This project creates a visual-mental-physical circuit between a Generative Adversarial Network (GAN), a co-robotic arm, and a five-year-old child. From training images to the latent space of a GAN, through pen on paper to a live human collaborator, it establishes a series of translational stages between humans and non-humans played out through the medium of drawing. Trained on a subset of the Rhoda Kellogg Child Art Collection, the neural network at the center of this piece learns its own representations of these images. The generated results, synthetic children's drawings, are of interest both for being outside of adult conventions and learned expression-like Dubuffet's art brut or Surrealist automatism-and for how they align machine learning with the human act of learning to draw.

The project layers many kinds of agency and embodiment: from the thousands of anonymous children who produced the original artwork used as training data, through the co-robot drawing from GAN-generated imagery, to the human child's active perception and graphic response to the robot. These questions of where we search for the other; when we attribute autonomy and intelligence; and why we might wish to escape our human subjectivities speak to core issues in the design and use of AI systems. This project is one attempt to think through those questions in an embodied way.