Nolan is an artist and researcher primarily working in sound, kinetic sculpture, and psychophysical practices. He is currently working on incorporating machine learning and hearing processes into his work that stem from his research as a PhD candidate at the Center for Computer Research in Music and Acoustics (CCRMA) at Stanford University. His current project deals with teaching the computer how to synthesize ‘industrial’ noise—electrical hums, power generator buzzes, server farm noise—from a large corpus of field-recorded industrial sounds. In doing so, the computer effectively learns to listen to the sounds representative of machines themselves and recreate them at will. Using these recursively learned sounds, his work explores to what extent neural networks can be used to form an epistemology of sound that is derived from both anthropomorphic and technocentric aims.