The Swiss Armed Forces and armasuisse: Together from the idea to the product
The Competence Centre Artificial Intelligence and Simulation (AISI) develops innovative solutions for organisations of national security. The latest example is an AI tool named NJÖRD, developed together with the Cyber-Defence Campus of armasuisse S+T for the Swiss Armed Forces. What used to take several days now works in just a few minutes. But what is NJÖRD and what is an inter-agency cooperation like?
Matthias Sommer, Competence Centre Artificial Intelligence and Simulation,
Lucas Ballerstedt, Staff, armasuisse Science and Technology
An AI tool named NJÖRD emerged as part of an inter-agency cooperation between the Federal Office for Defence Procurement armasuisse and the Swiss Armed Forces. NJÖRD is a good example of a successful cooperation. From the idea to the needs assessment and the development of a prototype to the final product development. But what is behind all of this? The area of Operational Studies and Training in the Swiss Armed Forces is also tasked with planning and conducting exercises at military strategic level. This also includes online exercises in simulations. Here, scenarios are executed in the programme, to which the participants in the exercise have to react. To enable a training environment that is as realistic as possible, these scenarios are supplemented with messages. These messages are intended to influence the decisions of the participants. In an ideal case, this means that each exercise is unpredictable for the participants and appears realistic. However, the problem is that these messages have to be created manually, which requires using a considerable amount of resources. In the NJÖRD project, the use of Large Language Models (LLM) was examined for creating these messages.
An LLM is a model of artificial intelligence (AI) and its purpose is to create and understand a text in a human-like manner. The understanding of LLMs is fundamentally different from the human understanding of language. Because AI does not distinguish between letters, words or sentences as they are interpreted by persons. Instead, LLMs use probability calculations and neural networks to find out how text modules are combined with each other. By using large quantities of text as training material, the probability with which a particular text module follows another text module can be calculated.
From the requirement to the demonstrator
Specifically, these messages must imitate the messages from message agencies in simulations and, depending on the exercise, cover various languages. Generating these messages demands considerable resources and can take up several days. The requirement was thus recognised to generate these messages via an LLM, in other words, an AI application, and armasuisse S+T was tasked with a corresponding feasibility study. In the study, a specific simulation scenario was used, consisting of 75 different events. Supplementary messages had to be generated for 73 of these events.
The approximate number of messages to be generated varied between five and thirty according to event. The messages were to be generated in different languages, including English, German, Russian, Finnish, Swedish, Norwegian, Estonian, Lithuanian, Latvian and French. There are thus a considerable number of fictitious messages to be produced. Four open source models were tested for the generation of the texts. Open source models are publicly accessible systems which can be further developed if necessary. The systems could thus be adapted to the needs of the Swiss Armed Forces and developed into a prototype. The specific end product consists of an application in which the exercise instructor could produce AI-generated messages on a graphical user interface (GUI) with just a few clicks. In this self-developed software, exercise instructors only need to enter a few core elements, and within a few seconds a fictitious message from a message agency appears. Thus for example, using the data “Europe-wide interference in the radar network in civil aviation leads to irregular interruptions in the military radar network. However, military network mostly stable.” the following fictitious message is created:
“An unexpected interruption in the military radar network of the Swiss Armed Forces was reported today. According to sources, short-term outages occurred in several regional control centres, while most systems remained stable. The responsible parties report technical errors and a possible load on the communication connections as the main causes. The defence ministry established that the network was widely intact and did not present a threat to national security. Civilian air transport radar continues to remain affected due to Europe-wide interference, but the network does not affect the military situation. Further investigations are pending. The task forces are working on rapid troubleshooting and are checking long-term solutions immediately without delay”.
From demonstrator to product development
The results of the feasibility study and the application demonstrator have proved to be of considerable use for the Swiss Armed Forces. What used to take several days can now be produced in just a few minutes. This success was possible thanks to the close cooperation between the Swiss Armed Forces and armasuisse S+T. In a further step, possible industry partners were sought to further develop the application demonstrator into a usable AI tool for the Swiss Armed Forces. Because a demonstrator is only used to demonstrate the intended purpose and its benefits and is not intended as an operative tool. Such a partner was found in the company IBM.
Interview with Miltiade Athanasiou
For us, it was important to know how we could get from “we want to” to “we can” to “we will do”.
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About Miltiade Athanasiou
Miltiade Athanasiou has been employed by the Confederation for over fifteen years. Following various other positions in Joint Operations Command at the Federal Criminal Police fedpol and in the Cyber Command project, he now works in Operational Studies and Training. He was the customer in the NJÖRD project. In his role, he represents the needs of the Swiss Armed Forces and was very closely involved in the project and in the cooperation with armasuisse S+T. He reflects on the project in the interview below.
Miltiade, how did you come to be the customer in the project for the Armed Forces?
Operational Studies and Training (op S) has various mandates. One of these concerns the planning and execution of training at military strategic level. In this context, it quickly became clear to us that we could use new technologies to support one of our tasks.
How did you recognise the need for such an AI model and where does this come from?
The requirement sprung mainly from the following two conclusions: First of all, AI enables us to generate a large number of open source messages based on a particular scenario in our work. Up to now, this work was performed manually, which means that each message was created individually. Secondly, we are a small (but excellent) team and have limited resources when it comes to performing strategic exercises.
Can you explain to us what the cooperation with armasuisse S+T was effectively like? How do you develop an AI-based application together, tailored to your requirements?
We started with nothing, but with a pretty good idea of our requirements. The cooperation with armasuisse S+T initially consisted of acquiring a better understanding of the current options of AI. It was therefore about benefiting from the expertise of armasuisse S+T. The next phase consisted of designing a first demonstrator quickly. This was decisive, because in this phase the fundamental questions were posed, in particular regarding the scope of the project. Over the entire project – which is not yet concluded – the close operation with armasuisse S+T was of utmost importance to us.
What is the added value of this AI-based application for your team?
The main added value is the time saved creating open source messages as part of the exercises. The result is very positive: We can now create hundreds of high-quality messages in French, German, Italian and English in just a few minutes, for which we previously needed several days.
Looking back, how do you assess the cooperation with armasuisse S+T?
The cooperation was very good, particularly with the project managers. I appreciated the short reaction times, as well as the simplicity of the result-oriented exchange. For us, it was important to know how we could get from “we want to” to “we can” to “we will do”. So it was a matter of researching together, contributing ideas and proposing realistic solutions.
The product is now under development. What are the next steps?
The work is currently being continued with IBM. At the same time, the AI model is to be developed. We have therefore taken up contact with Joint Operations Command, in particular, which likewise has requirements in this area. For us, it’s important that the AI model, which we’ve called NJÖRD, and the experiences from its development are accessible and serve the entire army system. Ultimately, we also work on other projects in the areas of anticipation and AI.