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Machine Intelligence in the fxyz Network

This document provides an introduction to the expansive domains of Machine Learning (ML) and Artificial Intelligence (AI) within the fxyz Network. It outlines our exploration across a broad spectrum of technologies and methodologies, aimed at enhancing capabilities in data understanding and interaction. Our focus areas have evolved to emphasize graph-based technologies significantly.

Key Technologies and Methodologies​

  • Machine Learning Algorithms: Fundamental for data analysis and predictive modeling.
  • Language Models and LLMs: Support natural language processing tasks and power our AI assistants.
  • Graph Agents: Assist in creating and manipulating graph-based data structures.
  • Vector Stores: Facilitate efficient storage and retrieval of vector data for ML applications, storing vector index as graphs with Neo4j Graph DB.
  • Graph Neural Networks (GNN) and Graph Convolutional Networks (GCN): Analyze and interpret graph-structured data.
  • SemanticInterfaceBot: Utilizes an open-source, fine-tuned model for sophisticated interaction across multiple interfaces including the Telegram app, web app, and a potential standalone app.

Language Models and LLMs​

Our network leverages state-of-the-art language models and Large Language Models (LLMs) to power various AI-driven functionalities:

  • Mistral MoE (Mixture of Experts): A highly efficient and powerful language model that we use for complex language understanding and generation tasks.
  • Llama3: An advanced open-source language model that provides robust natural language processing capabilities.
  • GPT-3.5 Turbo: Utilized through MeMGPT's endpoint for certain applications requiring its specific capabilities.
  • Custom Fine-tuned Models: We develop and fine-tune models on our specific datasets to enhance performance in domain-specific tasks.

These models are integrated into our AI assistants, data analysis tools, and user interfaces to provide sophisticated language understanding and generation across the network.

Transition to Graph-Based Technologies​

The iterative development of our AI platforms, including GenAI1 and GenAI2, underscores our dedication to AI advancement. The Docker GENAI stack, leveraging Docker's containerization technology, enhances the deployment and scalability of our AI applications. It provides robust deployment and management, integrates advanced ML models like GNN and GCN, and facilitates sophisticated data interactions through technologies like our Semantic Interface Bot.

Tools and Technologies​

Our transition towards a graph-centric approach includes the integration of various tools:

Graph Machine Learning (GML)​

We utilize machine learning and graph machine learning algorithms for various applications, including automated knowledge graph construction among other functionalities. GML is a vital area that leverages various algorithms for different tasks within data science and machine learning. It encompasses:

Understanding Graphs and GML​

Graphs are data structures consisting of nodes (or vertices) connected by edges (or relationships), often enhanced with properties that add further detail. Graph Machine Learning applies ML techniques to these structures for predictive and prescriptive analytics.

The Role of Compression in GML​

A significant challenge in GML is managing large, sparse graph data structures. Effective compression is crucial for maintaining essential signals for accurate prediction and inference, making the data manageable and useful for ML models.

Diverse Approaches in GML​

  • Classic Graph Algorithms: Include PageRank and Dijkstra's Shortest Path for tasks like community detection and pathfinding.
  • Non-GNN Graph Embeddings: Techniques like Node2vec and FastRP focus on representation learning.
  • Graph Neural Networks (GNNs): Advanced approach for end-to-end learning on graph-structured data.

For implementation, we utilize the Neo4j Graph Data Science library, which offers comprehensive tools and documentation.

Future Directions​

  • Continued exploration and integration of advanced graph-based AI tools
  • Enhancement of our AI platforms for improved scalability and performance
  • Development of more sophisticated AI agents for various network functions
  • Ongoing research into emerging LLMs and their potential applications in our network

By leveraging these diverse AI and ML technologies, the fxyz Network aims to create a sophisticated, adaptive, and intelligent ecosystem for financial and knowledge management.