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FundamentalAGI Blueprint

fundamentalAGI

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

  1. Mastermind Controller of Agency (MCA)
    • Central control unit managing various AGI functions.
    • Ensures coordination and execution of tasks across different modules.
  2. OpenMind Multi-Model Integration (OMMI)
    • Integrates various AI models for multi-modal processing.
    • Facilitates seamless communication between models for enhanced understanding and decision-making.
  3. WebMind Information Parser (WIP)
    • Parses and integrates information from the web and network resources.
    • Ensures real-time updates and knowledge acquisition from external sources.
  4. AutoMind Reasoning Environment (ARE)
    • Comprised of logic.py, reasoning.py, SocraticReasoning.py, and bdi.py.
    • Provides advanced reasoning capabilities, including logical reasoning, Socratic questioning, and belief-desire-intention (BDI) modeling.
  5. SimpleMind Neural Network (SMNN)
    • Utilizes JAX for creating and training neural network models.
    • Focuses on efficient learning and adaptability.
  6. AutoMindX Executable Folder (AMX)
    • Dynamic build environment located in the mindx rwx folder.
    • Allows for on-the-fly creation and execution of AI models and scripts.
  7. AGLM (Autonomous General Learning Model)
    • Built from memory.py parser and memory folder.
    • Central to the system’s learning capabilities, enabling autonomous general learning and continuous improvement.
  8. Memory System
    • Short-Term Memory (STM)
      • RAM-based memory for immediate data processing and tasks.
      • storage of each input response as timestamp.json
      • ./memory/stm
    • Long-Term Memory (LTM)
      • Database and ROM for storing and retrieving long-term knowledge.
      • ./memory/ltm
      • ./memory/context
      • ./memory/truth
      • ./memory/agents
      • ./memory/prompts
    • RAGE (Retrieval Augmented Generative Engine)
      • Uses AGLM to enhance learning by creating memory contexts from the context folder.

Detailed Architecture and Implementation Plan

1. Cognitive Architecture

  • Central Processing:
    • Implement the Mastermind Controller of Agency (MCA) to oversee and manage the entire system.
    • Ensure efficient task execution and coordination among different components.

2. Multi-Modal and Multi-Model Integration

  • OpenMind Multi-Model Integration (OMMI):
    • Develop a framework to integrate models for various tasks such as image recognition, speech processing, and natural language understanding.
    • Ensure seamless data flow and communication between these models to leverage their strengths.

3. Information Parsing and Real-Time Data Integration

  • WebMind Information Parser (WIP):
    • Develop robust parsing algorithms to gather information from the web and other network resources.
    • Ensure real-time data updates and integration into the system’s knowledge base.

4. Advanced Reasoning Environment

  • AutoMind Reasoning Environment (ARE):
    • Implement logic.py, reasoning.py, SocraticReasoning.py, and bdi.py to provide comprehensive reasoning capabilities.
    • Enable logical reasoning, advanced questioning, and belief-desire-intention modeling for complex problem-solving.

5. Neural Network Learning

  • SimpleMind Neural Network (SMNN):
    • Utilize JAX to create efficient and adaptable neural network models.
    • Focus on deep learning, reinforcement learning, and meta-learning for skill acquisition and adaptation.

6. Dynamic Build Environment

  • AutoMindX Executable Folder (AMX):
    • Set up a dynamic build environment for on-the-fly model creation and execution.
    • Ensure flexibility and adaptability in developing and deploying new AI models.

7. Autonomous General Learning Model

  • AGLM and Memory System:
    • Develop the AGLM using memory.py and the memory folder for autonomous learning capabilities.
    • Implement a dual memory system (STM and LTM) for efficient data processing and knowledge retention.
    • Integrate RAGE to enhance learning by creating contextual memories for improved performance.

Continuous Learning and Improvement

  • Online and Self-Supervised Learning:
    • Implement algorithms for continuous learning from new data without the need for complete retraining.
    • Utilize self-supervised learning techniques to generate training data and enhance decision-making.
  • Feedback Loops:
    • Establish continuous feedback loops for performance evaluation and strategy adjustment.
    • Incorporate human oversight and expert feedback to align with human values and ethical considerations.

Ethical Considerations

  • Safety and Robustness:
    • Ensure the system adheres to strict safety protocols, including fail-safes and error detection mechanisms.
  • Transparency and Accountability:
    • Design the system to be transparent in its decision-making processes.
  • Alignment with Human Values:
    • Implement ethical reasoning frameworks to ensure actions align with human values and ethical standards.

Implementation Phases

  1. Research and Development:
    • Conduct thorough research to identify the latest advancements in relevant fields.
    • Develop prototypes and conduct iterative testing to refine capabilities.
  2. Collaboration and Integration:
    • Collaborate with domain experts to integrate technologies and frameworks.
    • Ensure the system remains at the forefront of innovation.
  3. Deployment and Monitoring:
    • Deploy the system in controlled environments initially.
    • Gradually expand operational scope with continuous improvement and monitoring.

Conclusion

Creating funAGI requires integrating advanced AI techniques, robust memory systems, and ethical frameworks. By leveraging multi-modal integration, continuous learning, and adaptive reasoning, funAGI aims to achieve autonomous general intelligence capable of independent operation, continuous self-improvement, and adaptive interaction across diverse environments.

This blueprint outlines the fundamental components and implementation strategies necessary to develop the funAGI system, aiming to achieve a sophisticated level of autonomous general intelligence. More information and the first “working” SocraticReasoning environment is available at

fundamental AGI
https://github.com/autoGLM/funAGI

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