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, ...

As LLMs and diffusion models power more applications, their safety alignment becomes critical. Our research shows that even minimal downstream fine‑tuning can weaken safeguards, raising a key question ...

Aaron Y. Lee, MD, MSCI, explains how temporal optical coherence tomography modeling may improve longitudinal disease tracking and clinical decision-making.

A new study shows early specialization boosts short-term success but limits long-term excellence. Range, not early focus, ...

A type of cognitive training that tests people's quick recall seems to reduce the risk of dementia, including Alzheimer's disease ...

Models, by definition, are approximations: useful, informative, and inevitably incomplete, because they are the only way to ...

Few draft prospects highlight the philosophical divide between traditional scouting and model-driven evaluation quite like ...

Everyone bends the truth differently. Find out if you're a protective embellisher, a strategic omitter or another kind of ...

Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Evolving challenges and strategies in AI/ML model deployment and hardware optimization have a big impact on NPU architectures ...