Philosophy and Theory of Artificial Intelligence 2021
Author:   Vincent C. Müller
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2022
Volume:   63
ISBN:  

9783031091520


Pages:   248
Publication Date:   15 November 2022
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Philosophy and Theory of Artificial Intelligence 2021


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Overview

This book gathers contributions from the fourth edition of the Conference on ""Philosophy and Theory of Artificial Intelligence"" (PT-AI), held on 27-28th of September 2021 at Chalmers University of Technology, in Gothenburg, Sweden. It covers topics at the interface between philosophy, cognitive science, ethics and computing. It discusses advanced theories fostering the understanding of human cognition, human autonomy, dignity and morality, and the development of corresponding artificial cognitive structures, analyzing important aspects of the relationship between humans and AI systems, including the ethics of AI. This book offers a thought-provoking snapshot of what is currently going on, and what are the main challenges, in the multidisciplinary field of the philosophy of artificial intelligence.  

Full Product Details

Author:   Vincent C. Müller
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2022
Volume:   63
Weight:   0.559kg
ISBN:  

9783031091520


ISBN 10:   3031091523
Pages:   248
Publication Date:   15 November 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1: Cognitive architectures based on natural info-computation.- Chapter 2: Artificial intelligence systems, responsibility and agential self-awareness.- Chapter 3: Models, algorithms, and the subjects of transparency.- Chapter 4: Autonomy of attention.- Chapter 5: Toward out-of-distribution generalization through inductive biases.

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