{"id":16237,"date":"2018-05-01T20:04:34","date_gmt":"2018-05-01T20:04:34","guid":{"rendered":"https:\/\/one.sightlinemg.com\/c4isrnet\/uncategorized\/2018\/05\/01\/heres-how-intelligence-will-take-advantage-of-machine-learning-and-ai\/"},"modified":"2026-08-08T17:55:50","modified_gmt":"2026-08-08T17:55:50","slug":"heres-how-intelligence-will-take-advantage-of-machine-learning-and-ai","status":"publish","type":"post","link":"https:\/\/one.sightlinemg.com\/c4isrnet\/intel-geoint\/2018\/05\/01\/heres-how-intelligence-will-take-advantage-of-machine-learning-and-ai\/","title":{"rendered":"Here\u2019s how intelligence agencies will take advantage of machine learning and AI"},"content":{"rendered":"\n

Imagine an analyst tasked with watching hours of video footage or poring over thousands of images searching for a particular object. What if a machine could process this data for the analyst in seconds, freeing the analyst to perform more critical thinking rather than searching databases? <\/p>\n\n\n\n

Advancements in automation, machine learning and artificial intelligence are increasingly serving this purpose of \u201cunburdening\u201d analysts. With greater interest and adoption of these technologies into operations, there is more clarity of how they will work.<\/p>\n\n\n\n

Leveraging these tools will allow staffers to get back to performing analysis rather than poring over the data itself. But in interviews with industry representatives, what\u2019s becoming clearer is exactly how intelligence agencies can use these new technologies. In short: machines will pre-sort or manipulate data and then present its findings to a human for additional analysis, weeding out hours of manual labor.<\/p>\n\n\n\n

Critical to this development is understanding the difference between artificial intelligence, automation and machine learning. <\/p>\n\n\n\n

\u201cI consider AI to be cognition, independent cognition. A true AI would be cognizant of itself and be able to create and create from nothing,\u201d Rob Shaughnessy, senior manager at Deloitte, told C4ISRNET at the annual GEOINT symposium in Tampa, Florida. He noted that industry hasn\u2019t seen this characteristic yet. With machine learning, \u201cit can teach itself, but it\u2019s teaching itself within a tightly defined lane \u2026 It\u2019s being given boundaries to work in and learning within those boundaries.\u201d
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Shaughnessy added that machine learning is great for getting a machine to provide curated intelligence information to an analyst. It\u2019s not so great at making decisions. AI, on the other hand, will make an absolute decision or the best decision.<\/p>\n\n\n\n

However, \u201cyou don\u2019t always want to make the best decision. There could be political reasons to not make what is the best logical decision. That\u2019s for humans to do and that will always be for humans to do,\u201d he said.<\/p>\n\n\n\n

Some described the act of sifting through data to get to a decision as layered.<\/p>\n\n\n\n

\u201cWe look at analytics as, after you have the data, what\u2019s the intelligence you can get out of that data? Most of those analytics is how, with software, you can automatically produce some of these insights,\u201d Jane Chappell, vice president of global intelligence solutions at Raytheon, told C4ISRNET at the same conference. \u201cMachine learning, then, is just after you\u2019ve done that and seen, \u2018hey six times in a row when this happens, then that happens.\u2019 That\u2019s really the machine learning effort.<\/p>\n\n\n\n

\u201cThen AI \u2015 which really in this industry we\u2019re really not at an AI standpoint yet \u2015 that\u2019s just the code thinking on its own. We\u2019re really not to the point right now. We\u2019re [not] applying AI at any kind of scale in industry.\u201d<\/p>\n\n\n\n

After all, content or data is just that until it is turned into intelligence.<\/p>\n\n\n\n

What can machine learning do for analysts? <\/b><\/p>\n\n\n\n

\u201cYou can collect picture of \u2018x\u2019 for forever but if you\u2019re not making sense out of any of it, you just have pictures of \u2018x\u2019 and that doesn\u2019t help anybody,\u201d Mike Manzo, director of intelligence, threat and analytic solutions at General Dynamics told C4ISRNET at GEOINT. \u201cIt\u2019s content until we make sense of it. That sense could be made with a machine or that sense could be made with an analyst. Where it\u2019s heading is it\u2019s going to be made with both. Where the machine is really going through the monotony part and the analyst is really providing that values added piece and that\u2019s really where things are going.\u201d<\/p>\n\n\n\n

Officials within the U.S. government have stressed that these new processes are not designed to eliminate humans, but to make them better at their job. And it\u2019s imperative to leverage emerging technology to help make sense of the deluge of data in order to drive insights.<\/p>\n\n\n\n

\u201cAutomated intelligence using machines, I call it that way because I\u2019m not interested in the technology artificial intelligence, I\u2019m interested in the outcomes we achieve,\u201d Principal Deputy Director of National Intelligence Sue Gordon, said during a keynote presentation at the show.<\/p>\n\n\n\n

Gordon praised the National Geospatial Intelligence Agency and its director for its aggressive adoption of these technologies. During the conference, NGA Director Robert Cardillo announced a robust goal of applying automation, artificial intelligence and augmentation to every image NGA ingests by the end of this year.<\/p>\n\n\n\n

In its most simple form, leaders want these technologies to sift through the data and alert analysts of something significant.<\/p>\n\n\n\n

\u201cSpecific to a customer, like here at GEOINT and NGA, analysts are poring through mounds and mounds of data. We need to get the analyst focused on the analysis. Looking at what\u2019s important to determine what the best courses of actions are whether they\u2019re national security or policy or the warfighter,\u201d Damian DiPippa, senior vice president\/general manager mission and intelligence solutions business unit at ManTech told C4ISRNET.<\/p>\n\n\n\n

Manzo explained that the key is finding pinpoints from an analyst perspective having systems automated in the background to enable them to get to what they want to do, which is their job.<\/p>\n\n\n\n

\u201cWhat we\u2019re hearing from them is I want to be an analyst, I don\u2019t want to be a data gatherer, I don\u2019t want to be a discoverer,\u201d he said.<\/p>\n\n\n\n

Manzo continued; \u201cI want to know my data is pre-populated for me or is pre-stationed for me so when I get in in the morning I\u2019m not spending two-thirds of my day hunting for the things I know I need to do my job \u2026 I found all my data, so that\u2019s two-thirds of my day, now I have one-third of my day just to go through it all and by the time my day\u2019s finished I haven\u2019t actually gotten that far because I\u2019ve spent most of it looking for and now discovering.\u201d<\/p>\n\n\n\n

Manzo described how a human, working through a machine, might work in practice through the lens of an analyst tasked with monitoring North Korean missile launches. The analyst knows in order for a launch to occur a clear space has to be found, a transporter erector launcher has to be present along with fuel trucks and maybe even people that aren\u2019t normally in a particular area.<\/p>\n\n\n\n

The machine can look at imagery data and when certain thresholds are met, alert the human. The human analyst can then take control back after receiving an alert, and using their critical thinking and expertise determining if in fact what is being observed is preparation for a launch or simply a training exercise.<\/p>\n\n\n\n

Deloitte, similarly, displayed a machine learning capability that provided a great deal of insights from a video in a matter of seconds. The system was able to provide frame by frame analysis, object detection of objects in the video as well as concept detections such as power or time. The system can then provide timestamps of when these items happened in the video as well as a full timestamped transcript saving the analyst the time of watching the full video.<\/p>\n\n\n\n

Additionally, Deloitte demoed what Shaughnessy described as one of the simplest examples to look at but one of the hardest things to do from a machine learning perspective. The machine was able to learn what an M1 Abrahams tank was based on a video of an Abrams spinning around. Moreover, the machine was able to begin to pick out unique identifiers of the tank, such as its tracks, and identify a different variant of the vehicle.<\/p>\n\n\n\n

By having the machine identify tanks in pictures or full motion video, Shaughnessy said this makes a human\u2019s job not just easier, but it changes it because the analysts can ask more complex and better questions. <\/p>\n\n\n\n

Unburdening the analyst<\/b><\/p>\n\n\n\n

A more startling example of problems technology can solve for analysts is observing behavior at a fixed location. Sometimes, analysts have to stare at a particular building or compound monitoring every vehicle and person that enters and track where they go when they leave, Josh Nauman, chief engineer at Harris Geospatial Solutions, told C4ISRNET. While the analyst is following a vehicle out of the compound, they have no idea what else is going in and out of that building, so then they have to go back to where they started and watch it again.<\/p>\n\n\n\n

Harris is providing solutions that allow for the machine to monitor full motion video and imagery feeds to stare at the compound and track vehicles and persons going in and out alerting the analyst of all the instances. The system can also identify:<\/p>\n\n\n\n