A detailed account of our presentation and workshop at CCC 35C3 hacker conference where we proposed a new kind of software for the body based on the patterns of movement and physical interaction — and its implications for the development of AI.
In this workshop, conducted in collaboration with Nike Lab x Matthew Williams and ∞OS, we created a live interactive environment to stimulate cross-contextual learning and creativity among the participants. In this work we used text-to-network visualization tool, live space tracking, sonic queues and body-heatmaps to provide actionable live feedback to the workshop’s participants.
Text network visualizations are most often used to study and analyze data. However, how can an analytical tool become a musical instrument and what are the implications of that, both for the research process and for the creative work?
We now offer high quality large-format prints of network visualizations created using a combination of our own software (Textexture), open-source Gephi platform, iterative algorithms, fuzzy logic, trial and error, as well as intuition, magic, science, and experiment.
It is possible to order prints of the already existing text network visualizations or to create custom-made prints using your own source data (any text, social network, or any other connectivity that can be represented as a network).
Recommender systems are the basic building blocks of most online businesses today. They influence the news we read, the posts we see, the things we buy, the music we listen to. Based on the dataset of what is known about us, recommender system finds something we might be interested in. Technically recommender system is a combination of learning algorithms, statistical tools, and recognition algorithms – the areas commonly ascribed to the study of artificial intelligence.
We decided to study this emergent form of consciousness and conduct an attempt to communicate with a recommender system through the interview format. The results are presented in this report.
