Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...

For all their impressive capabilities, large language models (LLMs) often fall short when given challenging new tasks that require complex reasoning skills.

This month, the Machine Intelligence From Cortical Networks (MICrONS) consortium released the most comprehensive map ever assembled of a mammalian brain. The years-long effort painstakingly charted a ...

Finding relationships between bio-signals and health outcomes is complicated for many reasons, including sorting out irrelevant data.

The calibration set is often much smaller than the training data required for training machine learning algorithms. Usually just a few hundred spectra are enough for calibration.

By now, many people think they know what machine learning is: You “feed” computers a bunch of “training data” so that they “learn” to do things without our having to specify exactly how. But computers ...

A 2019 paper furthers machine unlearning research by introducing a framework that expedites the unlearning process by strategically limiting the influence of data points in the training procedure.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.