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Towards explainable fuzzy AIconcepts, paradigms, tools, and techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards explainable fuzzy AIby Vladik Kreinovich.
Reminder of title:	concepts, paradigms, tools, and techniques /
Author:	Kreinovich, Vladik.
Published:	Cham :Springer International Publishing :2022.
Description:	x, 130 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-031-09974-8
ISBN:	9783031099748$q(electronic bk.)

Towards explainable fuzzy AIconcepts, paradigms, tools, and techniques /
Kreinovich, Vladik.

Towards explainable fuzzy AIconcepts, paradigms, tools, and techniques /[electronic resource] :by Vladik Kreinovich. - Cham :Springer International Publishing :2022. - x, 130 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 10471860-9503 ;. - Studies in computational intelligence ;v. 216..

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$q(electronic bk.)

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

194058
Artificial intelligence.

LC Class. No.: Q335 / .K74 2022

Dewey Class. No.: 006.3

Towards explainable fuzzy AIconcepts, paradigms, tools, and techniques /
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520 $a 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.
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