BIO
Robert B. Lisek PhD is an artist, mathematician and composer whose work focuses on systems, networks and processes within the computational, the biological, and the social. He is involved in a number of projects which engage with the topics of media art, creative storytelling and interactive art. Drawing upon post-conceptual art, software art and meta-media, his work intentionally defies categorization. Lisek is a pioneer of art based on Artificial Intelligence and Machine Learning. Lisek is also a composer of contemporary music, and author of many projects and scores on the intersection of spectral, stochastic, concret music, musica futurista and noise. Lisek is a scientist who conducts research in the area of foundations of science, notably within mathematics and computer science. His research interests are category theory and high-order algebra in relation to artificial general intelligence. Lisek is a founder of Fundamental Research Lab, based in Southern California, and ACCESS Art Symposium. He is the author of 300 exhibitions and presentations, among others: SIBYL, ZKM Karlsruhe; SIBYL II, IRCAM Center Pompidou; QUANTUM ENIGMA, Harvestworks Center, New York, and STEIM, Amsterdam; TERROR ENGINES, WORM Center Rotterdam; Secure Insecurity, ISEA Istanbul; DEMONS, Venice Biennale (accompanying events); Manifesto vs. Manifesto, Ujazdowski Castle of Contemporary Art, Warsaw; NEST, ARCO Art Fair, Madrid; Float, Lower Manhattan Cultural Council, NYC; and WWAI, Siggraph, Los Angeles.
ABSTRACT
Evolutionary Strategies in Architecture and Art
The project proposes a new strategy for creating evolving architectural structures based on the idea of adaptation to a dynamically changing environment and with the use of advanced machine learning and AI methods. The evolving architecture uses physical and virtual processes that are transformed and assembled into structures based on environmental properties and capabilities. The project investigates a living dynamic system as a complex set of natural and cultural sub- processes in which each of the interacting entities and systems creates complex aggregates. It deals with natural processes, communication flows, information networks, resource distribution, dense noise masses, a large group of agents and their spatial interactions in the environment. By significantly expanding existing research, the project creates a meta-learning model useful for testing various aspects of adaptation to a complex dynamic environment. This refers to the difficulty of designing artificial agents that can intelligently respond to evolving complex processes.
