Panel: Surveillant Spaces: From Autonomous Surveillance to Machine Voyeurism
Computer vision is a branch of artificial intelligence that is concerned with developing algorithms to allow machines to process and respond to visual data. The degree to which the early pioneers in artificial intelligence underestimated the challenges involved is often illustrated with an anecdote about Marvin Minsky instructing a student to solve “the problem of computer vision” as a summer project. The first breakthroughs in computer vision came from the field of computational neuroscience and the work of David Marr, building low-level models of the visual cortex from the ground up. Nearly forty years later, the state-of-the-art in computer vision is still very much in the process of constructing relatively primitive representations from captured images. Nevertheless, the research has produced a wealth of techniques that can be applied to suitably structured scenes to extract meaningful information. Many of these techniques are simple enough that they can be implemented by novice programmers while more sophisticated techniques have become readily available through programming libraries and off-the-shelf software. The availability of computer vision technology provides a base for experimenting with machine autonomy in creative domains. In this panel I will discuss the possibility of developing autonomous machine performers that take advantage of the advances in computer vision by first reviewing some of relevant low-level and high-level techniques and showing how these can be integrated with machine learning systems. In particular, I will present this exploration in relation to my research developing self-motivated (curious) agents and my collaborations with Petra Gemeinboeck exploring the performativity of the gaze through the creation of machine augmented environments. In this workshop-like session we will explore the construction of a self-motivated machinic voyeur, examine what it sees, how it responds and what drives it.
- Rob Saunders is Senior Lecturer in Design Computing in the Faculty of Architecture, Design and Planning at the University of Sydney, AU. Rob’s research centres around creative application of computing and the computational modelling of creativity. Using techniques from machine learning, robotics and surveillance he has explored the role of curiosity in creative processes and developed models of creative systems at individual, social and cultural levels. His models of curious design agents have demonstrated useful abilities for autonomous design systems, including problem-finding and open-ended exploration. Rob works with artists and designers across a range of disciplines to support and engage in the creative application of computing and has applied his research in the development of design customisation systems, smart environments, interactive installations and robotic artworks.