Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

• URL: http://arxiv.org/abs/2205.01296v2
• Date: Wed, 4 May 2022 15:04:47 GMT
• Title: Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions
• Authors: Boris Kovalerchuk, R\u{a}zvan Andonie, Nuno Datia, Kawa Nazemi, Ebad Banissi
• Abstract summary: Integrated Visual Knowledge Discovery combines advances in Artificial Intelligence/Machine Learning (AI/ML) and visualization. Chapters included are extended versions of the selected AI and Visual Analytics papers and related symposiums. We aim to present challenges and future directions within the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to discuss the role of visualization in visual AI/ML.
• Score: 5.754786889790288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This volume is devoted to the emerging field of Integrated Visual Knowledge Discovery that combines advances in Artificial Intelligence/Machine Learning (AI/ML) and Visualization/Visual Analytics. Chapters included are extended versions of the selected AI and Visual Analytics papers and related symposia at the recent International Information Visualization Conferences (IV2019 and IV2020). AI/ML face a long-standing challenge of explaining models to humans. Models explanation is fundamentally human activity, not only an algorithmic one. In this chapter we aim to present challenges and future directions within the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to discuss the role of visualization in visual AI/ML. In addition, we describe progress in emerging Full 2D ML, natural language processing, and AI/ML in multidimensional data aided by visual means.

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