Joan Fontcuberta & Pilar Rosado
Joan Fontcuberta, Barcelona,1955 and Pilar Rosado, Sant Boi del Llobregat, 1965.
Joan Fontcuberta and Pilar Rosado have their work facilities at Roca Umbert Factory of the Arts in Granollers, Barcelona. This neighborhood situation allowed them to find shared concerns about art and technology that fueled their collaboration on different projects.
Joan Fontcuberta has developed both an artistic and theoretical activity, focused on the conflicts between nature, technology, photography, and truth. He is a self-taught artist and his training is mainly in communication and social sciences. From that theoretical starting point, his work has focused on the changing nature of images. However, beyond aesthetic qualities, images are understood as social and historical constructions that provide models for the real world and allow human interaction.
Pilar Rosado is an artist, teacher, and researcher. Graduated in Biology and PhD in Fine Arts, she has published various essays on the application of artificial vision models for the analysis of large collections of abstract art images, which provide alternative points of view for reflection and which question the conventions of our gaze. In her artistic practice, she explores political issues that can be addressed from the image and that implicate machine learning technologies, such as information management in the visual archives of the future, revision of collective memory, or artificial creativity.
Their work “Prosopagnosia” won the 15th edition of the ARCO-BEEP Electronic Art Award
Work at the collection: Prosopagnosia
Prosopagnosia is an artificially created speculation on the dialectic of facial recognition and the oversight of celebrity portraits in historical archives. Generative Adversarial Networks (GAN) have been used, which are deep neural network architectures composed of two networks that face each other (therefore, the term "adversary"), to create portraits that have never existed. Ian Goodfellow and other researchers from the University of Montreal introduced GANs in an article in 2014. The potential of these models is enormous because they can learn to mimic any dataset, that is, GANs can be taught to create haunting worlds, similar to ours in any domain: images, music, speech, or prose.