Beyond Algorithmic Genericism

Speaker(s):

Jon McCormack works at the nexus of art, technology and society. His experimental practice is driven by an enduring interest in computing and incorporates generative art, sound, evolutionary systems, computer creativity, physical computing and machine learning. Inspired by the complexity and wonder of the natural world, his work is concerned with electronic ‘after natures’: alternate forms of artificial life which, due to unfettered human progress and development, may one day replace a lost biological nature. His artworks have been widely exhibited at leading galleries, museums and symposia, including the Museum of Modern Art (New York, USA), Tate Gallery (Liverpool, UK), ACM SIGGRAPH (USA), Prix Ars Electronica (Austria) and the Australian Centre for the Moving Image (Australia). He is the recipient of over 17 awards for new media art and computing research including prizes at Ars Electronica (Austria), Nagoya Biennial (Japan), the 2012 Eureka Prize for Innovation in Computer Science and the 2016 Lumen Prize for digital art (still images). He is currently undertaking a Future Fellowship, funded by the Australian Research Council, that investigates new models for the generative design of digitally fabricated materials. Professor McCormack is the founder and director of SensiLab, a trans-disciplinary research space dedicated to the future of creative technology at Monash University in Melbourne, Australia. SensiLab’s collective research explores the untapped potential of technology, its impacts on society and the new possibilities it enables. Its dedicated research space – which opened in late 2017 – encourages enthusiasm, curiosity, seamless collaboration and unrestricted experimentation. jonmccormack.info sensilab.monash.edu

Description: With the recent renewed interest in art generated by Artificial Intelligence (AI), it is timely to re-explore the body of knowledge and critique around art made by algorithms. Since computers were first adopted as art machines a number of enduring criticisms have reoccured over the decades, often with different names, but ultimately similar conceptual foundations. Essentially they relate to issues of authorship (who is the author when an “intelligent” machine is involved in the art making process?), autonomy (how much of the decision making and creative judgment is absolved to the machine?), authenticity (can creative acts or outputs made by machines ever be authentic?) and intention (is it right to think of machines as artists?). As AI technologies are increasingly fetishised by technologists and artists, a renewed debate around these criticisms has reemerged. In this talk I want to specifically address the issue of algorithmic genericism: how can algorithmic art practices escape the spectre of being generic to the algorithm itself? How can a practice be informed to recognise the issues of authorship, autonomy, authenticity and intention and move beyond algorithmic genericism?