Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.

An inability to address AI security risks may create areas for intellectual property (IP) theft, swayed outputs, or general ...

Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

EPFL researchers have developed new software—now spun-off into a start-up—that eliminates the need for data to be sent to ...

In contrast to machine learning (ML), machine unlearning is the process of removing certain data or influences from models as ...

Mid-Atlantic Permanente Medical Group shows how trusted data and technology can turn value-based care into better outcomes, ...

Accurate simulation of crop growth processes for predicting final yield is critical for optimizing resource management, particularly in regions with variable climates and limited resource availability ...

Abstract: Conventional data-driven dynamic process monitoring methods usually rely on data collected at a single sampling rate. The effectiveness of these approaches typically diminishes when ...

Abstract: This white paper looks at threat modeling as a practical way for businesses to identify cyberrisk in an increasingly complex environment. Threat modeling enables enterprises to identify, ...

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.

SqlDBM, the leading cloud-native data modeling platform for the enterprise, today announces the release of its Model Context Protocol (MCP) Server live at Databricks Data + AI Summit 2025. This ...