While the digital impacts our mnemonic capacities, historical archives are also digitized affecting how we collectively remember the past. Together with a StyleGAN model, Andreas Refsgaard re-writes our history in the video work Aagaards Glasplader. A generative adversarial network (GAN) is a machine-learning system that can produce an image from a preexisting visual database. From this database, it intercepts patterns and information, after which it can demonstrate its own reverie of the world. Refsgaard has run portrait photos from the mid-19th century through the model, generating new portraits of a non-existing past accompanied by likewise generated historical biographies. It is just plain math, and yet this maneuver can be described as a symbiotic creative process between machine and human. Refsgaard’s work imitates how machine-vision, such as intelligent surveillance cameras and image recognition on social media, detects and analyzes objects, faces, and actions in our everyday surroundings. 

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                                                      Ida Schyum

cand.mag in art history