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This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models. It shows how visualization can bridge the gap between complex models or algorithms and human understanding while also facilitating data curation and model refinement. Therefore, visualization for artificial intelligence (VIS4AI) has become an emerging area that combines interactive visualization with machine learning techniques to maximize their values. VIS4AI techniques focus on every phase of the machine learning life cycle, from data preprocessing to model development and deployment. These techniques are closely aligned with the well-established data and model pipelines in machine learning. In the data pipeline, they contribute to improving data quality and feature quality, including training data cleaning and feature engineering. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering; and (2) model deployment by enabling decision explanation, model performance monitoring, and model maintenance.
This book provides a framework of VIS4AI and introduces the associated techniques in the two pipelines. It emphasizes the importance of interactive visualization in AI and presents various visualization techniques for different purposes. It also discusses the challenges and opportunities of VIS4AI and proposes several promising research topics for future work, such as improving training data using complementary modalities, online training diagnosis, fitting the dynamic nature of AI systems, and interactively pre-training and adapting foundation models. Overall, this book aims to serve as a resource for researchers and practitioners interested in both visualization and artificial intelligence.
Info autore
Shixia Liu
is a professor at Tsinghua University. Her research interests include explainable machine learning, visual text analytics, and text mining. Shixia was elevated to an IEEE Fellow in 2021 and inducted into IEEE Visualization Academy in 2020. She is an associate editor-in-chief of IEEE Transactions on Visualization and Computer Graphics and is an associate editor of Artificial Intelligence, IEEE Transactions on Big Data, and ACM Transactions on Intelligent Systems and Technology. She was one of the Papers Co-Chairs of IEEE VIS (VAST) 2016 and 2017 and is in the steering committee of IEEE VIS (2020–2023).
Weikai Yang
is an Assistant Professor at the Data Science and Analytics Trust, holding a joint appointment at the Computational Media and Arts Thrust (CMA) in the Information Hub, at The Hong Kong University of Science and Technology (Guangzhou). He received his Ph.D. in Software Engineering under the supervision of professor Shixia Liu and his B.S. degrees from Tsinghua University. His research primarily focuses on the intersections between visual analysis and machine learning, with the goal of helping general users to understand large-scale data and utilize machine learning models more effectively and efficiently by incorporating their knowledge and feedback.
Junpeng Wang
is a Research Scientist at Visa Research. He received his B.Eng. degree in software engineering from Nankai University in 2011, his M.S. degree in computer science from Virginia Tech in 2015, and his Ph.D. degree in computer science from the Ohio State University in 2019. Junpeng's research interests lie broadly in explainable artificial intelligence, visual analytics, and deep learning. He is the recipient of the 2021 IEEE TVCG Best Reviewer Award and multiple best paper awards, including the Best Paper Award at IEEE PacificVis 2018, the Best Paper Honorable Mention Award at IEEE VIS (VAST) 2018, and the Best Paper Award at IEEE VIS (SciVis) 2019.
Jun Yuan
is a Researcher at Tencent. His research interests lie in explainable artificial intelligence. He received his Ph.D. in Software Engineering under the supervision of Professor Shixia Liu and his B.S. degrees from Tsinghua University.
Riassunto
This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models. It shows how visualization can bridge the gap between complex models or algorithms and human understanding while also facilitating data curation and model refinement. Therefore, visualization for artificial intelligence (VIS4AI) has become an emerging area that combines interactive visualization with machine learning techniques to maximize their values. VIS4AI techniques focus on every phase of the machine learning life cycle, from data preprocessing to model development and deployment. These techniques are closely aligned with the well-established data and model pipelines in machine learning. In the data pipeline, they contribute to improving data quality and feature quality, including training data cleaning and feature engineering. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering; and (2) model deployment by enabling decision explanation, model performance monitoring, and model maintenance.
This book provides a framework of VIS4AI and introduces the associated techniques in the two pipelines. It emphasizes the importance of interactive visualization in AI and presents various visualization techniques for different purposes. It also discusses the challenges and opportunities of VIS4AI and proposes several promising research topics for future work, such as improving training data using complementary modalities, online training diagnosis, fitting the dynamic nature of AI systems, and interactively pre-training and adapting foundation models. Overall, this book aims to serve as a resource for researchers and practitioners interested in both visualization and artificial intelligence.
