As technology advances rapidly, the world is waking up to the reality that artificial intelligence (AI) will be woven into almost every aspect of our lives. Technological advances have also meant that threats have become more sophisticated. The defense industry was one of the earliest adopters of AI, and its appetite shows no signs of waning.
One of the challenges of AI adoption is a lack of data. Machine learning algorithms need data like human beings need air. Without accurate data, an AI system cannot predict consequences or even learn past patterns accurately. In a warzone, such indecision can prove fatal.
Synthetic data has come to the defense industry’s rescue. Here are 5 ways it’s helping defense forces operate more efficiently.
Simulated Warfare Platforms
Minimizing the loss of human life on battlefields is a critical objective that every theater commander wishes to achieve. While Terminator-like robot soldiers are still a fantasy, AI is helping commanders envision tactics and role-play strategies before sending soldiers into combat.
AI has the power to model an endless number of scenarios and provide commanders with data that can help them map various outcomes. There are only so many real-world scenarios that the military can draw from and all of these scenarios cover what has already taken place.
However, the objective of a war simulation is to cover everything that might happen. Synthetic data enriches real-world data and is fed into the AI algorithm. The result is a rich tapestry of scenarios that commanders can rely on.
Another reason why synthetic data is extremely effective is that real-world battlefields are messy. While modern equipment contains a large number of sensors, these instruments are ripe for disruption. In some cases, excessive glare from the sun can lead to incorrect data. Training AI on less than reliable data will only lead to mistakes on the battlefield.
Synthetic data generated from cleaned real-world data sets helps defense commanders model their war zone scenarios more accurately and develop better responses.
Warfare is increasingly conducted online. Many state agencies have weaponized their technical expertise to conduct hacks and intrusions into state assets. Many defense research labs store data in highly secure systems. However, these systems are vulnerable since attack methods have become increasingly sophisticated.
Malicious actors are increasingly using AI to learn a system’s vulnerabilities. Repeated failed attacks don’t indicate a static cybersecurity system’s resilience as much as the fact that attackers are learning more about their victim’s defenses. To counter this, cybersecurity professionals have recommended using continuous monitoring systems that use AI to mimic attackers.
Synthetic data helps cybersecurity teams model a wider variety of scenarios and train their algorithms to recognize more threats. The result is a dynamic security posture that keeps defense systems safe no matter how large the threat is.
Military leaders have been slow to adopt AI for logistics uses primarily due to its perceived ineffectiveness. The problem isn’t with AI as much as it is with the lack of data that the AI can learn from. Synthetic data solves this problem.
Synthetic data providers offer defense departments the ability to create an unlimited number of obstacles and environments. For example, modelers can create a large number of people, animals, objects, urban structures, and weather conditions to model logistics scenarios.
What’s more, they can even combine all of these factors in unlimited ways to generate dynamic environments that can stress test their plans. Synthetic data helps the military remove most of the guesswork surrounding logistics and helps them plan the most efficient routes, instead of sticking to the shortest or most convenient routes by default.
According to researchers, 86% of battlefield deaths occur in the first 30 minutes after injury. AI can help reduce the number of deaths through unmanned ground vehicles to provide aid and perform emergency surgery. The field of unmanned remote surgery is particularly promising since the battlefield isn’t a place one would expect to find highly specialized medical resources.
Synthetic data is helping private contractors and defense departments develop better learning models for AI. In many cases, synthetic data is better than real-world data since more permutations can be coded into the former. The result is a more robust AI that can handle a wider variety of healthcare scenarios.
Robotics and AI are also playing an important role in retrieving wounded personnel. Metal bodied unmanned ground vehicles or UGVs are currently operated remotely and can carry weight up to 500 pounds while reaching a speed of 10 miles per hour. Synthetic data can help these UGVs navigate better paths by training them to recognize battlefield obstacles more efficiently.
The threats that a country faces these days are numerous. More importantly, these threats are self-learning and change their nature constantly. It’s impossible for a human being to be able to sift through raw data and derive the right conclusions all the time.
AI assists in threat analysis by filtering out noise and alerting analysts to relevant data. However, to make this judgment, AI systems need to be feed synthetic data that helps differentiate between a real threat and a false positive. Synthetic data is accelerating the speed with which AI systems are learning this information, and the time isn’t far away when threat detection systems will be fully AI-powered.
Synthetic but Effective
Synthetic data might lead some people to think it’s made-up or somehow unreliable. However, synthetic data is often better than its real-world counterpart, thanks to advanced data modeling and cleaning procedures. The result is a less fatal battlefield and better identification of threats to avoid entering the battlefield, to begin with.