. On this topic, AI’s recent results have positioned this technology as a promising alternate avenue of experimentation. At the scale of the territory, urbanization patterns can take very different forms. The variety of city fabrics, conditioned by the surrounding landscape, infrastructure, and general location can 2. Imaginary Plans, M. del Campo, S. Manninger, 2019.

13 City-specific generated urban patterns, from the Urban Fiction project. By M. del Campo & S. Manninger.

display significant diversity among urban scenarios. Even though modeling this complexity generally represents an arduous task, the gradual improvement of AI models over the past few years has provided architects and urban planners with a renewed set of tools to study city patterns. The Urban Fiction2 project represents a step in this direction: a model, trained on the satellite imagery of major cities, can adapt city-specific textures to new user-defined patterns. As shown in Figure 13, the transposition of characteristics from one

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14

Urban Grid – New York Style

Urban Grid – London Style

context to another demonstrates the extent of this model’s agility at mimicking specific urban fabrics. Although speculative, the obtained results already forecast the potential contribution of this approach. 3. Chu et al., “Neural Turtle Graphics for Modeling City Road Layouts”, In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 4522-4530, 2019.

As the urban condition merges together multiple layers of information, other research initiatives attempt to disentangle them as distinct levels to then explore AI’s relevance to each one, in isolation. For instance, by focusing solely on the structure of road networks, the Neural Turtle Graphics (NTG)3 model attempts to learn and replicate the properties of circulation paths across

14 City-specific street network generation using the NTG model. By Nvidia Research.

chosen cities. Figure 14 displays some of NTG’s results, where a few urban grids have been generated, mimicking the road network style of specific cities; namely here in New York and London. Working as a negative of NTG, other initiatives have invested in the study and generation of urban block typologies. From Barcelona

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to New York or Paris, the form and organization of these blocks can vary drastically and display distinct infill strategies. Similarly to previously described experiments, these projects have investi4. See Rhee et al, 2021 / Tian, 2021 / Fedorova, 2021.

gated the generation of urban blocks4 for given cities, but also for specific immediate surroundings. As a matter of fact, the urban scale today witnesses a significant number of applied AI research initiatives; among many factors, this momentum benefits from the ever-increasing amount of data documenting cities’ multiple information layers: road networks, built environment, topographical data, etc. Mainstream mapping portals, like Google Maps, Open Street Maps, as well as GIS information gathered by institutional players, offer an almost endless source of high-quality data; a dynamic that today bolsters AI research’s application to the urban condition.

References & Resources Urban Fictions

M. del Campo & S. Manninger, 2019

Neural Turtle Graphics for City Road Layouts Chu et al., 2019

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Floor Plans

Closer to Price’s Generator experiments, the challenge of internal space planning stands as one of Architecture’s core concerns. The set of constraints pressuring this particular scale originates from various directions: the program, the structure, the facade openings, the building’s circulation, etc. Consequently, the layout of internal spaces 5. See Nauata et al. 2021 / Hu et al. 2020 / Chaillou 2019.

tries to balance and resolve these diverse influences, while translating the architect’s intent; a degree of complexity placing any technology aspiring to address internal space planning under acute pressure.

15 16 17 Internal layout generation. “Inputoutput” pairs, for various user-specified constraints. By S. Chaillou. Footprint Bedroom Entrance Window Livingroom Bedroom Bathr./Restr. Kitchen Circulation Closet Washing Room

In this respect, AI represents a leap forward. GAN models, for instance, have proven to be surprisingly adequate. Using a broad database of formatted internal layouts, recent research projects have studied this model’s ability to learn space programming and furnishing patterns5. Figures 15 and 16 display some of their results; these image-pairs showcase various mappings, from empty apartment footprints to their programmed counterparts, with respect to specific constraints (facade openings in Figure 15, facade openings and bedroom position in Figure 16). With Figure 17, these models are applied to space furnishing. Given a programmed layout, rough furniture outlines are placed to emulate potential setups.

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Footprint Door Window

Footprint Door Window

Footprint Door Window

Footprint Door Window

18 These generated floor plans provide an example of AI’s ability to lay out different functions under various input constraints, after only a few hours of training. However, evaluating these results against Architecture’s multiple requirements calls for a more nuanced assessment: given their imprecision, these floor plans should be considered as first drafts or initial attempts at finding a space planning strategy, rather than as final designs. In other words, these models, in the hands of architects, can provide a form of drafting assistance whose results expect further tuning and refinements.

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To formalize this rather iterative design process, a basic web inter-

18 4 steps of a generation sequence, using a simple web app interface. By S, Chaillou.

face can act as a simple yet efficient device. Figure 18 exhibits such an experiment, where a trained model, running in the background of a streamlined web app, reacts to users’ graphical input. Each frame displays a step of a typical design sequence. Each time, the architect’s intent is sketched in the left-side window while, simultaneously, the machine computes a solution displayed on the right. By drafting the constraints on the left, the architect iteratively regenerates solutions to narrow down the search and find an adequate typology. Space planning is in fact today a growing area of application for AI research. Many projects over the past 5 years have significantly pushed the envelope, mostly tackling controlled environments like apartment layout or office space zoning. However, as this area of research matures, these models have the potential to be applied to more complex programs with even more challenging constraints.

References & Resources AI & Architecture, an Experimental Perspective S. Chaillou, Towards Data Science, 2019

ArchiGAN: a Generative Stack for Apartment Building Design Nvidia Developer Blog, 2019

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Facades 6. Isola et al., “Imageto-image translation with conditional adversarial networks.”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125-1134, 2017.

More than a mere “wrapper”, a building envelope is as much a source of constraints and challenges for designers, as it is an expressive dimension of the built environment, conveying concepts such as style, typology, program, etc. Addressing this complex yet essential scale has therefore been on the roadmap of researchers over the past few years. AI has thus gradually found its way to the generation of building exteriors.

19 Series of generated facades. Each pair displays the “input” (left), and “output” (right) synthesized by the model. By Isola & al. Facade Molding Cornice Pillar Window Door Sill Blind Balcony Shop Deco Background

An early attempt has paved the way to current experiments on the topic: the application of Pix2Pix6 (a GAN model developed in 2017) to a dataset of annotated facades. This approach plays off the discretization of facade design into a composition of simple structuring elements (windows, cornices, pilasters, doors, balconies, etc.). The model then learns the mapping from an image representing the layouts of these elements – encoded using a vivid color code – to the facade’s real picture. Once trained, this network can texture a color map into an almost realistic-looking building and harmonize its style across the image (Fig. 19). At that point, an architect can use the model by creating new compositions, and generating somewhat realistic images of facades, prefiguring early on a given design’s potential appearance.

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Initial Massing

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21 However, the exterior of buildings wraps around more than single facades; entire city blocks often share a similar typology that adapts to the massing’s variations. Broadening the scope of generative AI to tackle this reality has recently led to more comprehensive results. 7. Kelly et al, “FrankenGAN: guided detail synthesis for building mass-models using style-synchonized GANs”, 2018.

Projects like FrankenGAN7 for instance, have provided promising demonstrations in this area. By taking as inputs the raw 3D massing of city blocks and a facade style reference, this model generates a highly detailed and textured envelope for all buildings. This approach

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Facades

is both style informed and geometry specific, which in turn creates

20

strikingly realistic building facades. Figure 20 details the generation

Steps of FrankenGAN generative pipeline. By Kelly et al.

pipeline, from a raw massing to a detailed one, while Figure 21 shows more results for different styles and city bock typologies.

21

The subject of facade generation is in fact one of these areas

Various textured city blocks, results of FrankenGAN. By Kelly et al.

where the scientific literature shows potential alignments between Architecture and other fields (the video game industry, satellite imagery, etc.). If their underlying motivations are quite distinct from Architecture’s agenda, as these domains refine and opensource their research, Architecture is likely to benefit from new tools, while diverting their usage to serve the discipline.

References & Resources Image-to-Image Translation with Conditional Adversarial Nets Isola et al., 2017

FrankenGAN

Kelly et al., 2019

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Perspectives

The representation in perspective of an architecture achieves more than the transcription of its 2D projections into 3D. It is also the translation of its textures, lighting, and general atmosphere, conveying the project’s potential perceived experience. While using computers to achieve this task, image quality and realism remain closely tied to the availability of computing power, and the extreme detailing of geometries by architects. By providing an alternate approach, AI has recently proven its ability to drastically reduce the computational time of renderings while allowing the inference of certain levels of detail. 8. K. Steinfeld, “Gan Loci”, 2019.

22 Generated urban scenes, for different styles, given a similar input image. By Kyle Steinfeld.

23 Various generated urban scenes, results of GAN-Loci. By Kyle Steinfeld.

GAN Loci8, a project realized in 2019, represents a step in this direction. This piece of research (Fig. 22) explores the possibility of transforming perspective views of initially white and neutral volumes into photorealistic urban scenes. Specifically, for a given perspective view, GAN Loci attempts to add facade-like textures, pathways, street furniture, pedestrians, cars, etc. More interestingly, the project goes even further to train different models on specific types of urban environment: suburban, public park, etc. To illustrate this reality, Figure 23 displays the results of the two different models, obtained for the same input image. However, since

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Input

9. Wang et al., Nvidia Research, 2019.

24 Urban scene, synthesized by Pix2pixHD. By Wang et al. 10. Park et al., Nvidia Research, 2019.

25 Generated landscape (right), given input mask (left), in GauGAN's interface. By Nvidia Research.

Output – Park Style

Output – Suburb Style

GAN Loci, projects like Pix2PixHD9 have addressed the same types of representation and, using a different methodology, represent a striking improvement in image quality (Fig. 24). More recently, GauGAN (2019)10, scales this approach to a more generalizable model, packaged into a streamlined interface (Fig. 25). The user is in charge of laying out patches of colors, corresponding to specific semantic categories (water, mountain, sky, etc.), while GauGAN almost instantaneously offers a rendered translation in the rightside window. Evidently, to fully support Architecture, these models still need to improve the precision of their outputs. However, these early

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25 experiments prefigure some of AI’s potential contributions. On the one hand, they dramatically reduce the computational time of rendering, from sometimes multiple hours to a few seconds, or even less. On the other, they allow simulating the detailing of scenes, with respect to specific learned styles. This latter aspect maybe opens one of AI’s most interesting contributions to Architecture.

References & Resources GAN-Loci

K. Steinfeld, Towards Data Science, 2019

GauGAN Demo

Nvidia Research, 2019

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Structures

Structural integrity is yet another challenge for architectural design. Finding the right form, able to handle a given building’s loads, can be a considerable challenge. If the construction industry often defaults to standard structural typologies (pre-set modules, frames, etc.), custom structures can sometimes be a better fit for certain projects. However, exploring new possibilities comes at a cost, as new 11. See Hoyer et al. 2019 / Miguel et al. 2019 / Mueller et Danhaive 2020.

designs require studying and simulating their underlying structural performance. In this respect, AI can significantly help architects explore alternate structural options, while being able to afford their respective analysis.

12. R. Danhaive, C.T. Mueller, “Design subspace learning: Structural design space exploration using performanceconditioned generative modeling”, Automation in Construction, 2021.

The challenge of structural form finding is today a thriving area for applied AI research. Multiple projects11 tackle AI’s potential to generate original structural designs, using models such as GA, VAEs, GANs and others. Using VAEs for instance, research developed at MIT12 investigates how various structures can be generated, while ensuring

26 VAE-generated structures By Mueller & Danhaive, MIT.

high-performance standards. Figure 26 displays some of their results: the exploration of the model’s latent space can yield a collection of diverse, and at times counterintuitive, truss structures. Each option however stays within strict performance bounds.

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Initial Shell Structure

Material Distribution

Thickness Distribution 5cm

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27 More generally, this approach to structural form finding provides users with the ability to consider design options potentially far removed from canonical patterns, while ensuring their respective efficiency. Additionally, as structural form finding is conditioned by the repartition of loads in resulting shapes, predicting the structural effort of a given design and the necessary distribution of material is an area in which AI has proven more than relevant in recent years. Instead of the traditional approach, using topological optimization – a precise

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yet computationally expensive methodology to simulate a design’s shape given the path of its internal loads – AI models can be used 13. From R. Danhaive Phd thesis, MIT, 2020.

27 AI-enabled prediction of an optimal material distribution for a shell structure By R. Danhaive, MIT.

to predict lighter material repartitions much faster. Recent research from MIT13 demonstrates this possibility. Figure 27 displays some of their results, where for a specific shell structure, an AI model predicts an optimal material distribution pattern, allowing then to decide on the layout of the shell’s various thicknesses. Both as a means of exploration or as an analytical lens, AI provides structural design with a renewed set of tools. In Architecture, if the pressure of structural concerns often hinders the design process, current models being developed could help address this challenge. Finally, if the results displayed in this segment are at an experimental stage, their integration with mainstream design tools is already underway. As a result, a more integrated approach to structural analysis in Architecture could offer greater autonomy to practitioners.

References & Resources Designing with data

N. Brown & C. Mueller, 2017

Digital Structures Lab

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Predictive Simulations

In times of growing ecological awareness, the built environment is expected to keep close tabs on its carbon footprint and energy performance. Part of Architecture’s strategy in addressing this concern has been to simulate expected building efficiency early in the design process. Solar radiation, wind flows, indoor thermal comfort are many dimensions that the industry’s simulations try to capture, to later inform the design phase as much as possible. However, given the budget and knowledge required to tap into these resources, such tools are almost exclusively used by trained experts. In this respect, AI has been able to lower the threshold and might be about to allow the dissemination of cheap, fast and simple predictive models across the industry. These “surrogate models” gradually represent a valid alternative to standard simulation engines. Looking at tangible examples will set the stage for this potential substitution. The estimation of wind flows around a project, for instance, is an essential part of assessing its impact on the immediate surroundings. The traditional approach, using computational fluid dynamics (CFD) simulations, sometimes lacks in accessibility for its cost and complexity. To mitigate these drawbacks, researchers have been training

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Simulated Wind Flow Physics-engine simulated result Wind Direction

Predicted Wind Flow AI-generated result Wind Direction

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AI models to predict the map of potential wind flows in a specific re28

gion, based on a simple site layout and its orientation (Fig. 28). These

Comparison between actual and AIpredicted wind flow. By Spacemaker Research.

models, although less accurate than actual simulations, are at times

14. For reference, see DaylightGAN: https://github.com/ TheodoreGalanos/ DaylightGAN

A handful of research projects have also been recently developed to

sufficient to assess a design’s efficiency, or to create benchmarks across vast collections of potential design options.

estimate internal building performance. DaylightGAN14 for instance aims at forecasting the potential reach of natural light within a project, given a floor plan footprint and its facade openings (Fig. 29). An-

29 Typical result of DaylightGAN. By T. Galanos. 15. Quintana et al., “Balancing thermal comfort datasets: We GAN, but should we?”, In Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp 120-129, 2020.

other project, ComfortGAN15, investigates the challenges of predicting a building’s indoor thermal comfort. Overall, as indoor conditions deeply impact buildings’ final energy efficiency, research aimed at their forecast constitutes a growing area of investigation today.

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Actual

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In parallel to these developments, serving such models to the end user remains another pressing challenge, vital to ensure their actual contribution to Architecture. Over the past decade, the deployment 30 Snapshot of a wind flow prediction in Spacemaker's web app.

of online platforms has provided the adequate infrastructure to that end: Spacemaker (Fig. 30), Covetool, Giraffe or InFraReD are only few examples of this growing ecosystem, offering simplified access to AI-based predictive models.

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References & Resources Wind Flow Prediction through Machine Learning T. Galanos, A. Chronis, O. Vesely, AIT, 2020

Pedestrian comfort: Why wind analyses are more relevant than ever Spacemaker's Blog, 2020

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The Outlooks of AI in Architecture

A Theoretical Perspective

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Alongside the mosaic of current applications, the discourse surrounding AI’s presence in Architecture is as fragmented as it is rapidly evolving. Since the intuitions of Negroponte and Price, AI itself has matured and improved, thus forcing the discipline to reinterrogate the early theorists’ assumptions concerning its presence and purpose in Architecture.

To address this reality, the following chapter orients the discussion towards three distinct avenues: AI’s contribution, adoption and prospects in Architecture. Through a collection of short articles, this triptych curates a broad landscape of perspectives, aggregating the visions from researchers, practitioners and entrepreneurs. Their perspectives together frame, evidence or challenge AI’s encounter with the discipline.

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AI’s gradual inception in Architecture calls for a broader reflection about its actual contribution. If the collection of current experiments certainly unveils its immediate benefits, the debates among theorists go further: to address the deeper purpose of AI’s presence in our field. Among many avenues, at least three salient directions are worth considering: the form, the context and the performance. First, the form, designates the importance of formal considerations, a long-standing tradition in Architecture; each period or movement offers a new approach and ethos to this burning topic. As AI can help shape our built environment, this technology reactivates the discussion. The context, then, addresses the relationship that any architecture entertains with its physical, cultural or symbolic surroundings. As AI enters the field, this essential dimension of the practice is invited to potentially evolve. The performance, finally, refers to the imperative of precising, if not simulating, an architecture’s expected efficiency. Since AI allows for more accessible predictions, the influence of performance on Design is an important reality to consider. The following segment will unfold this triptych, giving voice to researchers and theorists whose work shed a singular new light on these different topics.

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The Form Architectural Plasticity: The Neural Sampling of Forms by Immanuel Koh, Assistant Professor at Singapore University of Technology & Design

The Contribution 110

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Architecture, as a discipline of form-making, often absorbs new ways of form-thinking from other domains as well as it adapts new technologies as tools for form-modeling. This disciplinary tendency for formal appropriation has to do with its underlying plastic conception of form. With the recent breakthrough in Artificial Intelligence, architecture has again come to embrace the potential formal shift that deep learning might bring. Most notably is the use of a specific class of deep neural networks, known as generative adversarial networks (GANs). It first appeared in 2014 as a computer 1. Goodfellow et al., “Generative adversarial nets”, Advances in neural information processing systems, 27, 2014.

science research paper1, and within just a few years, mainly through the works of artists experimenting with GANs imagery, it quickly entered the public imagination. The generic nature of deep learning models lends itself well for architects to likewise appropriate GANs in their design explorations. However, unlike the abundance, accessibility, and ease of creating 2D datasets, few ventured into experimenting GANs with 3D datasets. This is mainly due to factors related to higher complexity in designing 3D-GANs, greater difficulty in encoding 3D geometries, larger computational load, longer training time, and more complicated code implementation using distributed cloud computing. Inevitably, current appropriation



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of GANs by architects who typically lack a strong understanding of its underlying mathematics, architectures and codes has only led to a plethora of initially inspirational-looking GAN-generated 2D images, but with limited or no 3D formal consequences. This essay aims to address this conceptual and technical lack in 3D form-making by introducing the notion of architectural plasticity, 2. I. Koh, Architectural “Sampling: A Formal Basis for Machine-Learnable Architecture”, PhD Thesis, École polytechnique fédérale de Lausanne, 2019.

elaborated with two key projects, to articulate the neural sampling2 of three-dimensionality, exteriority, interiority, and semantics.

From Neo-Plasticism to Neuro-Plasticity In May 1922 at the International Artists Congress of Düsseldorf, Theo van Doesburg, leader of the De Stijl movement in Holland, announced “We are preparing the way for the use of an objective universal

3. U. Conrads, “Programs and manifestoes on 20thcentury architecture”, MIT Press, 1971.

means of creation”3. For van Doesburg, “the universal means” is by way of a convergence of all artistic expressions into a single style, governed initially by the general principles of Piet Mondrian’s NeoPlasticism, and later by those of his own Elementarism. In his 1920 seminal essay “General Principles of Neo–Plasticism”, Mondrian not only defined Neo-Plasticism, but also laid out its six principles (or rules). In the second section of the text titled “Neo-Plasticism and Form”, he differentiates “morphoplasticism” from his neoplasticism. The former refers to traditional art that is figurative and naturalistic in the use of recognizable forms, or as he calls it painting “in-the-way-of-nature”, while the latter refers to the “representations of relationships” that use only abstract and pure forms, or as he calls it painting “in-the-way-of- art”. However, it was Theo van Doesburg’s 1924 manifesto “Towards a Plastic Architecture” and 1925 book

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“Principles of Neo-Plastic Art” that set the stage in envisioning a neoplastic architecture. Quoting the second and fifth points of his manifesto: “the new architecture is elemental; that is to say, it develops out of the elements of building in the widest sense” and “the new architecture is formless and yet exactly defined”. It is thus the “elemental” and the “formless” that would come to characterise the formal generative capacity of Neo-Plasticism. Yet, this formgenerativity remains inherently rule-based. Fast-forward a hundred years, today’s artificial intelligence is again “preparing the way for the use of an objective universal means of creation”. This “universal means” is The Master Algorithm – the title of a book by professor of computer science Pedro Domingos who also calls it the “general4. P. Domingos, “The master algorithm: How the quest for the ultimate learning machine will remake our world”, Basic Books, 2015.

purpose learner” or “universal learning algorithm”4. It is through learning that the machine is to “prepare the way” in abstracting data into algorithms, which in turn serves as the “means of creation”. The formal generative capacity of deep learning models is a result of its neuroplasticity — a general term used in neuroscience in referring to the rewiring of the brain and remapping of its functions. Analogically, instead of training a GAN model from scratch, by simply activating or deactivating a sample set of elemental neurons of a pretrained GAN, it could be rewired and thus made to remap its original functions in generating new images with different

5. D. Bau et al., “Rewriting a Deep Generative Model”, arXiv:2007.15646 [cs], 2020.

concepts5. Rather than directly editing the individual pixels of a given image, one would instead edit the individual neurons of the GAN to indirectly manipulate the features of any given image. Theo van Doesburg’s “plastic architecture” is recast here as a result of the plasticity afforded by deep neural networks — a conceptual shift from neoplasticism to neuroplasticity.

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Form-Sampling, Not Form-Finding The design of a chair has a long history of being used by architects as a potent exercise to experiment with formal possibilities engendered by new architectural concepts. The insights made would then be somehow transferred to the building domain, and typically with an indirect mental analogical translation in scales and functions. The project 3D-GAN-Ar-chair-tecture (2020) from the Neural Sampling Series takes a similar formal trajectory but implements a custom end-to-end 3D-GAN for a direct transference, simultaneously short-circuiting the typical bottleneck encountered by architects in trying to interpret 3D geometric interiority and exteriority from flat 2D GAN-generated images. Unlike existing small-data approaches in form generation,

1 An uncanny “chairness” expressed in this 3D-printed chair sampled from a GAN latent space trained with a dataset containing different chair designs. (3D-GAN-Ar-Chairtecture, 2020).

such as parametric modelling with McNeel Grasshopper3D or form finding with topology optimisation in Autodesk Generative Design, GANs are data-intensive approaches. Large input sample size is first required to effectively learn their implicit features prior to generating any novel output samples. Three 3D-GAN models were trained: one with 10,000 chairs from ShapeNet, the other with 4,000 high-rise building massings, and the last one with a combination of both. Once trained, their respective latent spaces were used for sampling 3D forms. The latent space is a structured exploratory space of probable

2 A smooth GAN interpolation across forms and scales between “chairness” (leftmost) and ‘buildingness’(rightmost) is directly translated and fabricated as an array of 3D prints. (3D-GAN-Ar-Chairtecture, 2020).

forms in high dimensions, where similar forms could be understood as being placed close together. This is the basis for not only generating new chairs (Fig. 1) or buildings, but for interpolating among chairs and buildings directly (Fig. 2). In fact, the project demonstrates that the creative use of GANs need not be constrained “in-the-way-ofnature” (e.g., scale, category, material and structure) since the task at hand is to explore the architectural plasticity of forms “in-the-way-ofart”, and thus the ideation of novel forms.

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Semantics of Interiority and Exteriority 3D-GAN-Housing (2021) from the Neural Sampling Series is part of a larger funded research project AI Sampling Singapore recently exhibited at the 17th Venice Architecture Biennale. It demonstrates the possibility of encoding specific semantics and 3D configurations of architectural programmes directly into the design of the training dataset using a custom 3D-GAN architecture. The initial dataset consists of 5,000 3D models sampled from Singapore Housing Development Board’s (HDB) flats – a ubiquitous high-rise public building typology where 80% of the population reside. A statistical exploratory data analysis of the dataset reveals that some key massing types could be identified, such as cluster blocks, L/U-shaped blocks, 3 A GAN training process (top left to bottom right) showing increasingly plausible 3D housing configurations that are semantically and structurally coherent. (3D-GAN-Housing, 2021).

4 A GAN latent walk showing the composite renderings of exteriority and interiority being smoothly interpolated as it maintains its learnt “housingness”. (3D-GANHousing, 2021).

slab blocks and point blocks, which in turn serves as a useful design intuition when evaluating the GAN’s learning and generative capacities during the training process. The GAN model is designed to not only learn and infer with increasing granularity the heterogeneity of the visible exterior form, but also those occluded interior spatial relations. Due to the high computational load, the GAN model was trained continuously for days with cloud GPUs until it reached a legible convergence (Fig. 3). The ability of GAN models in approximating a probabilistic and continuous distribution of the training dataset is evident from the smooth interpolation when sampling between different building forms (Fig. 4). The semantics and configurational coherence could likewise be observed in the plausible 3D relationships generated among different functional zones (e.g., living units, circulation, service cores and communal spaces), locally within each unit and globally within each building.

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When forms are no longer modelled directly, but sampled indirectly, the architect would have to harness the plasticity of deep neural networks in thinking about forms, and perhaps also in engendering a new aesthetic again paving the way towards a plastic architecture but now in the age of Artificial Intelligence.

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The Context The Sorcerer’s Apprentice by Kyle Steinfeld, Associate Professor at U.C. Berkeley

The Contribution 118

The Context

Recent developments in AI threaten to dislodge some of our basic assumptions about the nature and purpose of computational design tools. Creative designers ought to welcome the disruption. The use of computational tools in design has long been understood through the metaphor of computer “assistance”, wherein software stands by as an aide while we work on our design tasks. Affirming this metaphor, designers have come to expect that computers can be effective assistants insofar as they facilitate what has been described as a “reflective conversation” between an author and the salient materials of a design situation. But if design activity may be understood as a conversation with a willing assistant, then we must ask: what is the nature of this conversation? What is the topic, the context, and what are the terms by which participants converse? The variety of possible answers to such questions reveals that computational tools are not universal, but rather are cultural products strongly related to the details of the contexts in which they are produced and those in which they are applied. And yet, despite the contextual nature of computational design, we find that a single paradigm dominates the contemporary architectural toolkit.

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For more than 50 years, design technologists have struggled to coax computers to become better design aides. While a survey across this period reveals only mixed success, there are a number of notable bright spots. These bright spots – those times at which design technology has enjoyed a deep and wide-ranging impact on the culture and practice of architecture – have come when we presupposed that design is a rational activity, and is therefore best supported by a rational computational partner. This is how most contemporary tools for computer-aided design are understood today, and we find all around us examples of such an approach. From parametric modeling, to BIM, to simulations of building performance, most CAD software seeks to extend our capacity for reasoning about design in an analytical and deliberate way. Parametric modeling, for example, is a particularly insistent conversation partner, demanding that each idea flow from a more fundamental premise, thereby encouraging us to compose increasingly elaborate chains of logic. Similarly, building information (BIM) systems are meticulous bookkeepers that require us to elaborate on each tiny idea to an exhausting level of detail. As such, we may rightly understand contemporary CAD not as general design assistants, but rather as specific instruments of “machine-augmented rationalist design”. Such tools are more lab-assistant than raconteur, and while the conversations enabled by them are welcomed by some, when applied in the service of creative design, many of us find CAD to be boorish, tiresome, and fatiguing. While rationalist tools excel in supporting rational thinking, creative design requires tools for creative thinking. Late-stage design, a phase in which designs are refined into viable products, may be well-understood as a rational endeavor, and might therefore warrant the support of something like a lab assistant. Early-stage design, a phase in

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which the contours of a design concept are not yet clear, is different. Activities that tend to guide early-stage design, such as sketching and formal ideation, rely less on rationality and more on creativity, and therefore call for a very different sort of partner. With this in mind, we might ask: What are appropriate qualities for an early-stage design assistant? What sort of conversations are useful for enhancing creativity, and which capacities should such a tool facilitate? To address such questions, we must better understand the nature of creative design. In the early stages of a design, we are faced with a complex, confusing, and contradictory set of technical and social problems for which there exist no well-defined approaches to solve. And yet, somehow in this moment, designers regularly manage to manifest solutions. These are drawn from the materials of this problematic situation, yes, but are also assembled along the lines of a new order that seemingly appears from nowhere at all. Such creative leaps are less an act of reason and more an act of imagination: of finding connections among ideas that were not previously connected. While such solutions may appear to be conjured up in a “sudden illumination”, creativity is not magical, and many thinkers have sought to account for the ground from which creative leaps might spring. Robin Evans described creative design in terms of a “projective transmission” among an author's imagination, the instruments of drawing, and the dictates of geometric description. Nigel Cross referred to creative design as an “abductive leap” that relies on connections made between the direct experience of the author and the context surrounding a problem. What the many accounts share is that creativity is highly contextual, that it thrives in the recognition of new patterns, and that these new patterns are often improvisational modifications of those drawn from experience. With this in mind, how might our computational assistants support creative action?

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Insofar as creative design relies less on the mechanisms of reason and more on those of imagination, the failure of existing models of computer-aided design is clear. Our existing software programs are conceived of as tools of reason, not of imagination. For example, while we have tools that help us account for the connections across a complex program brief, we still require a tool for discovering those critical relationships that we're not yet aware of. While we have tools for finding forms based on the optimization of structural performance, we still require a tool that allows us to discover forms that recall selections from the enormous dataset of architectural precedent that we've inherited. While we have techniques to collate and visualize the cacophony of data related to an urban site, we still require a tool to identify those qualitative patterns that lend our most successful cities a sense of place. While we have tools conceived in the “lab assistant” model that excel in supporting rational design, we lack a computational aide that foregrounds the recognition of patterns, the application of precedent, and the awareness of context. What creative design requires is a “sorcerer's apprentice”, and the new breed of tools based on machine learning that are now being developed are just this. To illustrate the potential of positioning computational tools in this way, I'll briefly present below two projects I have recently completed that rely on machine learning techniques. First, I would mention

1 Generated urban scenes, results of GAN-Loci.

the GAN Loci project (Fig. 1), which applies generative adversarial networks (or GANs) to produce synthetic images that capture the predominant visual properties of urban places. Here, working across six cities in the US and Europe, urban image data was collected, processed, and formatted for training two known computational statistical models (StyleGAN and Pix2Pix) that identify visual

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patterns distinct to a given site and that reproduce these patterns to generate new images. Imaging cities in this way represents the first computational attempt to document the Genius Loci of a city: those forms, spaces, and qualities of light that exemplify a particular location and that set it apart from similar places. Seen as a design research tool, GAN Loci might better assist a general audience of designers in identifying tacit visual patterns found in our most successful cities. Next, I would mention a project still in development and that is currently being prototyped by a group of undergraduate students at UC Berkeley. Sketch2Pix is an interactive application for supporting augmented architectural sketching. Here, workflows are developed that allow for novice creative authors to train an ML model on the transformation from a “source” image depicting a sketch, consisting primarily of hand-drawn lines, to a “target” image depicting an architectural object that includes desired features such as color, texture, material, and shade/shadow. Using this system, designers can effectively train their own AI sketching assistant, trained on their own data, and for their personal use in sketching architectural forms. Some students in this course have trained assistants that

2 Samples of Sketch2Pix results.

recall traditional architectural forms (Fig. 2), such as one that evokes the glazed tubular tile roof forms of traditional Chinese architecture. Others have selected influences drawn from a local site, such as a collection that is based on the forms and textures of the produce grown in the California Central Valley. Seen as a conceptual drawing tool, Sketch2Pix might allow a general audience of designers to intentionally “mix in” a specific set of formal precedents as influences in their design process, thereby allowing the discovery of new solutions inspired by a wealth of seemingly unrelated forms.

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2 To conclude, while I do suggest such a metaphor for the production and application of software tools, I would like to be clear that machine learning is not magic. One of the central roles of a design technologist such as myself is the demystification of opaque technologies, and the suggestion that there is anything magical about the operation of a neural network would be malpractice. Neural nets are not “magic”, no, but neither are computers our “assistants”. There remain, however, some useful aspects to such ways of speaking and of thinking, and perhaps a certain value in a designer positioning themselves as a magician, so long as we remain willing to reveal the nature of our tricks.

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The Performance Artificial Intelligence for Human Design in Architecture by Renaud Danhaive & Caitlin Mueller, Digital Structures Lab, MIT

The Contribution 126

The Performance

Architecture as a discipline faces a growing challenge in the global climate crisis: approximately 40% of greenhouse gas emissions are due to the built environment. This impact may only increase as construction expands internationally to house the world’s exploding populations. These emissions are mostly due to energy consumed in the construction and operation of buildings and can vary significantly based on decisions made during the design process: what materials are used and how efficiently they are allocated, whether passive thermal strategies are engaged, etc. Even the most basic choices of a building’s massing, scale, shape, and constituent systems can have a great influence on environmental performance. Until recently, the climate impact of such design decisions was largely ignored, both because the scale of the problem was underemphasized and because designers lacked tools to measure it.

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Today, advances in computing and simulation have opened up new pathways for architects to design with data, especially data related to building performance. Finite element analysis, building energy modeling, and related methods are now accessible in software tools used in architectural design, rather than merely specialist engineering packages. Additionally, methods of optimization and design space exploration, which can guide architects towards better design options based on simulation data, are now available within the same software frameworks. Ideally, these tools and methods should be used as early in the design process as possible, so that the most impactful decisions are made with performance information as a key input. In reality, the integration of simulation data is often limited, sometimes relegated to just a validation of a crystallized design once all major decisions have been made. Since building performance is so important, and tools to measure and design with it are now available, why have architectural design processes not adopted this approach en masse? Beyond general disciplinary inertia, there are several important and fundamental limitations to the computational design methods described above that prevent wider adoption. First, many simulation software tools and methods remain cumbersome to connect with and slow to run, disrupting the pace of a fluid creative design process. Second, the parametric design spaces needed for optimization and exploration are often at odds with the more flexible and natural approaches used in analog design, constricting design freedom compared to methods architects are used to. Third, these design spaces often contain such vast expanses of data that they are virtually impossible for humans to understand and work with. Finally, due to the intrinsi-

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cally human nature of architecture and design, there is strong resistance to any process which purports to fully automate it. Because of these challenges, conventional computational design approaches are underutilized in favor of processes that remain human-centric. Recent advances in Artificial Intelligence and machine learning offer a way forward, connecting the power of performance-driven computing with the fluidity and creativity of human design. This may appear counterintuitive: Artificial Intelligence is often thought of as a means to replace humans in high-level tasks, for example in playing chess or performing surgery. Indeed, in recent years some have proposed AI-driven platforms that generate architectural artifacts, such as floorplans or facades. However, when completely isolated from human designers, such aspirations may be missing the point: the human experience of the built environment, arguably the most critical component of architecture, will always be best understood by a human designer. In our view, the most compelling and valuable applications of AI for architecture lie instead in methods where AI systems augment or collaborate with human intelligence. Several examples of such methods developed in our research are described below. Our first approach expands the method of surrogate modeling, originally developed to substitute a fast data-driven approximation for a slow simulation in black-box optimization processes (e.g. in aero1. Tseranidis, Brown, et Mueller, “Datadriven approximation algorithms for rapid performance evaluation and optimization of civil structures”, Automation in Construction, 72, pp 279293, 2016.

space engineering). In our work, we broaden the application beyond optimization to instantaneous performance feedback for designers in general1. Building on techniques developed for supervised learning of image-based content, such as convolutional neural networks, we have demonstrated tools that can accurately predict entire fields of simulation data in real time. For example, the entire displacement field

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2. R. Danhaive et C.T. Mueller, “Structural metamodelling of shells”, In Proceedings of IASS Annual Symposia, vol. 2018, no. 25, pp 1-4. International Association for Shell and Spatial Structures, 2018. 3. Brown et Mueller, “Design variable analysis and generation for performance-based parametric modeling in architecture”, International Journal of Architectural Computing 17, no. 1, pp 36-52, 2019.

of a structural surface or radiant exposure of a building facade can be displayed instantaneously as a designer explores conceptual options2. Our surrogate models are also highly portable, giving non-experts access to real-time performance data in lightweight interfaces such as web portals, removing the need for cumbersome file conversions and data transfers between disconnected software programs. We also tackle the question of design space parameterization, which traditionally requires a user to handcraft the parameters that drive variation across a design space. Using classical statistical methods and more recent developments in machine learning (including varia-

1

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Exploration path in a latent synthetic design space constructed using a variational auto-encoder for a roof design example. 4. R. Danhaive et C.T. Mueller, “Structural design space exploration with deep generative models: applications to shells and spatial structures”, 2020.

tional autoencoders), we can automatically synthesize a small number of variables that generate meaningful and large design variation in geometry while maintaining high performance3. We have demonstrated this approach on building-scale structures (Fig. 1), illustrating how, for example, the geometry of a long-span roof can be morphed in complex ways to generate many diverse forms (Fig. 2) that all perform very well, driven by a designer with only two synthesized parameters4. Finally, we are also actively developing design approaches that improve interfaces and modes of human-computer interaction. We have created tools that allow designers to collaborate with computing systems via interactive evolutionary algorithms, evolving

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2 5. C.T. Mueller et al., “Combining structural performance and designer preferences in evolutionary design space exploration”, Automation in Construction, pp 70-82. 2015. 6. R. Danhaive et C.T. Mueller, “Combining parametric modeling and interactive optimization for high-performance and creative structural design”, In Proceedings of IASS Annual Symposia, Vol. 2015, No. 20, pp 1-11, IASS, 2015.

design options to capture both human intent and numerical performance5,6. We have developed techniques for designers to tame the wilderness of expansive design spaces by clustering families of designs7 and guiding exploration using statistical and optimization-based methods. Finally, we are designing systems that allow designers to interact with complex digital modeling environments using natural human processes such as sketching. Specifically, in these sketch-based interfaces, designers can intuitively interact with complex parametric models using quick sketches and gen-

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7. N. Brown et C.T. Mueller, “Designing With Data: Moving Beyond The Design Space Catalog”, 2017.

erate three-dimensional models of structures or buildings and understand their associated performance8. In all of this research, our aspiration is to harness advances in machine learning and Artificial Intelligence to amplify human creativity in high-performance

8. Ong et al., “Machine learning for human design: Sketch interface for structural morphology ideation using neural networks”, In Proceedings of the IASS Symposium, 2020.

architectural design. Given the sustainability imperative now faced by the built environment, new tools and design approaches are needed to guide architects and engineers towards better solutions without limiting their imagination or freedom in this fundamentally human endeavor.

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The Outlooks of AI in Architecture

The Adoption

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AI may today stand more as a possibility than an evidence for architects. Whether the discipline at large will embrace, reject, or adapt this technology is still unclear and leaves the discussion wide open. Among many others, three facets of current debate speak to the state of ongoing reflections and concerns: the practice, the model and the scale. Although the practice of Architecture could soon begin relying on AI-enabled tool sets, the path to adoption still remains to be defined. The modalities of its integration and adaptation to the processes and needs of practitioners constitute an important avenue to explore and clarify.  The very notion of “model” is also at the core of AI’s adoption in Architecture. If the use of models is not a novelty for architects, with AI their definition is invited to evolve towards a deeper anchoring in mathematics and logic. Consequently, the discipline’s reflection on this evolution will condition AI’s inception in the field. Finally, the scale of AI’s deployment in Architecture constitutes a topic in itself. Technology’s successful dissemination is contingent upon its translation into adequate tool sets. Finding its best expression to match architects’ needs remains an open discussion and a topic of investigation. This segment offers to explore these three avenues, through the contributions of architects, scholars, and entrepreneurs whose work addresses the peculiarity and challenges of each theme respectively.

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The Practice The Data Challenge for Machine Learning in AECO by the Applied Research & Development Group, Foster + Partners

The Adoption

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The Practice

After the recession of the latest AI winter, the past decade showed a remarkable infiltration of Machine Learning (ML) techniques in various industries. Meanwhile, within the AECO industry (Architecture-Engineering-Construction-Operation), the question architects, engineers, and contractors alike are still asking is how could ML be used in the built environment in a meaningful (and profitable) manner? Of course, anyone who has used ML knows that the success of the system can only be as good as the quantity and quality of data to which the system is exposed. And there lies the problem with our industry, which is not the lack of data, but rather its abundance in formats that are incompatible with each other and do not match current ML requirements. Practically, the issue is not how ML can be used in AECO but rather how the industry can develop a structured data pipeline tailored to and appropriate for ML workflows.

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The Challenge of Original Datasets and the Value of Synthetic Data The AECO industry is producing vast quantities of data during all stages of a building’s life cycle. This is true not only through design to construction, but also during operation (the rise of IoT and Smart Buildings being the main contributor). Data produced or utilized in the built environment can range from climate and geospatial information through to brief requirements, sketches, drawings, images, performance simulation analysis, 3D BIM models, construction logistics and procurement or post-occupancy data gathered by sensors, 3D scans or HVAC monitoring systems (just to name a few). AECO data is derived from various sources, stored in different formats, and often includes high redundancy. Thus, it requires considerable effort before it can be utilized for any sort of ML endeavor. For it to become useful, it must be normalized. This process, which leads to the creation of meaningful datasets, is where the bulk of 1. S.I. Nikolenko, “Synthetic Data for Deep Learning”, Springer, 2021. 2. Kosicki et al., “HYDRA Distributed MultiObjective Optimization for Designers”, In Design Modelling Symposium Berlin, pp 106-118, Springer, 2020. 3. Abdel-Rahman et al., “Design of thermally deformable laminates using machine learning”, In Advances in engineering materials, structures and systems, pp 1016-1021, 2019.

the work lies. The challenge that the above process poses has led many researchers to the use of synthetic datasets – that is, data artificially generated – rather than original datasets – data collected from actual events or experiments. This has been an accepted practice within many industries, including automotive, healthcare, and financial services1. In the context of Architecture, this type of data can be derived from generative or parametric models, which could be accurate enough to replicate specific properties of the built environment. For large synthetic dataset production, distributed computing pipelines could be utilized – like Foster + Partner’s bespoke system Hydra2.

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)a( Input Displacement

)b(

Training Progress after 2, 10, 50, 200 epochs

Target layering

)c(

)d(

1

1 a,b) sample from the synthetic dataset showing different layering of the laminates and their simulated deformations. c) The model input the displacement values, while the target is the layering of the laminates. In the middle the training progress could be seen, as time passes the model is able to predict a layering close enough to the target layering. d) comparison between the simulated (bottom) and predicted (top) deformation3. 4. Tarabishy et al., “Deep learning surrogate models for spatial and visual connectivity”, International Journal of Architectural Computing, 18(1), pp 5366, 2019.

In collaboration with Autodesk, the Applied Research + Development (ARD) group at Foster + Partners has generated such synthetic datasets to prototype two ML systems – one for designing passively actuated laminates3 (Fig. 1) and another for the rapid assessment of visual and spatial connectivity for office layouts4. Both experiments demonstrated the immense potential in applying ML methods for supporting certain design tasks, and the challenges these pose due to our industry’s lack of appropriate datasets. But while opting to generate a synthetic dataset provides great control on the quality and amount of what is being generated, it is an idealization of reality and often should only be used as a starting point. So, the question remains: how to leverage the industry’s abundant original data?

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Show Me the Data! In recent years, ARD has been developing pipelines that could take advantage of both the agility and ease of use of synthesized data, as well as the 55 years of original data produced at Foster + Partners. One of the group’s earliest investigations was centered around the extraction of furniture layouts from residential floor plan data. This entailed the collection, labeling and augmentation of floor plans (in an image format) and was mainly focused on how this process could not only be mainstreamed, but also automated. While that initial exercise focused on using this data to train design-assist ML models, subsequent research focused on the development of surrogate ML models that could be used for an array of analyses from in-house simulation tools, and more specifically spatial and visual connectivity, a significant driver for planning the

2 Sample floor plans from a synthetic dataset4: 4,000 images of open layout and compartmentalized floor plans were generated along with their respective spatial connectivity and visual graph analysis to be used to train a ML model.

layout of offices (Fig. 2). The input to those analyses were office floor plans, and despite having an abundance of those in-house the decision was made to create a parametric model capable of mimicking spatial and furniture floorplate organization, which was subsequently analyzed and used to train the model. This decision to use a synthetic dataset may seem strange given the massive amount of data collated through the years in our arsenal. But retrieving any useful information from it is not a straightforward proposition. While (or because) data retention is straightforward and – nowadays – relatively cheap, not much thought is given to which data is valuable or how to retain it in a consistent manner. Additionally, for big companies operating for decades, there is

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2 rarely a clear definition of the lifecycle of a piece of data within a team, let alone cross-department or even cross-business. In that sense, trying to dig through the archives looking for specific data, taking into consideration the number of formats produced from various software, under ever-changing file naming conventions has proven to be much more challenging than developing the ML model itself. This is a hurdle that is removed from synthetic datasets, where quite a lot of thought has been given in advance to the way data will be created. This usually entails proper tagging, labeling, or captioning of different elements, comparable formats, and consistent naming conventions. The above investigations made one thing abundantly clear: special pipelines and tools need to be put in place to allow for the smart retrieval and labeling of data; the process of curating a task-oriented dataset must not include excessive ceremony. One approach could be processing data during its creation, allowing the encapsulation

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The Adoption

Asset Categories of pre-existing knowledge about the data to the medium it is being stored or archived in at the source. Structuring data like that is both challenging and time-consuming; how does one pick a system and a structure that is rich enough, transdisciplinary, ages well and is user-friendly? At Foster + Partners, we are in the process of specifying data structures that allow for ease of traversability and cross-referencing. In the meantime, we have been developing tools that facilitate the retrieval of items of interest from our on-premises data stores. One such tool is our bespoke File-Seeker, a parallelized breadth-first tool for traversing file system directories on network drives. The way the tool was designed allows for plugging in different file format pars-

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ers to search the content of the files for elements or labels of interest. Although it allows the user to traverse millions of files in only a fraction of the time compared to publicly available search tools, the

3 A diagram showing the allocation of data under specific file format categories both in terms of raw size and number of files as a percentage from a total of around 74 million files on our warm storage constituting data for only the currently active projects. Images and PDF/page layout files being collectively the highest in size and number.

application really depends on some assumptions associated with name conventions, file type and folder locations being true before starting the search. These limitations led us to our current investigations (Fig. 3), where we are evaluating different techniques for crawling and indexing data, using non-intrusive ways for “on-theedge” labeling, and tagging of newly created daily data. Adding a semantic layer on top of all data streams produced in the office can make it format-agnostic, which in turn would result in an easy, company-wide access to information through an integrated desktop application ecosystem.

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Future Outlook Historically, the AECO industry is well known for being highly resistant to change. This characteristic, paired with the emerging nature of ML research, is posing a lot of challenges on deep integration of ML. To take full advantage of ML, AECO first needs to reassess its standing as a data-driven industry. Being a data-driven organization requires a comprehensive approach, where its corporate culture and tools are tailored in such a way as to deliver high-quality products (designs) at a faster pace than traditional practices. With ML, this translates into building a data pipeline: a live ecosystem which collects and combines data coming from various sources and disciplines before it is used in predictive models. In a ML pipeline, incoming data is transformed through a series of steps, 5. Treveil, Omont, Stenac, Lefevre, Phan, Zentici, Lavoillotte, Miyazaki & L. Heidmann, “Introducing MLOps”, O’Reilly, 2020.

linking data and code to produce models and predictions5. Since new data is being gathered all the time, such systems need to be constantly updated and redeployed, there also needs to be a governance framework to evaluate and manage the lifecycle of data and models.

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This idea of continuous product deployment is encapsulated by Machine Learning Operations (MLOps)5. Those ML models, automatically performing specific design tasks, could be part of a bigger system that still relies on designers’ experience. For this to work, data must flow freely between design stages, departments and different companies, which highlights the importance of data interoperability — traditionally one of the biggest bottlenecks for the AECO industry. The processes mentioned above are as much an opportunity as they are a challenge. Their success depends not only on the amount of available data, but also on other factors, like capacity and willingness for change, availability of financial and other resources, existence of implicit biases or even corporate culture, all of which can provide either a massive leverage or an insurmountable challenge. But the bottom line is this: whoever succeeds in putting these structures in place will be in an incredibly advantageous position in the future; data is power, and ML goes a long way in providing the means to harness it.

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The Model Shadowplays: Models, Drawings, Cognitions by Andrew Witt, Associate Professor at Harvard University Initial publication in Log #50

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The Model

“Model is a generalization, form is a special case.” – Buckminster Fuller, Synergetics 2, 1979

In its classical guise, an architectural model is both a tangible artifact and a proxy for the imagined reality that it is designed to resemble. With its volume, shadow, and tactility, a model stakes a claim for an architectural idea as embodied fact. A model looks like a building and vice versa: they share a common and specific form, conjoined as a dyad of original and copy. They exist together in a zone between actual and ideal, fact and fiction. A model is an intermediary between appearance and imagination, anchored in the form of a specific object. If an architectural model is a designed artifact, a 21st-century scientific model is more mathematical or logical than physical and spatial. As philosopher Ian Hacking notes, “A model in physics is something you hold 1. I. Hacking, “Representing and Intervening: Introductory Topics in the Philosophy of Natural Science”, Cambridge University Press,1983.

in your head rather than your hands”1. Climatic, economic, and ecological models are all austere mathematical systems instead of thick objects. Visual resemblance is irrelevant to the behavior of systems that scientific models aim to encode and make operative. If the architectural model relies on resemblance, the scientific model rests on the numeric language of deep and hidden structures. When the two poles of architectural and technoscientific models are juxtaposed, a spectrum of practices opens between them. Archi-

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tectural models evoke specific imaginations through tangible material objects, while logical scientific models posit the mathematical interactions of abstract entities and phenomena. Yet separating the disparate tactics of architectural and scientific models is ever more confounding due to the proliferation of digital instruments that surreptitiously import the modus operandi of the exact sciences into the practice of design. Abstract varieties of quasi-scientific digital models are increasingly supplanting physical models whose function rests merely on appearance. The once tautological connection between model, resemblance, and representation in architecture is giving way to new relationships between epistemic abstraction and technique. What is emerging is an increasingly scientific intuition – if not an explicit understanding – of model as a creative and evaluative matrix that exceeds the scaled specification of a single building. By attending to models that are not precisely architectural but on a continuum between architectural and technoscientific, the roles, possibilities, and futures of architectural modeling can be critically reframed. In particular, the dichotomization of visual resemblance and instrumental abstraction that dominates discussion of models can be critiqued and perhaps overcome. If the building is dislodged as the exclusive focus of model representation, the more disciplinary functions of the model emerge, such as its capacities for cultural encapsulation, propagation, and diffusion. Here I consider two types of models – skiagraphic models of 19th-century shadow rendering and image-based neural network models of 21st-century Artificial Intelligence – that abandon any pretense of resemblance to buildings in favor of more abstract roles within the knowledge culture of design. Each type of model brings visual and mathematical rigor – geometric rigor for the skiagraphic model and statistical rigor for the neural

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model – to bear on systems of perception and representation. Their function is not merely instrumental: both play critical roles in encoding visual practices beyond the direct specification of buildings. In both episodes, models are not scaled miniatures but tools for training architectural perception and creation by both humans and machines. In their philosophical account of scientific models, the biomathematicians Philip Gerlee and Torbjörn Lundh remind us that the word model descends from the Latin “modulus, a diminutive form of modus, mean2. P. Gerlee and T. Lundh, “Scientific Models: Red Atoms, White Lies and Black Boxes in a Yellow Book”, p 123, Springer, 2016.

ing a small measuring device”2. A model, then, is a ruler against which to gauge, to delimit, and to judge. Models are devices to dimension not only buildings but the culture of architecture – its practices, conventions, styles, and processes. Yet the appearance of buildings is never a distant concern of the architect, and even the relentless abstractions of scientific models can be hacked for freshly intense and unexpected kinds of design invention. New computational forms of neural vision open up strange kinds of glitched, warped, and liquid transformations. In this respect, visuality mediated by calculation models confirms the philosopher of science Bas C. van Fraassen’s observation that “distortion, infidelity, lack of resemblance in some respect, may in general be

3. B. C. van Fraassen, “Scientific Representation: Paradoxes of Perspective” p 13, Oxford University Press, 2008.

crucial to the success of a representation”3. These new models are engines to mutate representation itself.

Modeling Shadows Architectural models are specific artifacts, but they are also evidence of disciplinary vision and concretized conventions that invite the user to behold the idea of a building in a particular way. The status of architectural models in the larger pantheon of representations is brought into relief through their sometimes peculiar and even

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competitive relationship with architectural drawings. Models rarely exist alone as the only representations of buildings. Instead, they are one in an entourage of other representations – drawings chief among them – that collectively delineate the world of a project. Modeling and drawing are conjoined practices and nowhere is that more apparent than in the atmospheric realm of shadows. With its illusion of solidity, the shaded drawing seems to exceed the merely documentary qualities of technical plans and adopt the appearance of a three-dimensional model. This conflation between drawing and model is readily discernible in the artfully rendered drawings of the 19th-century French Beaux-Arts architects. Buildings were drawn as if they were models, shadows cast as if the sectional thickness were cut away. Among the most facile hands was Jean-Jacques Lequeu (1757–1826), whose remarkable drawings seem to close the gap between drawing and model. The shadows cast in Lequeu’s interiors evoke his contemporary Jean-Baptiste Rondelet’s monumental sectional maquette of the Pantheon in 4. “Les règles de la Science des Ombres Naturalles,” quoted in P. Duboy, “Lequeu: An Architectural Enigma”, p 14, MIT Press, 1987.

Paris as much as they do actual buildings. Much scholarship on the enigmatic Lequeu’s work rightly focuses on his flamboyant imagination or meticulous craft. Yet shadows held a definite priority in Lequeu’s technique. His “Architecture Civile”, an unpublished drawing manual in which he claimed to outline “the rules of the science of natural shadows”, is substantially devoted to the tonal and

1 Plans, elevations, and sections of two domed projects of the French architect Jean-Jacques Lequeu, assembled as a single drawing, and rendered with precise shadows.

geometric intricacies of rendering shade4. In Lequeu’s drawings, we see a virtuosic manifestation of skiagraphy, the projective science of rendering shadow. As a disciplinary practice, skiagraphy altered the common precedence between drawings and models. To produce a skiagraphic drawing, the meticulous

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draftsperson projectively constructs the shadows of a complex

A skiagraphic construction from Jean-Jacques Lequeu’s manuscript Architecture Civile, ca. 1820, showing the classical technique derived from descriptive geometry. Source: Bibliothèque Nationale de France.

model – a building, a fragment, or an entirely contrived object – with

5. W. Muschenheim, “Curricula in Schools of Architecture: A Directory”, Journal of Architectural Education 18, no. 4, p 56, 1964.

utable schools of architecture, such as the Bartlett, into the 1960s5.

exquisite precision. In its pure form, skiagraphy was an academic exercise to mold a visceral intuition of light. Between the early 19th and mid-20th centuries, skiagraphic drawings were de rigueur in architecture schools across Europe and the United States. The practice persisted well into the 20th century and was taught at repAs much as almost any architectural drawing practice, skiagraphy defined a family resemblance among architectural drawings of a certain style and from a certain period. What is the object of representation in a skiagraphic drawing? The apparent focus of depiction would seem to be the model itself. But in fact, skiagraphic models are more like props, merely incidental to the object’s shadows, which are the proper focus. The true test of the draftsperson’s skill is not the rendering of the model per se but rather the rendering of the epiphenomenal shadows. The secondary effects of the shadows are elevated to the primary object of attention. Drafting these shadows entails a sophisticated and systemic analysis of the atmospheric conditions that surround the model as well as the occluded geometry of the model itself. In other words, skiagraphy requires a concept of a world system that the rendered model inhabits. Unmoored from representational obligations, the model becomes a pretext for disciplinary training. The model can thus take on functions

6. Thomas DaCosta Kaufmann, “The Perspective of Shadows: The History of the Theory of Shadow Projection”, Journal of the Warburg and Courtauld Institutes 38, p 258, 1975.

that ignore the conventions of building per se and instead attend to systems of representation themselves. In his account of shadow projection, art historian Thomas DaCosta Kaufmann calls practices like skiagraphy “modeling shadows”6. Kaufmann notes that specially constructed models played an essential role in the training of Re-

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2 naissance painters’ visual intuition for the construction of light and shade: “Vasari says that his contemporaries continued to use ‘rounded’ models of clay or wax before drawing their cartoons, in order to 7. Ibid., p 260. 8. M. Hirst and C. Bambach Cappel, “A Note on the Word Modello”, The Art Bulletin 74, no. 1, pp 172–73, 1992.

see shadows in sunlight. He says that Michelangelo had used models and that the sculptor Jacopo Sansovino had supplied wax models for a number of painters”7. The models Michelangelo and other painters used were physical props or maquettes that furnished the space of a painting8. These models were not intended as representations, ex-

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cept in the highly indirect fashion that they simulated the shadows of specific figures. It was the resemblance of shadows that mattered, not the resemblance of model to object. Instead, models were expedients to initiate practitioners and introduce a disciplinary way of seeing. In 19th-century France, skiagraphy spanned architectural and engineering practices and thus inevitably impinged on technoscientific culture. In Paris, then the European nucleus in the professionalization of both architecture and engineering, skiagraphy was an indispensable part of both architectural training at the École des Beaux-Arts and engineering training at the École Polytechnique. As the birthplace of Gaspard Monge’s descriptive geometry, France was a fertile soil for a quasi-scientific model of skiagraphy to take root. Monge developed a theory of shadows that was an integral part of later editions of his Géométrie descriptive and which led the way for a considerable literature of manuals, including Lequeu’s, for training aspiring draftspeople 9. See Gaspard Monge and Barnabé Brisson, Géométrie descriptive: augmentée d’une théorie des ombres et de la perspective extraite des papiers de l’auteur (Paris: Gauthiers-Villars, 1922). This publication followed Monge’s 1798 edition published by Baudouin in Paris.

in “les dessins des ombres”9. French sculptor Eugène Guillaume, who straddled the porous boundary between the arts and engineering as both director of the École des Beaux-Arts and professor of drawing at the École Polytechnique, framed the pedagogical philosophy of skiagraphic studies as a precise mathematical exercise: “The very essence of drawing is purely mathematical, since the only two modes by which it can be envisaged, the geometrical or the perspectival, both that which would be applied to draw lines and to trace shadows, rest on exact laws: the truths of mathematics. This manner of consideration is justified by language that the artist and the mathematician each employ in their own sphere, using the same words of line, plan, proportion, symmetry, equilibrium, and retaining the same meaning... drawing itself is a science”. Drawing shadows was not an act of pure intuition and perception, but rather a deliberate practice of exact construction.

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In a skiagraphic drawing, the raison d’etre of the models was to provide an entry into a representational system and a suitable challenge of skill for the eye, mind, and hand of the composing draftsperson. Models functioned as pretexts for a specific mode of disciplinary training. Through them, the architect was viscerally and autonomically sensitized to the behaviors, moods, and subtleties of shadow. Physically, skiagraphic models could be abstract platonic forms, odd and awkward assemblages, mechanical contrivances, mathematical maquettes, or fragments of actual architectural models. In the Beaux-Arts context, Corinthian capitals or fragmented entablatures arranged in still-life tableaux were favorite examples. Other skiagraphic models were improbable piles of odd forms, generative devices designed to induce as much variety and difference in their shadows as possible. Skiagraphic models were furniture in a regime of a highly mathematical representation. Not all skiagraphic models were even physical maquettes. They could be, and often were, more abstract entities fancifully imagined and projectively constructed entirely within the drawing itself. Whether physical objects or mathematical entities, the models’ highly contrived forms were intended to probe the limits of representation, beholden not to the demands of building but rather to the private and autonomous conventions of architectural drawing. The skiagraphic model was a parafactual entity that existed purely as the linchpin of a specific practice of architectural seeing and drawing. The model had a definite physical presence and form, yet that form was secondary to its role as an object of initiation. It never served to clarify or expedite the design of a building. On the contrary, it defined the architect, her intuition, and her vision. In that sense it was an entirely cultural artifact, a resolutely architectural model that nevertheless had nothing to do with building, only with seeing and drawing.

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Beau-Arts Deepfakes In the 1950s, a quite distinct kind of model began to emerge in the work of mathematical psychologists interested in visual perception. These were not models of individual things (like buildings) or even models of explicit systems (like skiagraphic geometry) but models of perception itself. In his remarkable 1950 paper “Mathematical Biophysics, Cybernetics, and Significs”, mathematical psychologist Anatol Rapoport was among the first to use the term “model” to describe the function of neural networks, the reticulated structures posited as the basis of organic nervous systems. He recounted the convergence between biological perception and electronic calculation: “The two programs of research, a mathematical theory of the nervous system on the one hand and the development of electronic computers on the other, proceeded along parallel lines . . . Workers from both fields soon found themselves talking to each other in a language which was a curious mixture of psycho-physiology (neurons, synapses, refractory periods, threshold, etc.) and electronics (feedbacks, vacuum tubes, amplifiers, transformers, 10. A. Rapoport, “Mathematical Biophysics, Cybernetics and Significs”, Synthese 8, no. 3/5, p 189, 1950-1951.

etc.).”10 When encoded electronically, these new neural models took on the comportment of human or animal reflexes, reactions, and cognitions. They could be “trained” and generate new internal associations in response to serialized stimuli. In short, they could learn to sense. One of the earliest computational neural networks was an optical mechanism for the discretization of light and shadow. In 1958, psychol-

11. F. Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”, Psychological Review 65, no. 6, pp 386–408, 1958.

ogist Frank Rosenblatt introduced the perceptron, the mathematical framework for an interconnected matrix of retinal sensors configured to detect gradients of illuminance11. Rosenblatt attacked the assumption of eidetic resemblance between model and modeled object in neural representation, claiming that “the images of stimuli may never

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really be recorded at all ... the central nervous system simply acts as an intricate switching network, where retention takes the form of new 12. Ibid., p 386.

connections, or pathways, between centers of activity”12. Rosenblatt’s argument is suffused with multiple models: coded models, physiological models, conceptual models. As befitted an essentially electrical apparatus, the neurons of Rosenblatt’s network could be readily and continuously tuned to inculcate particular kinds of visual training. In the 70 years since Rapoport’s account of neurocomputation, models that learn have made fitful but dramatic advancements toward a distinctly novel fusion of perception, computation, and creation. New tactics of deep learning like generative adversarial networks have relentlessly pushed the limits of computational perception and affiliated techniques of image generation. Generative networks are models that are not explicitly and deliberatively theorized but instead emerge autonomically from iteratively applied statistical encodings. Taking enormous archives of images as feedstock for training, neural models build matrices of statistical probabilities that encode perceptual and creative behavior. On the one hand, the process of encoding a neural network is akin to the use of compression and encryption algorithms, distilling the vast array of images to numeric correlations. On the other hand, neural training is like teaching a child to recognize and draw shapes through the reinforced repetition of countless examples of ascending complexity. Image by image, a distinct intuition is formed through cybernetic feedback. If classical architectural models were explicit physical representations, neural net models are black-boxed codices of relational connections. Though they are computationally deterministic, neural networks are not rulesets per se. Instead of semantically articulated rules, neural

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3 A matrix of architectural drawings generated from a neural network trained on thousands of Beaux-arts drawings, including plans, sections, and elevations. In many cases those drawing types are hybridized or morphed into a new and ambiguous arrangement through the encoding of the neural network. Project team: Andrew Witt, Gia Jung, Claire Djang, 2020.

models are vast ledgers of numeric correlations. Derived from probabilistic and statistical associations as opposed to explicit logical rules, when visualized in their raw form these models appear almost as noise even to the educated eye. Yet the imagery produced by suitably trained models is marvelously specific and inescapably legible. When trained on the Beaux-Arts drawings of Lequeu and thousands of others, a neural model begins to draw in the luxurious style of figured volumes, filigreed details, and crepuscular shadows as convincingly as any suitably trained draftsperson. What emerges in these drawings is a statistical skiagraphy: shadow rendered not with the constructive principles of descriptive geometry but with the stochastic processes of neurocomputation. The unmistakable forms of domes, colonnades, entablatures, and other telltale elements of the Beaux-Arts vocabulary appear in a mannered chiar-

4 A matrix of sections generated from a neural network trained on thousands of Beauxarts drawings. Though derived entirely from unsupervised training, a characteristic technique of shadows begins to emerge. Project team: Andrew Witt, Gia Jung, Claire Djang, 2020.

oscuro calculated from tensors of probabilities. The images have impressionistic and atmospheric qualities, as if seeing the precise lines of the building through a light fog. There is a striking vagueness about these images; they are more sketches than renderings. Yet that belies their calculational precision, and what may glancingly appear as fluid washes of watercolors are actually grayscales calibrated by an exact science of machine learning. Neural networks not only capture and reproduce the specific atmospheric subtleties that elude more procedural means of computation, they also have the capacity to blur the line between distinct forms of architectural representation. If neural models are trained on several different genres of drawing, they fluidly hybridize all these disparate types in their generated images. Mutations of plan, section, and perspective are vivisected into strange new quasi-montages that seamlessly blend them all together. Plan melts into section, coalescing or dissipating through the technical intermedi-

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ary of the neural model. Like skiagraphic models, the drawings do not refer to specific buildings and, indeed, often depict something that defies consistent interpretation. Instead, they are artifacts of pure disciplinary representation. In this they resemble another BeauxArts staple, the composite drawing juxtaposing plan, section, and elevation in one patchwork image. The neural model becomes a mixing chamber to reformat all the myriad forms of modeling and drawing in a common and continuous visual language. In circumscribing and relating a corpus of images, neural networks delimit specific territories of imagination. More than models, neural networks are maps. They statistically interpolate disparate images and thereby plot a gradient of interstitial architectures. Instead of modeling one form or a discrete set of forms, they offer a model of visual invention itself, and with it, a continuous and seamlessly variable terrain of endless and endlessly different forms. Like the Situationist dérive, latent walks through the neural space allow the user to wander through the dream space of a trained intuition, each step generating a new and surprising result. This continuity is of a totally different order than parametric or combinatorial variation, where incremental changes of scale or density retain the essential topological organization of a space. Instead, neural variation ranges across type and topology, setting up liquid interpolations between improbable and chimerical forms.

Modelers, Human, and Machine Despite their mathematical origins, neural models transcend the strictly deductive and largely instrumental methods of most contemporary scientific modeling. As models of thought and perception, they expose the qualitative vagaries of taste and style to exact modeling. In doing so, neural models confound the direct relationship between visual

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resemblance and representation. Resemblance is merely a secondary effect of a model that can learn and manipulate entire representational systems. In this paradigm, the architectural model of the future will take on increasingly protean roles, representing not only specific buildings but also the disciplinary intuitions and cultural nuances of architecture itself. At one time, a finite and specific architectural model might have been interpreted as a representational terminus, a definitive conclusion of a search, or the birth of an architectural idea in an embodied reality. Even today, most digital 3D models merely replicate or amplify the qualities of eidetic resemblance characteristic of physical maquettes. In shifting from embodiments of specific forms to generalized encodings of representational processes, models become ever more expansive and open-ended vessels of design culture. Now even imaginations, aspirations, and obsessions are amenable to modeling. Once proxies for buildings that were manipulated by architects, models are on the verge of integrating the operative tastes and judgements of the architect herself. Inanimate models and human authors have always maintained unconfused and distinct places in architecture. As artificial intelligences model, incubate, and encapsulate cognition, that careful distinction between made and maker, thought and thinker may seem as antiquated as physical maquettes themselves. Between the maquette and the architect there is a new actor and mediator, the quasi-intelligent model that embeds human intuitions and hallucinates endlessly elastic images, drawings, and buildings. In the realm of imagination, the gap between the neural-generated deepfake and the human-imagined model is eroding. When models can download and contain patterns of thought, the cease to be distinct objects and simply become the way architecture is created.

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The Scale Scaling AI in the AEC: The Spacemaker Case by Carl Christensen, Co-founder and CTO at Spacemaker AI

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In late 2016, Spacemaker AI was incorporated in Norway. We the founders — Havard Haukeland, Carl Christensen, Anders Kvale — formulated a bold vision to fundamentally change the way design, engineering and project teams worked. Frustrated with the inefficiencies of the planning process, especially in the early stages, we saw an opportunity to find a better way to design our cities. Did something like this already exist, or if it didn’t, would it be impossible to build? We set out to find a way to realize our ambition and, in the process, create something that would have a global impact. Bringing Spacemaker to life has been both a joy and an epic undertaking — not to mention the challenge to change an entire industry that has largely remained unchanged for decades. Central to our vision was a plan to create a game-changing AI technology platform that would empower practitioners all over the world. However, utilizing AI to successfully solve these challenges would require designers (ie. primarily architects) to not only adopt, but to truly embrace this technology into their workflow on a massive scale.

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Impediments to the Adoption of AI Technology in AEC Design

By its very nature, AI has an air of magic and mystery to it: taking complex information, processing it, then quickly and effortlessly providing predictions or suggestions where humans and traditional computation struggle. While these properties contribute to making the idea of AI alluring and attractive, it can also become an impediment to adoption. Applying AI in a design process in AEC, we argue that this is particularly true. In many processes augmented by AI, there is a yardstick with which to measure — a reference for quality. In applications like predicting the likelihood of rain tomorrow, or the most effective route to take while avoiding traffic jams, results can be discussed with a high degree of objectivity. The design process, however, is deeply subjective. There is no commonly accepted norm for perfect design. Any suggestion from an AI (or a human) is inherently subjective. In such a process, a “Black Box” AI quickly becomes a challenge. While a human can contextualize and make a compelling argument for the merits of their proposal, an AI cannot. If we were to overcome the challenges of subjectivity, we still need the AI to capture intent. When observing a design process in AEC,

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intent is driven by a multitude of local conditions, soft and hard constraints as well as subjective preferences and needs. Capturing and describing all of these, and requiring the “operator” of the AI to enter them all a priori is impractical and likely infeasible. This makes the AI inherently hard to control. An often underestimated challenge with AI is the technical complexity of operating it. While interesting results found in singular experiments are common and frequently published, these results often require significant setup and fine tuning to work, with a human-in-theloop evaluating results for feasibility. The technical competence and resources required for such an undertaking makes AI inaccessible to most practitioners. Another challenge is intellectual property. If the AI learns from me, does it also steal? How can I trust that as I use it, it does not become smarter at my expense? When the sources of knowledge and effects of training become unintuitive, it becomes harder to gain this trust. But most importantly, to be adopted, workflows enabled by the AI would need to be attractive and compatible with the creative process of design. At its core, this process is both incremental and iterative in nature. A designer wants to interact with and augment a proposed design, and stakeholders want to have their say. Compromises must be made. An AI that creates “finished’’ design proposals by taking in information and turning it into designs, is neither iterative nor incremental in nature. Rather than augmenting the process, it replaces the process, becoming a competitor to the designer, not a complement.

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The Spacemaker Approach Facing all these challenges, but determined to succeed, we started out with a very conscious approach to the identity of the Spacemaker AI platform. If it had been a person, how would we describe it? Rather than being perceived as an “oracle” providing opaque, finished designs, Spacemaker was to be a sage; a knowledgeable and wise advisor helping the user in their pursuit of better designs and outcomes. For good measure, we added a sprinkle of magic to the identity: enough to be exciting and alluring, but humble enough to avoid detraction. Fully embedding AI elements in a process requires meaningful ways to connect with the existing process. In Spacemaker’s case, we did not see how it would be possible to leverage existing tools or workflows in making our value proposition viable. No cohesive platform existed where those workflows existed. Information was fragmented, and no design tools were built to collect or process the types of information we needed. We decided that we needed to build a fully integrated design platform from scratch, encompassing the necessary data capture, design process and evaluation for desired outcomes that we required. This included automatically setting up a detailed digital twin of the physical environment of the proposed design, and building fully automated, powerful simulations to predict the impact of design options and

1 Spacemaker’s various predictive analyses for a given site.

changes to sustainability, buildability, zoning and constraints (Fig. 1). A crucial premise in building the platform was to be technology-agnostic. While, in principle, it was clear that AI would be a key enabler,

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Facade Daylight Analysis

Facade Sunlight Analysis

Noise Analysis

Wind Speed Analysis

1 we needed to be focused on outcomes for users, not technology. Value creation to users would be the only priority. As a result, we would be very pragmatic to the definition of AI. The proof of value would be results and adoption, rather than academic scrutiny. In order to make our platform accessible to users with minimal friction and technical competence, we built a fully web-based Saas environment, powered by elastic, serverless cloud services. In so doing, we could “productize” AI, encapsulating complexity and the resource intensive systems needed to deliver and continuously evolve our offering. In addition, an uncompromising focus on ease of use and accessibility that would allow non-technical users to adopt the platform with little or no training would be key. To gain trust, we formulated terms of usage in clear language, describing ownership of data and how training is performed. In combination with the incremental nature of our design assistance, this instills confidence that contributing to the platform’s learning does not unreasonably extract or replicate specific designs.

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The Adoption

How to accomplish the goal of supporting and augmenting the designer rather than becoming a competitor? We realized that we needed to avoid the “Black Box” fallacy. Rather than view the design process as an “input/output” problem, we dived into the increments and iterations that make up the unpredictable journey from idea to finished product. Thinking of automation and AI as design assistance rather than “generative design”, providing an “AI on the shoulder” supporting the designer towards intended outcomes, we broke capabilities down into small parts, offering a multitude of different ways to “nudge” a design forward, rather than “pushing” too far.

2 Multiple massing suggestions in the Spacemaker App for a given site and a set of user-specified constraints.

3 Multiple apartment layout suggestions in the Spacemaker App, given a program mix, and selected typologies.

4 Sketching an apartment building in the Spacemaker App.

The designer is always in control of selecting the appropriate level of control at any given time. Going wide, the designer can ask for a handful of options for a scope she controls, ie. one part of a site (Fig. 2) or a floor (Fig. 3). Exploring an idea, she can sketch with simple lines (Fig. 4) or build on components of previous designs with embedded knowledge. Going deep, she can perform detailed freehand design. All of these methods, can be fluently combined and iterated upon. At any given time, increments of “magic” are so small you can intuitively accept them as meaningful. Building blocks of logic empowering subjectivity. The AI disappears into the fabric of the creative process, and the user forgets it is there, helping her focus on intent and outcomes. In focusing on outcomes, stakeholders are a key part of a successful iterative design process. To empower a collaborative process we built the platform to be deeply collaborative, where a team of stakeholders shares a common truth, providing input, understanding different perspectives and coming to terms with the many inevitable compromises.

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4 AI has the potential to empower designers to imagine and realize a better built environment for humanity and a better tomorrow for our planet. For this to become reality, we believe that AI needs to be disseminated into the architecture practice at scale. With Spacemaker, we strive to do our part in contributing to this shift and as we observe the increasing number of initiatives in academia and the industry doing the same, the future of AI in design is brighter than ever!

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The Outlooks of AI in Architecture

The Prospects

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Independently of AI’s immediate contributions and potential adoption, the architectural agenda is filled with longer-term prospects. Among them, at least three seem about to evolve with AI’s advent: the style, the ecology and the language. The notion of style, first, belongs to Architecture’s core concerns. For historical, cultural or functional reasons, style conditions the form of any architecture. AI revives this discussion by offering new ways to study the diversity of Architecture’s stylistic landscape. Ecology, then, stands as yet another pressing contemporary matter for the discipline. The significant impact of the built world on the environmental balance sheet calls for a more informed design process. AI can provide architects with the means to address certain critical ecological dimensions of Architecture, and contribute to the discipline’s broader environmental strategy. Finally, the language and its analogy with Architecture is a long-standing discussion. Concepts borrowed from linguistics now provide expressive frameworks to Architecture. AI can renew this analogy by providing the discipline with an alternate schema. The upcoming segment rounds up this chapter’s theoretical landscape. The articles gathered in this last part explore plausible scenarios for Architecture, as AI could soon shed new light on preexisting crucial discussions in our field.

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The Style Strange, but Familiar Enough: Reinterpreting Style in the Context of AI by Matias del Campo & Alexandra Carlson, SPAN, Michigan University

The Prospects

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1. L. Wright, “In the Cause of Architecture, II. What ‘Styles’ Mean to the Architect”, Architectural Record, 1928.

Architecture has a very complicated relationship to the term

2. M. Carpo, “Digital Style”, Log No.23, Anyone Corp, pp 41-52, 2011.

proposed to rid the discipline entirely from the term style4 in an

3. N. Leach, “There is No Such Thing as a Digital Building, A Critique of the Discrete”, AD Architectural Design, No. 89, Issue 2, Wiley, London, UK, pp. 136-141. 4. H. Muthesius, “Stilarchitektur und Baukunst”, Verlag v. Schimmelpfeng,1903. 5. S. Giedion, “Space, Time, Architecture”, Cambridge University Press, 1941. 6. W. C. Behrendt, “Der Kampf um den Stil im Kunstgewerbe und in der Architektur”, Deutsche Verlag, 1920. 7. W. C. Behrendt, “Der Sieg des neuen Baustils”, Fritz Wedekind, 1927. 8. P. Behrens, “Stil?”, Die Form: Zeitschrift für gestaltende Arbeit, 1: pp 5–8, 1922. 9. R. Hausmann, et al. “Aufruf zur elementaren Kunst”, De Stijl, 1921.

“style”. It is a charged term1; it’s a loved term2, it’s a despised term3. Ever since the German architect and writer Hermann Muthesius attempt to cleanse the domain from the frivolous formalistic escapades of the 19th century and its historicism, the discussion has been ongoing weither style at large is a valid area of inquiry in the architectural discourse at all. Sigfried Giedion, the quintessential modern architecture critic, vehemently criticized the concept of style, proclaiming that “There is a word we should refrain from using to describe contemporary architecture. This is the word ‘style’. The moment we fence architecture within a notion of ‘style’, we open the door to a formalistic approach”5. If style was a taboo for some – such as Hermann Muthesius – for others, such as the influential architecture critic Walter Curt Behrendt, it represented a cornerstone of the discipline, in that new styles were both intrinsic and necessary6,7. Others, like Peter Behrens, pondered the idea that style is nothing but the result of the design process. Difficult, if not impossible, for contemporaries to discern8. There were even calls to abandon style to discover a new style (Style 2.0?), using negation to affirm the fundamental importance of style9.



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1 Despite these attempts to get rid of the term, the practice of categorizing buildings with specific similar features into a style has prevailed.

1

Style is like a Zombie; it is undead – neither really dead nor really alive – it repeatedly emerges in conversations about architecture. For

A walk through the latent space (learned visual space) of a Gothic architecture dataset.

example, the notion of style is inescapable when dealing with ques-

10. J. V. Maciuika, “Art in the Age of Government Intervention: Hermann Muthesius”, Sachlichkeit, and the State, 1897–1907.

self recognized this. He rejected the Bauhaus, which he inspired and

2 Four snapshots, taken during the training process (see previous figure), displaying the model's gradual improvement over time. 11. I. Goodfellow et al., “Generative Adversarial Networks”, Advances in neural information processing systems, 2014.

tions about the history of architecture. Who would refuse well-established terms such as Baroque or Gothic? (Fig. 1) Even Muthesius himhelped form as “Just another Style”10. To this very day, this discussion rages on. Reject or accept that style is part of architectural inquiry? The recent, impressive advances in the field of machine vision, specifically Deep Neural Networks, have thrown this discussion of both historical and new styles into a new light, as well as what “style” can be. Deep Neural networks are algorithms loosely based upon the human visual system11. They can take in vast corpora of images (Fig. 2), more significant than any human or groups of humans can process, and learn to extract salient visual features from images that allow them to achieve an often greater-than-human level of performance on visual tasks like image classification. These algorithms are

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trained similarly to how architecture students are trained; they are

shown a set of images, curated by a human, and are provided a supervisory signal that guides how they learn a style like “Baroque”, etc. However, in contrast to architectural education, where a professor will tell the students which specific visual features define a particular style, (for example, the presence of fluted columns, ellipses, and voluptuous figures define a Baroque object) the visual features that neural networks learn to extract is only constrained by what visual information is present in the training data and the network’s task performance. Humans as trainers of these algorithms do not engineer or specify them beforehand. Style can be defined by the statistical distributions of visual features that end up being learned by neural networks; their learned features capture the probability of specific texture distributions based upon how they exist in the training dataset or in a given image. This data-driven style does not consider the context through which to understand it. For example, the lens of intellectual interrogation; Neural Networks lack the ability for a crucial discussion around aspects of style referring to why a building has come into being. Motivations behind the design, such as a particular theory, ideology, or political conviction, are a priori missing when training/ collecting datasets or in labeling. While Architecture has been hauling the baggage of debating style around for the entirety of the last century, it seems that the debate about the term style is way more innocent in computer/machine vision circles. In machine vision, the term is clearly used to describe

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3 the collection of visual features that capture a specific morphological quality of an object. Recognized features would include, for example, symmetry, proportion, and repetition, as well as spatial and compositional techniques. In the context of machine vision, style refers to how architecture is manifest; it indicates the specific ornamental motives, 12. D. Ascher Barnstone, “Style Debates in Early 20th-Century German Architectural Discourse”, pp. 1–9, Architectural Histories, 2018

material palette, color, pattern, construction, and technical systems. This means that computer vision scientists can describe “Hans Hollein” or “Coop Himmelblau” as being a style, whereas art historians or architects would not. Or, as Deborah Ascher Barnstone put it: “When style refers to why a building has come into being, it alludes to the

3 Exploring the “Style” of SPAN. A collection of 2243 images created by SPAN between 2010 and 2020 was used as a dataset for the StyleGAN2 neural network. Walking through the learned visual space of the SPAN design universe.

motivations behind design, such as satisfying functional imperatives, site conditions, a spiritual movement or a philosophical concept, or responding to societal circumstances”12. However, through the lens of algorithmic, data-driven Style (Fig. 3), the definition of style within the realm of Architecture starts to change, transform, mutate, and produce new and strange objects.

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These generated objects are not a copy of existing styles, even though those objects are based on existing data in the form of historical architecture images. It is not merely a copy; it falls into its own category. The result presents a provocation for the architect’s 13. G. Harman, “Weird Realism: Lovecraft and Philosophy”, p 93, Zero Books, Hants, 2012.

mind: what are we seeing in the strange, defamiliarized13, and alien images resulting from this process? As Demis Hassabis, the CEO of DeepMind, explains, there are three categories that need to be observed in this case: aspects of interpolation within a dataset (which machines are very good at), aspects of extrapolation (a profoundly human ability), and invention – the last one being profoundly difficult

14. Lecture on Creativity and AI by Demis Hassabis to the Royal Academy of Arts, September 17th 2018.

to achieve even by humans, let alone machines14. This statement epitomizes the tension between style as it is known in machine vision and style as it is known in architecture: data-driven style is not a new architectural style; it is a mash-up of existing architectural styles, of textural and geometric features that have been captured by a given dataset or image. The results remind us of what we have seen; they are familiar but strange. Behrens observed that “every period has its unique style, including ours”, although “a style is not recognizable in one’s own time but rather can only be perceived at a later time”15.

15. P. Behrens, “Stil?”, Die Form: Zeitschrift für gestaltende Arbeit, pp 5–8, 1922.

4 This plan is the result of a StyleGAN interpolating/ transforming between Baroque and Modern plans. The voluptuous pouches of the Baroque interpolated with the asymmetry of Modern plans.

Riffing on Peter Behrenses argument, it would mean that it might not be up to us as contemporary witnesses to define a particular style – that might be the job of an art historian down the line – it is also doubtful whether we have the necessary distance to evaluate the current actions that lead to the provocative imagery (Fig. 4) resulting from the use of Neural Networks as a design method. However, what can be observed is the incredible influence that neural networks have on human designers; the images generated by Neural Networks can act as a stimulus for the human mind to interpret

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4 them in ways that ultimately push the architecture discourse further. Because they are based on existing information, they are familiar enough to be construed as architecture but strange enough to provoke us and challenge us as designers. Ultimately, neural networks as a design tool provoke questions about the boundaries of design or the value of the history of our discipline. Simultaneously these images explore aspects such as agency, authorship, and design ethos in a posthuman design ecology. Currently, many parts of this posthuman design ecology are blank spots – waiting to be charted.

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The Ecology InFraReD: Accessible Environmental Simulations by AIT’s City Intelligence Lab, A. Chronis, T. Galanos, S. Duering, N. Khean

The Prospects

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The Ecology

The impact of climate change on urban environments is no longer projected but measured. In 2020 262 deaths and $98.9 billion worth of damages have occurred due to extreme climate events in the United 1. A. Smith, “2020 U.S. Billion-Dollar Weather and Climate Disasters, In Historical Context”, 10.13140/ RG.2.2.25871.00166/1, 2021. 2. V. Bertollini, “Here’s What Building the Future Looks Like for a 10-Billion-Person Planet”, Redshift, 2018.

3. United Nations Environment Programme, “2020 Global Status Report for Buildings and Construction: Towards a Zero-emission, Efficient and Resilient Buildings and Construction Sector”, Nairobi, 2020.

States alone1. The construction industry is still the largest contributor of greenhouse gas emissions, with more than 38% of the total annual emissions attributed to the construction and operation of buildings2; a metric which itself does not account for the significant impact of the construction industry on every aspect of our ecosystems, from urban heat islands to water and waste management etc. If one considers that we are currently building more than 11,000 buildings per day3 with around 3,600 more projected to be built daily by 2050 if the urbanization rate continues, it’s easy to conclude that we need to calculate and mitigate the environmental impact, both in terms of energy demand but also in terms of the direct effect that buildings have on their environment, such as for example their thermal, solar or wind properties. Despite the immense impact of the construction industry on the environment, we have very little understanding of how our constructions affect their environment, especially during the crucial phases of their conception. Early design stages when most important design decisions are made significantly affect the design outcomes, and by the time the designs are finalized, massing volumes, orientations and other fundamental environmental design aspects can change very little. It is common knowledge that early design stages require fast but also accurate

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environmental simulation feedback to have a maximal positive effect on the climatic aspects of design. The evaluation however of the environmental impact of both new and existing buildings is not trivial. Environmental simulations can be quite complex, time consuming and difficult to set up. Moreover, they often require higher technical expertise, not commonly found in architecture and planning offices. As an example, a typical wind simulation – a Computational Fluid Dynamics (CFD) simulation – takes days to setup and many hours to run. A wind comfort simulation can take up to a few days simply to run. This complexity makes the inclusion of such simulations prohibiting for fast, early-stage design cycles; and in most cases impossible to include at any design phase. The integration of environmental simulations in both computational and standard design systems has undoubtedly increased in the re4. Ladybug Official Website: https://www. ladybug.tools/

cent year. Ladybug tools4, as an example, have made environmental simulations accessible to a much greater audience. However, the barriers of environmental simulations and specifically simulations speed and domain expertise remain. To overcome these barriers, further to integration, faster simulation models are also needed. One way to do this is using Artificial Intelligence. The intense recent development of AI models has revolutionized simulation speeds in other domains, and environmental simulations can benefit from this development. ML models can be used to predict simulation results in a fraction of the time required to conventionally run them. InFraReD, the intelligent framework for resilient design, developed by

5. T. Galanos & A. Chronis, “A deep-learning approach to real-time solar radiation prediction”, Routledge, 2021.

the City Intelligence Lab (CIL) of the Austrian Institute of Technology, is aiming to do exactly that, to use AI to overcome the environmental simulation barriers in architectural and urban design5.

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InFraReD is based on deep learning models, trained with large simulation datasets developed by the CIL. To produce these simulation datasets the CIL has developed a distributed simulation pipeline that produces thousands of simulation results (Fig. 1), automating

1

the simulation processes from the geometry input to the simulation

A large solar radiation simulation dataset used to train InFraReD’s machine-learning models.

output. These simulation results are then used to train deep learning models to learn the relationship between geometry input and simulation output. In doing so, the whole simulation workflow is overcome, and the result is directly produced. As an example, a CFD simulation result that takes 8 hours to produce is predicted within a few seconds (Fig. 2). InFraReD’s models are trained using data from many

2 An actual solar radiation simulation compared with the AI-predicted simulation result from InFraReD.

cities around the world and simulation results from fundamental environmental models, such as wind comfort (CFD), solar radiation and sunlight hours calculations. The accuracy of the simulation predictions is quite high (ranging from 85 to 95%), making InFraReD very useful, especially for early design stages.

2 The main goal of the development of InFraReD is to make environmental simulations more accessible and to increase their integration at all stages of design. A simulation result that is available in seconds

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3 can have a significant impact in early design stages by allowing designers to make well-informed decisions based on the environmental impact of their designs. However, for these results to be accessible, the integration of InFraReD’s AI models in the design process is also needed. For that reason, InFraReD is developed as a modular, open-ended architecture that allows easy integration in both existing as well as new design systems. InFraReD’s models can be currently accessed through three different approaches: as a cloud-based app

3 Wind analysis in InFraReD’s web app interface.

(Fig. 3) that allows end users to design or upload their designs on the cloud and get instant environmental feedback; as a Grasshopper plugin that allows more expert users to directly integrate InFraReD’ AI models in standard computational workflows such as Grasshopper; as well as through an API that allows other design platforms to integrate InFraReD and provide fast environmental feedback to their users. This open-ended deployment approach aims to maximize the accessibility of InFraReD’s models and thus maximize the accessibility of environmental simulation to diverse users. Further to using AI to predict the simulation results, InFraReD also aims to address the barrier of the lack of domain expertise to understand and effectively use these results to steer design decisions through a key performance

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The Prospects

indicators (KPI) approach. InFraReD’s models compute not only the standard performance maps and point values that a user would find in environmental simulation platforms, but also a series of useful KPIs

4 InFraReD’s KPI-based design explorer.

that help drive design decisions (Fig. 4). These can be for example the percentage of unsafe areas in terms of pedestrian wind comfort or the areas with excess solar radiation and thus extreme thermal conditions. Through a comprehensive design explorer that allows an intuitive comparison of different design options and a performance tracker which helps the user understand how to improve the environmental performance based on specific KPIs, InFraReD aims to help the user focus on meaningful design metrics that can steer their design to improved performance. The increasing integration of environmental simulations in design systems, as discussed, can significantly help designers and planners understand the environmental impact of their designs. It can be argued that for most users, integration itself gives access to previously inaccessible environmental simulation models. Integra-

6. Radiance Official Website: https://www. radiance-online.org 7. EnergyPlus Official Website: https://energyplus. net 8. OpenFOAM Official Website: https://https:// www.openfoam.com

tion frameworks, such as for example Ladybug, enable a designer to incorporate results from state-of-the-art simulation models like Radiance6, EnergyPlus7 or OpenFOAM8, all being environmental simulation standards. The integration of these models, though, still does not reduce the speed and domain expertise burden which InFraReD is trying to overcome. Integration however also enables a fundamentally different way of optimizing the environmental impact of designs. The examples of computational optimization or algorithmic exploration of designs that couple advanced computational techniques – such as genetic algorithms, simulated annealing or self-organizing maps – with environmental simulations, mainly solar

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4 radiation or energy simulations, are numerous. For these algorithmic design explorations and optimization methods, the computational demand of environmental simulation that InFraReD’s simulation prediction models overcome is the biggest bottleneck. If we take as an example the simplest environmental simulation – a solar radiation calculation that takes only a few minutes to perform – and we assume a design search for a mere thousand different options to explore, we still need a few hours for this optimization run. If we then consider the more complex wind simulations, which need at least a few hours to perform, it is easy to conclude that an optimization run is simply not possible. It is evident that the ability to obtain environmental simulation results of such complex simulation models in seconds can lead to unprecedented levels sof fine-tuning of design problems, thus potentially significantly reducing the environmental impact of future constructions while still allowing designers a great amount of freedom on spatial configurations. The aim of InFraReD is exactly that, to make environmental simulations accessible to both traditional as well as advanced design processes and allow designers and planners to make more environmentally conscious design decisions.

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The Language Semanticism: Towards a Semantic Age for Architecture by Stanislas Chaillou, Architect, Data Scientist

The Prospects

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The Language

For a long time, Architecture has benefited from fruitful analogies with linguistics. The language is a rich matrix that provides both a system and a free canvas where creations are expressed according to rules and transgressions. Quite naturally, architects have harvested its lexicon and frameworks to describe and think about Architecture. Over the past decades, the discipline has in fact considerably borrowed from grammar and its concepts: the translation of Architecture into formal languages has corresponded to a need to formulate, organize, and replicate architectural information. Although this effort has proven to be very instructive for the discipline, a strict grammatical conversion does not fully account for many aspects of Architecture: at the very least it represents a missed opportunity. Today, we believe that semantics offers a new angle to revive the analogy between Architecture and linguistics. This alternate ap1. S. Chaillou, “Latent Architecture: a semanticist’s perspective”, Architectural Research Quarterly 24, pp 309-313, 2020.

proach should allow for a more adequate dialogue between technology and the architectural agenda. Built upon the latest development in Artificial Intelligence, we will call “Semanticism”1 this new momentum for Architecture.

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The Prospects

Rules of Design, Design of Rules A short glance at the past century reminds us how concepts, borrowed from linguistics, have made their way into many other disciplines. This chronology could in fact begin with Gottlob Frege’s 2. G. Frege, “Begriffsschrift”, Louis Nebert Verlag, 1879.

seminal work on formal languages. In his book, “Begriffsschrift”2, the German philosopher attempts to ground logic into arithmetic. For Frege, the rigor of arithmetic would help formulate a “pure” language, so as to provide a powerful framework for thought processes. Frege, and later the British logician Bertrand Russell, are going to deploy an entire corpus where the formulation of complex sets of rules will offer an early expression of formal languages. Since then, the discussion has matured among linguists; the relevance of formal languages has also grown, as computer science came to adopt some of their characteristics for shaping many programming languages. By capillarity, Architecture has also found an interest in this approach, as theorists started to investigate the ben-

3. J. Gips & G. Stiny, “Shape Grammars and the Generative Specification of Painting and Sculpture”, In IFIP congress, Vol. 2, No. 3, pp 125-135, 1971.

1 Typical shape grammar procedure. By J. Gips & G.Stiny. 4. P. Schumacher, “Parametricism as Style - Parametricist Manifesto”, 11th Architecture Biennale, Venice, 2008.

efits of a rule-based design process. In this respect, shape grammar and parametric modeling represent a golden age for the formalization of design. The work of James Gips and Georges Stiny offers compelling examples of these attempts at defining a rule-based logic for the organization of compositions (Fig. 1). Their seminal publication in 19713 displays such systems and demonstrates the originality of this approach. With parametric modeling and the advent of computers, rules are formulated into scripts. Functions and parameters are then woven into entire procedures for the machine to follow. Patrik Schumacher’s manifesto in 20084 reaffirms Parametricism dependence on this type of rule-based approach.

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Rule 3

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Initial Shape

Rule 1

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Step 1

...

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The Prospects

In a nutshell, with shape grammar and parametric modeling, architects have explored Design as a process built on logic and rules. Under this definition, Architecture could be translated into heuristics explicitly declared by architects, to then be communicated to computers as exact procedures to follow. This grammatical momentum in Architecture remains, to this day, a notable moment for theory and formal research.

A Semantic Momentum The influence of grammatical concepts however is soon going to fade away, for the benefit of new frameworks grounded in semantics. In linguistics, semantics allows moving past the oversimplification of the relationship between language and meaning into a strict logic-based mapping. If the language appears to convey more than the sum of its parts, this new discipline hopes to help address the question of meaning and its deep complexity. In computer science, this shift is later echoed by the development of new frameworks to represent information. Following the semantic principles laid down in linguistics, computer scientists are going to investigate the possibility of reflecting their language content in the very structure of their code. Object-Oriented Programming (OOP), that is the organization of code using the abstraction of objects with attached properties, is a direct expression of this reality. 5. T. Berners Lee , “Semantic Web Road Map”, W3C, 1998.

The Semantic Web, as explained by Tim Berners-Lee5, father of the Internet, is another manifestation of the same principles: the Web as we know it today is built on an infrastructure of nodes and connections whose denomination and organization reflect the

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The Language

6. W3C OWL Working Group, “OWL 2 Web Ontology Language”, 2012.

content they host. The Web Ontology Language (OWL)6 maps out the entirety of this structuration. In a nutshell, from OOP to OWL, the semantic principles and their benefits are going to profoundly shape technology. In Architecture, the reflection is soon going to move along the same line thanks to a few theorists. The British-American architect and design theorist Christopher Alexander remains a central figure of this movement. Alexander in his books lays down his idea of comparing built forms to patterns, whose nesting and imbrication would explain the morphology of our built environment’s fabric. For him, this approach goes hand in hand with the attempt to exhaustively map the categories and types of systems composing the built

7. C. Alexander, “The Pattern Language”, Oxford University Press, 1977.

world. In the Pattern Language (1977)7, Alexander goes through this process of declaring a quasi-ontology of built forms, category by category, type by type, so as to describe and explain their potential relationships. The underpinnings of BIM very much proceed from the same intuitions. The information of BIM models is indeed

8. The ifcOWL initiative, by turning the universal BIM format (IFC) into a proper ontology, give us the opportunity to contemplate how much the underlying BIM schema relies on a deeply semantic structuration.

organized following an OOP schema8, declaring families, types, elements, their respective properties and their ways of interacting with one another. It is not unreasonable to say that the semantic principles profoundly irrigate technology and, by capillarity, many creative fields today. The porosity between both spheres seems in fact considerable and leads us today to anticipate a profound evolution in Architecture: Semanticism, that is, the application of the semantic principles both as a descriptive and generative framework for the discipline.

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The Prospects

Semanticism Today, semantics provides a robust descriptive framework that permeates most of Architecture’s tool set. However, recent AI projects are currently demonstrating its generative capability. This new avenue of research is about to round up a “semantic momentum” for the discipline. “Semanticism” gives a name and a direction to this reality. As shape grammar did yesterday, the ambition of Semanticism is to help architects both describe and generate the shapes and forms that populate our built environment. If the former is well underway, the latter is still nascent and lacks a clear definition. In an effort to delineate its upcoming contribution to Architecture, we believe semantic generation differentiates itself from previous methodologies in at least three distinct ways. First, for its abstraction potential: previous generative methodologies mainly operated on raw geometry or low-level numerical data. Semanticism, on the contrary, formulates architectural information so as to convey its content through its form. Through the wealth of potential

2 Conversion from semantic abstractions to space layouts using AI. Top: S. Chaillou Middle: Nauata et al. Bottom: images generated using OpenAI’s Glide model.

9. The ArchiText project offers an ideal example of this type of application and is accessible at the following address: https://architext.design/

abstractions – categorical, graphical, textual, etc. – information gets formatted to reflect high-level architectural concepts. Figure 2 presents this reality: in an image, room colors encode a program (categorical); in a graph, nodes represent rooms while connections denote a doorway (graphical); in a sentence, expressions depict the general features of an actual space layout (textual)9. Semanticism’s approach to translation represents also a unique opportunity. Although it is straightforward to turn an architecture into a semantic abstraction, it is much harder to reverse this process. If previous generative paradigms would employ explicit rule-based systems to do so, Seman-

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Outputs

Graph Input

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Outputs

House with “A“Ahousing floor Three Bedrooms planand with three Two bedrooms”. Bathrooms.”

2

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The Prospects

ticism relies on the learning process of certain AI models to achieve the mapping from abstraction to forms. This transformation is therefore induced rather than deduced, observed rather than described, learned rather than declared: this difference sets aside Semanticism from previous generative methodologies. As a result, the forms obtained have the potential to be better informed and well-rounded than with previous approaches (Fig. 2). Finally, Semanticism’s use of “multimodal” generation contributes to its relevance for Architecture: using certain AI models, one semantic abstraction can be translated into multiple designs, so as to render Architecture’s vast diversity. In simpler terms, one input maps to multiple outputs. Consequently, in Figure 2, four different options are obtained each time for a unique input. This “one-to-many” translation is an essential aspect of semantic generation that addresses the variety of built forms. As with any new paradigm, however, we believe Semanticism is a two-edged sword, with its epistemic gains and challenges. The formulation of abstractions is the first immediate challenge Semanticism faces. As shown in Figure 2, many representation modes can help abstract and encode Architecture semantically. This “game of formulation” is a challenge that will require extensive work and refinement over the next few years. The next important facet of Semanticism is its “polysemic” potential. Polysemy corresponds in linguistics to the fact that a term can refer to various meanings. By analogy, a semantic abstraction can be translated into a field of shapes, rather than to a single form. This polysemy can liberate the design process by providing architects with a wealth of designs. However, training AI models to achieve this “one-to-many” translation is an arduous technical challenge. Elaborating training processes able to keep

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The Language

these models’ generative spectrum as wide as possible will be one of Semanticism’s most pressing imperatives. Context embedding eventually confers to Semanticism a clear advantage over previous generative methodologies. As training sets can carry many implicit features  – typological, cultural, or demographic information to only name a few – it is a unique opportunity for designers to embed some crucial dimensions of Architecture in their generative tools. AI models, while operating the translation from abstractions to forms, can take into account these various influences. It remains therefore essential to control the training process as these biases can both act as a way of incorporating relevant contextual information or add irrelevant notions to the generation process.

Towards a Technology of the Specific Although Semanticism is in its early days, we believe a “semantic moment” is underway. This momentum foreshadows a deeper purpose and a greater potential. Semanticism is somewhat at odds with those theories that, in Architecture, have aimed at placing a generic style above the particularity of cultures or the singularity of locations. Semanticism offers the means to anchor Architecture back into its immediate context. And if technology often rhymes with the uprooting of our practice, in contrast Semanticism provides us with a renewed methodology to ground the form we design into the specificity of a given site, a certain place, or a particular culture. No “international style 3.0” or “space design automation”; rather a framework for architects, and maybe others, to observe, describe, and create architectures mindful of the particular, aware of the singular, closer to the peculiar. This potential constitutes, in essence, Semanticism’s greatest promise for Architecture.

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The rapid pace of innovation presents architects with an ever-growing technological landscape. The “disruption” rhetoric, however, too often prevents practitioners from understanding the actual dynamic between Technology and its applications. Taking the opposite route, this book has tried to clarify and illustrate AI’s distinct potential with regard to Architecture. To conclude, we wish to condense its message to a few final assertions. Most evidently, AI aspires to democratize the analytical and assist the sensitive in Architecture. Simpler, faster and cheaper predictions, coupled with the ability to process a wide and diverse array of mediums – from textual to geometrical or visual inputs – confers to AI a distinct relevance to the many facets of the architectural agenda. The experiments and research projects presented in this book speak to this immediate contribution. AI is also an invitation to reestablish observation as creativity’s springboard. As seen earlier, building upon the notion of statistical learning, AI-en-

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Closing Remarks

abled tools can derive their functioning principles from the information collected across multiple observations. Rather than modeling Architecture using explicit context-agnostic rules, AI can help study architectural patterns in context. Consequently, an age of AI in Architecture could correspond to an increased understanding and proximity to the unique character of singular conditions. Far from the uprooting of the practice, AI can give architects new means to refine the adequacy between their design and the specificity of contextual or cultural factors. An age of AI in Architecture also carries the potential to play off the porosity between research and practice, even more so than previous technological revolutions did. The synergies between architectural practice’s project-based mindset and AI’s research-based culture can be the bedrock of a new approach to Architecture, provided practitioners and researchers establish meaningful bridges between both worlds. It is, finally, an age that expects much more from technology than the mere promise of automation. The sole autonomous replication of architectural patterns by computers does not harvest AI’s full potential. On the contrary, the dynamic relationship between designers and AI through a “gray1. A. Witt, “Grayboxing”, pp 69-77, Log #43, 2018.

boxing”1 approach is a perspective far more likely to benefit Architecture in the long run. The relationship between both worlds is not yet a set reality. However, as Architecture engages with AI, it just so happens that the world around us is watching: many other creative fields, looking to embrace AI as a new methodology, still wrestle with its adoption and are today witnessing the vibrant discussions unfolding in our field. The discipline has here the unique opportunity to set a lasting precedent, and inspire practitioners, well beyond the realm of Architecture.

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References & Resources

The Incredible Inventions of Intuitive AI A conference by M. Conti, TedX, 2017

AI and Creativity: Using Generative Models To Make New Things by Google Brain, 2017

AI & Architecture: Towards a New Approach

Digital Culture in Architecture

A conference by S. Chaillou, 2020

A conference by Antoine Picon, 2010

The Routledge Companion to Artificial Intelligence in Architecture

Atlas of Digital Architecture

I. As, P. Basu, Routledge, 2021

L. Hovestadt, U. Hirschberg and O. Fritz, Birkhaeuser, 2020

Architectural Intelligence

Architecture, Design, Data

M. W. Steenson, MIT Press, 2017

P. G. Bernstein, Birkhaeuser, 2018

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References & Contributors

202

Image Credits Foreword @ Stanislas Chaillou, 2020

Artificial Intelligence, Another Field Fig. 1: @ S. Chaillou, 2021 Fig. 2: @ AT&T, photographer: Jack St. Fig. 3: @ Historic American Engineering Record Fig. 4: @ B. G. Buchanan and E. H. Shortliffe Fig. 5: @ Image Courtesy of NVIDIA Fig. 6: @ OpenAI

The Advent of Architectural AI Fig. 1: @ S. Chaillou, 2020 Fig. 2: @ Historic American Buildings Survey (Library of Congress) Fig. 3: @ Safdie Architects Fig. 4: @ Electronic edition of Sutherland’s Sketchpad dissertation, image adapted to format Fig. 5: @ C. M. Highsmith Archive, Library of Congress Fig. 6: @ Z. Hadid Architects Fig. 7: @ S. Chaillou, 2020 Fig. 8: @ Cedric Price fonds, Canadian Centre for Architecture

AI's Deployment in Architecture Fig. 1: @ S. Chaillou, 2021 Fig. 2: @ S. Chaillou, 2021 Fig. 3: @ S. Chaillou, 2021 Fig. 4: @ S. Chaillou, 2021 Fig. 5: @ S. Chaillou, 2021 Fig. 6: @ S. Chaillou, 2021 Fig. 7: @ S. Chaillou, 2021 Fig. 8: @ S. Chaillou, 2021 Fig. 9: @ S. Chaillou, 2021 Fig. 10: @ S. Chaillou, 2021 Fig. 11: @ S. Chaillou, 2021 Fig. 12: @ S. Chaillou, 2021 Fig. 13: @ SPAN M. del Campo & S. Manninger 2019 & 2020 Fig. 14: @ Image Courtesy of NVIDIA Fig. 15: @ S. Chaillou, 2021 Fig. 16: @ S. Chaillou, 2021 Fig. 17: @ S. Chaillou, 2021 Fig. 18: @ S. Chaillou, 2021 Fig. 19: @ Isola & al. Fig. 20: @ Kelly & al. Fig. 21: @ Kelly & al. Fig. 22: @ K. Steinfeld Fig. 23: @ K. Steinfeld Fig. 24: @ Wang & al. Fig. 25: @ Image Courtesy of NVIDIA Fig. 26: @ Mueller & Danhaive 2020 Fig. 27: @ Danhaive 2020 Fig. 28: @ Spacemaker AI

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References & Contributors

Fig. 29: @ T. Galanos Fig. 30: @ Spacemaker AI

The Outlooks of AI in Architecture The Form Fig. 1: @ I. Koh Fig. 2: @ I. Koh Fig. 3: @ I.Koh Fig. 4: @ I.Koh

The Context Fig. 1: @ K. Steinfeld Fig. 2: @ K. Steinfeld Fig. 3: @ K. Steinfeld Fig. 4: @ K. Steinfeld

The Performance Fig. 1: @ Mueller & Danhaive Fig. 2: @ Mueller & Danhaive Fig. 3: @ Mueller & Danhaive Fig. 4: @ Mueller & Danhaive

The Practice Fig. 1: @ Foster + Partners, 2021 Fig. 2: @ Foster + Partners, 2021 Fig. 3: @ Foster + Partners, 2021 Fig. 4: @ Foster + Partners, 2021

Fig. 3: @ Andrew Witt, 2021. Fig. 4: @ Andrew Witt, 2021.

The Scale Fig. 1: @ Spacemaker AI Fig. 2: @ Spacemaker AI Fig. 3: @ Spacemaker AI Fig. 4: @ Spacemaker AI

The Style Fig. 1: @ SPAN M. del Campo & S. Manninger, 2019 Fig. 2: @ SPAN M. del Campo & S. Manninger, 2020 Fig. 3: @ SPAN M. del Campo & S. Manninger, 2020 Fig. 4: @ SPAN M. del Campo & S. Manninger, 2020

The Ecology Fig. 1: @ Chronis, 2021 Fig. 2: @ Chronis, 2021 Fig. 3: @ Chronis, 2021 Fig. 4: @ Chronis, 2021

The Language Fig. 1: @ G. Stiny Fig. 2: @ S. Chaillou, 2021, @ Nauata et al, @ S. Chaillou

The Model Fig. 1: @ Bibliotheque Nationale de France Fig. 2: @ Bibliotheque Nationale de France

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Contributors’ Biographies ARD Group The Applied Research and Development team (ARD) at Foster & Partners is an integrated multi-disciplinary team of architects and engineers. The ARD’s expertise ranges from art, aerospace engineering, and computer science to landscape architecture, structural engineering and applied mathematics.

City Intelligence Lab The City Intelligence Lab (CIL) is an interactive digital platform to explore novel forms and techniques for the urban development practice of the future. As incubator for intelligent solutions the lab fosters the co-creation of digital urban planning workflows and processes, applying augmented reality and interactive design interfaces to create simulations, generative design and artificial intelligence solutions.

Immanuel Koh Immanuel Koh holds a joint appointment as an assistant professor in Architecture & Sustainable Design (ASD) and Design & Artificial Intelligence (DAI) at the Singapore University of Technology and Design (SUTD), where he now directs Artificial-Architecture. He obtained his PhD between the School of Computer Sciences and the Institute of Architecture at EPFL.

Matias del Campo Dr. Matias del Campo is a registered architect, designer, and educator. He is an Associate Professor at Taubman College, University of Michigan, and director of the AR2IL at UoM. He conducts research on advanced design methods in architecture, through the application of Artificial Intelligence techniques.

Andrew Witt Andrew Witt is an associate professor in practice in Architecture at the Harvard GSD, teaching and researching on the relationship of geometry and machines to perception, design, construction, and culture. Witt is also co-founder of Certain Measures, a Boston/Berlinbased design and technology studio.

Renaud Danhaive & Caitlin Mueller Renaud Danhaive and Caitlin Mueller are respectively post-doctoral associate and associate professor at MIT’s Digital Structures Lab (DS Lab). The DS Lab’s work focuses on the synthetic integration of creative and technical goals in the design and fabrication of buildings, bridges, and other large-scale structures.

Carl Christensen Carl Christensen is co-founder and CTO at Spacemaker AI. The company, founded in 2016, provides an online platform for real-estate developers, architects and other stakeholders in the AEC industry to make early stage datadriven decisions.

Kyle Steinfeld Kyle Steinfeld is an associate professor of Architecture at U.C. Berkeley. His academic and scholarly work investigates the relationship between the creative practice of design and computational design methods. More generally, his creative work happens at the intersection of AI and Environmental Design.

Alexandra Carlson Alexandra Carlson is a PhD candidate at the Robotics Institute, University of Michigan. Her current research focuses on robust computer vision for autonomous vehicles, specifically on realistic noise modeling in images.

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Index

Index Architecture, Engineering, Construction (AEC) 136, 137, 162, 164 aesthetics 48 AI winter 20, 22, 137 Alexander, Christopher 193 AlexNet 26 algorithm 174 AlphaGo 26 Architecture Machine Group (AMG) 44, 56 Artificial Neural Network (ANN) 16, 69, 71 ArchiCAD 52 Archigram 38 Archistar 58 Architecture Biennale 58, 116 Applied Research & Development Group (ARD) 136, 139, 140 Arsenal Pavilion 8 Artificial Intelligence (AI) 7, 17, 33, 56, 57, 60, 63, 64, 78, 107, 109, 111, 113, 117, 119, 129, 133, 135, 137, 148, 163, 171, 182, 189, 194, 199 Ascher Barnstone, Deborah 177 AutoCAD 46 autonomous car 24 Bardeen, John 16 Bauhaus 36, 174 Bayesian networks 24, 69 Behrendt, Walter Curt 173 Behrens, Peter 173, 178 Bell Lab 16 Berners Lee, Tim 192 Bézier, Pierre 43 Building Information Modeling (BIM) 52, 120, 138, 193 Bradford Shockley, William 16 Buckminster, Fuller 147 Computer-Aided Design (CAD) 33, 42, 43, 46, 49, 56, 120 CATIA 46 Computational Fluid Dynamics (CFD) 102, 182, 184 Christensen, Carl 163 City Intelligence Lab (CIL) 182, 184

classification 26, 174 cloud 25, 111, 116, 167, 185 Convolutional Neural Network 71 ComfortGAN 104 convolution 71 Cornell Aeronautical Laboratory 16 CoveTool 58, 104 Cross, Nigel 121 CYC 22 DaCosta Kaufmann, Thomas 152 DALL-E 30 DARPA 22, 24 DARPA Grand Challenge 24 Dartmouth Summer Research Project 17, 64 Dassault Systemes 46 data 46, 65, 66, 68, 81, 114, 122, 124, 128, 129, 130, 136, 137, 178 database 25, 26, 72, 78 dataset 90, 116, 122, 138, 139, 140, 176, 178 Deep Blue 24 Deep Learning 13, 24, 25, 70, 111, 113, 184 DeepMind 26, 178 Delve 58 Devol, George 18 Dymaxion House 36 ecology 171, 179, 180 efficiency 54, 100, 102, 104, 109 ELIZA 17, 18 EnergyPlus 187 Engelberger, Joseph 18 Evans, Robin 121 evolutionary algorithm 24, 132 expert system 20, 21, 24 feedback loop 17, 24, 70 File-Seeker 142 finite element analysis (FEA) 128 floor plan 56, 57, 78, 80, 86, 88, 104, 140 Foster & Partners 8, 136, 138, 140 FrankenGAN 92 Frege, Gottlob 190 Fuller, Buckminster 36 Generative Adversarial Network (GAN) 27, 71, 72, 86, 90, 98, 111, 113, 114, 122

GAN Loci 94, 96, 122, 124 GauGAN 96 Gehry, Frank 46 Geisberg, Samuel 49 Generator 57, 86 genetic algorithm 187 Gerlee, Philip 149 Giedion, Sigfried 173 Gips, James 190 Giraffe 104 Geographic Information System (GIS) 85 Glymph, Jim 46 Graph Neural Network (GNN) 71 Goodfellow, Ian 30, 72 GPT-3 30 Graphics Processing Unit (GPU) 25, 116 grammar 189, 194 Grasshopper 50, 51, 52, 58, 114, 185 Gropius, Walter 36, 40 Habitat 67 38 Hacking, Ian 147 Hadid, Zaha 50 Hanratty, Patrick 42, 44 hardware 16, 25, 42 Harvard 8, 146 Hassabis, Demis 178 Haukeland, Havard 163 Houser Brattain, Walter 16 Hydra 138 hyperparameter 67 IBM research 24 ImageNet 26 InFraReD 104, 184, 185, 186 interface 43, 44, 51, 52, 89, 96, 130, 131, 132 Internet 25, 192 interpolation 116, 160, 178 knowledge base 21 Koh, Immanuel 110 Kvale, Anders 163 Ladybug 182 language 30, 52, 147, 154, 156, 160, 171, 189, 190, 192 latent space 74, 75, 76, 98, 114 Le Corbusier 38 Lenat, Douglas 22 206

Lequeu, Jean-Jacques 150 Lighthill, James 20 Lincoln Laboratory 42 linguistics 30, 171, 189, 196 Lundh, Torbjörn 149 Machine Learning (ML) 24, 58, 65, 68, 71, 122, 124, 129, 131, 136, 137, 138, 140, 144, 145, 158, 182 McCarthy, John 17, 22 McCulloch, Warren 16, 69 McDermott, John P. 22 McLaughlin, Robert W. 37 Media Lab 56 Minsky, Marvin 17, 20 Massachusetts Institute of Technology (MIT) 8, 42, 44, 56, 98, 101, 126 modularity 36, 38, 40 Modulor 38 Mondrian, Piet 112 Monge, Gaspard 154 Moretti, Luigi 48 Muthesius, Hermann 173 MYCIN 21 Negroponte, Nicholas 44, 56, 57, 58, 107 neoplasticism 113 neuroplasticity 113 Natural Language Processing (NLP) 17 Neural Turtle Graphics (NTG) 84 Nvidia 30 Object-Oriented Programming (OOP) 192, 193 OpenAI 30 OpenFOAM 187 optimization 27, 100, 122, 128, 129, 132, 187 OWL (Web Ontology Language) 193 Papert, Seymour 20 parameter 48, 49, 54, 56, 65, 67, 69, 70, 71, 130, 190 Parametricism 48, 50, 190 pattern 18, 27, 40, 68, 82, 86, 100, 121, 122, 161, 177, 193, 200 Perceptron 16, 20, 156

performance 26, 27, 30, 71, 98, 102, 104, 109, 120, 126, 127, 128, 131, 133, 138, 174, 176, 186 Pitts, Walter 16, 69 Pix2Pix 90, 96, 122 platform 50, 104, 129, 163, 166, 167, 168, 185, 186 Plugin City 38 polysemy 196 Price, Cedric 57, 86, 107 procedure 17, 48, 50, 190, 192 Pro/ENGINEER 49 program 18, 21, 22, 42, 49, 50, 72, 80, 89, 122, 194 programming language 52, 190 PRONTO 42 PTC 49 R1 22 Radiance 187 Rapoport, Anatol 156 reinforced learning 68 Revit 48, 52, 58 Rhino 48 robotics 18, 68 Rondelet, Jean-Baptiste 150 Rosenblatt, Frank 13, 16, 156 rule 21, 36, 40, 46, 48, 49, 52, 56, 80, 150, 157, 158, 189, 196, 200 Russell, Bertrand 190 Safdie, Moshe 38, 40 Schumacher, Patrik 50, 190 Schwarz, Jacob T. 22 Selfridge, Oliver 17 semantic 52, 96, 116, 143, 189, 193 Semanticism 188, 189, 194, 196 shape grammar 190, 192, 194 ShapeNet 114 Simon, Hebert 18 Sketch2Pix 124 SketchPad 42, 43, 49, 52 software 42, 43, 44, 48, 51, 56, 119, 125, 128, 130 Solomonoff, Ray 17 Spacemaker 8, 58, 104, 162, 163, 166 Stadium N 48 Stanford University 21, 24 Stanley 24 Steinfeld, Kyle 118 Stiny, Georges 190 structural design 98, 101

style 50, 77, 84, 90, 93, 112, 149, 152, 171, 173, 174, 178, 197 StyleGAN 30, 122 supervised learning 68, 129 surrogate model 102, 129, 130 Sutherland, Ivan 42, 49 technology 7, 13, 30, 33, 73, 81, 82, 86, 109, 120, 135, 163, 189, 193, 197, 199 thermal comfort 102 training 13, 25, 65, 68, 69, 74, 77, 78, 80, 116, 165, 196 transistor 16 typology 89, 90, 92, 98, 116 UC Berkeley 124 Unimate 18 UNISURF 43 Unité d’Habitation 38 unsupervised learning 68 Urban 2 44 Urban 5 44, 56, 57 Urban Fiction 82 Variational Autoencoder (VAE) 71, 73, 74, 98 van Doesburg, Theo 112, 113 van Fraassen, Bas C. 149 Vectorworks 46 visual programming 50, 52 Volkswagen Electronics Research Lab 24 Walt Disney Concert Hall 46 web app 58, 89 Weizenbaum, Joseph 18 wind flow 102, 104 Winslow Ames House 37 Witt, Andrew 67, 146 XKool 58 Zaha Hadid Architects 50

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