Foundations for Thinking Machines in Artificial General Intelligence

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.