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id cf2019_032
authors Khean, Nariddh; Alessandra Fabbri, David Gerber and M. Hank Haeusler
year 2019
title Examining Potential Socio-economic Factors that Affect Machine Learning Research in the AEC Industry
source Ji-Hyun Lee (Eds.) "Hello, Culture!" †[18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 254
summary Machine learning (ML) has increasingly dominated discussions about the shape of mankindís future, permeating almost all facets of our digital, and even physical, world. Yet, contrary to the relentless march of almost all other industries, the architecture, engineering and construction (AEC) industry have lagged behind in the uptake of ML for its own challenges. Through a systematic review of ML projects from a leading global engineering firm, this paper investigates social, political, economic, and cultural (SPEC) factors that have helped or hindered MLís uptake. Further, the paper discusses how ML is perceived at various points in the economic hierarchy, how effective forms of communication is vital in a highly-specialized workforce, and how MLís unexpected effectiveness have forced policy makers to reassess data governance and privacy; all the while considering what this means for the adoption of ML in the AEC industry. This investigation, its methodology, background research, systematic review, and its conclusion are presented.
keywords Machine learning ∑ Artificial intelligence ∑ Research and development ∑ Architecture, engineering, and construction industry ∑ Social factors ∑ Political factors ∑ Economic factors ∑ Cultural factors
series CAAD Futures
type normal paper
email n.khean@unsw.edu.au2
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