Machine learning

By adding rewards for logical reasoning steps, a new training framework aims to make large language models more robust and reliable.

A machine learning model called Fragle helps detect cancer and spot signs of relapse by quantifying DNA fragments in the blood.

A consistent rulebook of prompts could be the key to advancing artificial intelligence models, enhancing their image classification performance even when dealing with unknown objects.

A new framework aligns data from multiple sensors across time, boosting the accuracy of machine failure predictions.

A new AI-driven computational pipeline paired with rigorous lab-based testing accelerates the discovery of novel molecules for efficient organic solar cells.

A new machine learning tool could help predict cancer treatment outcomes for more effective and targeted treatments.

Departing from costly simulations and trial-and-error methods, researchers harness machine learning and automation to efficiently discover new recipes for green hydrogen catalysts.

A new machine learning model identifies key dementia features from complex clinical datasets, potentially making diagnoses faster and less subjective.

A new training strategy enables robots to efficiently collaborate and learn in dynamic environments, improving their performance in real-world tasks.

Machine-learning-based predictions intelligently analyse complex sensor data to enhance equipment maintenance and reliability.

A*STAR researchers develop machine learning models that quickly and accurately simulate chemical reactions involving complex liquid interactions.

Advanced photonic processors boost machine learning efficiency by handling vast data streams in parallel, with potential benefits for enhanced patient monitoring and cloud computing.