HUMANIKRONThe U.S. Army is actively adopting artificial-intelligence algorithms and Data Science methods to analyse the streams of data coming from border sensors in real time. This makes it possible to automate the detection of potential threats and anomalies, greatly cutting the reaction time to critical events compared with traditional monitoring methods.
My take: For developers this case is a classic example of solving the "information overload" problem. In complex data-processing systems the core issue is often not a shortage of information but an excess of noise that hides the critical signals. For business it means a chance to move from a reactive model to a preventive one: instead of reacting to an event after it is recorded, the system should predict the probability of an incident from patterns. In my own practice I will use this approach to build intelligent filters in complex monitoring systems. My goal is an architecture where AI cuts off false-positive signals at the stage of collecting and pre-processing data, letting end users or operators focus only on the scenarios that need immediate intervention. Such an approach significantly optimises human-resource costs and raises the overall efficiency of a security or logistics system by automating routine analytics.
Source: army.mil. Original →