System Aesthetics: Generative Adversarial Network – FIELD.IO
  • Description
  • The works in this series are part of an extensive research project by FIELD, exploring the most relevant machine learning algorithms in code-based illustrations. The field of Artificial Intelligence has seen a major acceleration in recent years, especially in the specialisation of machine learning algorithms, controlled by large corporations with access to huge amounts of personal user data and computing power. These algorithms are now the invisible force that increasingly shapes our lives, deciding who we meet on social dating platforms, about our mortgage and insurance prospects, and controlling our environments at home and in public spaces. Their development happens behind proprietary shields and outside of public scrutiny, making a grassroots understanding or the emergence of independent alternatives nearly impossible. Often, the decision-making of neural networks is obscure even to the humans who developed and trained them, making A.I. a veritable “black box”. As visual artists, we find it important to understand and discuss these fundamental forces that change our society. We have started a deeper exploration of the less accessible information that is out there, such as scientific papers and open source code publications, to develop an understanding of these algorithms’ inner workings, and translate it into visual metaphors that can contribute to a public debate.
  • metadata / artifact / image
  • 7099
  • can.nft$.v1.2021.11
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