Technical Perspective: Bridging AI with Real-Time Systems

Artificial intelligence (AI) and machine learning models are making progress at an unprecedented rate and have achieved remarkable performance in several specific tasks such as image classification, object detection, automatic control, strategy games, some types of medical diagnoses, and music composition. The exceptional performance of machine learning models in perception tasks makes them very attractive for being adopted in a large variety of autonomous systems, which must process sensory data to understand the environment and react in real time to accomplish a given task. Examples of such autonomous systems include self-driving cars, advanced robots operating in unknown environments, and interplanetary space probes. These systems must not only perceive the objects in the scene and their location with a high accuracy, but they also must predict their trajectories and plan proper actions within stringent timing constraints. Consider, for instance, an autonomous car driving in an urban environment.