Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

Duolingo leverages AI to transform content creation, personalization, and scalability, positioning itself as a dominant, ...

A multi-institutional team of researchers led by Virginia Tech's Fralin Biomedical Research Institute at VTC has for the first time identified specific patterns of brain chemical activity that predict ...

Self-driving cars did not disappear. They simply slipped out of the spotlight. While attention shifted to generative AI, ...

Alibaba's open-source RynnBrain AI model enables robots to understand environments and perform tasks, advancing embodied AI..

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

Today’s standard operating procedure for LLMs involves offline training, rigorous alignment testing, and deployment with frozen weights to ensure stability. Nick Bostrom, a leading AI philosopher and ...

An AI-powered model developed at the University of Michigan can read a brain MRI and diagnose a person in seconds, a study suggests. The model detected neurological conditions with up to 97.5% ...

Schizophrenia is a severe and often highly debilitating psychiatric disorder characterized by distorted emotions, thinking patterns and altered perceptions of reality, as well as mental impairments.

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

3D illustration of high voltage transformer on white background. Even now, at the beginning of 2026, too many people have a sort of distorted view of how attention mechanisms work in analyzing text.

Abstract: This paper proposes an optimal design method for surface-mounted permanent magnet synchronous motors (SPMSMs) based on electromagnetic performance prediction using a convolutional neural ...