Oliver Bown

Keywords and research areas: Computational Creativity, Generative Music & Art, Musical Metacreation, Interactive Media Art & Design, Creative AI.

Oliver Bown is an academic interested in understanding artistic and musical creativity using digital technology, and its relation to society. 

He has worked in the field of Computational Creativity — the study of the automation of creative tasks — since 2007, seeing massive transformations in technological capability leading up to the current explosion in AI art and music. During this time he has worked as a creative coding practitioner, making his own music improvising systems which have been featured at the London Science Museum, the North Sea Jazz Festival, BBC Radio 3, the Computer Music Journal, the New Interfaces for Musical Expression Conference, and the International Symposium on Electronic Art, amongst other venues. 

As a practitioner he has also worked with other emerging media arts technologies, most notably low-cost networked computing for distributed audio and visual experience design. His collaborations with media arts collective Squidsoup have involved the development of massively multiplicitous distributed audio arrays, exhibited worldwide at venues including Kew Gardens in London, The Scottsdale Museum of Contemporary Art, Burning Man, and Salisbury Cathedral UK. He developed this work to enable rich audio synthesis over distributed networks with his HappyBrackets framework for distributed creative coding, which has been used in the commission of further multiplicitous media artworks.

Parallel to this creative practice, his research uses diverse methodological approaches to understanding technology, art and creativity, centred on a strongly social perspective on creative art and music practice. His 2021 book Beyond the Creative Species (MIT Press) summarises this body of work, applying literature on the psychology and social dynamics of creativity to questions of how we understand the creative autonomy of machines, how we design for interactions with creative AI, and how creative AI will impact cultures of creative practice. Related to this he is interested in how evolutionary theory provides a powerful framework for thinking about the dynamics of complex sociotechnical assemblages, connecting the work of evolutionary theories such as niche construction and multi-level selection theory with social theories such as actor-network theory and Bourdieu’s field of cultural production. His research methods combine creative practice research, design research and anthropological methods.

He is also a member of the UK electronic music duo Icarus, and the Australian improvising ensemble Tangents, an Australian Music Prize nominated band who have had their music reviewed in the Australian, Pitchfork, and SMH and featured on ABC radio and the BBC, amongst other media channels.

To see more of Oliver's work including his free book Beyond the Creative Species Making Machines That Make Art and Music click here.

David White

I am a lead investigator in the Face Research Lab at UNSW. We study face perception with a focus on individual differences in people's ability to perform face processing tasks. Although we all look at many faces each day, we do not all share the same abilities to process the important social information that they contain.

These individual differences have implications for theoretical understanding of perceptual processing and social cognition, as well as for people's everyday lives. They are also of substantial applied interest in settings where accurate face identification decisions are critical to identity management processes, for example in government, police, private industry and courts. Errors in these decisions can have profound social consequences, such as identity theft, acts of terrorism or wrongful convictions.

Our work in this area encompasses performance testing of humans and technology. Interest in facial recognition technology is both applied and theoretical. From an applied perspective, we are interested in how people use and collaborate with facial recognition technology when making face identity judgments – for example in criminal investigations and forensic science evidence reports. From a theoretical perspective, we are interested in the potential for modern facial recognition technology – Deep Neural Networks – to model aspects of the face processing system in humans. 

To find out more about David and his work click here.