self-contained
single-channel projection + single channel video (8:21 of a real-time animation), pix2pixHD neural network
2019
A neural network, trained to see the world as variations of the artist’s body, enacts a process of algorithmic interpretation that contends with a body as a subject of multiplicity. After training on over 30,000 images of the artist, this neural network learns to creates surreal humanoid figures unconstrained by physics, biology and time; figures that are simultaneously one and many. The choice of costumes and the movements performed by the artist to generate the training images were specifically formulated to optimize the legibility of the artist within this computational system. self-contained explores the algorithmic shaping of our bodies, attempting to answer the question: how does one represent themselves in a data set?
This work was included in the Fractured: Digital Topographies show at Lithium Gallery in Chicago.
To see how the self-contained series was created, click here.
