How machine learning can support human creative practices

Abstract:

Recently, there has been an explosion of interest in machine learning algorithms capable of creating new images, sound, and other media content. Computers can now produce content that we might reasonably call novel, sophisticated, and even compelling. When researchers, artists, and the general public discuss the future of machine learning in art, the focus is usually on a few basic questions: How can we make content generation algorithms even better and faster? Will they put human creators out of a job? Are they really making ‘art’? In this talk, I propose that we should be asking a different set of questions, beginning with the question of how we can use machine learning to better support fundamentally human creative activities. I’ll show examples of how prioritising human creators—professionals, amateurs, and students—can lead to a new understanding of what machine learning is good for, and who can benefit from it. For instance, machine learning can aid human creators engaged in rapid prototyping of new interactions with sound and media. Machine learning can support greater embodied engagement in design, and it can enable more people to participate in the creation and customisation of new technologies. Furthermore, machine learning is leading to new types of human creative practices with computationally-infused mediums, in which a broad range of people can act not only as designers and implementors, but also as explorers, curators, and co-creators.

Bio:

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.