Zizi is a procession of faces of drag artists generated by data in constant transition, morphing and changing shape. Their gender, sexuality, whether they are real or artificial, is all uncertain. Drag is a celebration of gendered and sexual otherness. It’s loud, bold and beautiful.
Machine learning algorithms make distinctions based on biases and weightings in a training dataset. They can also predict features to generate new instances. Zizi tackles head-on the lack of representation in training datasets. Here, the permanent becoming of a neural network (GAN) represents the fluidity, ambiguity and transition of drag artists. The work is a celebration of difference, which invites us to reflect on bias in our data driven society.
Jake Elwes (UK), born 1993, is currently based in London. His recent works have looked at Artificial Intelligence, investigating the technology, philosophy and ethics behind it. Jake graduated with a BA in Fine Art from the Slade School of Fine Art (UCL), London in 2017, having also attended SAIC, Chicago (Erasmus), in 2016 and Central St Martins (foundation) in 2012.
Jake has exhibited in the UK and internationally, including the ZKM, Karlsruhe, Germany; Today Art Museum, Beijing, China; CyFest, Venice; Edinburgh Futures Institute, UK; Zabludowicz Collection, London; Frankfurter Kunstverein, Germany; Bloomberg New Contemporaries 2017, Newcastle & London, UK; Ars Electronica 2017, Linz, Austria; Victoria and Albert Museum, London; City Loop, Barcelona; Nature Morte, Delhi, India and the Centre for the Future of Intelligence (CFI), Cambridge, UK.
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