Modern Warfare — AI the military space in 2023
The last global conflict, as horrible as it was, brought us transistors, ball pens, radar, and many other inventions created in face of imminent danger. We are again witnessing an unstable period. In this article, I want to share my views on how the defense industry is rapidly modernizing in 2023, with the widespread adoption of state-of-the-art machine learning.
Since I combine years of experience working in tech as well as wearing a uniform I’m most interested in the impact on the defense sector, but most of this is applicable to other verticals as well.
Logistics and transportation
The last few years brought us back to high-scale and high-intensity conflicts between nations. Before looking into all the science fiction weapons and Black Mirror technology let’s tackle the most overlooked area — logistics. As gen. Pershing said, “Infantry wins battles, logistics win wars”.
Most of the armed forces are part of an alliance of some sort like NATO, BRICS, or EU. This means that any group, from a small multi-national unit to a tank battalion will contain various forms of similar, yet very different equipment. An alliance would have 3–6 different types of main tanks or fighter jets. All of them would need different ammunition, fuel, and spare parts. That’s a quartermaster’s nightmare.
However this is not a new problem, in fact, most retailers and FMCG companies already built massive sets of ML models to solve such issues like:
- identifying the possible impacts of supply disruptions, demand changes, or discrepancies
- automating administrative tasks like filing paperwork
- forecasting customer demand through data-driven data analytics
- analyzing traffic and weather conditions to optimize routes, saving time, fuel, and money
- utilizing smart warehouses like UK’s Ocado
We already see that the defense sector is adopting this technology, but in 2023 this is going to rapidly speed up, as more and more heavy equipment is being transported around the globe and investments in such systems have a huge ROI.
Another big push is happening on the predictive maintenance front. It’s much easier to schedule spare parts delivery when you know exactly when and where they will be needed. Last year already we had digital twins on the NVidia Omniverse platform utilized to train predictive maintenance models for advanced weapon systems to the point where the accuracy is so high that mechanics call it witchcraft.
Intel gathering and cybersecurity
Cyberspace is this day a real domain of conflict alongside sea, air, land, and space. Machine learning is changing the landscape here as well, both on the blue and red team side.
Defense
Security Operations Center — SOC, which the main job is to:
- maintain security monitoring tools
- investigate suspicious activity
- perform routine checks of TTPs
- ensure compliance
is constantly overworked and understaffed, which is even more true in times of conflict. SOCs are already utilizing ML-based tooling for many years, however, the upcoming year is bringing new quality into that space. The research team of SOCCRATES program published an algorithm that was developed for automatically suggesting courses of action to the SOC operator. It analyzes the structure of attack graphs generated from models of the monitored ICT infrastructure and fits defense actions into a specified available budget. This is a crucial capability to win the attrition war with your opponent.
Another interesting spot for AI-driven innovation is ISTAR which stands for intelligence, surveillance, target acquisition, and reconnaissance. It has never been easier to build a data lake with multiple data sources, easily searchable, and with real-time streaming capabilities. Now we can also fully utilize it. IMO 2023 will be a year of multimodal models, which will allow us to combine text, video surveillance, audio recording, and more into a single model. Imagine DALL·E combined with Chat-GPT.
Offense
Sometimes the best defense is a powerful and well-timed offense.
The big disruptor is already mentioned Chat-GPT and tons of similar models like NVidia NeMo Megatron. They were immediately taken by criminals and used to create high-quality phishing and other types of social engineering attacks. We are already preparing for to LLMs be used also in info ops, election meddling, and other similar activities performed by state actors or associated APT groups.
What was earlier possible only as a targeted red team operation will be used this year on a mass scale to achieve strategic and tactical goals with little to no human oversight.
Everyone is trying to weaponize AI, and there is a good reason for that. Just 24% of cybersecurity teams are fully prepared to manage an AI-related attack, according to a recent Gartner survey. What is often overlooked however is that every AI system and every ML model is also a possible attack vector, which is completely new and mostly uncovered by existing security procedures. This can be as simple as malicious attackers finding small variations in the model data inputs that can result in redirected and undesired model outputs. But also more elaborate attacks like data poisoning, transfer learning attacks, or hostile model extraction stealing years of your research work are happening more and more frequently. In my next article, I will describe a full spectrum of attacks against ML models and how a good build MLOps pipeline can help to counter that with ie. inserting noise and intentional randomness into the training data and proper tuning of the model.
Aiming and sighting systems
Today the conflicts are not fought by two lines of soldiers standing in front of each other on an empty field but in highly populated areas where it’s hard to distinguish friend or foe. In order to eradicate civilian casualties a lot of effort is put into enhancing the accuracy of target recognition. This is gaining importance with the growing usage of autonomous weapon systems. The main technology making an impact here is the ability to export lite machine learning models, capable of running directly on the device/weapon system, which allows it to act in near real-time.
Not only identification is important. In target-rich environments, it’s crucial to do correct prioritization, which will use available autonomous weapons to destroy targets that would cause the highest casualties amongst friendly troops.
Another recent advancement in sighting systems is NGSW-FC. Next Generation Squad Weapon — Fire Control is an advanced optic that contains a laser range finder, and a ballistic calculator and provides the shooter with an automatic, adjusted aimpoint. This capability transforms the “average” shooter into a “marksman” capability on the first shot, making him more accurate. What happens this year is the ongoing integration of such scopes into wider battlefield control systems so that firing solutions could be based on the entire dataset of battlefield consciousness instead of a single device point of view.
Drones
Drones are already a common part of the landscape of any modern conflict. They are used to gather information, carry munitions and support ground elements in their operations. This year however their capabilities are going to be greatly increased by two new capabilities. Creation and control of drone swarms and multi-domain operations on network-centric battlefields.
Swarm intelligence research is very advanced now, and its military application into swarms of drones is based on nature — the birds or fish can move together in formation, while also moving individually. And so can UAVs UGVs etc.
Swarm intelligence is pretty simple, and based on three types of movement:
separate — stay a certain distance apart
align — move towards the destination
cohere — try to stay together
Thanks to that there is no need for a single control point or any reliance on connectivity with the operator.
Where it evolves is multi-domain operations with drones. Autonomous Multi-domain Adaptive Swarms-of-Swarms (AMASS) is a new project in development now, where DARPA’s Strategic Technology Office is seeking innovative proposals from industry sources capable of creating a common C2 language meant to protect unmanned, autonomous swarms of different types against adversary anti-access/area-denial capabilities.
The program aims to create a swarms-of-swarms system that will simultaneously counter multiple adversarial assets and enable warfighters to operate within the A2/AD environment. An approach used here is to utilize techniques from the IoT world, like mesh twin learning which I described in a video in a manufacturing context.
Decision support systems
The basics of decision support systems are nicely described by my teammate here so I will focus only on the new things coming into the picture.
DSSs are commonly used now in strategic decision-making, wargames, and tabletop exercises. What we are working on right now is integrating it into real-time tactical decision-making. DSS is trained on data coming from battlefield simulators which are taking into account squad-level tactics and situations. In order to be able to perform reinforcement learning and also give this capability to a single infantryman it integrated with the TAK system which the operator can carry on his plate carrier and see hints about a mission at hand, probabilities of success of certain actions, and dynamically adapted priorities and mission parameters. All of the data from the unit is later fed back to the MLOps pipeline and used to improve the DSS iteration by iteration.
Enhanced combat casualty care
War is a dangerous game, and in order to lower the risk we invest heavily in combat casualty care. There are two systems developed right now that caught my attention.
The first one is automated triage and evacuation planning. Whenever someone gets injured and needs specialized help the MEDEVAC is called. From a data science point of view, it’s a good data source being a standardized 9-line format. So automated voice recognition models are used to get the request in real time into the system when medevac is being called, and a team with proper equipment, transportation means, and security is automatically dispatched taking into account all external priorities. This allows a soldier to get help quickly in a “Golden Hour”. The first hour after the occurrence of a traumatic injury is considered the most critical for the emergency stabilization of a casualty.
A big factor impacting survivability is also immediate care, delivered typically under TCCC — Tactical Combat Casualty Care. An issue here is that not everyone can be a trained medical professional, with all the knowledge of a world-class surgeon. But everyone can talk to a Chat-GPT-like model over the radio, describe patient conditions and get the best possible advice on how to proceed. This turns everyone with a radio into an AI-enhanced combat medic doing clinical reasoning in the same way as top doctors. And there is no limit to such calls, each and every injury gets proper attention and advice. Such systems are currently tested in real conditions and I can’t wait to publish the overall results but even an initial look shows a great life-saving potential of such an approach.
Conclusions
Key decision-makers on the state level understood that technological superiority is a key to controlling a modern battlefield. Widespread adoption and dynamic improvements of AI-based capabilities are already in motion and only going to speed up. The key factor is now the speed of translation of ML models, data platforms, and other tools from the civilian world into the public sector. Open source technologies are a key enabler to getting the latest and greatest software, benefiting from innovation yet still staying in control, being able to audit the source code, and trusting the automation in charge of powerful weapons.
