Designer and Fulbright fellow Stanislas Chaillou has created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans. After an initial study in the potential of AI-generated floor plans, Chaillou's project developed into training and tuning an array of models on specific architectural styles: Baroque, Row House, Victorian Suburban House, & Manhattan Unit. The study reveals how style carries a fundamental set of functional rules that define mechanics of space and control the internal organization of the plan.
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