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

Learning Models for Interactive Melodic Improvisation

. This research addresses the problem of the computer interacting with a live, improvising musician in the jazz/blues setting. We introduce BoB, a model of improvisation that enables the computer to trade solos with a musician in an adaptive, user-specific manner. We develop unsupervised learning methods for autonomously customizing the model via improvised examples and demonstrate the powerful musical abstractions that emerge when applied to Charlie Parker's Mohawk improvisations. Our key technical contribution is the development of an architecture that naturally enables unsupervised learned knowledge, perception and generation to be tightly coupled. 1 Introduction This research addresses the problem of the computer interacting with a live, improvising musician in the jazz/blues setting. The long-term research goal is to create computer music algorithms and tools that enhance the organic, adaptive, melodic evolution that takes place when a soloing improviser practices alone with a..