Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques (Studies in Computational Intelligence, 1047, Band 1047) - Hardcover

Kreinovich, Vladik

Hardcover

ISBN 10: 3031099737 ISBN 13: 9783031099731

Verlag: Springer, 2022

Inhaltsangabe

Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.

Über diesen Titel

Über die Autorin bzw. den Autor

Vladik Kreinovich is Professor of Computer Science at the University of Texas at El Paso. His main interests computations and intelligent control. He has published 13 books, 39 edited books, and more than 1,800 papers.

Vladik is Vice President of the International Fuzzy Systems Association (IFSA), Vice President of the European Society for Fuzzy Logic and Technology (EUSFLAT), Fellow of International Fuzzy Systems Association (IFSA), Fellow of Mexican Society for Artificial Intelligence (SMIA), Fellow of the Russian Association for Fuzzy Systems and Soft Computing. He is Treasurer of IEEE Systems, Man, and Cybernetics Society

Von der hinteren Coverseite

Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2022
• Sprache: Englisch
• ISBN 10: 3031099737
• ISBN 13: 9783031099731
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 140
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland