Dance performance traditionally follows a unidirectional relationship where movement responds to music. While AI has advanced in various creative domains, its application in dance has primarily focused on generating choreography from musical input. We present a system that enables dancers to dynamically shape musical environments through their movements. Our multi-modal architecture creates a coherent musical composition by intelligently combining pre-recorded musical clips in response to dance movements, establishing a bidirectional creative partnership where dancers function as both performers and composers. Through correlation analysis of performance data, we demonstrate emergent communication patterns between movement qualities and audio features. This approach reconceptualizes the role of AI in performing arts as a responsive collaborator that expands possibilities for both professional dance performance and improvisational artistic expression across broader populations.

Designers are helping to shape the businesses, products, and services in our changing economy.

Machine learning is in the midst a renaissance. Technical advancements in machine learning are enabling a broad assortment of new possibilities for designers to better engage with and understand users. Yet, these technologies also bring new challenges and require new ways of thinking about the design of user interfaces and interactions. Using intuitive analogies and tangible examples, Patrick Hebron offers an introduction to contemporary machine-learning systems and explores how these emerging capabilities will transform the next generation of computing interfaces, such as search engines, intelligent assistants, connected homes, and open-world video games. This webcast is intended for user interface and user experience designers as well as anyone interested in how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction.

If there's any comfort offered during the current debate around robots, automation and the future of work, it's that robots can't do creativity. Machines are great for automated, precise, repetitive work; not so great for creative, expressive work. Beating beneath the discussion is a steady pulse of fear that once the technology leaps from apprentice to creative independent agent, robots could cause mass unemployment, bring about a dystopian society and steal our very reason for being. Yet there are some who argue that robots getting creative could actually make the world a better place. Machines will analyse and come up with solutions for environmental problems, such as infrastructure and design, that humans couldn't possibly conceive, for example.

This paper discusses emerging creative practices that involve interacting with generative computational systems, and the effect of such cybernetic interactions on our conceptions of creativity and agency. As computing systems have become more powerful in recent years, real time interaction with intelligent computational processes and models has emerged as a basis for innovative creative practices. Examples of these practices include interactive digital media installations, generative art works, live coding performances, virtual theatre, interactive cinema, and adaptive processes in computer games. In these types of activities computational systems have assumed a significant level of agency, or autonomy, that provoke questions about shared authorship and originality that are redefining our relationship with technologies and prompting new questions about human capabilities, values and the meaning of productive activities.