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 ...

What it takes to get useful health data from your smartwatch Training an algorithm is an essential part of translating our bodies’ signals into early diagnoses.

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 ...

Care must be taken to ensure that traces of data are not left behind in the unlearning process. Compatibility: Machine unlearning algorithms should ideally be compatible with existing ML models.

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.

Learn about types of machine learning and take inspiration from seven real world examples and eight examples directly applied to SEO.