ARCHITECTURAL SAMPLING by Immanuel Koh
The spatial and formal conception of architecture, and thus its modes of design perception and representation, directly contributes to its machine-learnability; and consequently, its capacity in leveraging today’s machine learning apparatus for design innovation. If text can be sampled and synthesised in Natural Language Processing, image in Image Processing and sound in Audio Signal Processing, how can architectural forms and spaces be likewise sampled for generating new designs? What is a sampling unit of architectural form? This lecture will endeavour to construct a theoretical and technical framework with the concept of Architectural Sampling. Foundational to this new design theory, is the overcoming of architecture’s own longstanding set of conceptual and perceptual assumptions, namely figure/ground, parts/whole and shapes/grammars; and replaced with one that is ontologically ‘flattened’, ‘resolutional’ and ‘probabilistic’.