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.
Leveraging both artificial intelligence and field knowledge may prove to be a superior strategy for predicting machine health.
Three A*STAR scholars—Sarah Luo, Caroline Wee, and Kaicheng Liang—have been selected for the highly competitive National Research Foundation Fellowship out of over 134 applicants worldwide.
Computing devices are finally reaching their limits. But recently discovered particles called magnetic skyrmions could redefine these limits, according to Anjan Soumyanarayanan.
A new deep learning method increases the accuracy and range of applications for computer vision platforms.
Tipped off by artificial intelligence, a research team is testing whether a rheumatoid arthritis drug could be repurposed as a COVID-19 treatment.