This comprehensive guide provides an extensive overview of the key theories, methodologies, and applied frameworks that enable AGI systems to exhibit aspects of human intelligence.
As the role of artificial intelligence grows in our everyday lives, so does the need for AI with greater capabilities. Unlike narrow AI, confined to specific tasks, artificial general intelligence seeks human-like adaptability, reasoning, and learning across domains. Integrating cognitive, mathematical, and computational concepts, it presents multidimensional solutions to create more natural human-AI interactions. This book examines the theoretical foundations, cognitive architectures, and practical methodologies shaping artificial general intelligence. It highlights the significance of human-like emotional intelligence in AI and its potential to create more natural, empathetic, and intuitive human-AI interactions, using techniques such as facial expression analysis, speech emotion recognition, and physiological signal processing. From healthcare to customer service, affective AI is being used to enhance user experiences by tailoring interactions to the emotional states of individuals. The book also discusses the ethical dilemmas posed by affective AI, such as emotional manipulation, bias in emotion detection, and the impact of AI-driven emotional decisions on human behavior. Balancing rigor with practical insight, the volume provides a roadmap for researchers, practitioners, and policymakers to study artificial general intelligence's evolution and transformative potential.
Readers will find the volume:
- Discusses different applications of affective artificial intelligence across various industries;
- Introduces the fundamental concepts of reinforcement learning for different applications;
- Presents the state-of-the-art of transfer learning analysis through contributions from industry and academia.
Audience
Engineering research scholars, students, IT professionals, network administrators, artificial intelligence and deep learning experts, and government research agencies.
T. Saravanan, PhD is an Assistant Professor at the Gandhi Institute of Technology and Management, Bengaluru, India with more than ten years of teaching experience. He has published many research papers, book chapters, and Indian patents. His research interests include computer networks, fuzzy logic, and wireless sensor networks.
P. Preethi, PhD is an Associate Professor in the Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India. She has six books and has published 28 articles in international journals and conferences. Her areas of interest include cloud computing, network security, and machine learning.
Sumaya Sanober, PhD works in the Computer Science Department at Old Dominion University, Virginia, United States. She has published many articles in national and international journals and conferences, and serves as a reviewer on multiple boards. Her research interests include machine learning, artificial neural networks, pattern recognition, web services, cloud computing, and testing tools.
N. Thillaiarasu, PhD is an Associate Professor in the School of Computing and Information Technology, REVA University, Bangalore, India, with more than 12 years of teaching experience. He has more than 75 publications to his credit, including articles, books, and book chapters. His areas of interest include cloud computing, security, IoT, and machine learning.
S. Balamurugan, PhD is the Director, Intelligent Research Consultancy Services, Coimbatore, Tamil Nadu, India. He has published 75 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.
