AI systems built with Large Language Models (LLMs) present unique challenges that traditional observability tools weren't designed to handle:
Source: https://www.youtube.com/watch?v=kNrkHWjZaeM
Graph analytics have evolved significantly with the advent of GPU acceleration, enabling faster computations and larger-scale graph processing. In this paper, we present insights from an in-depth discussion with Joe Eaton, NVIDIA Distinguished System Engineer, on how RAPIDS and cuGraph revolutionize graph analytics. We explore GPU-accelerated ETL, the scalability of NetworkX on GPUs without code modification, and the integration of graph analytics with machine learning approaches such as graph neural networks (GNNs) and graph embeddings. The discussion also touches on current trends in graph analytics, the increasing demand for dynamic and multimodal graphs, and the role of knowledge graphs in generative AI applications.
Title: Hypergraph and Metagraph Architectures for Secure and Scalable Situational Intelligence in the Department of Defense
Abstract: Situational intelligence within the Department of Defense (DoD) demands real-time, secure, and scalable information processing solutions. This paper explores the adoption of Hypergraph and Metagraph architectures as a next-generation approach to data management, secure information exchange, and trust mechanisms. We contextualize these architectures within existing DoD frameworks and emerging best practices in distributed ledger technologies (DLT), trust-based consensus mechanisms, and decentralized interoperability. Drawing from recent advancements in directed acyclic graphs (DAGs), we analyze the capabilities of the Hypergraph framework, focusing on its Proof of Reputable Observation (PRO) consensus and Metagraph-based modular data governance. This paper details practical applications for the DoD, including real-time intelligence fusion, autonomous system coordination
| # -*- coding: utf-8 -*- | |
| """prefect-colab-event-mentions.ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1KZRhjRazTGl7tjyG91Y9uJNDyxxcI2CY | |
| """ | |
| # Commented out IPython magic to ensure Python compatibility. |
flowchart TD
%% Home Page Navigation
subgraph Home["Home Page (app.py)"]
A[User] -->|Selects analysis mode| B[Sidebar Navigation]
B --> C{Available Pages}
C -->|COVID Navigator| Page1
C -->|COVID Event Graph Explorer| Page2
C -->|Global Network Analysis| Page3
C -->|Feb 2025 Navigator| Page4
| import os | |
| import asyncio | |
| from prefect import flow, task, get_run_logger | |
| from prefect.tasks import task_input_hash | |
| from prefect.blocks.system import Secret, JSON | |
| from prefect.task_runners import ConcurrentTaskRunner | |
| from prefect.concurrency.sync import concurrency | |
| from pathlib import Path | |
| import datetime | |
| from datetime import timedelta |
title: Competency Questions - economics & finance author:
Type: markdown Size: 304 bytes Created: 2025-02-10 01:37:02 UTC Modified: 2025-02-10 01:37:02 UTC
---
This document provides a comprehensive overview of the Constellation Network's Hypergraph, a decentralized network protocol. The Hypergraph utilizes a directed acyclic graph (DAG) structure, enabling parallel transaction processing for superior scalability compared to traditional blockchains. Its layered architecture features a Global L0 layer for final consensus and immutable data storage, and independent, customizable subnetworks called metagraphs that handle specific functions and data types before submitting snapshots to the Global L0. Key themes include the network's innovative consensus mechanism (Proof-of-Reputable Observation), its flexible tokenomics model (Metanomics) for the native DAG token, and the powerful functionality of metagraphs as building blocks for diverse applications. The overall purpose is to detail the structure and function of this novel blockchain alternative, highlighting its advantages in scalability, security, and interoperability.