How Artificial Intelligence Is Transforming the Intelligence Community

By Elana Lyn Gross, Contributor

The United States intelligence community analyzes millions of data points a day to solve crimes and proactively prevent them. But the process of reviewing these data points — including mugshots, satellite images, and malware threats — is time-consuming for human analysts who are also responsible for producing high-level analysis. In response, The United States intelligence community has been developing artificial intelligence to help analysts more efficiently and effectively evaluate data.

Robert Cardillo, the director of the National Geospatial-Intelligence Agency (NGA), is a prime example. He reportedly wants robots to perform 75 percent of the tasks currently completed by intelligence analysts who are responsible for collecting and interpreting images from drones, satellites, and other devices. And in 2016, Barack Obama’s White House released a white paper with strategic recommendations for increasing the government’s use of artificial intelligence. Government organizations, including the NGA, Central Intelligence Agency (CIA), and the Federal Bureau of Investigation (FBI), all actively built or used AI technology to improve the data analysis process in 2017.

Here’s a look at how these organizations are transforming operations using AI.

The NGA

NGA analysts currently evaluate millions of images from drones and satellites to identify site changes that could indicate potential threats, such as military testing. “A significant chunk of the time, I will send [my employees] to a dark room to look at TV monitors to do national security essential work,” Cardillo told reporters, including Foreign Policy. “But, boy, is it inefficient.”

Cardillo predicts that the amount of data will continue to increase and wants to use machine learning technology to interpret the images more efficiently. “Instead of analysts staring at millions of images of coastlines and beachfronts, computers could digitally pore over images, calculating baselines for elevation and other features of the landscape,” Foreign Policy reports. “NGA’s goal is to establish a ‘pattern of life’ for the surfaces of the Earth to be able to detect when that pattern changes, rather than looking for specific people or objects.”

In 2017, the NGA awarded four contracts — totaling nearly $2 million — to advance its use of AI and automation, according to a statement from the agency. Although the contracts have different core objectives, the overarching goal is to use technology to effectively and efficiently analyze large quantities of data. The contracts will enable task automation, identify similarities between analysts’ projects to foster collaboration, use machine learning to recommend hypotheses and strategies, and sort through data to alert analysts to pertinent information. “This research provides NGA with great opportunities to explore how humans and machines can team together to sift, sort, and process in a data rich environment,” Amanda Weaver, a senior geospatial intelligence officer, said in the statement. “Analysts seek new, different and efficient ways to process information for customer consequence,” she added.

The CIA

Last September, the CIA had 137 artificial intelligence pilot projects in progress, according to Dawn Meyerriecks, the CIA’s deputy director for science and technology. Meyerriecks revealed the CIA’s AI plans at the 2017 Intelligence and National Security Summit. The organization also has its own investment capital arm, In-Q-Tel, according to Business Insider reporting. The technology that the CIA invests in draws insights into the type of artificial intelligence the CIA is using or hopes to use. For example, Cylanceuses artificial intelligence and machine learning to identify malware and stop it in less than one second, and Orbital Insight provides large-scale analysis of satellite imagery.

The FBI

Like the CIA and NGA, the FBI is using artificial intelligence to analyze large data sets faster than human analysts could. Kimberly J. Del Greco, the deputy assistant director of the criminal justice information services division at the organization, explained the technology in a statement before the House Committee on Oversight and Government Reform in early 2017.

The FBI’s Next Generation Identification system maintains a mugshot repository called the Interstate Photo System (IPS), she said. Authorized local, state, tribal, and federal law enforcement agencies can run automated facial recognition (FR) searches. “The automated FR software uses pattern-matching approaches developed within the field of computer vision,” Del Greco said. “The algorithm performance is entirely dependent upon the patterns that the algorithm developer finds to be most useful for matching. The patterns used in automated FR algorithms do not correlate to obvious anatomical features such as the eyes, nose or mouth in a one-to-one manner, although they are affected by these features. ” she added. A law enforcement agent submits a “probe” photo, and the system returns two to 50 “candidate” photos of people who may be a match. Del Greco reported that tests conducted by the FBI have shown that the process “returns the correct candidate a minimum of 85 percent of the time within the top 50 candidates.” The agent is responsible for reviewing the photos further to check if it is a match.

Future Intelligence

It’s likely that the United States government’s use of AI will only increase. At the request of the research agency of the Office of the Director of National Intelligence, Harvard’s Belfer Center for Science and International Affairs published a 132-page report detailing proposed policy initiatives. “Future progress in artificial intelligence has the potential to be a transformative national security technology, on a par with nuclear weapons, aircraft, computers, and biotech,” the authors wrote.

While some members of the intelligence community have expressed fear that AI technology will replace jobs or fail completely, the intelligence community’s artificial intelligence plans currently have one important thing in common: machines and humans working in tandem, with humans still responsible for oversight and strategic thinking.