Name: Interdependent Human-Machine Teams: The Path to Autonomy (en Inglés)
SKU: 63406971
Price: 188.37 USD
Availability: InStock
Author: Lawless William,Mittu Ranjeev,Sofge Donald
ISBN: 9780443292460

portada Interdependent Human-Machine Teams: The Path to Autonomy (en Inglés)

Interdependent Human-Machine Teams: The Path to Autonomy (en Inglés)

Formato

Libro Físico

Editorial

Academic Press

Autor

Lawless William,Mittu Ranjeev,Sofge Donald

Idioma

Inglés

N° páginas

448

Encuadernación

Tapa Blanda

ISBN13

9780443292460

N° edición

Interdependent Human-Machine Teams: The Path to Autonomy (en Inglés)

Lawless William,Mittu Ranjeev,Sofge Donald (Autor) · Academic Press · Tapa Blanda

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Reseña del libro "Interdependent Human-Machine Teams: The Path to Autonomy (en Inglés)"

Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public. It establishes the meaning and operation of "shared contexts" between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems.Investigates how interdependence is the missing ingredient necessary to produce operational autonomous systemsIntegrates concepts from a wide range of disciplines, including applied and theoretical AI, quantum mechanics, social sciences, and systems engineeringPresents debates, models, and concepts of mutual dependency for autonomous human-machine teams, challenging assumptions across AI, systems engineering, data science, and quantum mechanics