Artificial Intelligence

Get Smart: Though still a nascent technology, AI promises to transform the design process and the built environment.
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Architectural Record
By Clifford A. Pearson

Learning Objectives:

  1. Outline the history of AI.
  2. Describe the possible design-process advantages and efficiencies of AI.
  3. Describe ways AI is being deployed to create architectural components, buildings, and cities that are responsive to environmental conditions and users’ needs.
  4. Discuss the privacy and security concerns associated with the collection of vast amounts of data necessary to use AI as a design tool.

Credits:

AIA
Approval Pending
IACET
0.1 IACET CEU*
AIBD
1 AIBD P-CE
AAA
AAA 1 Structured Learning Hour
AANB
This course can be self-reported to the AANB, as per their CE Guidelines
AAPEI
AAPEI 1 Structured Learning Hour
MAA
MAA 1 Structured Learning Hour
NLAA
This course can be self-reported to the NLAA.
NSAA
This course can be self-reported to the NSAA
NWTAA
NWTAA 1 Structured Learning Hour
OAA
OAA 1 Learning Hour
SAA
SAA 1 Hour of Core Learning
 
This course can be self-reported to the AIBC, as per their CE Guidelines.
This course is approved as a Structured Course
This course can be self-reported to the AANB, as per their CE Guidelines
Approved for structured learning
Approved for Core Learning
This course can be self-reported to the NLAA.
Course may qualify for Learning Hours with NWTAA
Course eligible for OAA Learning Hours
This course is approved as a core course
This course can be self-reported for Learning Units to the Architectural Institute of British Columbia

DEPENDING ON the people you talk to, architects approach artificial intelligence (AI) with a range of anticipation, skepticism, or dread. Some say algorithms will handle drudge work and free designers to focus on the more creative aspects of their jobs. Others assert that AI won’t live up to its hype—at least not in the near future—and will make only marginal improvements in the profession. And a third group worries that software that learns on its own will put a lot of architects out of work.

PHOTOGRAPHY: © MORPHOGENESIS LAB WSU

MORPHOGENESIS LAB’S installation, Wisteria, moves and changes color in response to the biometric data of people moving beneath it.

Science fiction writers have been imagining robots that think like human beings for more than 100 years. But the field of artificial intelligence really began in the middle of the last century with British mathematician Alan Turing’s 1950 paper “Computing Machinery and Intelligence.” In 1956, at a conference hosted by Dartmouth College in New Hampshire, mathematician John McCarthy coined the term “artificial intelligence” and, with a group of participants, explored how to “make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves,” according to the event’s proposal.

Imbuing computers with true intelligence, though, has proved to be more difficult than originally imagined. Sixty-five years after the Dartmouth conference, computers can process huge amounts of information, analyze it to find correlations and patterns, and then make predictions based on those patterns. What makes AI different from previous forms of computation is Machine Learning (ML), which employs algorithms that get better at performing certain tasks the more they do them; they learn without having to be programmed to do each step. The bigger the data set used to “train” an algorithm, the better it will perform. By 1997, IBM had developed a chess-playing program called Deep Blue that was able to beat Gary Kasparov, the world chess champion at the time. Today, Google Translate does a pretty good job of recognizing text in one language and communicating it in another. A program known as GPT-3 will take a few word prompts and write a paragraph of text that seems at first glance to have been written by a person. Algorithms allow autonomous vehicles to navigate city streets, radiologists to identify cancerous tumors, and online shopping services to recommend products to their customers.

PHOTOGRAPHY: © COURTESY REDDYMADE

VISITORS TO the Smithsonian’s Futures exhibition can speak into Reddymade’s me+you installation and then have an AI-driven system translate their voices into color and light.

But computers still don’t think like people. They have no awareness of anything beyond their own predetermined capabilities and don’t have anything close to common sense. They know only what they have been shown and lack the ability to generalize from one task to another. A 2016 Obama Administration report on the future of AI identified the technology’s potential to “open up new markets and new opportunities for progress in critical areas such as health, education, and the environment,” but admitted that “it is very unlikely that machines will exhibit broadly applicable intelligence comparable to or exceeding that of humans in the next 20 years.”

 

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Originally published in Architectural Record
Originally published in December 2021

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