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. 

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.

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).