Set In Stone
Set In Stone is a series of marble faces, generated by AI, as it learns to create and update its bias’ on gender. First, the AI is trained to generate masculine marble faces, fixed, immovable. Then marble feminine faces are added. It learns to change, a transgender neural network, updating its knowledge and its experience as it goes. Then the marble starts to give way, non-conforming self-expression, colour, and joy emerge as the gender becomes unfixed, non-binary. Through this evolving conceptual artwork, Rosenbaum is exploring how the machine creates images representing gender and if it holds onto its trained bias. Rosenbaum examines what the gender shift looks like and what transgender and non-binary self-expression and self-aware aesthetics are beyond biological essentialism. The datasets used by the machine are created by Rosenbaum using 3D modelling, echoing the idealised masculinity and femininity of classical statuary with a more inclusive bent. Rosenbaum is purposely training bias into the machine and then attempting to unbias it. The artwork evolves as the machine learns that there are multiple genders and gender expressions.
The works you see on the wall and the screen are moments in time in the training of the Deep Convolutional Generative Adversarial Network as Rosenbaum manipulates the dataset the machine relies on to challenge its bias. As it learns it generates sample images to show its progress and understanding. The samples are entirely represented in the video to represent the full training of the neural network to date. The works on the wall consist of the sample rendered into a mosaic of a face. They are chosen by the application. This is part of my ongoing research project and is an evolving conceptual piece.
J. Rosenbaum is a Melbourne AI artist and researcher working with 3D modeling, artificial intelligence, and extended reality technologies. Their work explores posthuman and postgender concepts using classical art combined with new media techniques and programming.