New technology can quickly and accurately monitor glucose levels in people with diabetes without painful finger pricks to draw blood. A palm-sized device developed by researchers at the University of Waterloo uses radar and artificial intelligence (AI) to non-invasively read blood inside the human body.
The COVID-19 pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in machine learning are providing an opportunity to adapt clinical trials and lay the groundwork for smarter, faster and more flexible clinical trials in the future.
On 18 September the European Commission published a report on the Ethics of Connected and Automated Vehicles (CAVs). Written by an independent group of experts, the report includes twenty recommendations on road safety, privacy, fairness, AI explainability and responsibility, for the development and deployment of connected and automated vehicles.
A team led by computer scientist Lydia Kavraki used a machine learning approach to predict the quality of scaffold materials produced by 3D-printing, given the printing parameters. The work also found that controlling print speed is critical in making high-quality implants.
Apps that can precisely identify shards, coins or heel bones: archaeology has embraced artificial intelligence. Alex Brandsen is working on a search engine that scans vast quantities of text from an archaeological viewpoint.
Researchers have used a combination of AI and quantum mechanics to reveal how hydrogen gradually turns into a metal in giant planets.
Dense metallic hydrogen – a phase of hydrogen which behaves like an electrical conductor – makes up the interior of giant planets, but it is difficult to study and poorly understood. By combining artificial intelligence and quantum mechanics, researchers have found how hydrogen becomes a metal under the extreme pressure conditions of these planets.
Liquid-phase transmission electron microscopy (TEM) has recently been applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. Researchers from the University of Illinois have developed a machine learning workflow to streamline the process of extracting physical and chemical parameters from TEM video data.
Since the early 1930s, electron microscopy has provided unprecedented access to the world of the extraordinarily small, revealing intricate details that are otherwise impossible to discern with conventional light microscopy. But to achieve high resolution over a large sample area, the energy of the electron beams needs to be cranked up, which is costly and detrimental to the sample under observation.