Towards a Human-Like AGI Architecture: General Intelligence Framework (GIF)

Abstract

Artificial Intelligence (AI) has achieved significant breakthroughs but remains limited by its specialization and inability to generalize across domains, unlike human cognition. Current models such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) excel at specific tasks but struggle with real-time adaptability and cross-domain generalization. This paper introduces the General Intelligence Framework (GIF), an approach designed to bridge this gap by mimicking human-like cognitive processes. By integrating Deep Learning (DL), Spiking Neural Networks (SNNs), and neuromorphic hardware, the framework fosters Real-Time Learning (RTL) and adaptability. The proposed framework holds potential for industries like robotics, healthcare, education, astronomy, defense, autonomous systems, etc.…, where flexible, adaptive AI is critical. We hypothesize that the framework will enable AI systems to handle unforeseen inputs and tasks without requiring extensive retraining, representing a step toward achieving Artificial General Intelligence (AGI).