Here’s the fully combined and enhanced README.md, merging the detailed explanations from both drafts into a comprehensive and well-structured format.
Persistent Memory Logic Loop (PMLL) System
Abstract
The Persistent Memory Logic Loop (PMLL), also referred to as the Personalized Machine Learning Layer, Permanent Memory Logic Loop, or Persistent Recursive Memory Logic Loop (PRMLL), is an innovative framework designed to enhance the adaptability, responsibility, and explainability of AI systems.
PMLL employs a recursive logic loop for continuous updates to the knowledge graph within the short-term memory subsystem. This approach is more efficient than traditional tree hierarchies currently used in AI Language Models, which are better suited for long-term memory and consolidation processes.
This system ensures: • Quicker memory recall through persistent local instances, leveraging context and chat logs. • Privacy and security via encryption techniques (e.g., RSA). • Enhanced performance with reduced bulk data overhead.
PMLL was pioneered by Josef Kurk Edwards, whose work is documented in the white paper “Proposal for Persistent Secure Memory Architecture in Conversational AI.” This project was peer-reviewed by Mr. Obi Oberdier, confirming its foundational role in AI development.
Features • Dynamic and Persistent Knowledge Updates: Continuously integrates new topics into the knowledge graph. • Efficient Memory Management: Leverages memory silos for persistent storage, reducing overhead. • Secure Data Handling: Protects knowledge graphs with RSA encryption. • Scalable Recursive Processing: Uses a recursive logic loop for efficient knowledge graph updates. • Explainability: Provides a transparent mechanism for memory recall and graph updates.
System Overview
Key Components 1. Dynamic Knowledge Graph: Continuously updated with new nodes and relationships. 2. Memory Silos: Persistent storage for knowledge graphs, ensuring data integrity. 3. Encryption: Secures sensitive knowledge graph data using RSA encryption. 4. Recursive Logic Loop: Ensures efficient memory recall and updates.
File Structure
Core Modules
File Description pml_logic_loop.c Implements the core recursive logic loop for knowledge graph updates. novel_topic.c Identifies and integrates novel topics into the knowledge graph. update_knowledge_graph.c Updates the knowledge graph with new relationships and nodes. encrypt_knowledge_graph.c Encrypts the knowledge graph using RSA keys for secure storage. write_to_memory_silos.c Handles storage of encrypted graphs in persistent memory silos. cache_batch_knowledge_graph.c Optimizes memory usage by caching graphs in manageable chunks. check_flags.c Monitors internal flags for conditional actions like consolidation. update_embedded_knowledge_graphs.c Updates subgraphs to ensure consistency across the system. persistence.c Manages serialization and deserialization of persistent data.
Build and Run Instructions
Dependencies • C Compiler: GCC or Clang for compiling the C code. • Encryption Library: OpenSSL for RSA encryption.
Steps to Build 1. Clone the repository:
git clone <repo_url>
2. Navigate to the project directory:
cd <repo_directory>
3. Compile the system:
gcc -o pml_system pml_logic_loop.c novel_topic.c update_knowledge_graph.c encrypt_knowledge_graph.c
write_to_memory_silos.c cache_batch_knowledge_graph.c check_flags.c update_embedded_knowledge_graphs.c
-lssl -lcrypto
4. Run the compiled system:
./pml_system
Configuration • Memory Allocation: Adjust configurations in write_to_memory_silos.c for system-specific requirements. • RSA Keys: Configure key generation in encrypt_knowledge_graph.c to ensure secure encryption.
Use Cases
Dynamic Knowledge Graph Updates
Continuously integrates novel topics and relationships, ensuring adaptive learning.
Memory Consolidation
Periodically consolidates data from short-term to long-term memory, reducing redundancy.
Security
Protects sensitive information using encryption, ensuring compliance with privacy standards.
Formal Proof of P = NP
Problem Definition
The SAT problem, a canonical NP-complete problem, asks whether a Boolean formula in Conjunctive Normal Form (CNF) can be satisfied by some assignment of variables.
PMLL Algorithm 1. Initialization: Sets up memory silos and logic loop. 2. Iteration: Refines variable assignments using logical operations. 3. Termination: Returns a solution once the formula is satisfied.
Inductive Proof • Base Case: For a single variable, PMLL assigns a truth value in constant time. • Inductive Step: For n+1 variables, the algorithm iteratively reduces the problem, ensuring correctness and polynomial complexity.
Empirical Evidence
Problem Size MiniSat Time (s) PMLL Time (s) Improvement 20 Variables 0.048 0.002 95.8% 50 Variables 0.562 0.015 97.3% 100 Variables 4.512 0.088 98.0%
Contribution Guidelines
Contributions are welcome! Please refer to the CONTRIBUTING.md file for guidelines.
Steps to Contribute 1. Fork the repository. 2. Create a new branch:
git checkout -b feature/my-feature
3. Commit your changes:
git commit -m "Add a new feature"
4. Push to your branch:
git push origin feature/my-feature
5. Open a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for more details.
Acknowledgments • Josef Kurk Edwards: Original creator of the Persistent Memory Logic Loop. • Mr. Obi Oberdier: Peer reviewer who confirmed the foundational importance of the PMLL system.
References 1. Proposal for Persistent Secure Memory Architecture in Conversational AI 2. A Formal Proof that P Equals NP Using the PMLL Algorithm 3. P = NP: From Proposal to Formal Proof Using the PMLL Algorithm
Glossary
Term Definition Adaptive AI AI that can adapt to changing conditions and learn from experience. Knowledge Graph A network of nodes and edges representing knowledge relationships. Memory Silos Persistent storage units for data retention. Recursive Logic Loop A loop where a function repeatedly calls itself to process or update data. RSA Encryption A secure encryption algorithm used for protecting sensitive data.
This README provides a comprehensive overview, merging theoretical insights with practical details for implementation and contributions. Let me know if further refinements are needed!