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. 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.
*Neural networks are programming models which are biologically inspired and learn from observing data. GANs (generative adversarial networks) are neural networks which learn to mimic through generation and refinement.

Exhibitions

Data • Glitch • Utopia, Gazelli Art House, London, UK (2023)
»Biomedia«, ZKM | Center for Art and Media, Karlsruhe, Germany (2021 -22)
The Book of Sand, Aiiiii Art Center, Shanghai (2021 - 22)
Forschungsfall Nachtigall, Museum für Naturkunde, Berlin, Germany (2019 -20) ID. ART:TECH | CYFEST-12 (during Venice Biennale), Ca’ Foscari Zattere, Venice, Italy (2019)
Zabludowicz Collection | Invites (solo display), London, UK (2019)
Lying Sophia and Mocking Alexa, Today Art Museum, Beijing, China (2019)

Jake Elwes (b.1993) is an artist living in London currently working to queer artificial intelligence with drag performers. Across projects that encompass moving-image installation, sound and performance, Elwes’ work finds unusual ways of demystifying, mapping and subverting technology. Their work searches for poetry and narrative in the successes and failures of digital systems. Works include deepfake drag in The Zizi Project (2019 – ongoing), glitching oppressive algorithms in Machine Learning Porn (2016) and reframing AI generated marsh birds back into nature in CUSP (2019). They have been making art exploring the aesthetics and ethics of machine learning systems since the very first generative AI models in 2016. Elwes’ work also calls for us to challenge who builds these systems and for what purpose, and whether we as artists and queers can reclaim these technologies to build our own digital utopias.

Elwes studied at The Slade School of Fine Art, UCL (2013-17) and their work has been exhibited in museums and galleries internationally, including the Victoria and Albert Museum, London; Pinakothek der Moderne, Munich; Somerset House, London; ZKM, Karlsruhe; Today Art Museum, Beijing; Frankfurter Kunstverein; Fotomuseum Winterthur, Switzerland; Honor Fraser Gallery, LA; Fundacion Telefonica Museum, Madrid; Ars Electronica, Austria; Zabludowicz Collection, London; Sculpture in the City, London; Science Gallery Dublin; RMIT Gallery, Melbourne; Onassis Foundation, Athens; E-WERK Freiburg, Germany; Museum für Naturkunde, Berlin; Nature Morte, Delhi; Centre for the Future of Intelligence, Cambridge and they have been featured on ZDF aspekte, ARD ttt (DE), BBC Radio 4 Front Row, and BBC1’s Kill Your TV - History of Video Art (UK).