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.
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1. Introduction
2. Persistent Homology & Multi-Agent Team Efficiency
3. AI Insights from the Cognitive Sciences
4. Designing Artificial Ethical Minds
5. Action, Ecology and The Science of Life
6. Safety Framework for Human-Machine Learning
7. Autonomy: Evidence from Robotics
8. Autonomous Human-Machine Teams: Data Dependency and AI
9. 'Human-AI Teaming’. A Review of the National Academies of Science Report
10. Late Binding Dependence in Collaborating Systems
11. Leveraging Manifold Learning and Relationship Equity Management for Symbiotic Explainable AI
12. Understanding Interference Within and Between Human-Machine Teams
13. AI Trust Framework and Maturity Model
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An interdisciplinary approach to addressing critical issues in the development of autonomous human-machine systems
Investigates how interdependence is the missing ingredient necessary to produce operational autonomous systems
Integrates concepts from a wide range of disciplines, including applied and theoretical AI, quantum mechanics, social sciences, and systems engineering
Presents debates, models, and concepts of mutual dependency for autonomous human-machine teams, challenging assumptions across AI, systems engineering, data science, and quantum mechanics
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Produktdetaljer
ISBN
9780443292460
Publisert
2025-01-01
Utgiver
Vendor
Academic Press Inc
Høyde
229 mm
Bredde
152 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
300