Content creation is a fundamental activity for developing identities in modern individuals. Yet creativity is hardly addressed by computer science. This project addresses the issue of content creation from the perspective of Flow machines. Flow machines are interactive systems that learn how to generate content, text or music, in the user’s style. Thanks to controlled generation mechanisms, the user can then steer the machine to generate content that fits with their intentions. Flow interactions induce a multiplicative effect that boosts creativity and prompts the user to reflect on their own style. This vision stems from the success stories of several computer-assisted musical systems that showed how interactive dialogs with self-learning interactions provoke flow states.
To enables full control of stylistic generation, the scientific challenge is the reification of style as a flexible texture. This challenge will be addressed by pursuing three original directions in the fields of statistical learning and combinatorial optimization: 1) the formulation of Markov-based generation as a constraint problem, 2) the development of feature generation techniques for feeding machine learning algorithms and 3) the development of techniques to transform descriptors into controllers.
Two large-scale studies will be conducted with well-known creators using these Flow machines, during which the whole creation process will be recorded, stored, and analyzed, providing the first complete chronicles of professional-level artifacts. The artifacts, a music album and a novel, will be published in their respective ecosystems, and the reaction of the audience will be measured and analyzed to further assess the impact of Flow machines on creation. The technologies developed and the pilot studies will serve as pioneering experiments to turn Flow machines into a field of study and explore other domains of creation.
Field of science
- /natural sciences/computer and information sciences
Call for proposal
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Funding SchemeERC-AG - ERC Advanced Grant
KT13 0XW Weybridge