Rebecca Fiebrink

Rebecca Fiebrink

University of the Arts London

Dr Rebecca Fiebrink makes new accessible and creative technologies. As a Reader at the Creative Computing Institute at University of the Arts London, her teaching and research focus largely on how machine learning and artificial intelligence can change human creative practices. Fiebrink is the developer of the Wekinator creative machine learning software, which is used around the world by musicians, artists, game designers, and educators. She is the creator of the world’s first online class about machine learning for music and art. Much of her work is driven by a belief in the importance of inclusion, participation, and accessibility: she works frequently with human-centred and participatory design processes, and she is currently working on projects related to creating new accessible technologies with people with disabilities, and designing inclusive machine learning curricula and tools. Dr. Fiebrink previously taught at Goldsmiths University of London and Princeton University, and she has worked with companies including Microsoft, Smule, and Imagine Research. She holds a PhD in Computer Science from Princeton University.

Machine learning as (Meta-) instrument

Computer scientists typically think about machine learning as a set of powerful algorithms for modeling data in order to make decisions or predictions, or to better understand some phenomenon. In this talk, I’ll invite you to consider a different perspective, one in which machine learning algorithms function as live and interactive human-machine interfaces, akin to a musical instrument. These “instruments” can support a rich variety of activities, including creative, embodied, and exploratory interactions with computers and media. They can also enable a broader range of people—from software developers to children to music therapists—to create interactive digital systems. Drawing on a decade of research on these topics, I’ll discuss some of our most exciting findings about how machine learning can support human creative practices, for instance by enabling faster prototyping and exploration of new technologies (including by non-programmers), by supporting greater embodied engagement in design, and by changing the ways that creators are able to think about the design process and about themselves. I’ll discuss how these findings inform new ways of thinking about what machine learning is good for, how to make more useful and usable creative machine learning tools, how to teach creative practitioners about machine learning, and what the future of human-computer collaboration might look like.