人工智能、架构、可访问性和数据公正——ACADIA特刊

Dana Cupkova, A. Wit, Matias del Campo, Mollie Claypool
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引用次数: 0

摘要

近年来,随着将人工智能(AI)集成到设计、表现和生产中的新数字技术变得更加突出,建筑研究领域呈现出快速发展的趋势。随着任何范式转变和变革性技术的迅速出现,新的紧张局势和对人类远离设计和制造行为的恐惧出现了。在建筑之外,人工智能已经在工程、IT和社会/政治科学等领域发挥了重要作用,人们对人工智能对人类的影响及其劳动伦理的讨论也在不断加深。架构师必须制定关键的度量标准,理解隐含的偏见,并探索新的方法,以更好地理解这些变革性技术在他们自己的领域内的影响和含义。对于架构来说,现在比以往任何时候都更迫切地需要在学科内塑造人工智能框架的代理。传统上,由于在数字制造和机器人生产中使用的物理基础设施和工具的高成本和陡峭的学习曲线,建筑技术的进步受到限制。然而,最近人工智能技术的突破似乎使人工智能提供的数字网络越来越多地分配给那些已经有技术接入能力的人。这种范式转变的结果是,新的经济和劳动力模式出现了,人工智能的使用再次引发了围绕作者角色、数据所有权和学科内合作模式的问题。在这个人工智能日益普及的新时代,在机器学习(ML)框架的帮助下,看似快速的设计自由,一系列关键问题通过本卷中的文章浮现出来:
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AI, architecture, accessibility, and data justice—ACADIA special issue
In recent years, the field of architectural research has trended towards rapid evolution as new digital technologies that integrate artificial intelligence (AI) into design, representation, and production have become more prominent. As with any paradigm shift and rapid emergence of transformative technology, new tensions and fears of human distancing away from acts of design and making arise. Outside of architecture, AI already plays a significant role in fields such as engineering, IT, and the social/political sciences, with a deepening discourse on its effect on humanity, and the ethics of its labor. Architects must develop critical metrics, understand implicit biases, and probe new methodologies to better understand the impacts and implications these transformative technologies have within their own territory. It is now more urgent than ever for architecture to take a stance on shaping the agency of AI frameworks within the discipline. Traditionally, advances in architectural technologies were limited in access due to the high monetary costs and steep learning curves in the physical infrastructure and tools utilized in digital fabrication and robotic production. However, recent breakthroughs in AI technologies have seemed to enable the digital networks provided by AI to be increasingly distributed to those already abled by technological access. As a result of this paradigm shift, new models of economy and labor arise, and the use of AI yet again opens questions surrounding the role of authorship, ownership of data, and models of collaboration within the discipline. In this new era of increased AI ubiquity and seemingly rapid design freedom aided by machine learning (ML) frameworks, a series of critical questions emerge through the articles curated in this volume:
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CiteScore
3.20
自引率
17.60%
发文量
44
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