London chapter of AnitaB.org presents this talk by Rebecca Fiebrink as part of the Coding in Gender Equality collaboration with IKLECTIK and Female Pressure, series of talks, performances and workshops focusing on women pioneers in experimental digital arts and research, taking place in November 2018. Full program can be found here.
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’ll 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.
Dr. Rebecca Fiebrink is a Senior Lecturer at Goldsmiths, University of London. Her research focuses on designing new ways for humans to interact with computers in creative practice, including on the use of machine learning as a creative tool. Fiebrink is the developer of the Wekinator, open-source software for real-time interactive machine learning whose current version has been downloaded over 15,000 times. She is the creator of a MOOC titled “Machine Learning for Artists and Musicians,” which launched in 2016 on the Kadenze platform. She was previously an Assistant Professor at Princeton University, where she co-directed the Princeton Laptop Orchestra. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research, and Smule, where she helped to build the #1 iTunes app “I am T-Pain.” She holds a PhD in Computer Science from Princeton University.