Researchers at the Massachusetts Institute of Technology believe they've found a way to determine if people have coronavirus based on the sound of their cough through artificial intelligence. The human ear is incapable of picking up on the acoustic variations of a coronavirus cough, but AI can, the researchers say. The technology could be a could indicator of whether or not someone should get tested for the virus, even if they don't have symptoms. A team of researchers began collecting audio recordings of coughs in April. The samples were voluntarily submitted on a website using a cell phone and laptop.

Ian Goodfellow is an author of the popular textbook on deep learning (simply titled "Deep Learning"). He invented Generative Adversarial Networks (GANs) and with his 2014 paper is responsible for launching the incredible growth of research on GANs. He got his BS and MS at Stanford, his PhD at University of Montreal with Yoshua Bengio and Aaron Courville. He held several research positions including at OpenAI, Google Brain, and now at Apple as director of machine learning. This recording happened while Ian was still at Google Brain.

A new artificial intelligence (AI) system being developed at MIT's Computer Science and Artificial Intelligence Laboratory is being trained by researchers to aid humans in identifying potential cyber-attacks. Typically, when trying to pinpoint possible attacks, analysts are required to sift through massive amounts of data to find abnormalities and discrepancies--a method that is time-consuming and tedious. Anchored on the idea that AI never gets tired, the new computer based method means that humans can identify cyber-attacks more efficiently. AI2 for instance--MIT's new system, which honed its ability to identify threats after reviewing three months worth of log data from an unidentified ecommerce platform--can review millions of log lines every day. Once it spots something suspicious, a human can then take over and promptly check for possible signs of a security breach.