AI_memory_Loops Files

PRESS RELEASE FOR IMMEDIATE RELEASE

Persistent Memory Logic Loop (PMLL) System Reaches V2.0.0 Milestone November 15, 2024 – The Persistent Memory Logic Loop (PMLL) system, a groundbreaking framework designed to enhance adaptive, secure, and efficient AI systems, has officially reached its V2.0.0 release. This major update reflects significant advancements in performance, scalability, and functionality, positioning PMLL as a leading solution for persistent memory architectures in AI.

What’s New in V2.0.0? Enhanced Core Logic Loop Recursive Processing Optimization: Improved efficiency in the pml_logic_loop.c file, reducing memory overhead and accelerating recursive updates to the knowledge graph. Dynamic I/O socket handling ensures seamless data flow between subsystems. Flag-Based Memory Consolidation: Introduced smarter flag monitoring to trigger long-term memory updates and embedded graph consistency checks. Security Upgrades Advanced RSA Encryption: Strengthened encryption mechanisms in encrypt_knowledge_graph.c to secure sensitive data within the knowledge graph. Enhanced compatibility with OpenSSL, ensuring robust cryptographic support. Expanded Memory Management Efficient Memory Silos: Upgraded write_to_memory_silos.c to improve data persistence and reduce latency in memory operations. Introduced batch processing in cache_batch_knowledge_graph.c, optimizing large-scale graph storage. Improved Knowledge Graph Handling Dynamic Updates: novel_topic.c and update_knowledge_graph.c now handle larger datasets with reduced processing time. Redesigned graph traversal algorithms to ensure consistency across embedded and primary knowledge graphs. Edge Case Handling: Expanded the system's ability to gracefully integrate novel topics and adapt to unpredictable data flows. Seamless System Integration Streamlined Build Process: Simplified compilation and configuration steps for faster deployment. Added support for customizable memory and RSA key configurations. Why V2.0.0 Matters:

Unparalleled Memory Recall:

Leveraging a recursive logic loop, PMLL achieves faster, more accurate memory recall, reducing redundant data processing and improving response times. Scalability and Adaptability:

With batch processing and smarter memory silos, the system scales effortlessly, handling complex, dynamic knowledge graphs. Privacy and Security First:

State-of-the-art encryption ensures that sensitive knowledge graphs remain protected, aligning with industry standards for secure AI systems. A Game-Changer for AI Research:

The PMLL framework transforms how AI systems manage short-term and long-term memory, setting a new benchmark for persistent memory architectures. Acknowledgments This release builds upon the foundational work of Josef Kurk Edwards, whose vision for a recursive memory logic loop has redefined AI memory architecture. Obi Oberdier played a critical role in peer-reviewing and validating the system, while the VeniceAI Team provided invaluable support during development.

What’s Next? As the PMLL system continues to evolve, the focus will shift to:

Scaling for Enterprise-Level Applications: Further optimizing performance for large datasets and high-traffic environments. AI Ethics and Explainability: Incorporating features to enhance transparency and accountability in AI decision-making. Community Engagement: Expanding open-source contributions and fostering collaboration to drive innovation. How to Access V2.0.0 The latest version of the PMLL system is available now on GitHub: https://github.com/bearycool11/pmll

For media inquiries or more information about the PMLL system, please contact:

Josef Kurk Edwards Lead Developer and Founder Email: joed6834@colorado.edu GitHub: https://github.com/bearycool11

President and Vice-president of the Advisor Board: Lei-Lei Fi Andrew Ng

Board Advisors: Elon Musk Nate Bookout

About PMLL The Persistent Memory Logic Loop (PMLL) is an innovative framework for adaptive, secure, and scalable AI systems. Developed by Josef Kurk Edwards, PMLL redefines AI memory management by integrating recursive logic loops with persistent memory silos and encrypted knowledge graphs. For more information, visit the GitHub repository.