Title: AI, Innovation and Global Transformation: Interdisciplinary Perspectives on Technology, Business and Society
Editors: Dr. J. Preetha, and Dr. Siddhartha Mehrotra
ISBN: 978-81-69857-64-2
Chapter: 3
DOI: https://doi.org/10.59646/809/03
Author: S. Chandrasekar
Abstract
Artificial General Intelligence (AGI) represents a prospective stage in artificial intelligence in which computational systems would possess broad, flexible, and transferable cognitive capabilities across diverse tasks rather than being optimized for a narrow application. Unlike Narrow AI, which generally performs specific functions within predefined domains, AGI aims to integrate reasoning, learning, planning, perception, language understanding, knowledge acquisition, adaptation, and autonomous decision-making. This chapter examines the conceptual evolution from Narrow AI toward AGI, emphasizing advances in foundation models, multimodal learning, reinforcement learning, neural-symbolic integration, autonomous agents, and continual learning. A structured methodology is used to analyze the capabilities and limitations of contemporary AI systems against proposed AGI characteristics. A mathematical framework models general intelligence as a multi-dimensional optimization problem involving learning efficiency, reasoning accuracy, transferability, adaptability, autonomy, and resource efficiency. Synthesized results demonstrate the increasing capabilities of integrated AI architectures while highlighting persistent challenges in generalization, robust reasoning, alignment, interpretability, autonomy, and evaluation. The chapter concludes with future directions for responsible AGI development.
Keywords: Artificial General Intelligence, Narrow AI, Foundation Models, Machine Reasoning, Autonomous Agents, Generalization