Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery
暫譯: 人工智慧與視覺化：推進視覺知識發現
Kovalerchuk, Boris, Nazemi, Kawa, Andonie, Răzvan
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商品描述
This book continues a series of Springer publications devoted to the emerging field of Integrated Artificial Intelligence and Machine Learning with Visual Knowledge Discovery and Visual Analytics that combine advances in both fields. Artificial Intelligence and Machine Learning face long-standing challenges of explainability and interpretability that underpin trust. Such attributes are fundamental to both decision-making and knowledge discovery. Models are approximations and, at best, interpretations of reality that are transposed to algorithmic form. A visual explanation paradigm is critically important to address such challenges, as current studies demonstrate in salience analysis in deep learning for images and texts. Visualization means are generally effective for discovering and explaining high-dimensional patterns in all high-dimensional data, while preserving data properties and relations in visualizations is challenging. Recent developments, such as in General Line Coordinates, open new opportunities to address such challenges.
This book contains extended papers presented in 2021 and 2022 at the International Conference on Information Visualization (IV) on AI and Visual Analytics, with 18 chapters from international collaborators. The book builds on the previous volume, published in 2022 in the Studies in Computational Intelligence. The current book focuses on the following themes: knowledge discovery with lossless visualizations, AI/ML through visual knowledge discovery with visual analytics case studies application, and visual knowledge discovery in text mining and natural language processing.
The intended audience for this collection includes but is not limited to developers of emerging AI/machine learning and visualization applications, scientists, practitioners, and research students. It has multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery, visual analytics, and text and natural language processing. The book provides case examples for future directions in this domain. New researchers find inspiration to join the profession of the field of AI/machine learning through a visualization lens.
商品描述(中文翻譯)
這本書延續了Springer出版的一系列專注於整合人工智慧（Artificial Intelligence, AI）和機器學習（Machine Learning, ML）與視覺知識發現（Visual Knowledge Discovery）及視覺分析（Visual Analytics）新興領域的出版物，結合了這兩個領域的進展。人工智慧和機器學習面臨著長期存在的可解釋性（explainability）和可理解性（interpretability）挑戰，這些挑戰是建立信任的基礎。這些特性對於決策制定和知識發現至關重要。模型是對現實的近似，充其量只是轉換為算法形式的解釋。視覺解釋範式對於解決這些挑戰至關重要，因為當前的研究在深度學習中對圖像和文本的顯著性分析中顯示了這一點。視覺化手段通常對於發現和解釋所有高維數據中的高維模式是有效的，但在視覺化中保持數據屬性和關係卻是具有挑戰性的。最近的發展，例如一般線坐標（General Line Coordinates），為解決這些挑戰開啟了新的機會。
本書包含了在2021年和2022年於國際資訊視覺化會議（International Conference on Information Visualization, IV）上發表的擴展論文，共有18章來自國際合作者。本書建立在2022年出版的《計算智慧研究》（Studies in Computational Intelligence）前一卷的基礎上。當前的書籍重點集中在以下主題：無損視覺化的知識發現、通過視覺分析案例研究應用的AI/ML視覺知識發現，以及文本挖掘和自然語言處理中的視覺知識發現。
本書的目標讀者包括但不限於新興AI/機器學習和視覺化應用的開發者、科學家、實務工作者和研究生。書中有多個當前AI/機器學習與視覺化整合的例子，涵蓋視覺知識發現、視覺分析以及文本和自然語言處理。本書提供了該領域未來方向的案例示例。新研究者通過視覺化的視角找到靈感，加入AI/機器學習領域的專業行列。
