Deep render

Intelligent Algorithms (IA) are increasingly influential, becoming embedded and intertwined in cognitive and creative processes. This practice-based PhD addresses the urgent need for novel approaches in contemporary art that offer insights into how these algorithms can be used and presented outside of their common interfaces and the vested interests for which they were developed. The project focuses on Machine Learning (ML) and Artificial Intelligence (AI) curated systems that "recommend" and push content on users, for example, internet search engines, social media, news and content feeds. The research centres on the probing and prodding, dissecting and inverting, looping and prying of such networks and their facilitating algorithms, on and offline.

The submitted artworks present a rethinking of techniques to process, organise, and display online content, while reflecting the dissonant environment that internet users face, i.e., with multiple tabs open, a video streaming in the background, twitter, the news, reddit, or Instagram open on their phone. This kind of media ‘collage’ is how most users encounter content and information: in fragments, on multiple devices and interfaces. These overlapping streams of hypermedia content are incorporated into the artworks that reorganise the collected material using true randomness to recast content dynamically in unpredictable ways.

Methods used in this project combine activating and activated practices that work together to examine how intelligent algorithms operate, either internally through its processes or externally on individuals. The research takes the form of a semi-fictional memoir in the form of a ‘subreddit novella’ and ‘Deep Render’ artworks that employ IA algorithms within custom programs that disrupt their normal flow of information to produce generative worlds, spontaneous narratives, moving images, and objects. Both the novella and the ‘Deep Renders’ are reposted online and presented in their respective hosting platforms. This process of reinsertion and feedback is central to the methodology of the PhD that seeks to use the research materials to subtly alter biased systems and decouple data and art practice from networks of control.