funAGI Objective: Develop a comprehensive Autonomous General Intelligence (AGI) system named FundamentalAGI (funAGI). This system integrates various advanced AI components to achieve autonomous general intelligence, leveraging multiple frameworks, real-time data processing, advanced reasoning, and a sophisticated memory system. Design will be modular for dynamic adaptation using modern object oriented programming technique primary in the Python language. Components of funAGI: the big picture Detailed Architecture and Implementation Plan 1. Cognitive Architecture 2. Multi-Modal and Multi-Model Integration […]

Building a rational Autonomous General Learning Model with Retrieval Augmented Generative Engine to create a dynamic learning loop with machine.dreaming for machine.learning as a self-healing architecture. MASTERMIND uses the Autonomous General Learning Model (aGLM) enhanced by the Retrieval Augmented Generative Engine (RAGE) to create a sophisticated AI system capable of intelligent decision-making and dynamic adaptation to real-time data. This combination leverages the strengths of both components to ensure that responses are not only based on […]

Overview This document provides a comprehensive general explanation of an Augmented General Intelligence (AGI) system framework integrating advanced cognitive architecture, neural networks, natural language processing, multi-modal sensory integration, agent-based architecture with swarm intelligence, retrieval augmented generative engines, continuous learning mechanisms, ethical considerations, and adaptive and scalable frameworks. The system is designed to process input data, generate responses, capture and process visual frames, train neural networks, engage in continuous learning, make ethical decisions, and adapt to […]