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Machine Learning for Creativity and Design
Tom White · Mattie Tesfaldet · Samaneh Azadi · Daphne Ippolito · Lia Coleman · David Ha

Mon Dec 13 08:15 AM -- 06:00 PM (PST) @
Event URL: https://neuripscreativityworkshop.github.io/2021/ »

Machine co-creativity continues to grow and attract a wider audience to machine learning. Generative models, for example, have enabled new types of media creation across language, images, and music--including recent advances such as CLIP, VQGAN, and DALL·E. This one-day workshop will broadly explore topics in the applications of machine learning to creativity and design, which includes:

State-of-the-art algorithms for the creation of new media. Machine learning models achieving state-of-the-art in traditional media creation tasks (e.g., image, audio, or video synthesis) that are also being used by the artist community will be showcased.

Artist accessibility of machine learning models. Researchers building the next generation of machine learning models for media creation will be challenged in understanding the accessibility needs of artists. Artists and Human Computer interaction / User Experience community members will be encouraged to engage in the conversation.

The sociocultural and political impact of these new models. With the increased popularity of generative machine learning models, we are witnessing these models start to impact our everyday surroundings, ranging from racial and gender bias in algorithms and datasets used for media creation to how new media manipulation tools may erode our collective trust in media content.

Artistic applications. We will hear directly from some of the artists who are adopting machine learning--including deep learning and reinforcement learning--as part of their own artistic process as well as showcasing their work.

The goal of this workshop is to bring together researchers and artists interested in exploring the intersection of human creativity and machine learning and foster collaboration between them, as well as promote the sharing of ideas, perspectives, new research, artwork, and artistic needs.

Discord invite --> https://bit.ly/3puzVuM

Author Information

Tom White (Victoria University of Wellington School of Design)

Tom is a New Zealand based artist investigating machine perception. His current work focuses on creating physical artworks that highlight how machines “see” and thus how they think, suggesting that these systems are capable of abstraction and conceptual thinking. He has exhibited computer based artwork internationally over the past 25 years with themes of artificial intelligence, interactivity, and computational creativity. He is currently a lecturer and researcher at University of Wellington School of Design where he teaches students the creative potential of computer programming and artificial intelligence.

Mattie Tesfaldet (McGill University & MILA)

Mattie Tesfaldet (they/them) is a computer vision and machine learning researcher, artist, and DJ based in Montréal, Canada. They are pursuing their PhD at McGill University and Mila researching generative models for visual content creation, specifically, looking for novel and interesting ways images and videos can be represented with neural networks. Outside of academia, they like to apply their research with the aim of exploring the intersection of human creativity and artificial intelligence. Particularly, developing new AI-based mediums for communication, expression, and sharing of visual imagery.

Samaneh Azadi (UC Berkeley)
Daphne Ippolito (University of Pennsylvania)
Lia Coleman (RISD)

Lia Coleman is an artist, ML practitioner, & educator. In her visual art practice, she uses neural networks to create her work. Currently she is working with RunwayML to create an explainable ML video series. She has been an adjunct professor at the Rhode Island School of Design, teaching graduate courses on creative uses of machine learning. Her work has been shown at Science Gallery Detroit, Tribeca Film Festival, New York University, NeurIPS, Mozilla Festival, Gray Area, and Partnership on AI. Her writing on AI art has been published by Princeton Architectural Press, DISEÑA, and Neocha Magazine. She holds a BSc in Computer Science from Massachusetts Institute of Technology, and has completed a residency at the School for Poetic Computation.

David Ha (Google Brain)

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