A supercomputer beat a human chess champ 30 years ago, paving a path for AI dominance

In 1996, IBM's Deep Blue faced off against Garry Kasparov, the greatest chess mind on Earth — and changed history.

Fentanyl or phony? Machine learning algorithm learns to pick out opioid signatures

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...
EurekAlert!

Spin symmetry breaking unlocks new path to highly efficient hydrogen catalysts

Spin density symmetry breaking in single-atom catalysts can significantly enhance the performance of hydrogen evolution ...
Frontiers

Explainable Artificial Intelligence in Toxicology: Building Trust in Predictive Models

Artificial intelligence (AI) and machine learning (ML) hold significant promise in advancing the field of toxicology by ...
University of Birmingham

Machine‑learning framework could transform prediction of drug side effects

Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.
Open Heart

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation

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 ...
European Medical Journal

AI Predicts Cardiometabolic Multimorbidity Risk in Type 2 Diabetes

Researchers develop an AI tool to predict cardiometabolic multimorbidity risk in type 2 diabetes, aiding early intervention and personalised care. Find out more.
IEEE

Interpretable Multiphysics Field Optimization Approach for HSPM Machine Based on Deep Reinforcement Learning

Abstract: High-speed permanent magnet (HSPM) machines are pivotal in aerospace, industrial automation, and electric vehicles owing to their exceptional power density and compact structure. However, ...
IEEE

Interpretable Machine Learning Models for Vitamin A Deficiency Screening in School-Aged Children

Abstract: Vitamin A deficiency (VAD) is one of the leading causes of visual impairment globally. While blood tests are common approaches in developed countries, socioeconomic and public perspectives ...
Phys.org

Interpretable machine learning to accelerate nanocatalyst discovery

A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures. Metal nanoparticles catalyze reactions ...
Frontiers

Interpretable machine learning model for predicting covert hepatic encephalopathy in patients with cirrhosis: a multicenter study

Background and aim: Covert hepatic encephalopathy (CHE) is a neurocognitive complication affecting 40.9–50.4% of patients with cirrhosis. It often remains undiagnosed owing to its subclinical nature ...