Paradigm Reversal – Connectionist Technologies for Linear Environments

Abstract

- Applications of computing in environmental design practices (architecture, urban design, landscape) are mostly following technological developments. Further, they even amplify the purpose of the field of development where the technology is borrowed from. Hence, architectural computing saw nearly twenty years of form-finding and fabrication, stemming from parametric models developed in industrial design around the end of the 1980s. With the onset of pervasive data harvesting through sensory networks and mobile computing, architecture again is jumping on a bandwagon: utilization and user-centered optimization. AI and machine learning make use of the massive amounts of data collected to optimize all areas of cognitive and behavioral life. Environmental design in industry and academia is following suit and appears to commit to optimize all spatial and social dynamics in buildings and cities. Questions about what to optimize are rarely asked other than known operational and capital domains. Original research into spatial computing and machine learning did not focus on optimization but sought alternative representations of environments through distributed heuristic computing and big data for design and planning. The field of architectural research appears to be at the crossroads of a possible paradigm reversal, where an early enthusiasm gives way to market demands. This article briefly sketches out the path from a 1990s paradigm shift to a 2020s paradigm reversal based on technological developments and market demands.

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