Artificial General Intelligence

Artificial General Intelligence presents a systematic account of the theoretical foundations, experimental architectures, and engineering challenges that define this field. The book surveys core learning paradigms-supervised, unsupervised, and reinforcement learning-and examines their limitations when applied to broad problem spaces. Subsequent chapters introduce hybrid models that integrate symbolic knowledge with neural computation, followed by detailed case studies of multi task agents and continual learners. We present design principles for scalable architectures, methods for safe exploration under uncertainty, and strategies for transparent decision making. The text also addresses system evaluation, offering metrics that extend beyond accuracy to include adaptability, resilience, and interpretability. Throughout, emphasis is placed on practical implementation: code snippets illustrate key algorithms, while sidebars highlight deployment considerations such as latency, energy efficiency, and hardware constraints.