The Defense Advanced Research Projects Agencies is looking to apply the same kind of machine learning to the radio spectrum as is used by advanced systems for applications ranging from voice recognition to management of internet-of-things devices to autonomous vehicles. DARPA has issued a broad agency announcement for a new Radio Frequency Machine Learning Systems (RFMLS) program that will address the need for enhanced situational awareness regarding the ever-changing composition of RF signals in the IoT and spectrum sharing. Machine learning is widely used to manage data and images, but the similar work in the radio spectrum offers unique challenges, making a more compelling case for developing a native approach. "What I am imagining is the ability of an RF machine learning system to see and understand the composition of the radio frequency spectrum – the kinds of signals occupying it, differentiating those that are'important' from the background, and identifying those that don't follow the rules," said DARPA's Microsystems Technology Office Program Manager Paul Tilghman. An RFMLS would be able to discern subtle differences in the RF signals among identical, mass-manufactured IoT devices and identify signals intended to spoof or hack into these devices.