Artificial General Intelligence: Principles and Practices
Book:Artificial General Intelligence: Principles and Practices
Editors: T. Saravanan, P. Preethi, Sumaya Sanober, N. Thillaiarasu & S. Balamurugan
Publication date: July 2026
Publisher: Wiley–Scrivener
Edition: 1st edition
ISBN: 978-1-394-42267-8
Length: approximately 472–480 pages
Summary
Artificial General Intelligence: Principles and Practices is a large edited collection examining what would be required to move from today's narrow AI, which performs specialized tasks, toward Artificial General Intelligence (AGI) capable of learning, reasoning, adapting, and transferring knowledge across many domains. Rather than treating AGI simply as a matter of scaling neural networks, the book approaches intelligence from cognitive, mathematical, symbolic, neural, and computational perspectives. Its opening sections discuss definitions of general intelligence, cognitive architectures, Bayesian reasoning, algorithmic learning, symbolic versus neural approaches, and especially neuro-symbolic systems that attempt to combine explicit logical reasoning with the pattern-recognition strengths of neural networks.
A major theme is generalization: an AGI should not merely solve tasks seen during training but learn how to learn. Consequently, several chapters examine self-supervised learning, transfer learning, meta-learning, continual learning, common-sense reasoning, memory, and adaptation to changing environments. The book also considers explainability, proposing architectures in which reasoning can be inspected rather than remaining entirely inside a neural-network “black box.” Reinforcement learning and decision-making under uncertainty receive separate treatment, while multimodal chapters investigate how an intelligent system could combine vision, language and sound into a unified representation of its environment.
The final sections move from fundamental AGI questions toward applications and possible future technologies. Topics include multimodal perception, anomaly detection, computer vision, agricultural AI, assistive systems for visually impaired users, quantum computing and brain–computer interfaces. The concluding chapter speculates about the progression from narrow AI through increasingly autonomous systems toward AGI and potentially superintelligence, while also mentioning security and ethical issues.
Main topics
This looks most useful as a broad survey/reference volume for postgraduate students and researchers who want exposure to many of the approaches currently associated with AGI rather than a single rigorous theory of general intelligence. The publisher explicitly targets engineering students, researchers, IT professionals and AI/deep-learning specialists.
Its greatest strength is breadth: cognitive architectures, mathematical foundations, neuro-symbolic reasoning, meta-learning, multimodality and future technologies are gathered in one volume. Its weakness is the same breadth—the table of contents suggests that the book sometimes drifts from AGI itself into general contemporary machine-learning applications. I would therefore regard it as an AGI overview and research anthology, rather than a definitive technical textbook explaining how AGI can actually be built.
BOOK
Book:Artificial General Intelligence: Principles and Practices
Editors: T. Saravanan, P. Preethi, Sumaya Sanober, N. Thillaiarasu & S. Balamurugan
Publication date: July 2026
Publisher: Wiley–Scrivener
Edition: 1st edition
ISBN: 978-1-394-42267-8
Length: approximately 472–480 pages
Summary
Artificial General Intelligence: Principles and Practices is a large edited collection examining what would be required to move from today's narrow AI, which performs specialized tasks, toward Artificial General Intelligence (AGI) capable of learning, reasoning, adapting, and transferring knowledge across many domains. Rather than treating AGI simply as a matter of scaling neural networks, the book approaches intelligence from cognitive, mathematical, symbolic, neural, and computational perspectives. Its opening sections discuss definitions of general intelligence, cognitive architectures, Bayesian reasoning, algorithmic learning, symbolic versus neural approaches, and especially neuro-symbolic systems that attempt to combine explicit logical reasoning with the pattern-recognition strengths of neural networks.
A major theme is generalization: an AGI should not merely solve tasks seen during training but learn how to learn. Consequently, several chapters examine self-supervised learning, transfer learning, meta-learning, continual learning, common-sense reasoning, memory, and adaptation to changing environments. The book also considers explainability, proposing architectures in which reasoning can be inspected rather than remaining entirely inside a neural-network “black box.” Reinforcement learning and decision-making under uncertainty receive separate treatment, while multimodal chapters investigate how an intelligent system could combine vision, language and sound into a unified representation of its environment.
The final sections move from fundamental AGI questions toward applications and possible future technologies. Topics include multimodal perception, anomaly detection, computer vision, agricultural AI, assistive systems for visually impaired users, quantum computing and brain–computer interfaces. The concluding chapter speculates about the progression from narrow AI through increasingly autonomous systems toward AGI and potentially superintelligence, while also mentioning security and ethical issues.
Main topics
- Foundations of AGI: What general intelligence actually means and how it might be formally characterized.
- Cognitive architectures: Memory, reasoning, planning, learning and goal-directed behavior.
- Neuro-symbolic AI: Combining neural networks with symbolic logic and explicit knowledge representation.
- Learning and generalization: Self-supervised learning, meta-learning, transfer learning and lifelong learning.
- Common-sense reasoning: Giving AI systems background knowledge and inference abilities closer to human reasoning.
- Explainable AGI: Making decisions and reasoning processes interpretable.
- Reinforcement learning: Model-based versus model-free approaches.
- Multimodal intelligence: Integrating language, images, sound and contextual information.
- Future AGI: Quantum computing, brain–computer interfaces, autonomous learning and possible post-AGI systems.
- AGI is presented as an architectural problem, not merely a scaling problem. The book repeatedly argues that general intelligence will require memory, reasoning, adaptation, knowledge representation and learning to operate together.
- Hybrid neuro-symbolic approaches receive considerable attention. The editors appear to regard the combination of neural learning and symbolic reasoning as an important route toward systems capable of both flexibility and reliable logical reasoning.
- Learning to generalize is central to AGI. Meta-learning, transfer learning, self-supervised learning and continual learning are treated as mechanisms that could allow an AI to reuse knowledge instead of being retrained separately for every problem.
- This is more a research collection than a unified textbook. The 19 chapters are written by different contributors, and some later chapters—such as plant-disease detection, industrial anomaly detection and web-spyware classification—are conventional applied machine-learning topics whose connection to AGI is relatively indirect.
This looks most useful as a broad survey/reference volume for postgraduate students and researchers who want exposure to many of the approaches currently associated with AGI rather than a single rigorous theory of general intelligence. The publisher explicitly targets engineering students, researchers, IT professionals and AI/deep-learning specialists.
Its greatest strength is breadth: cognitive architectures, mathematical foundations, neuro-symbolic reasoning, meta-learning, multimodality and future technologies are gathered in one volume. Its weakness is the same breadth—the table of contents suggests that the book sometimes drifts from AGI itself into general contemporary machine-learning applications. I would therefore regard it as an AGI overview and research anthology, rather than a definitive technical textbook explaining how AGI can actually be built.
BOOK
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