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Published by Springer International Publishing AG, CH, 2022
ISBN 10: 3031099737 ISBN 13: 9783031099731
Language: English
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Add to basketHardback. Condition: New. 2022 ed. 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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Published by Springer International Publishing AG, Cham, 2023
ISBN 10: 3031099761 ISBN 13: 9783031099762
Language: English
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Published by Springer International Publishing AG, Cham, 2022
ISBN 10: 3031099737 ISBN 13: 9783031099731
Language: English
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Add to basketPapeback. Condition: New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Published by Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031099761 ISBN 13: 9783031099762
Language: English
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Published by Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3031099737 ISBN 13: 9783031099731
Language: English
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Published by Springer International Publishing, Springer International Publishing Sep 2023, 2023
ISBN 10: 3031099761 ISBN 13: 9783031099762
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.
Published by Springer International Publishing, Springer International Publishing Sep 2022, 2022
ISBN 10: 3031099737 ISBN 13: 9783031099731
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketBuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.
Published by Springer International Publishing AG, Cham, 2022
ISBN 10: 3031099737 ISBN 13: 9783031099731
Language: English
Seller: AussieBookSeller, Truganina, VIC, Australia
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Add to basketHardcover. Condition: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.