sculpture television buddha (after Something Pacific) by Robert Twomey
Measurable Creative AI SIGGRAPH 2021
August 2021 Time TBD
SIGGRAPH 2021 is a virtual event and this workshop will be conducted online. Our invited speakers will provide prerecorded video talks and participate in live panel discussions.
The idea of applying AI to creative endeavors activates the games/vfx industries, artists, manufacturing, tech company labs, and academics, but much of what is published on the topic shows there is still a great degree of siloing within these various fields. SIGGRAPH is an ideal context for these diverse constituencies to develop shared perspectives on goals, outcomes, and metrics for this emerging field.
This half-day workshop for SIGGRAPH pulls from the industries above to give a wide view on what “creative AI” means in practice and what people’s goals and aspirations are in the space. Framed with panel discussion from field experts, we will conduct a hands-on session around “measurable” goals to collaboratively define, apply, and iterate on metrics for evaluating creative AI. Partnering with the Art Gallery and Art Papers programs, we have unique subjects for this analysis drawn from the creative projects present at the conference.
This workshop aims to bring people together to talk about common goals (the “Measurable” part of the workshop title) and objectives in this space as well as inform the direction that researchers take when developing creative AI tools, and potentially form the foundation for new collaborative relationships.
This workshop will consist of a combination of pre-recorded presentations along with a one hour long live panelist discussion.
Kenric Allado-McDowell - K Allado-McDowell is a writer, speaker, and musician. They are the author, with GPT-3, of the book Pharmako-AI, and are co-editor, with Ben Vickers, of The Atlas of Anomalous AI. They record and release music under the name Qenric. Allado-McDowell established the Artists + Machine Intelligence program at Google AI. They are a conference speaker, educator and consultant to think-tanks and institutions seeking to align their work with deeper traditions of human understanding.
Stephanie Dinkins - Stephanie Dinkins is a transmedia artist who creates platforms for dialog about race, gender, aging, and our future histories. Dinkins’ art practice employs emerging technologies, documentary practices, and social collaboration toward equity and community sovereignty. She is particularly driven to work with communities of color to co-create more equitable, values grounded social and technological ecosystems. Dinkins is a professor at Stony Brook University where she holds the Kusama Endowed Professor in Art.
Ethan Edwards - Ethan Edwards is a researcher in Experiments in Art and Technology (E.A.T.) at Nokia Bell Labs, an initiative which fuses art with engineering to humanize technology. He works directly with scientists and artists to help facilitate collaboration and builds technology which crosses these domains. He is a creative technologist, having graduated with an MFA in Sound Art from Columbia University and has had work featured in museums, galleries, and performances around the world. His independent artwork explores traditional aesthetic themes in radically new media contexts. He has designed and led numerous large scale exhibits at Nokia Bell Labs.
Eunsu Kang - Eunsu Kang is an artist, a researcher, and an educator who explores the intersection of art and machine learning, one of the core methods for building AI. She has been making interactive art installations and performances, teaching art-making using machine learning methods, and recently looking into the possibility of creative AI. She is also a co-founder of Women Art AI collective.
Sang Leigh - Sang Leigh is an Assistant Professor of the School of Industrial Design at Georgia Institute of Technology. His research focuses on augmenting humans and their creativity, through forming a symbiotic and tactile relationship between humans and computers. His Machine Poetics research group investigates novel user interfaces, interactive programming, and human-robot interaction for enhancing our creative processes and learning.
To be discussed with the panelists
On The Subjectivity of Evaluation: Evaluating a creative work can be considered subjective. Can/should we account for this subjectivity in our evaluation of artificially intelligent creative systems? Or should we strive for objective measures of creative success?
On Context in Evaluation: Evaluating creative work is dependent on history. When evaluating a creative work, can we account for bodies of work that have come before it?
On Explainability of Evaluation: How important is intelligibility of the evaluation of creative work? Can we have evaluation measures that do not clearly explain themselves?
On Parts of Creativity for Evaluation: Novelty, surprisingness, value, etc. can be considered aspects of creativity. Can these aspects be enumerated and defined, or are there mysterious qualities to creative work? Does a work need to have each aspect to be considered creative? If not, can the relationship between the presence of these aspects in a work be mapped out systematically?
On Uses: What can we do with creative AI evaluation measures? Say we have an incredible AI creativity evaluation function today: what should we do with it?
On Value: Do you find that research into evaluating creativity is valuable? In what ways do you see this research contributing to culture or knowledge?
Call for Participation
Ahmed Elgammal is a professor at the Department of Computer Science at Rutgers University. His research areas include data science in the domain of digital humanities. His work on knowledge discovery in art history and AI art generation received wide international media attention, and his art has been shown in several technology and art venues in Los Angeles, Frankfurt, San Francisco, and New York City.
Hyeju Jang is a postdoctoral fellow at the University of British Columbia. Her research interests include natural language processing, computational linguistics, discourse analysis, and text mining in various domains. She has been working on computationally modeling creative uses of language, such as metaphor, in order to capture how they are used in discourse context and identify a broader spectrum of predictors that contribute towards their detection and generation.
Eunsu Kang is an artist, a researcher, and an educator who explores the intersection of art and machine learning as well as the possibility of creative AI. She started her artist career with video installations and single channel videos. She left her tenured art professor position to design and teach new courses (Art and Machine Learning, Creative AI) at the Machine Learning Department of Carnegie Mellon University. Recently she co-founded the Women+ Art AI collective.
James McCann is an Assistant Professor in the Carnegie Mellon Robotics Institute. He is interested in systems and interfaces that operate in real-time and build user intuition, including systems that enable and enhance creativity.
Jean Oh is a faculty member at the Robotics Institute at Carnegie Mellon University. She is passionate about creating robots that can collaborate with humans in shared or remote environments, continuously improving themselves through learning, exploration, and interactions. Jean co-designed a new graduate-level course on Creative AI at CMU and was a co-organizer of the first workshop on Measuring Computational Creativity at ISEA’20.
Peter Schaldenbrand is a graduate student and technical staff member at Carnegie Mellon University. His research interests include creating machine learning models that perform creative tasks and artificial intelligence in education. Recently, he has been focusing on a robot artistic painting project.
Robert Twomey is an Assistant Professor of Emerging Media Arts at the University of Nebraska-Lincoln, and a Visiting Scholar with the Clarke Center for Human Imagination at UC San Diego. His work as an artist and engineer explores how emerging technologies transform sites of intimate life. He has presented his work at SIGGRAPH (Best Paper Award), the Museum of Contemporary Art San Diego, and has been supported by the National Science Foundation, the California Arts Council, Microsoft, Amazon, and NVIDIA.
Jun-Yan Zhu is an Assistant Professor in the School of Computer Science of Carnegie Mellon University. He studies computer vision, computer graphics, computational photography, and machine learning, with the goal of building intelligent machines, capable of recreating our visual world. Jun-Yan has co-organized several relevant workshops and tutorials including CVPR 2020 Tutorial on Neural Rendering, ICCV 2019 Workshop on Image and Video Synthesis, and CVPR 2018 Tutorial on Generative Adversarial Networks.