The US National Geospatial Intelligence Agency (NGA) has adopted a new strategy to deal with a huge amount of data coming from inside and outside, and this strategy relies on new methods and technologies, including artificial intelligence.
NGA, an intelligence support agency of the US Department of Defense, analyzes information and images from satellites collected by government and military agencies, including the National Reconnaissance Office, and turns it into concrete intelligence, Defense News reported.
“The growth in geospatial intelligence data from government and commercial sources in the United States and around the world has become astounding,” said agency director David Sharp, who was speaking at a symposium in Missouri on Wednesday about the features of this new strategy. One of the biggest challenges we face: managing all the data.”
To deal with these challenges, he said, the agency has adopted a new data strategy “to direct its efforts to develop technologies and methods needed to address the flow of information.”
Among the goals of this transformation are to “make data accessible and improve its reuse,” and “to securely create, manage, and share trusted data with the speed and accuracy that our customers’ missions require.”
He spoke of “improving data assets so that they can be easily reused for expected and unexpected purposes.”
He said the agency would focus heavily on artificial intelligence and machine learning “to enhance our productive capacity.”
The site’s report indicated that former officials at the agency noted that the amount of data that reaches the agency daily is so huge that it is impossible for human analysts to process it on their own.
To carry out its mission, the agency needs to “automate” a lot of analyst jobs using machine learning so that humans can focus on more difficult problems.
Part of the strategy is driven by the Ministry of Defense’s pursuit of the use of artificial intelligence to quickly identify and respond to combat targets, according to the report.
“Warfighters (military commands) are challenging us to be able to get machines to understand where data is going, how fast, what it is, and drive it through infrastructure like a smart content delivery network,” Sharp said.