Max Cooper

Morphosis uses artificial neural networks to create morphing images of scale. The system explores how natural structures from the most tiny to the most huge, share aesthetic properties, as recognized by the trained network, and recreated in continuous flowing sequence via these connections. It’s a study of the seemingly infinite nature of space and natural physical structure, which can loop back on itself to give endless visual exploration and variation.

Jake Elwes

A familiar childhood location on the Essex marshes is reframed by inserting images randomly generated by a neural network (GAN*) into this tidal landscape. Initially trained on a photographic dataset, the machine proceeds to learn the embedded qualities of different marsh birds, in the process revealing forms that fluctuate between species, with unanticipated variations emerging without reference to human systems of classification. Birds have been actively selected from among the images conceived by the neural network, and then combined into a single animation that migrates from bird to bird, accompanied by a soundscape of artificially generated bird song. The final work records these generated forms as they are projected, using a portable perspex screen, across the mudflats in Landermere Creek.