What's possible when AI models can learn from additional health data modalities to create a more complete understanding of ...

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 ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Overcome Excel PowerPivot limitations by importing data into Power BI Desktop. Publish to the service and connect multiple ...

Database provider Couchbase has unveiled a comprehensive suite of model hosting and data processing capabilities for building, deploying and governing agentic AI applications. By bringing data and ...

Network data models provide a representation of a network's configuration, state and policies. These models abstract various network components and describe how they interact with each other, which ...

So-called “unlearning” techniques are used to make a generative AI model forget specific and undesirable info it picked up from training data, like sensitive private data or copyrighted material. But ...

AI models never remain static; they inevitably drift over time. This makes continuous output monitoring and model drift mitigation vital to any ongoing AI strategy. AI systems are developed using ...

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 ...

Data models are used to represent real-world entities, but they often have limitations. Avoid these common data modeling mistakes to keep data integrity. Data modeling is the process through which we ...