Artificial General Intelligence (AGI) represents a significant advancement in Artificial Intelligence, aiming to develop systems capable of performing a wide range of cognitive tasks with human-like efficiency and adaptability. Unlike narrow AI, which is limited to domain-specific applications, AGI seeks to achieve generalization, enabling machines to transfer knowledge across different contexts and solve unfamiliar problems. This paper presents a comprehensive analysis of the fundamental concepts underlying AGI, including reasoning, learning paradigms, and cognitive adaptability. It further examines various computational approaches such as symbolic AI, deep learning models, hybrid neuro-symbolic systems, and reinforcement learning frameworks.
Additionally, the study highlights critical challenges in AGI development, including scalability, lack of common-sense reasoning, data dependency, and explainability issues. Ethical concerns such as bias, safety, and the alignment problem are also discussed, emphasizing the need for responsible and controlled deployment. Despite significant progress by leading research organizations like Open-AI and Google Deep-mind, achieving true AGI remains an open problem. The paper concludes by outlining future research directions and the potential societal impact of AGI across domains such as health-care, education, and scientific discovery.
Keywords
Artificial General Intelligence (AGI), Generalization, Deep Learning, Reinforcement Learning, AI Ethics
Conclusion
Artificial General Intelligence (AGI) represents a significant milestone in the evolution of Artificial Intelligence, with the potential to transform how machines interact with and understand the world. Unlike narrow AI, AGI aims to achieve human-like intelligence by integrating reasoning, learning, and adaptability across multiple domains. This paper examined the core concepts, models, architectures, and key challenges associated with AGI, highlighting the gap between current capabilities and the goal of true general intelligence.
Despite notable progress in Machine Learning and related technologies, several challenges such as generalization, common-sense reasoning, computational complexity, and ethical concerns remain unresolved. Addressing these issues requires not only technical advancements but also strong ethical frameworks and responsible research practices.
In conclusion, while AGI is still in the developmental stage, its potential impact on society is immense. With continued research, interdisciplinary collaboration, and careful regulation, AGI can be developed in a way that maximizes its benefits while minimizing risks, ultimately contributing to the advancement of humanity.
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How to Cite This Paper
Aboni Mohan Sahu, Rohit Kumar Yadav, Ajay Kumar, Zaid Alam, Kumar Amrendra (2026). Artificial General Intelligence: A Comprehensive Study of Concepts, Models, and Limitations. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
