Combining artificial intelligence algorithms with radiologist assessment can improve the accuracy of detecting cancer in screening mammograms
Read article: Crowdsourced AI challenge aims to improve mammography accuracy
Anatole von Lilienfeld explains how machine learning is creating new opportunities for research across all areas of the physical sciences
Read article: A broader range of experiments
Artificial intelligence-derived image features predict prostate cancer recurrence with high accuracy
Read article: Opening the AI box: can deep learning predict cancer recurrence?
New technique could be used to design a wide range of optical devices
Read article: Artificial intelligence reveals how light flows around nanoparticles
Complex 3D maps of chemical reactions could soon be acquired using better light sources
Read article: Machine learning stabilizes synchrotron beams
An artificial intelligence-powered algorithm trained to identify skin conditions doesn't work effectively on black skin
Read article: AI dermatology tool needs more diverse skin types in its training datasets
An algorithm developed by Cambridge physicist Gareth Conduit and inspired by many-body quantum mechanics is the driving force behind a novel materials-science spin-out
Read article: Neural networks extract information from sparse datasets
Combining forces with other numerical approaches, as well as collaborating with other research groups across the world, adds strength to data-driven techniques for materials science
Read article: Machine learning collaborations accelerate materials discovery
An AI system used to detect breast cancer in mammograms achieved a comparable performance to 101 radiologists
Read article: Artificial intelligence versus 101 radiologists
Proof-of-concept demonstration done using two superconducting qubits
Read article: Machine learning is implemented on an IBM quantum processor
