A computational platform designed by A*STAR researchers accurately predicts a machine’s functional lifespan even with limited data.
By linking past observations with future possibilities, a novel framework could help computers predict human actions more accurately.
A novel machine learning framework makes it faster and easier to train robots that can perform tasks with unprecedented precision.
Machine learning and artificial intelligence have the power to transform chemical research, says A*STAR National Science Scholar Jacqueline Tan.
From advanced materials to atomic super sensors, A*STAR scientists are harnessing the power of quantum to the fullest, ushering in the next generation of trailblazing technologies.
Deep learning algorithms for predicting machine failures in industrial settings can be compressed without compromising their performance, say A*STAR researchers.
By giving algorithms the ability to generalize, researchers are expanding the range of problems that can be tackled with artificial intelligence.
By tapping into the inner workings of cells, Jinmiao Chen uses novel analytical technologies to understand why immune responses vary greatly among individuals.
Artificial neural networks are now being used to make 3D-printed metal structures more accurately—and stronger—than ever before.
A generative adversarial network has been used to develop audio classification technologies that require much less training data.
A new sequencing technique called PORE-cupine combines artificial intelligence to reveal ribonucleic acid structures in cells.
Horizontal Technology Programme Offices will bring A*STAR’s deep capabilities to bear on real-world issues facing Singapore and the wider world, says Deputy Chief Executive (Research), Andy Hor.