As AI models grow more complex, a new white-collar gig workforce has emerged to review and guide systems. A new category of ...

Traditional data architectures are rigid and siloed, limiting agility, experimentation and the cross-domain insights required ...

Explore how AI is shaping cybersecurity in 2026, enhancing security operations, API governance, and compliance amidst ...

Allianz and Anthropic announce a global partnership to integrate Claude AI models into insurance workflows, focusing on ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

In 2026, boards won’t ask if you use AI — they’ll ask if you truly understand, control, and can explain how it’s steering the ...

Mount Sinai analysis looks at the effectiveness of electrocardiograms analyzed via deep learning as a tool for early COPD detection ...

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality globally. Effective management ...

Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiogramsJournal: eBioMedicine ...

Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical tool for interpreting the predictions of complex deep learning models. While XAI has been increasingly applied in various ...

When a medical AI system once recommended denying a patient treatment, the doctors hesitated—but couldn’t explain why. The algorithm’s reasoning was invisible, locked inside a mathematical “black box.

According to Stanford AI Lab, researchers have successfully optimized the classic K-SVD algorithm to achieve performance on par with sparse autoencoders for interpreting transformer-based language ...