June 21, 2022
Imagining artificial intelligence (AI) military applications can make one imagine scenarios like those in the Terminator movies, but in reality, AI solutions for defense are much more mundane and focused on improving decision-making for humans, whether aircraft maintenance personnel; pilots; or Intelligence, Surveillance, and Reconnaissance (ISR) analysts, says John Canipe, business development manager, Air Force, at SparkCognition Government Systems, in a conversation we had at his company’s headquarters in Austin, in Texas. We also discussed the difference between AI and machine learning (ML), how AI is applied in several military fields, and more. Edited excerpts follow.
MCHALE: Please provide a brief description of your responsibility within SparkCognition Government Systems and your group’s role within the company.
CANIPÉ: As Director of Business Development, Air Force, my current responsibilities are product development, capture management, product pricing/licensing, and new and recurring sales generation.
MCHALE: We often see AI/ML [artificial intelligence/machine learning] in the same sentence, or used to describe the same thing, but what is the actual difference between AI and ML?
CANIPÉ: Differentiating AI from ML is a struggle everyone is having right now. We see AI as a broad umbrella term, with ML as the heartbeat of AI, enabling real applications of putting data and getting a result, rather than data and tools. That’s why we call SparkCognition and SparkCognition Government Systems (SGS) machine learning companies.
Speaking of both, it helps if we remove the fictional AI in movies like “Terminator” from the discussion, because that’s just fantasy and not reality. Instead, we need to focus AI on solving critical behind-the-scenes issues our military faces, like search functionality in the depot maintenance world. Although it’s not a Hollywood title, it’s still vital for mission prep. This significantly reduces aircraft downtime. This allows this maintainer to look back in time and see what happened, how it happened, and extract that data quickly. Without this capability, such problem solving could take a week or more, costing time and money.
MCHALE: What design trends and requirements are driving AI innovation in military applications?
CANIPÉ: There are many opportunities to improve the current legacy systems the military uses. These platforms and systems are not going away. By adding AI solutions to already existing platforms to drive cognitive capabilities, data filtering, etc. it extends the life of the platform while improving its capabilities. An example of a program addressing this concept is the Kaiju project, which explores AI solutions to integrate cognitive electronic warfare (EW) at the edge.
There is a big push towards improving aircraft readiness. How to solve this manager-level readiness challenge with AI is the million dollar question. Achieving a maintenance readiness above 50% to 60% will impact the entire defense community.
For deployed applications, AI requirements will focus on battlespace management scenarios to accelerate combatant decision-making at the edge of battle. This is done by AI algorithms that help filter the ISR [intelligence, surveillance, and reconnaissance] data close to the sensor, so that the human operator monitoring the flow of an unmanned aerial system (UAS) sensor module – for example – can more quickly decide what information is actionable and relay that actionable information to commanders on the terrain, accelerating the sensor to the shooter process.
This amounts to accelerating human decision-making – whether the human in question is a depot-level maintainer, a fighter pilot, or an ISR specialist analyzing data from a UAS sensor. There are so many decisions to make that can overwhelm the cognitive load of a human mind.
MCHALE: How does AI improve autonomy?
CANIPÉ: From an F-35 pilot’s perspective, the goal will be to have a self-contained wingman, but with instructions on what and how to attack a target. The autonomous wingman will lighten the cognitive burden of the F-35 pilot, thereby improving decision-making speed. Over the past few years we have seen research and development around this technology. Funding has focused on prototyping to see if these capabilities are actually feasible. The Kaiju project has done a lot of this kind of R&D, prototyping, and testing.
MCHALE: Predictive maintenance, decision making, and autonomous navigation are three of the best-known AI capabilities. Are there others the US Department of Defense (DoD) is investing in?
CANIPÉ: The DoD is focused on updating most of its software and evaluating its current processes. This, in turn, will give the DoD a great opportunity to take advantage of new technologies. In the spirit of speed, however, we build our solutions to integrate with existing systems, enabling a seamless user experience and helping the DoD reduce additional software costs.
MCHALE: What are the weak points of acquisition with AI? Are they technological? Bureaucratic?
CANIPÉ: AI is a software-as-a-service (SaaS) model, which is a new mindset for government. The DoD is used to owning the technology it purchases, such as a tank or aircraft, and the electronics on board the platform, such as computer hardware or software operating systems. It is not a SaaS model. Some in government are still trying to figure out the cost of maintaining an AI SaaS product, asking questions like: How do I acquire AI? Where is the IP address? Who owns it? Can we create it ourselves?
MCHALE: How has military technology changed since you served as a pilot in the Air Force and how is AI enabling this evolution?
CANIPÉ: Since 2018, there has been a cultural shift within the Air Force, from a culture of procurement and quick wins to a culture of innovation, so that they are not left behind. It is a longer term strategy. It’s amazing to see the change over the past few years. What’s critical is that the push for innovation comes from the top, from Air Force leaders down to individual Airmen.
MCHALE: SparkCognition founder Amir Husain said AI can be applied to every stage of warfare and almost any activity. How is the DoD progressing in mastering these areas? Where should the emphasis be?
CANIPÉ: AI can and is applied in all areas of warfare, and it can be an exciting reflection on future possibilities and applications. However, the focus should not be on the exciting projects that grab the headlines. Instead, we believe the DoD will realize exponential value by focusing on smaller wins that may be less appealing to a wider audience, but will deliver real ROI in cost savings, speed of decision and preparation for the DoD mission. These small gains will pave the way for some of these most exciting projects when the DoD has a more intimate understanding of the procurement and deployment cycles of AI solutions.
MCHALE: In the future, what disruptive technology or innovation will be a game-changer in AI/ML? Predict the future.
CANIPÉ: The founder of my former company in the oil and gas industry said that the future winners will be those who can bring all the data together in one place and make sense of it. It was about 10 years ago. This statement is true today. A major challenge currently facing the DoD is the dispersion of data, spread across many different databases and stakeholders. The goal is to streamline a decision maker’s access to the right data and ensure that the right protocols are in place to act on that data. Once this access is unlocked, the “unknown unknowns” will be easier to identify and deal with, unleashing innovation across the DoD.