This research presents the implementation of AI-driven design approaches for final-year architectural design students, aiming to enhance environmental decision-making in their projects. It explores motivations, challenges, and outcomes of integrating AI tools into architectural practice. The use of AI facilitated evidence-based design and increased design exploration. Students showed a strong interest in quantitative environmental design. The research highlights the need for adapting architectural education to incorporate AI and sustainability, aligning with recent reforms in the field. It offers valuable insights for educational bodies, practitioners, and program administrators, emphasizing the importance of AI in training future architectural professionals.
AI, as a gamechanger, is revolutionizing the architectural and urban scenes so drastically that the way our buildings and cities will look like and be experienced will fundamentally change. The overarching purpose of the paper is to take a modest step towards understanding the paradigmatic role of AI in changing the form of our built environments. The paper conceptualizes AI’s role in the design process as essentially the suggestion of hybrid solutions, transforming the nature of the built environment to a hybrid between its static and non-static, real and virtual forms. The basic question to pose is the role of AI technology in assisting the design process, gearing it towards the creation of hybrid forms that redefine the relationship between humans and their built environment in a meaningful way and that also address the complex problems of our contemporary society which hinge upon the resilience of the architecture and urban form of the space we inhabit. AI technology is being increasingly embedded into our inhabited environment affecting the built expression of architecture as well as the process and practice of designing architecture.
This research delves into the potential of implementing artificial intelligence in architecture. It specifically provides a critical assessment of AI-enabled workflows, encompassing creative ideation, representation, materiality, and critical thinking, facilitated by prompt-based generative processes. In this context, the paper provides an examination of the concept of hybrid human–machine intelligence. In an era characterized by pervasive data bias and engineered injustices, the concept of hybrid intelligence emerges as a critical tool, enabling the transcendence of preconceived stereotypes, clichés, and linguistic prejudices. This paper not only explores the applied and generative capacities of AI-enabled workflows but also suggests fundamental approaches that can enhance the creative process and confront the embedded biases and injustices within data-driven systems.
We conducted an experiment to explore how Machine Learning (ML) can be utilized as a tool in urban studies research. The current study aims to compare two methodologies to identify urban indicators of the residents’ well-being focusing on three transects across two local watersheds in Jacksonville. The study is framed within the theory of transect analysis. The goal of this experiment was to compare an analogical transect analysis method (AT) to Machine Learning one (MLT) to understand (1) what kind of contribution the latter approach can provide to the development of transect analysis methodologies, and (2) if and how it can connect digitally generated site analysis to local knowledge.The experiment’s findings highlight the ability of the ML algorithms to find noticeable patterns of built environment from aerial imagery. However, local knowledge is indispensable to interpret results in a meaningful way. The combination of the two approaches emphasizes the complementary nature of them and shows how ML methods can be a tool at the service of communities.
As contemporary artificial intelligence (AI) tools are showing the ability to decipher intricate systems through the identification of correlations within vast datasets, the capacity to record, store, and utilize this data nowadays takes a central position in the cultural debate of an increasing number of disciplines. Architectural scholars, however, seem more interested in the explicit expression of AI, such as the development of new tools, rather than in how AI can challenge the ontology and epistemology of architectural design. Discussion of the research of scholars like Maurizio Ferraris, or Shoshanna Zuboff who investigates the societal consequences of data-harvesting practices, may reveal an ongoing change in the accumulation, preservation, and exploitation processes of architectural knowledge. This research intends to explore how one of the raw materials on which AI is nurtured, namely the abundance of data, has the potential to shape and guide forthcoming developments in architectural design.
Since early 2021, the discourse concerning the potential and impacts of artificial intelligence on architecture has radically expanded. Discussions have largely focused on the heightened levels of productivity or efficiency that can be achieved within the existing ecology of architectural production processes, as well as the potential disruptions that may arise through human–AI co-authorship of the built world. What this paper asserts is that these dominant narratives appear to be extensions of quite conventional storylines which either frame artificial intelligence as a hyper-computational prosthetic for the enhancement of the architect or architectural office or as a critically disruptive force that will trigger micro- to macro-scale reconfigurations of the domain of built- environmental authorship. The dilemma is that we appear to be thinking of AI on old models of brute-force computation (i.e., Deep Blue) or dystopian conceptions of AI systems that can readily cross-pollinate with and radically disrupt existing societal configurations and dynamics (i.e., HAL-9000). What we have not quite considered are the real capacities and limits exhibited by artificial neural networks anchored around self-play reinforcement learning models (i.e., AlphaZero).
AI’s potential is in its ability to sift through vast amounts of contextual data that can drive design decisions. It offers the opportunity to process information about a virtually limitless number of subjects, at a conscious or unconscious level. This has created what Thom Mayne describes as a “paradigm shift” in our perception of site. The architect can now curate, directing results based on AI to restore specificity to projects that, through the distance created between the real site and the computer model, we have lost along the way. We rely on technological workarounds to fix many designable issues in our buildings – an over-reliance on climate control, a willingness to place buildings directly in harm’s way (i.e., building on flood plains), and an ignorance of the context of a site (from archeology to gentrification). This paper offers a provocation: AI can create a return to site and construction sensibilities by harnessing layered data sets such as orientation, topography, climate, or even social fabric. Can AI chart a course towards less reliance on technological band-aids in the production of building?
This essay aims to explore the unexpected intersection of AI-generated images, metaphor, and fantasies of knowledge. Expanding on the notions of metaphor in the philosophy of Jose Ortega y Gasset, we view the AI-generated images (such as those from mis-trained StyleGAN models) as ‘objects’ in possession of an inherent surplus that transcends their perceived qualities and constituent parts. Metaphors reveal latent qualities of ‘objects’ that extend beyond human perception. We consider how these image-objects operate not merely as generators of visual metaphors but as metaphor-like entities themselves, shifting our perception of reality. As a result, these image-objects behave like disembodied expressions of a new kind of knowledge. This feeling, or fantasy of knowledge, is what Steven Connor calls ‘epistemopathy.’ By examining the interplay between human imagination, AI-generated outputs, and the epistemopathic experiences they evoke, we explore how AI becomes a manifestation of the human desire for expanded knowledge and imaginative exploration. In the blurring of the boundaries between our own understanding and AI’s perplexing output, we are confronted with a sort of knowledge boundary that conflates reverie with reasoning.