Partner POV | Data Preparation is Key to Expanding the Military Benefits of AI

This article was published C4ISRNET - Media for the Intelligence Age Military and written by Jason Dunn-Potter, Solution Architect for Defense and National Security at Intel, and a retired U.S. Army Chief Warrant Officer 5. 

When it comes to artificial intelligence and edge computing across the U.S. Department of Defense, we're no longer in the talking phase — we're in the doing phase.

That's evidenced by the Joint All Domain Command and Control effort and the recent introduction of the DoD's Chief Digital and Artificial Intelligence Office.

Indeed, we're seeing the positive impact of AI in virtually all areas of industry but with the most potential in defense operations, across all operational domains (Air, Land, Sea, Space & Cyber). Air traffic controllers can now use AI-type analytics to keep track of "good aircraft" (i.e., a passenger jet) and "questionable aircraft" (an unexpected aircraft moving in an abnormal manner, such as traveling at Mach speed or moving like a weather balloon).

Thanks to AI-driven image processing and its ability to analyze different characteristics of objects, including size, weight, speed and more, military commanders can gain better situational awareness and accurately determine whether an object seen from afar or on the radar is innocuous or a possible threat.

All this information can be processed and delivered in near real-time thanks to Field Programmable Gate Arrays, which is a fancy term for CPU accelerators. Commanders and troops operating at the tactical edge can make split-second decisions without having to wait for information to be processed at a centralized cloud location. Using AI in this fashion provides new capabilities to commanders without taking away any of their legacy decision-making tools.

Essentially, AI is enhancing leaders' ability to shorten their OODA Loop cycles and enable real-time decision-making. OODA stands for "Observe, Orient, Decide, Act" — all within a matter of seconds. AI is making it happen right now, giving fighter pilots flying at supersonic speeds and ground units engaged in military operations the information they need, when they need it, to understand what's happening and enabling them to make better decisions on what to do next faster.

And yet these successful use cases are only possible if organizations take the right steps to appropriately prepare their data and AI models.

Here's how to prepare your data for AI

Before you start down the AI road, you need to be clear about your goals and objectives. The bottom line: What problem do you want AI to solve? What information are you seeking? What data is relevant?

Data must be accurate and clean

The less trustworthy the data is upon ingestion, the less accurate the AI's recommendations will be. This is particularly challenging, as so much information is being collected from so many different sensors, each with its own proprietary operating system and metadata formats.

Data must be de-duplicated

Collecting millions of data points can result in duplicate datasets. This can make it difficult to understand which data to prioritize or result in conflicting information that could adversely affect missions: for example, a Medivac pilot engaged in a rescue mission could receive multiple recommendations about where to fly or a troop may end up with the wrong intelligence about the location of an enemy deployment. Also, during HA/DR missions (Humanitarian Assistance or Disaster Recovery), delivering the wrong aid to the wrong place can have ghastly results.

The quantity of data being ingested must be managed 

There may come a point where workloads overwhelm the AI's ability to process data. Thus, it's important for agencies to regulate data ingestion and only include what's necessary. This practice, which we like to call "Digital Discipline," is especially critical when dealing with edge devices that, unlike a centralized cloud, can only manage small chunks of data.

Data must be relevant and timely

For example, a military operation could be relying on multiple radar arrays located in the sky, on a ship and on the ground. While each radar signal is homed in on the same object, data originating from the largest and fastest transmitters will be delivered first. A good AI engine can timestamp the data and differentiate between timely and stale information, with "stale" meaning data that's milliseconds older than more current data. Yet those milliseconds could be what saves troops in the event of an incoming missile strike, which makes ensuring the immediacy of the data critical. In this environment, seconds matter.

Data must be properly formatted 

Data must be formatted correctly so the AI can interpret it effectively. Consider a person who opens the same webpage on Chrome and Edge web browsers yet sees different things; the information being digested is the same, but the way it's presented is different due to data formatting issues. Data must be formatted consistently and properly for the AI to make sense of it and deliver accurate recommendations. Also, building in data converters takes time away from data delivery.

Data integrity must be protected

The appropriate cybersecurity measures must be in place to ensure that data security and integrity are maintained. Once data is compromised, it not only becomes a national security issue but it also compromises trust in the AI system. If commanders' and troops' confidence in the AI is undermined, they will begin second-guessing the computer's recommendations.

AI engine must be trained and kept current on mission objectives

This is crucial to delivering outputs and recommendations to meet your goals. None of the above tactics will be very useful if the AI itself does not understand the mission objectives.

By addressing these points, defense organizations will not only be able to deliver more effective battlefield operations, but they'll also be able to gain better insights into handling events that impact soldiers' and civilians' lives. For instance, the military will have greater situational awareness when it responds to domestic crises, such as a hurricane or other natural disasters. Supply chain managers will be able to pinpoint and predict the availability of the beans, bullets, and band-aids as well as other critical supplies.

In short, when one talks about "military intelligence" today, they're likely referring to how hard-fought information is gleaned and massaged by AI. That intelligence is shining through and will continue to do so if agencies take the right steps to ensure the cleanliness, accuracy and integrity of their data.