Experience Driven Design of Creative Systems

The key contribution of this paper is to describe and demonstrate a novel application of grounded theory to the analysis of a human/machine music performance. Rather than attempting to measure the ‘creativity’ of our machine improviser, we instead proposed an investigation of the experiences of humans- in this case the designer, the performer and the listener. We report the design of an AI system chosen to perform in a specific creative context - a jazz-inflected musical performance in this case - and explore the specific experiences of these human actors through the performance itself. The performance is one which is a commonplace one where a single human musician interacts and performs with a single autonomous system. We describe this system which improvises by training pitch and event sequence models in real time from a live audio input and then uses a riffing behaviour to generate output in the form of note sequences with varying timbre. However, the main thrust of this paper is to propose a new methodology for understanding the role of the system through the interplay of experiences of audience, designer and performer throughout the performance, and describe how our time based media annotation system can be used to support that methodology. We present the results of this grounded ontology methodology applied to the text-based commentaries between system engineer, performer and listener. We argue that by developing an understanding of these inter-related experiences we can understand the desired and potential role of computational systems in creative contexts which can help in the design of new systems and help us curate new kinds of performance scenarios

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency

Can Machines Be Artists? A Deweyan Response in Theory and Practice

To speak comfortably of the machine artist (as outlined in the call for papers for this Special Issue) makes key assumptions about what it is to be an artist. It assumes, for instance, that the experience of living as an artist, which includes the socialisation, hard work, single-mindedness, and focused energy of creative activity, is incidental rather than essential since these aspects are not comfortably applicable to machines. Instead, it supposes that what is essential is the artistic product, and it is the similarity of human and machine products that makes it possible to speak of machine artists. This definition of art in terms of products is supported by modern psychological theories of creativity, defined as the generation of novel ideas which give rise to valuable products. These ideas take place in the mind or brain, regarded as a closed system within whose workings the secret of creativity will eventually be revealed. This is the framework of what is widely referred to as “cognitivism”. This definition in terms of novel ideas and valuable products has been widely assumed by artificial intelligence (AI) and computational creativity (CC), and this has been backed up through a particular version of the Turing Test. In this, a machine can be said to be a creative artist if its products cannot be distinguished from human art. However, there is another psychological view of creativity, that of John Dewey, in which a lived experience of inquiry and focus is essential to being creative. In this theory, creativity is a function of the whole person interacting with the world, rather than originating in the brain. This makes creativity a Process rather than a Cognitivist framework. Of course, the brain is crucial in a Process theory, but as part of an open system which includes both body and environment. Developments in “machine art” have been seen as spectacular and are widely publicised. But there may be a danger that these will distract from what we take to be the most exciting prospect of all. This is the contribution of computer technology to stimulate, challenge, and provoke artistic practice of all forms

The Machine as Art/ The Machine as Artist

The articles collected in this volume from the two companion Arts Special Issues, “The Machine as Art (in the 20th Century)” and “The Machine as Artist (in the 21st Century)”, represent a unique scholarly resource: analyses by artists, scientists, and engineers, as well as art historians, covering not only the current (and astounding) rapprochement between art and technology but also the vital post-World War II period that has led up to it; this collection is also distinguished by several of the contributors being prominent individuals within their own fields, or as artists who have actually participated in the still unfolding events with which it is concerne