“超越数据驱动美学,”，由麻省理工学院建筑系校友兼研究员 Alexandros Haridis, 于 6 月 30 日在麻省理工学院凯勒画廊展出, 审视了 20 世纪和 21 世纪将计算转变为建筑和应用艺术中创造性生产和审美判断媒介的努力。展览利用哲学,数学,计算机科学,和设计计算,将算法,理论,和机器学习系统转化为物理装置和交互式可视化。
“Beyond Data-Driven Aesthetics,” by MIT Architecture alumnus and researcher Alexandros Haridis, on view at the MIT Keller Gallery through June 30, examines 20th- and 21st-century efforts to transform computing into a medium for creative production and aesthetic judgment in architecture and the applied arts. Drawing on philosophy, mathematics, computer science, and design computation, the exhibition translates algorithms, theories, and machine-learning systems into physical installations and interactive visualizations.
Q: “超越数据驱动美学,” 的灵感来源是什么以及它探索了哪些问题?
Q: What inspired “Beyond Data-Driven Aesthetics,” and what questions does it explore?
A: “Beyond Data-Driven Aesthetics” 的概念起源来自三个交叉的研究路线。
A: The conceptual origins of “Beyond Data-Driven Aesthetics” emerged from three intersecting lines of research.
同时,我自己的研究已经集中在审美判断和评估,上，而且我越来越清楚，许多以“new”形式公开提出的与人工智能相关的问题实际上在20世纪有着更长的历史。例如，, 1956 年达特茅斯夏季研究项目, 是 AI 领域的基础性事件, 创建和评估过程被确定为未来 AI 研究应解决的人类智能的七个关键维度之一。
At the same time, my own research was already focused on aesthetic judgment and evaluation, and it became increasingly clear to me that many of the questions presented publicly as “new” in relation to AI actually have a much longer history across the 20th century. For example, in the 1956 Dartmouth Summer Research Project, a foundational event for the field of AI, creation and evaluation processes were identified as one of seven key dimensions of human intelligence that future AI research should address.
其次, 该展览受到设计计算和形状语法研究的影响，这些研究通过基于规则的方法, 而不是纯粹的数据驱动学习来研究人类洞察力和计算之间的关系。最近对美学理论 — 的解释性研究来自塞缪尔·泰勒·柯勒律治, 奥斯卡·王尔德, 甚至约翰·冯·诺依曼 — 等人物，对我来说尤其重要。这些研究探讨了哲学和文学文本中阐述的美学价值和比较理论是否可以揭示当代建筑和设计中数字计算和人工智能模型的可能性或局限性。
Second, the exhibition was influenced by research in design computation and shape grammars that investigates relationships between human insight and computation through rule-based methods, rather than purely data-driven learning. More recent interpretative studies of aesthetic theories — drawing from figures such as Samuel Taylor Coleridge, Oscar Wilde, and even John von Neumann — have been especially important to me. These studies examine whether theories of aesthetic value and comparison articulated in philosophical and literary texts may reveal possibilities or limitations in contemporary models of digital computation and AI in architecture and design.
最后,展览的动机是使用设计,制造,和数据可视化作为解释数学概念,算法,和“黑盒”机器学习系统的方法。跨学科, 研究人员越来越多地使用重建和可视化技术来使计算系统更加有形和可解释—，从计算机科学中的神经网络可视化到建筑和策展实践中的软件重建和数字制造。
Finally, the exhibition was motivated by the use of design, fabrication, and data visualization as methods for interpreting mathematical concepts, algorithms, and “black box” machine-learning systems. Across disciplines, researchers increasingly use reconstruction and visualization techniques to make computational systems more tangible and interpretable — from neural network visualization in computer science to software reconstruction and digital fabrication in architecture and curatorial practice.
Q:您如何将计算和美学的研究转化为展览?
Q: How do you translate research on computation and aesthetics into an exhibition?
A: 展览的方法是询问特定研究论文或书籍中的哪些内容准确捕捉了其最突出的想法,，然后使用设计以视觉, 空间, 和体验形式解释该想法。展览借助软件重构,实体制作,、数据可视化,等设计技术，将富含算法思想,抽象概念,和数学公式,的书面资料转化为包括交互,物质形式,和数字可视化在内的空间故事。
A: The approach of the exhibition is to ask what exactly in a particular research paper or book captures its most salient idea, and then use design to interpret that idea in a visual, spatial, and experiential format. Drawing on design techniques such as software reconstruction, physical making, and data visualization, the exhibition takes written sources that are dense with algorithmic ideas, abstract concepts, and mathematical formulas, and translates them into stories in space that include interaction, material form, and digital visualization.
展览本身围绕五个主题领域:审美衡量,审美准则,算法美学,审美挪用,和审美新颖性。每个主题都充当选择性 “window” 的功能，进入从特定出版物 — 书籍或研究论文中提取的审美判断的独特计算方法。这些主题的标题源自每个出版物的核心概念。例如, “measure” 指的是数学家 George Birkhoff 在 1930 年代的工作，以数学方式量化美学价值, 而 “novelty” 检查机器学习系统 AICAN 如何根据平衡熟悉度和偏差的认知美学理论来判断生成的图像来自已知的艺术风格。
The exhibition itself is organized around five thematic areas: Aesthetic Measure, Aesthetic Guidelines, Algorithmic Aesthetics, Aesthetic Appropriation, and Aesthetic Novelty. Each theme functions as a selective “window” into a distinct computational approach to aesthetic judgment drawn from a specific publication — a book or research paper. The titles of these themes are derived from concepts central to each publication. For example, “measure” refers to mathematician George Birkhoff的 work in the 1930s to quantify aesthetic value mathematically, while “novelty” examines how the machine learning system AICAN judges generated images according to a theory in cognitive aesthetics that balances familiarity and deviation from known artistic styles.
在所有五个案例,中，关键的见解是设计本身可以作为一种解释性翻译的方法—一种使可见的,有形的,和体验的方式，技术领域的传统学术学术通常仅通过文字和类似文字的表征装置,（例如科学图表和表格）进行交流。
Across all five cases, the key insight is that design itself can function as a method of interpretative translation — a way of making visible, tangible, and experiential what traditional academic scholarship in technical domains typically communicates only through words and word-like representational devices, such as scientific diagrams and tables.
Q: 接下来您希望探讨什么问题?
Q: What questions are you hoping to explore next?
A: “Beyond 数据驱动美学” 被认为既是一个研究展览，也是一个持续的平台，用于研究计算系统如何参与审美判断, 生成, 的过程以及跨建筑和应用艺术的转变。
A: “Beyond Data-Driven Aesthetics” is conceived both as a research exhibition and as an ongoing platform for investigating how computational systems participate in processes of aesthetic judgment, generation, and transformation across architecture and the applied arts.
展览 — 的核心问题之一以及架构, 设计, 和工程领域的研究人员越来越关注 — 的核心问题是超出纯粹性能或功能要求的计算评估。这适用于许多不同的设计空间,，无论是建筑物,结构形式,还是日常产品。展览’的案例研究表明，其中许多问题早在当前对计算和AI,的兴趣之前就已经存在，并且至少从20世纪初就已经通过一系列计算和理论评估模型得到了解决。
One of the central questions of the exhibition — and one that researchers across architecture, design, and engineering are increasingly focusing on — is computational evaluation beyond purely performative or functional requirements. This applies to many different design spaces, whether buildings, structural forms, or everyday products. The exhibition的 case studies suggest that many of these questions long predate current interest in computing and AI, and have been approached through a range of computational and theoretical models of evaluation since at least the early 20th century.
At the same time, I’m increasingly interested in how these ideas can move into broader applications related to the built environment. In particular, I am interested in how research connected to “Beyond Data-Driven Aesthetics” can help designers and engineers better understand how computation — whether rule-based or data-driven — can inform us about what contributes positively to human experience in relation to the spaces and objects people inhabit and use.
Finally, a direction I continue to explore is the methodological role of design itself as an interpretative device. Through software reconstruction, visualization, and physical making, the exhibition uses design to translate opaque computational systems into more legible, tangible, and experiential artifacts. More broadly, this opens questions not only about mechanizing “beauty” or “taste” (the traditional preoccupation of aesthetic formalism in the 20th century), but also about how traditional forms of research scholarship and communication may evolve through spatial, visual, and public-facing formats.