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 ...
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 ...
Once, the world’s richest men competed over yachts, jets and private islands. Now, the size-measuring contest of choice is clusters. Just 18 months ago, OpenAI trained GPT-4, its then state-of-the-art ...
How agencies can use on-premises AI models to detect fraud faster, prove control effectiveness and turn overwhelming data ...
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 ...
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs ...
Is it possible for an AI to be trained just on data generated by another AI? It might sound like a harebrained idea. But it’s one that’s been around for quite some time — and as new, real data is ...
MongoDB said additional partners and offerings are expected to be added to the startup program over time.
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 ...
The world is changing rapidly, and if businesses want to keep up, there is no alternative but to change with it. Customer behavior, market conditions, and the technological landscape are in a constant ...
AI’s future doesn’t depend on ever-larger models but on better, human-curated data. AI risks bias, hallucinations and irrelevance without expert oversight and high-quality training sets. AI is a paper ...