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Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques/ by Vladik Kreinovich.
作者:	Kreinovich, Vladik.
面頁冊數:	X, 130 p. 31 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Data Engineering. -
電子資源:	https://doi.org/10.1007/978-3-031-09974-8
ISBN:	9783031099748

Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques
Kreinovich, Vladik.

Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques[electronic resource] /by Vladik Kreinovich. - 1st ed. 2022. - X, 130 p. 31 illus.online resource. - Studies in Computational Intelligence,10471860-9503 ;. - Studies in Computational Intelligence,564.

Why Explainable AI? Why Fuzzy Explainable AI? What Is Fuzzy? -- Defuzzification -- Which Fuzzy Techniques? -- So How Can We Design Explainable Fuzzy AI: Ideas -- How to Make Machine Learning Itself More Explainable -- Final Self-Test.

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.

ISBN: 9783031099748

Standard No.: 10.1007/978-3-031-09974-8doiSubjects--Topical Terms:

1226308
Data Engineering.

LC Class. No.: Q342

Dewey Class. No.: 006.3

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