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

Title

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

Creator

Puliyanmakkal, Jiran Kurian; Rohini, V.

Description

Artificial Intelligence (AI) has achieved significant breakthroughsbut remains limited by its specialization and inability to generalize acrossdomains, unlike human cognition. Current models such as Large LanguageModels (LLMs) and Multimodal Large Language Models (MLLMs) excel atspecific tasks but struggle with real-time adaptability and cross-domaingeneralization. This paper introduces the General Intelligence Framework(GIF), an approach designed to bridge this gap by mimicking human-likecognitive processes. By integrating Deep Learning (DL), Spiking NeuralNetworks (SNNs), and neuromorphic hardware, the framework fosters Real-Time Learning (RTL) and adaptability. The proposed framework holdspotential 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 handleunforeseen inputs and tasks without requiring extensive retraining,representing a step toward achieving Artificial General Intelligence (AGI). 2025 Jiran Kurian Puliyanmakkal and Rohini V. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Source

Journal of Computer Science;Volume;21;Issue;7;pp.1637-1650

Date

01-01-2025

Publisher

Science Publications

Subject

Artificial General Intelligence (AGI); General IntelligenceFramework (GIF); NeuromorphicComputing; Real-Time Learning (RTL); Spiking Neural Networks (SNNs)

Coverage

Puliyanmakkal J.K., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Rohini V., Department of Computer Science, Christ (Deemed to be University), Bangalore, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 15493636;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.3844/jcssp.2025.1637.1650

https://www.scopus.com/pages/publications/105012831361?origin=resultslist