Artificial Intelligence in Architecture and Built Environment Development 2024: A Critical Review and Outlook, 13th part: R&D Anew
Being right and wrong at the same time, Chaillou reveals the core of the misconception when concluding his paper [329]: ... our belief is that a statistical approach to design conception shapes AI’s potential for Architecture. Its less-deterministic and more holistic character is undoubtedly a chance for our field. Rather than using machines to optimize a set of variables, relying on them to extract significant qualities and mimicking them all along the design process is a paradigm shift. Then, nesting models one after the other will ultimately allow ... to encapsulate relevant pieces of expertise. „Relevant pieces of expertise“ is the keyword, indeed. However, it is not the expertise hidden in input-output pairings but pieces of expertise representing steps and milestones of the (design) process as the AI-driven-robotics benchmarks of Figure-1, GR00T, and others introduced within the state-of-the-art (2) section suggest. The statistical approach and supervised-learned GANs, on the contrary, appear doomed to remain just an etap of the history of the deployment of AI in architecture that was treading a dead end. In this field, supervised-learned GANs exhausted their potential before they could deliver qualified results.