Tag: neural networks

Since early 2021, the discourse concerning the potential and impacts of artificial intelligence on architecture has radically expanded. Discussions have largely focused on the heightened levels of productivity or efficiency that can be achieved within the existing ecology of architectural production processes, as well as the potential disruptions that may arise through human–AI co-authorship of the built world. What this paper asserts is that these dominant narratives appear to be extensions of quite conventional storylines which either frame artificial intelligence as a hyper-computational prosthetic for the enhancement of the architect or architectural office or as a critically disruptive force that will trigger micro- to macro-scale reconfigurations of the domain of built- environmental authorship. The dilemma is that we appear to be thinking of AI on old models of brute-force computation (i.e., Deep Blue) or dystopian conceptions of AI systems that can readily cross-pollinate with and radically disrupt existing societal configurations and dynamics (i.e., HAL-9000). What we have not quite considered are the real capacities and limits exhibited by artificial neural networks anchored around self-play reinforcement learning models (i.e., AlphaZero).