Vol. 15 No. 3 (2025): IJCRT, Volume 15, Issue 3, 2025
Journal Article

Foundations for Thinking Machines in Artificial General Intelligence

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  • Nazeer Shaik,
  • Dr. T. Murali Krishna,
  • Dr. P. Chitralingappa
Nazeer Shaik
Department of CSE, Srinivasa Ramanujan Institute of Technology, Anantapur.
Dr. T. Murali Krishna
Department of CSE, Ashoka Women’s Engg. College, Kurnool.
Dr. P. Chitralingappa
Department of CSE, Srinivasa Ramanujan Institute of Technology, Anantapur.
DOI: https://doi.org/10.5281/zenodo.16736236
Categories

Published 2025-08-04

Keywords

  • Artificial General Intelligence,
  • Modular Architecture,
  • Meta-Cognition,
  • Hybrid Intelligence

How to Cite

Nazeer Shaik, Dr. T. Murali Krishna, & Dr. P. Chitralingappa. (2025). Foundations for Thinking Machines in Artificial General Intelligence. IJCRT Research Journal | UGC Approved and UGC Care Journal | Scopus Indexed Journal Norms, 15(3), 50966–50973. https://doi.org/10.5281/zenodo.16736236

Copyright (c) 2025 IJCRT Research Journal | UGC Approved and UGC Care Journal | Scopus Indexed Journal Norms

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The development of Artificial General Intelligence (AGI) stands as a grand challenge in the field of artificial intelligence, aiming to create systems that possess human-like cognitive abilities across diverse tasks and environments. While recent advancements in deep learning and multi-modal models have led to impressive capabilities, current AI systems remain limited in adaptability, reasoning, and self-awareness. This paper presents a unified, modular framework for AGI design—called the Modular AGI Framework (MAF)—that integrates symbolic reasoning, neural learning, episodic memory, meta-cognition, and human feedback alignment. Through a detailed comparison with existing systems such as GPT-4, Gato, SOAR, and OpenCog, we demonstrate the proposed architecture’s superior performance across key AGI metrics, including generalization, causal reasoning, adaptability, and interpretability. This work contributes a structured pathway toward realizing truly thinking machines and outlines the essential components necessary for safe and scalable AGI systems.

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