CUSP

A familiar childhood location on the Essex marshes (UK) 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. The work both augments and disrupts the natural ecology of the location, as flocks of native birds enter a visual dialogue with these artificial ones.

Title: CUSP

Medium: 4K digital video with audio, 13 minute loop

Artist: Jake Elwes

Year: 2019

Location: On display at Pedion tou Areos

Credits
Camera: Toby Elwes

Machine learning models
Progressive Growing of GANs for Improved Quality, Stability, and Variation (2018)
WaveNet: A Generative Model for Raw Audio (2016)

Datasets
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop (2015)
Customised dataset of marsh bird images from Flickr community (2019)

Special thanks
The machine learning community, in particular at Nvidia and Deepmind
Maitreyi Maheshwari and the team at Zabludowicz Collection

Glossary: neural network, GAN, dataset