We need to learn our letters before we can learn to read and our numbers before we can learn how to add and subtract. The same principles are true with AI, a team of NYU scientists has shown through laboratory experiments and computational modeling. In their work, researchers found that when recurrent neural networks (RNNs) are first trained on simple cognitive tasks, they are better equipped to handle more difficult and complex ones later on. 

Humans no longer have exclusive control over training social robots to interact effectively, thanks to a new study from the University of Surrey and the University of Hamburg.  

The study, which will be presented at this year’s IEEE International Conference on Robotics and Automation (ICRA), introduces a new simulation method that lets researchers test their social robots without needing human participants, making research faster and scalable.

Early detection of left ventricular systolic dysfunction (LVSD) can identify patients at risk of developing heart failure and yet echocardiography is unavailable for many patients in resource-limited settings. An artificial intelligence (AI)-enabled electrocardiogram (ECG)-based algorithm was investigated as a screening method to detect LVSD in a prospective study of almost 6,000 adult patients who attended healthcare facilities in Kenya. Nearly 1 in 5 participants were found to have LVSD using the AI-ECG algorithm and this method performed well compared with echocardiography.  Larger screening studies with the AI-ECG algorithm are now warranted.