About this Project
Algorithms have aided the creation of artworks for centuries. Before the age of digital computing, artists, composers, and writers harnessed automatic processes through analog means. While the rapid advancement of computation has brought attention and innovation to this interaction, the full implications of algorithmic processes for art have yet to be understood and development in many areas has been slow. Machine learning, one of the most productive topics in computer science, has scarcely been explored in its application to visual art and design. In its fundamental capacity to both learn features and generate statistical variants, machine learning suggests a particularly promising method for algorithmically engaging with both the making and the reception of art.
This diverse collaborative team will explore the question: how can machine learning methods be deployed within the creation and evaluation of visual art and design? To address this question, the team will pursue dual programs of public-facing discourse and laboratory-based research. Public programs, including an exhibition and symposium, will approach the topic broadly with a desire to engage the community in ideas and work not well-represented on campus. The laboratory research will focus on two large data sets: 1) contemporary abstract painting and 2) virtual models of buildings and their furnishings. Results of this research are expected to provide a new category of data to the field: artworks labeled for machine learning applications. The research team will also explore more speculative possibilities of generating content through machine learning methods.
April 14, 2017
Faculty Fellow Jason Salavan (Critical Computation) will be the first artist exhibiting in a new installation space in downtown Chicago, reports Chicagoist.
There are no events associated with this project yet.