Interdependent Human-Machine Teams: The Path to Autonomy

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 policy makers, and to the public. The book establishes the meaning and operation of shared contexts? between humans and machines, policy makers, and the public. This book 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 INDICE: 1. Introduction2. Persistent Homology & Multi-Agent Team Efficiency3. AI Insights from the Cognitive Sciences4. Designing Artificial Ethical Minds5. Action, Ecology and The Science of Life6. Safety Framework for Human-Machine Learning7. Autonomy: Evidence from Robotics8. Autonomous Human-Machine Teams: Data Dependency and AI9. 'Human-AI Teaming'. A Review of the National Academies of Science Report10. Late Binding Dependence in Collaborating Systems11. Leveraging Manifold Learning and Relationship Equity Management for Symbiotic Explainable AI12. Understanding Interference Within and Between Human-Machine Teams13. AI Trust Framework and Maturity Model

• ISBN: 978-0-443-29246-0
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/01/2025
• Nº Volúmenes: 1
• Idioma: Inglés