As the military tries to enable artificial intelligence on the battlefield, building databases and coding software are only part of the picture. AI goals like the Army's desires for autonomous vehicles or creating "hyper-enabled operators" will require computing systems to become more efficient, military technologists said during a recent panel. The challenge is in deploying "edge computing," where the high-power processors needed to run AI systems are dispersed in the field rather than located only in a central cloud system. Better hardware at the edge means less reliance on communications networks that can be denied, degraded or jammed by enemy forces. For example, sending raw data from the field to a central AI system just for an unmanned vehicle to determine if a road is turning right or left is not realistic, the experts said.

On Saturday, the US Air Force is expected to launch its secret space plane, X-37B, for a long-duration mission in low Earth orbit. The robotic orbiter looks like a smaller version of the space shuttle and has spent nearly eight of the past 10 years in space conducting classified experiments for the military. Almost nothing is known about what X-37B does up there, but ahead of its sixth launch the Air Force gave some rare details about its cargo. In addition to its usual suite of secret military tech, the X-37B will also host a few unclassified experiments during its upcoming sojourn in space. NASA is sending up two experiments to study the effects of radiation on seeds, and the US Air Force Academy is using the space plane to deploy a small research satellite.

In the last decade, artificial intelligence ("AI") applications have exploded across various research sectors, including computer vision, communications and medicine. Now, the rapidly developing technology is making its mark in weather prediction. The fields of atmospheric science and satellite meteorology are ideally suited for the task, offering a rich training ground capable of feeding an AI system's endless appetite for data. Anthony Wimmers is a scientist with the University of Wisconsin–Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) who has been working with AI systems for the last three years. His latest research investigates how an AI model can help improve short-term forecasting (or "nowcasting") of hurricanes. Known as DeepMicroNet, the model uses deep learning, a type of neural network arranged in "deep" interacting layers that finds patterns within a dataset.

The U.S. Navy is testing tiny robot drones that fly in swarms like cicadas to collect data. The CICADs - or'close-in covert autonomous disposable aircrafts - are designed to be cheap enough that a bunch can be dropped simultaneously from the sky and even into storm conditions like hurricanes. The Naval Research Lab has been working on the technology in various ways since 2011, but the focus of this specific iteration - MK5 - is a shape that would allow them to be stackable. The stackable robots disperse to their own GPS coordinates to collect data. Currently, 32 can be released at once. Once landed, they send the data back to the aircraft they were dropped from.