Neural Glitch

Mario Klingemann describes himself as “an artist and a skeptic with a curious mind.” His work spans and incorporates a wide range of tools and technologies, including neural networks and deep learning, computer code and algorithms, and artificial intelligence and generative art (work produced by autonomous or semi-autonomous systems). The driving force behind his evolving aesthetic is an interest in the idea that artificial intelligence has the potential to generate surprising new images and perspectives. As technology advances, the question then becomes for the artist, “What does one hope to find?”

Neural Glitch is a technique Klingemann started exploring in April 2018, in which he manipulates generative adversarial networks, or GANs, which are a class of machine learning system that use training data sets to create new data. Due to the complex structure of the GANs’ neural architectures, the introduced glitches cause the models to misinterpret the input data in interesting ways, some of which could be interpreted as glimpses of autonomous creativity. According to Klingemann, “One interesting aspect of this process is that on one side the same input data can yield very different results depending on the glitch, whilst at the same time different input data, transformed by the same glitched model chain, will result in a coherent style and show the same semantic misinterpretations.”

As a pioneer in producing art with artificial intelligence, computer learning, and other technologies, Klingemann’s work has been shown at the Museum of Modern Art in New York City and the Centre Georges Pompidou in Paris. He has worked with a variety of prestigious institutions, including the British Library, Cardiff University, and New York Public Library. He is currently an artist in residence at Google Arts & Culture. 

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For more information about the artist and his work, visit http://quasimondo.com/.

Cite this Article

Klingemann, Mario. “Neural Glitch.” Issues in Science and Technology 36, no. 2 (Winter 2020).

Vol. XXXVI, No. 2, Winter 2020