"Producing an image using machine learning, as opposed to any other method, creates different experiences, expectations, histories, traces and contexts for the viewer to consider. I am interested in the form these associations may take and how they might be registered. I have used machine learning here as a process that enables me to accentuate the central ideas that emerged out of The Fall of the House of Usher, a 12-minute animation based on Watson and Webber’s 1928 silent film. Each still is created by a neural net which has been trained on ink drawings that I have made. Three separate neural nets are used – the first trained on drawings of the original frames, the second on drawings made of the results of the first net, and the third on drawings made of the results of the second. By manipulating the reciprocity and feedback between the original film, my drawing and this form of technology, I have been able to heighten and intensify the film’s original motifs and to liberate fugitive aspects of memory to create a sense of the uncanny that is partly machine-made.
Working in this way, I am not interested in programming a machine to draw like a human or in producing a drawing that does not acknowledge its origin. The shiny, robotic quality of much digital art appears to neuter the messiness of the world, I am interested, however, in the opposite approach: how to use a medium that is cold, sterile and algorithmic to maintain and accentuate a sense of human touch. By laboriously creating by hand datasets based on the original film and then by processing and reproducing these by using specially modified algorithms, I have created a system of loops and feedbacks that use and enhance machine learning as an integral part of the work’s material and process. The simultaneous progression from hand drawn still, to its first feedback, to its ‘final’ form is registered in the The Fall of the House of Usher’s three contiguous projected screens."