Artwork Spotlight
Workshop: Machine Learning for Creativity and Design

Iterative Iterative

Erin Smith


I began by feeding thousands of images of my past artwork into a GAN. As a ceramic artist, the source imagery were photographs of sculptures and pottery I made between 2015-2019. The GAN required me to augment the data in multiple ways, including photographing my work from all sides. My vision was that the GAN would "imagine" my future work based on past work. However, the GAN was limited in ways that made this unfeasible. It was trapped within my prior work and could not imagine the new. I developed a collaboration with the machine where the shortcomings of the GAN become generative of new form. I prompted the GAN with my past work, and I interpreted its output as a prompt to either create the unknown half or interpret the output in 3 dimensions. It was within this unknown space that a void was created which offered the opportunity for novelty. As the GAN navigated this void, attempting to create new form from old, I also navigated this void. I embrace and utilize these digital shortcomings as a marking of time within technology and my own art practice.