Can machines think? In 1950 Alan Turing famously answered this question in the affirmative by means of a so-called “imitation game” in the course of which an examiner is asked to distinguish between humans and machines while communicating via a typewriter. He argued that, once the responses appear indistinguishable, the machine can be correctly be understood as engaged in thought. In an article entitled “Computing Machines and Intelligence,” Turing approached the subject of artificial intelligence from multiple perspectives, one of them inspired by the English mathematician Ada Lovelace. Here Lovelace figures as Turing’s foil, arguing that machines are incapable of thought because they are incapable of “tak[ing] us by surprise.” Turing counters by stating that machines are a frequent source of surprise, behaving unpredictably and, thereby, generating surprises.
Surprise Machines reprises Turing’s experiment some seven decades later by means of an AI-based curatorial experiment that relies upon “black box” algorithms whose behaviors, once set in motion, cannot be predicted by their programmers. It sets out to visualize the entire universe of Harvard Art Museums’ collections, with the aim of opening up unexpected vistas on the more than 250,000 objects that make them up. (Dario Rodighiero)
Curatorial A(i)gents presents a series of machine-learning-based experiments with museum collections and data developed by members and affiliates of metaLAB (at) Harvard, a creative research group working in the networked arts and humanities.