Foundations for Thinking Machines in Artificial General Intelligence
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866902329818611712 |
|---|---|
| author | Nazeer Shaik Dr. T. Murali Krishna Dr. P. Chitralingappa |
| author_facet | Nazeer Shaik Dr. T. Murali Krishna Dr. P. Chitralingappa |
| contents | <p><span>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.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16736236 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Foundations for Thinking Machines in Artificial General Intelligence Nazeer Shaik Dr. T. Murali Krishna Dr. P. Chitralingappa Artificial General Intelligence Modular Architecture Meta-Cognition Hybrid Intelligence <p><span>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.</span></p> |
| title | Foundations for Thinking Machines in Artificial General Intelligence |
| topic | Artificial General Intelligence Modular Architecture Meta-Cognition Hybrid Intelligence |
| url | https://doi.org/10.5281/zenodo.16736236 |