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Transparency and Interpretability for Learned Representations of Artificial Neural Networks
Taschenbuch von Richard Meyes
Sprache: Englisch

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Beschreibung
Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AI¿s decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed lighton how to adopt an empirical neuroscience inspired approach to investigate a neural network¿s learned representation in the same spirit as neuroscientific studies of the brain.
Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AI¿s decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed lighton how to adopt an empirical neuroscience inspired approach to investigate a neural network¿s learned representation in the same spirit as neuroscientific studies of the brain.
Über den Autor
Richard Meyes is head of the research group "Interpretable Learning Models" at the institute of Technologies and Management of Digital Transformation at the University of Wuppertal. His current research focusses on transparency and interpretability of decision-making processes of artificial neural networks.
Inhaltsverzeichnis
Introduction.- Background & Foundations.- Methods and Terminology.- Related Work.- Research Studies.- Transfer Studies.- Critical Reflection & Outlook.- Summary.
Details
Erscheinungsjahr: 2022
Genre: Informatik, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xxi
211 S.
3 s/w Illustr.
70 farbige Illustr.
211 p. 73 illus.
70 illus. in color. Textbook for German language market.
ISBN-13: 9783658400033
ISBN-10: 365840003X
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Meyes, Richard
Auflage: 1st edition 2022
Hersteller: Springer Fachmedien Wiesbaden
Springer Fachmedien Wiesbaden GmbH
Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, D-65189 Wiesbaden, juergen.hartmann@springer.com
Maße: 210 x 148 x 13 mm
Von/Mit: Richard Meyes
Erscheinungsdatum: 28.11.2022
Gewicht: 0,348 kg
Artikel-ID: 125797342
Über den Autor
Richard Meyes is head of the research group "Interpretable Learning Models" at the institute of Technologies and Management of Digital Transformation at the University of Wuppertal. His current research focusses on transparency and interpretability of decision-making processes of artificial neural networks.
Inhaltsverzeichnis
Introduction.- Background & Foundations.- Methods and Terminology.- Related Work.- Research Studies.- Transfer Studies.- Critical Reflection & Outlook.- Summary.
Details
Erscheinungsjahr: 2022
Genre: Informatik, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xxi
211 S.
3 s/w Illustr.
70 farbige Illustr.
211 p. 73 illus.
70 illus. in color. Textbook for German language market.
ISBN-13: 9783658400033
ISBN-10: 365840003X
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Meyes, Richard
Auflage: 1st edition 2022
Hersteller: Springer Fachmedien Wiesbaden
Springer Fachmedien Wiesbaden GmbH
Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, D-65189 Wiesbaden, juergen.hartmann@springer.com
Maße: 210 x 148 x 13 mm
Von/Mit: Richard Meyes
Erscheinungsdatum: 28.11.2022
Gewicht: 0,348 kg
Artikel-ID: 125797342
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