Mirror Ritual: Human-Machine Co-Construction of Emotion

Mirror Ritual is an interactive installation that challenges the existing paradigms in our understanding of human emotion and machine perception. In contrast to prescriptive interfaces, the work's real-time affective interface engages the audience in the iterative conceptualisation of their emotional state through the use of affectively-charged machine generated poetry. The audience are encouraged to make sense of the mirror's poetry by framing it with respect to their recent life experiences, effectively `putting into words' their felt emotion. This process of affect labelling and contextualisation works to not only regulate emotion, but helps to construct the rich personal narratives that constitute human identity.Comment: Paper presented at ACM TEI Conference 2020 Arts Track, Sydney Australi

Holon: a cybernetic interface for bio-semiotics

This paper presents an interactive artwork, "Holon", a collection of 130 autonomous, cybernetic organisms that listen and make sound in collaboration with the natural environment. The work was developed for installation on water at a heritage-listed dock in Melbourne, Australia. Conceptual issues informing the work are presented, along with a detailed technical overview of the implementation. Individual holons are of three types, inspired by biological models of animal communication: composer/generators, collector/critics and disruptors. Collectively, Holon integrates and occupies elements of the acoustic spectrum in collaboration with human and non-human agents.Comment: Paper accepted at ISEA 24, The 29th International Symposium on Electronic Art, Brisbane, Australia, 21-29 June 202

Deep Learning of Individual Aesthetics

Accurate evaluation of human aesthetic preferences represents a major challenge for creative evolutionary and generative systems research. Prior work has tended to focus on feature measures of the artefact, such as symmetry, complexity and coherence. However, research models from Psychology suggest that human aesthetic experiences encapsulate factors beyond the artefact, making accurate computational models very difficult to design. The interactive genetic algorithm (IGA) circumvents the problem through human-in-the-loop, subjective evaluation of aesthetics, but is limited due to user fatigue and small population sizes. In this paper we look at how recent advances in deep learning can assist in automating personal aesthetic judgement. Using a leading artist's computer art dataset, we investigate the relationship between image measures, such as complexity, and human aesthetic evaluation. We use dimension reduction methods to visualise both genotype and phenotype space in order to support the exploration of new territory in a generative system. Convolutional Neural Networks trained on the artist's prior aesthetic evaluations are used to suggest new possibilities similar or between known high quality genotype-phenotype mappings. We integrate this classification and discovery system into a software tool for evolving complex generative art and design

The Enigma of Complexity

In this paper we examine the concept of complexity as itapplies to generative art and design. Complexity has many different, dis-cipline specific definitions, such as complexity in physical systems (en-tropy), algorithmic measures of information complexity and the field of“complex systems”. We apply a series of different complexity measuresto three different generative art datasets and look at the correlationsbetween complexity and individual aesthetic judgement by the artist (inthe case of two datasets) or the physically measured complexity of 3Dforms. Our results show that the degree of correlation is different for eachset and measure, indicating that there is no overall “better” measure.However, specific measures do perform well on individual datasets, indi-cating that careful choice can increase the value of using such measures.We conclude by discussing the value of direct measures in generative andevolutionary art, reinforcing recent findings from neuroimaging and psy-chology which suggest human aesthetic judgement is informed by manyextrinsic factors beyond the measurable properties of the object beingjudged

Building Simulations with Generative Artificial Intelligence

In this chapter, we explore the possibilities of generative artificial intelligence (AI) technologies for building realistic simulations of real-world scenarios, such as preparedness for extreme climate events. Our focus is on immersive simulation and narrative rather than scientific simulation for modelling and prediction. Such simulations allow us to experience the impact and effect of dangerous scenarios in relative safety, allowing for planning and preparedness in critical situations before they occur. We examine the current state of the art in generative AI models and look at what future advancements will be necessary to develop realistic simulations