Description: This groundbreaking volume is designed to meet the burgeoning needs of the research community and industry. This book delves into the critical aspects of AI's self-assessment and decision-making processes, addressing the imperative for safe and reliable AI systems in high-stakes domains such as autonomous driving, aerospace, manufacturing, and military applications. Featuring contributions from leading experts, the book provides comprehensive insights into the integration of metacognition within AI architectures, bridging symbolic reasoning with neural networks, and evaluating learning agents' competency. Key chapters explore assured machine learning, handling AI failures through metacognitive strategies, and practical applications across various sectors. Covering theoretical foundations and numerous practical examples, this volume serves as an invaluable resource for researchers, educators, and industry professionals interested in fostering transparency and enhancing reliability of AI systems.
Brief description: Hua Wei is Assistant Professor at the School of Computing and Augmented Intelligence at Arizona State University. He specializes in data mining, artificial intelligence, and machine learning. His work has been awarded multiple best paper awards and his research has been funded by agencies including the National Science Foundation and the U.S. Department of Energy.
Review Quotes: 'This book offers a fascinating exploration of the astounding relationship between metacognition and AI. It provides readers with a comprehensive understanding of how AI systems can be designed not only to make accurate predictions but also to learn from their mistakes and improve over time. The authors explore various methods for enhancing trust in AI models by incorporating aspects of human cognitive processes, providing practical insights for building more reliable and transparent AI technologies.' Todd C. Hughes, Scientific Systems Chief Innovation Officer