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Accurate processing of covariance information related to environmental variations and sensor noise is paramount to the performance of statistics-based estimators and is the key enabler for optimally combining information originating from multiple heterogeneous sensors and subsystems. 

The U.S. Defense Advanced Research Projects Agency (DARPA) is interested in organizing information from separate sensors in an effort to enhance artificial intelligence (AI) and machine learning technologies. DARPA has recently published its Enabling Confidence project of the Artificial Intelligence Exploration (AIE) program for this purpose.

Covariance is a measure of how two random variables differ; if the greater values of one mainly correspond with the greater values of the other — and the lesser variables tend to show similar behavior — the covariance is positive. Conversely, when the greater values of one variable mainly correspond to the lesser values of the other, the covariance is negative. According to, the Enabling Confidence project will develop scalable ways to generate covariance information for machine learning systems to enhance performance when combining several subsystems. It encourages using machine learning approaches like deep learning and Bayesian techniques.

DARPA researchers want to know if a machine learning system output covariance can reflect sensor and environmental covariance information. 

They want to evaluate if computer scientists can develop machine learning subsystems hierarchically to increase inference accuracy? Can systems integrators combine these kinds of machine learning systems with statistics-based estimation, like Kalman filters, to reduce errors?

The AIE program is one key element of DARPA’s broader AI investment strategy that will help ensure the U.S. maintains a technological advantage in this critical area. Past DARPA AI investments facilitated the advancement of “first wave” (rule based) and “second wave” (statistical learning based) AI technologies, according to