IEEE VIS 2024 Content: Generative AI for Visualization: Opportunities and Challenges

Generative AI for Visualization: Opportunities and Challenges

Rahul C. Basole -

Timothy Major -

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Room: Bayshore III

2024-10-17T17:00:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T17:00:00Z
Exemplar figure, described by caption below
The iterative phases of the end-to-end visualization workflow (A-G) and types of generative AI opportunities (Creativity, Co-Pilot, and Automation) within them.
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Keywords

Generative AI, Art, Artificial Intelligence, Machine Learning, Visualization, Media, Augmented Reality, Machine Learning, Visual Representation, Professional Knowledge, Creative Process, Domain Experts, Generalization Capability, Development Of Artificial Intelligence, Artificial Intelligence Capabilities, Iterative Process, Natural Language, Commercial Software, Hallucinations, Team Sports, Design Requirements, Intelligence Agencies, Recommender Systems, User Requirements, Iterative Design, Use Of Artificial Intelligence, Visual Design, Phase Assemblage, Data Literacy

Abstract

Recent developments in artificial intelligence (AI) and machine learning (ML) have led to the creation of powerful generative AI methods and tools capable of producing text, code, images, and other media in response to user prompts. Significant interest in the technology has led to speculation about what fields, including visualization, can be augmented or replaced by such approaches. However, there remains a lack of understanding about which visualization activities may be particularly suitable for the application of generative AI. Drawing on examples from the field, we map current and emerging capabilities of generative AI across the different phases of the visualization lifecycle and describe salient opportunities and challenges.