Creating Scalable AGI: the Open General Intelligence Framework
Daniel A. Dollinger, Michael Singleton

TL;DR
This paper introduces the Open General Intelligence (OGI) framework, a modular, scalable architecture designed to enable true Artificial General Intelligence by integrating multiple specialized modules for complex, real-world problem-solving.
Contribution
The paper presents a novel systems architecture for AGI, emphasizing modularity, dynamic processing, and multi-modal integration to overcome limitations of current siloed AI models.
Findings
OGI enables real-time multi-modal data integration.
The framework supports scalable and adaptable intelligent systems.
It incorporates principles inspired by human cognition.
Abstract
Recent advancements in Artificial Intelligence (AI), particularly with Large Language Models (LLMs), have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed architectures, which are designed to handle only one data modality (data type) at a time. This single modal approach hinders their ability to integrate the complex set of data points required for real-world challenges and problem-solving tasks like medical diagnosis, quality assurance, equipment troubleshooting, and financial decision-making. Addressing these real-world challenges requires a more capable Artificial General Intelligence (AGI) system. Our primary contribution is the development of the Open General Intelligence (OGI) framework, a novel systems architecture that…
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Taxonomy
TopicsComputability, Logic, AI Algorithms
MethodsSparse Evolutionary Training
