Coinciding with his current project on our Media Wall, Mario Klingemann will be in conversation with Daniel Rourke discussing his recent work which employs machine learning to create visual imagery in a process he calls “Neurography”. In recent months, attention has been drawn to the use of adversarial neural networks to produce entirely fictitious photographic images and their potential use and mis-use in a post-truth age. Reflecting on the ways in which machines are being trained at an increasing rate to learn new skills - from creating to interpreting images - Mario will unpack the creative possibilities presented by neural networks. 

Two CycleGAN models trained on face transformations are passing their outputs back and forth creating an infinite loop of ever-changing Turing patterns. 

This is an experiment where 2 CycleGAN models are pitted against each other and transform their reciprocal output images in a feedback loop. The models have originally been trained on transforming faces which is why you see a lot of eyes and noses being generated. The process is seeded with an initial image but it quickly descends into a semi-stable state that reminds of complex cellular automata or Turing patterns.