Google, Microsoft and Nvidia are among the names vying to make forecasts more accurate for longer.

The project for which Hulsebos received the grant is called DataLibra, which runs from 2024 to 2029. Over those five years, ...

Training artificial intelligence models is costly. Researchers estimate that training costs for the largest frontier models ...

O n Tuesday, researchers at Stanford and Yale revealed something that AI companies would prefer to keep hidden. Four popular ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

Abstract: In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results. Nevertheless, ...

This isn't really an issue (discussions are disabled) and it isn't exactly related to the Device model group, but since you, the Smart Data Model maintainers, have a lot of experience with JSON-LD and ...

Singapore-based AI startup Sapient Intelligence has developed a new AI architecture that can match, and in some cases vastly outperform, large language models (LLMs) on complex reasoning tasks, all ...

Figure 1. Average result across 8 benchmarks for different methods. High performance at the same selection ratio indicates high efficacy, while achieving similar performance with a smaller selection ...

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, ...

Willis, a WTW business, has integrated Moody’s detailed global flood data into its proprietary risk management tools, building upon its partnership with the company. In the past, Willis has ...