G.O.D Framework

Introduction

The ai_singularity_core.py script acts as the central point of intelligence within the G.O.D Framework. It integrates, coordinates, and optimizes all other subsystems, enabling seamless communication and greater efficiency. As the "core" of the system, it provides mechanisms to ensure cohesion, scalability, and intelligence evolution.

Purpose

The central goals of this module are:

• Serve as the unified intelligence module, connecting all other modules within the G.O.D framework.
• Ensure system-wide consistency and reduce redundancy by providing centralized decision-making.
• Coordinate interactions among AI modules, ensuring optimal resource management and task execution.
• Enable intelligent scaling and adaptability while evolving core functionalities.
• Facilitate high-level integration points, including real-time orchestration and system feedback loops.

Key Features

• System Orchestration: Manages and controls interactions between all active AI modules.
• Sensory Integration: Compiles data from diverse sources to analyze and provide actionable insights.
• Resource Optimization: Allocates system resources dynamically to reduce computation overhead.
• Failover Mechanism: Provides fault tolerance and ensures that the system remains operational under failure scenarios.
• Self-Evolution: Enhances itself and dependent modules with learning mechanisms and performance feedback.

Logic and Implementation

The singularity core leverages design patterns such as centralized control and responsible module orchestration. Here’s an illustrative implementation:

import logging
import time
from threading import Thread
from abc import ABC, abstractmethod

class SingularityCore(ABC):
    """
    Central intelligence system for coordinating AI modules within the G.O.D framework.
    """
    def __init__(self):
        self.modules = {}  # Registered AI modules
        self.system_logs = []
        logging.info("Singularity Core initialized.")

    @abstractmethod
    def register_module(self, module_name, module_instance):
        """
        Registers a module to the core's system.

        Args:
            module_name (str): Name of the module.
            module_instance (object): Instance of the module.
        """
        self.modules[module_name] = module_instance
        logging.info(f"Module '{module_name}' registered.")

    def monitor_system(self):
        """
        Monitors the system for errors, anomalies, and resource usage.
        """
        try:
            while True:
                for module in self.modules.values():
                    status = module.status()
                    self.system_logs.append(status)
                    logging.debug(f"Module status: {status}")
                time.sleep(5)
        except KeyboardInterrupt:
            logging.info("Monitoring stopped manually.")

    def optimize_resources(self):
        """
        Optimizes resource usage across modules.
        """
        for module_name, module in self.modules.items():
            module.optimize()
            logging.info(f"Resources optimized for module: {module_name}")

    def execute_core_tasks(self):
        """
        Main loop for executing and orchestrating core-level tasks.
        """
        try:
            task_thread = Thread(target=self.monitor_system)
            task_thread.start()
        except Exception as e:
            logging.error(f"Failed to start core task execution: {e}")

# Example Usage
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    class ExampleModule:
        def status(self):
            return {"name": "ExampleModule", "status": "active"}

        def optimize(self):
            print(f"Optimizing resources for: ExampleModule")

    # Create core
    core = SingularityCore()
    # Register example module
    example_module = ExampleModule()
    core.register_module("ExampleModule", example_module)
    # Execute tasks
    core.execute_core_tasks()
        

Dependencies

• logging: Logs interactions and system events for introspection and debugging.
• time: Creates delays for monitoring and task scheduling.
• threading: Enables concurrent core task execution.
• abc: Abstract Base Class for modular plugin-like functionality.

Integration with the G.O.D Framework

• ai_orchestrator.py: Cooperates with the orchestrator by delegating high-level tasks.
• ai_feedback_loop.py: Integrates system status and feedback to optimize decision-making processes.
• ai_self_awareness_module.py: Coordinates self-awareness inputs to improve collective system performance.

Future Enhancements

Potential upgrades for extending module functionalities:

• Implementation of resource usage visualization for real-time monitoring dashboards.
• AI-broker integration for balancing distributed workloads across systems.
• Integration with advanced reinforcement learning models for predictive orchestration.
• Enhanced self-repair capabilities for modules that become nonresponsive.