Impact of deep learning on computer vision


The technological challenges that must be addressed before autonomous cars can be unleashed onto the streets are quite significant. Using deep learning techniques, the computer can look at hundreds and thousands of pictures, e.g., an electric guitar, and start to learn what an electric guitar looks like in different configurations, contexts, levels of daylight, backgrounds and environments. Sitting behind all this intelligence are neural networks; computer models that are designed to mimic our understanding of how the human brain works. The following year there were of course multiple deep learning models and Microsoft broke records recently when its machine was able to beat their human control subject in the challenge.

Waze for War: How the Army Can Integrate Artificial Intelligence


Machine learning software agents isolate images of potential Russian covert elements agitating protests, cross referencing cell phone pictures posted on social media with police traffic cameras, and more sensitive collection platforms. While many commercial applications of artificial intelligence are based on identifying patterns and trends using big data, most military applications focus on autonomous systems. Existing artificial intelligence programs in the Department of Defense include Navy unmanned undersea and aerial vehicle programs such as the Low-Cost Unmanned Aerial Vehicle Swarming Technology (LOCUST), and Air Force/DARPA ventures such as the Gremlin anti-surface-to-air missile drone program. Concepts range from larger logistics convoys composed of one manned vehicle and a large number of autonomous vehicles to combat formations mixing manned and unmanned platforms.