Creating Scalable AGI: the Open General Intelligence Framework
Daniel A. Dollinger, Michael Singleton
TL;DR
Current AI systems are narrow and siloed, lacking natural generalization across data modalities and real-time adaptability. The paper proposes the Open General Intelligence (OGI) macro design, combining a Dynamic Processing System with modular Framework Areas and a multi-layered memory fabric to enable real-time, multi-modal cognition. Key contributions include macro design guidance (multi data type support, specialized modules, interconnected fabric, cognitive switching), the dynamic routing and weighting mechanism with $w_t$ defined by $w_t = Φ(C,E_t) = softmax(g(C,E_t))$ inside the simplex $Δ^n$, and the specification of executive control, autonomous processing, IO integration, and memory components. The approach offers a scalable, context-aware reference architecture aimed at solving real-world tasks with greater reliability and openness for empirical validation and future enhancements.
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 serves as a macro design reference for AGI. The OGI framework adopts a modular approach to the design of intelligent systems, based on the premise that cognition must occur across multiple specialized modules that can seamlessly operate as a single system. OGI integrates these modules using a dynamic processing system and a fabric interconnect, enabling real-time adaptability, multi-modal integration, and scalable processing. The OGI framework consists of three key components: (1) Overall Macro Design Guidance that directs operational design and processing, (2) a Dynamic Processing System that controls routing, primary goals, instructions, and weighting, and (3) Framework Areas, a set of specialized modules that operate cohesively to form a unified cognitive system. By incorporating known principles from human cognition into AI systems, the OGI framework aims to overcome the challenges observed in today's intelligent systems, paving the way for more holistic and context-aware problem-solving capabilities.