Open this publication in new window or tab >>2023 (English)Artistic output (Unrefereed)
Abstract [en]
The Cascades is a solo exhibition and multi-channel installation that brings together an ongoing series of work by artist Daniel Shanken. The installation focuses on networks of information that run in the background of our lives, filtered and curated by intelligent algorithms that push bias and dissonance.
Incorporating live content feeds from multiple sources online such as YouTube videos and comments, internet radio, Google Image search, Reddit, and Twitch, the work builds an environment that fluctuates with incoming content scraped from the internet in real-time. Custom-trained machine learning algorithms respond to and alter the work, while offering insights through laser-projected text. Using randomness derived from physical sources such as radioactive decay and atmospheric noise, material is shuffled and displayed in unpredictable combinations that are never the same. This is in contrast to most search engines and hosting sites that control content and information through tailored interfaces that often exclude voices hidden in the noise.
The installation creates a spontaneous environment that allows viewers to enter through interfaces other than the normal internet screen space confronted on a daily basis. The work reflects the remnants of discarded technological production, oversaturated media landscapes, data centres, landfills, and mining tunnels. It draws on the intangible data-points, AI algorithms, and rendered objects often encountered in the periphery.
The Cascades was developed through research conducted during Shanken’s completed PhD with the Contemporary Art Research Group (CARG) at Kingston University’s Centre for Practice Research in the Arts (CePRA).
Place, publisher, year, pages
London: Kingston University, 2023
Keywords
art, installation, video art, internet art, generative art
National Category
Visual Arts
Research subject
Artistic research
Identifiers
urn:nbn:se:umu:diva-234219 (URN)
2025-01-172025-01-172025-01-20Bibliographically approved