Computer science applied to architectureE xpanding Systems of Representation and Computational Signifiers from Big Data, Simulation, and Robotics activating Artificial Intelligence

Abstract

Computer Science Applied to Architecture: Multidimensional Architecture of Information by Expanding Big Data, Simulation, and Robotics, in Artificial Intelligence. This Doctorate Thesis investigates the histories and theories of representation in architecture interrelating technology and culture through linguistics in computational design and robotic fabrication. The thesis addresses mutidimensional spatial representation in the information age, to propose a means to address the displacement of deep informational structures in computational design through Big Data, Robotics, Materials, and AI (Artificial Intelligence) through a diverse set of methodologies from Machine Learning to Artificial Neural Networks, to Generative Adversarial Networks. The thesis argues that necessarily innovation in architecture involves a displacement of the deeper systems of representation. Within the information age, the displacement of systems of representation implies the displacement of the coding, binary signals, cryptography, algorithms, meta-algorithms and three-dimensional Cartesian systems of representation that structure and pre-determine architecture design. The thesis develops concepts and applied case studies for the emerging e-Architect, an autonomous blockchain AI platform.