Even as large language models have been making a splash with ChatGPT and its competitors, another incoming AI wave has been quietly emerging: large database models. Even as large language models have ...

How agencies can use on-premises AI models to detect fraud faster, prove control effectiveness and turn overwhelming data ...

Algorithms, markets, supply chains and national security systems make real-time decisions based on data as their primary input. Enterprise valuations rest on the integrity and reliability of their ...

Occasionally one may hear that a data model is “over-normalized,” but just what does that mean? Normalization is intended to analyze the functional dependencies across a set of data. The goal is to ...

Distributed database consistency models form the backbone of reliable and high-performance systems in today’s interconnected digital landscape. These models define the guarantees provided by a ...

When developing machine learning models to find patterns in data, researchers across fields typically use separate data sets for model training and testing, which allows them to measure how well their ...

Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI ...

Learn how this new standard connects AI to your data, enhances Web3 decision-making, and enables modular AI systems.

Last month, destructive wildfires blazed across Maui, Hawaii, killing at least 100 individuals and destroying some 3,200 acres of land. Residents critici zed government leaders, especially those ...

Why does MCP avoid direct credential ownership by AI? Learn how the Model Context Protocol separates intelligence from ...

In the ever-evolving world of sports analytics, cycling stands on the brink of a data-driven revolution. By leveraging innovative data models, inspired by diverse industries, new opportunities for ...