Tectonics of Artificial Intelligence

This study delves into the nuanced understanding of the concept of Latent Space within the field of architecture, emphasizing its transformative potential. Latent Space, a term often used in machine learning, refers to an abstract multi-dimensional space that represents the learned features of data. By integrating this concept with architectural tectonics—the art and science of construction—we propose a novel framework that reconceptualizes space as both physical and informational. The research begins by examining the parallels between Latent Space in machine learning and tectonic practices in architecture. In machine learning, Latent Space enables the discovery of patterns and relationships within data that are not immediately apparent. Similarly, architectural tectonics involves the articulation of structural elements and materials to create meaningful spaces. By drawing these parallels, we aim to establish a theoretical foundation that bridges the gap between AIdriven data exploration and traditional architectural methods The paper then explores how