Accumulation Study is a sculptural study in which a robotic arm gathers material towards itself. The piece was made to perform an automated mode of production, specifically the accumulation process involved in predictive machine learning models. The form was adapted from an open-source Mime Industries .svg file, and has been influenced by the work of Anicka Yi and Lee Bul.
This project concept came out of an interest in machine learning. I have found that while predictive machine learning models can be used to generate eerily accurate pattern predictions, they more interestingly obfuscate any semblance to the users from which the data was pulled, resulting in a hyper-specific set of characteristics about multiple people that will never be made visible, human, or intelligible to those receiving its predictions. This sculpture incorporates transparent, imperfectly shaped cubes. Each cube can be thought of as a complex set of data. The sculpture performs an abstraction process similar to that of machine learning, although it doesn’t generate any predictions and it doesn’t “learn.” Instead, it offers a collective, portrait of many different cubes– once visible and now only partially recognizable when in the greater structure–that resembles the abstraction process central to machine learning. The sculpture will demonstrate a “plurality” of portraits in a similar way that machine learning offers. I am inspired by the work of Anicka Yi and Brian House.
Introduction to Physical Computing