Dynamic Abstractions: Building the Next Generation of Cognitive Tools and Interfaces
This workshop provides a forum to discuss, brainstorm, and prototype the next generation of interfaces that leverage the dynamic experiences enabled by recent advances in AI and the generative capabilities of foundation models. These models simplify complex tasks by generating outputs in various representations (e.g., text, images, videos) through diverse input modalities like natural language, voice, and sketch. They interpret user intent to generate and transform representations, potentially changing how we interact with information and express ideas. Inspired by this potential, technologists, theorists, and researchers are exploring new forms of interaction by building demos and communities dedicated to concretizing and advancing the vision of working with dynamic abstractions. This UIST workshop provides a timely space to discuss AI’s impact on how we might design and use cognitive tools (e.g., languages, notations, diagrams). We will explore the challenges, critiques, and opportunities of this space by thinking through and prototyping use cases across various domains.
Authors
- Hai Dang (ORCID: https://orcid.org/0000-0003-3617-5657)
- Jingyi Li (ORCID: https://orcid.org/0000-0002-8253-887X)
- Ian Arawjo (ORCID: https://orcid.org/0000-0001-8910-0822)
- Sangho Suh (ORCID: https://orcid.org/0000-0003-4617-5116)
- Josh Pollock (ORCID: https://orcid.org/0000-0001-5141-0999)
- Ryan Yen (ORCID: https://orcid.org/0000-0001-8212-4100)
- Arvind Satyanarayan (ORCID: https://orcid.org/0000-0001-5564-635X)
- Rubaiat Habib Kazi (ORCID: https://orcid.org/0009-0007-9781-0032)
- Nazmus Saquib (ORCID: https://orcid.org/0000-0002-7037-2737)
- Hariharan Subramonyam
Institutions
- Vassar College (US)
- Adobe Systems (United States) (US)
- Pomona College (US)
- University of Toronto (CA)
- University of Bayreuth (DE)
- Université de Montréal (CA)
- Stanford University (US)
Publication Details
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1145/3672539.3686706
- Citations
- 2
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.50