Washington-based Starcloud launched a satellite with an Nvidia H100 graphics processing unit in early November, sending a chip into outer space that's 100 times more powerful than any GPU compute that ...

Dynamic predictive modeling using electronic health record data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of ...

AI systems are increasingly being integrated into safety- and mission-critical applications ranging from automotive to health care and industrial IoT, stepping up the need for training data that is ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

Abstract: Data-driven Quality of Experience (QoE) modeling using Machine Learning (ML) is a key enabler for future communication networks as it allows accelerated and unbiased QoE modeling while ...

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, ...

My name is Bill Burkett, and I am a data modeler. I don’t call myself that often and sometimes have misgivings about doing so. I often get the feeling that being a “data modeler,” when considered in ...

A team has developed a new method that facilitates and improves predictions of tabular data, especially for small data sets with fewer than 10,000 data points. The new AI model TabPFN is trained on ...

Missing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant ...