A new app, which will be used to collect data to develop machine learning algorithms that could automatically detect whether a person is suffering from COVID-19 based on the sound of their voice, their breathing and coughing, has been launched by researchers at the University of Cambridge.
Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.
As countries adopt different approaches, the question remains: how best to enact a lockdown without compromising critical food supply chains in the short and longer term?
On 22nd March 2020, Cambridge University Botanic Garden closed its gates to protect visitors and staff during the global coronavirus pandemic. Coinciding almost exactly with the start of Spring, this felt like a particularly cruel blow.
Those on low incomes are also more likely to have lost jobs or pay, and less able to complete work tasks from home. Researchers warn the COVID-19 downturn is likely to “increase inequality between young and old”.
Following from last week's call for governments to use machine learning and AI techniques to help in the fight against the COVID-19 pandemic, Professor Mihaela van der Schaar gives an update on a working proof of concept she has built using anonymised data from Public Health England.
A new rapid diagnostic test for COVID-19, developed by a University of Cambridge spinout company and capable of diagnosing the infection in under 90 minutes, is being deployed at Cambridge hospitals, ahead of being launched in hospitals nationwide.
Four researchers at the University of Cambridge have won advanced grants from the European Research Council (ERC), Europe’s premier research funding body.