Machine Learning in Art Specialist, Reader at the Creative Computing Institute at University of the Arts London & Department of Computing at Goldsmiths University of London
Speaking topics
› How machine learning is changing music and art
› A bicycle built for two: Humans and computers making music together
› Can computers be creative?
› Understanding machine learning as a tool for human creativity
› How does machine learning really work, and why should you care?
Abstracts
Machine learning techniques are now capable of creating new images, sound, and other media with increasing sophistication. They can also analyse and model all sorts of creative human activities, from the motions of a dancer to the actions of a professional music recording studio editing a track. In this talk, I’ll describe the state of the art in these techniques and explore what this means for the future of human creative practice.
Ada Lovelace imagined the potential for computers to revolutionise music-making in 1843. Since then, computers and musicians have embarked on wild explorations together, from synthesising sounds that never existed before, to designing bespoke musical instruments for people with disabilities, to enabling people around the world to make music together over the Internet. I’ll take you on a brief tour of this landscape, revealing a history of musical and technical ingenuity, and exploring how technologies like AI, VR, and blockchain are changing music today.
With an AI-generated “portrait” recently selling for over $400,000 at a Christie’s auction, you may be wondering: is this the future of art? Can machines really be creative? What does the future look like for human artists and other creators? In this talk, I’ll explain the state of the art in “creative” AI technology and invite you to imagine with me what the future may hold.
While machine learning may not seem inherently “creative,” it is currently changing creative practice in art, music, design, and other domains. Not only can machine learning generate new content, but it can be used to help humans explore and navigate new ideas, to make creation accessible to new audiences, and to enable creators to communicate their ideas to computers through sound, image, gesture, and other means. In this talk, I’ll introduce you to state-of-the-art examples of machine learning supporting human creativity, and leave you with a new perspective on how machine learning technologies may change our world.
Machine learning probably lies behind many activities of your daily life, from making business decisions, to choosing entertainment, to interacting with friends online; yet, you may know little about how machine learning works. In this talk, I will teach you the basics of machine learning using simple explanations and many interactive examples, many of which are freely available online and simple enough for you to use with your children. These examples will help you reason about how machine learning is likely to continue changing our world, and to grapple with ethical questions such as those around algorithmic bias and automated decision-making.
Bio note
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.
