Beyond Drones And Robots: Untapped Potential In The ADF

The future is now! Flight Lieutenant Jacob Simpson demonstrates how Artificial Intelligence and Machine Learning are practical and achievable solutions to current problems faced by the Australian Defence Force. Yet a lack of AI literacy amongst personnel runs the risk of the ADF missing opportunities to exploit this technology through bottom-up innovation. Building such initiatives are necessary to ensure the Forces continue to improve at all levels, not just large scale technologies. FLTLT Jacob Simpson offers realistic possibilities for developing AI literate personnel to take advantage of their specialised service experiences.

The impact of Artificial Intelligence (AI) technology is considered so significant that it is likened to the onset of electricity. If that statement is true, the Australian Defence Force (ADF) is currently not prepared to exploit the technology, and will continue to be on the back foot until it can effectively develop AI literacy amongst uniformed personnel. The successful implementation of AI not only requires an understanding of what AI is and an appreciation of what it can realistically achieve, but also an extensive knowledge across all domains in which it is being deployed. The development of AI for medical purposes, for example the automation of the detection of tumours in MRI scans, necessitates an expertise in both medicine and deep learning algorithms. The ADF will similarly require a cross-domain approach - through both military and AI expertise - to solve the problems that are unique to Defence, such as inefficiencies found within the domains of intelligence, command and control, or military logistics. Therefore, the key to success is two fold. It must support a bottom-up approach; combining technical AI knowledge and military expertise found within silos of excellence. And it must also be coupled with specific education at all ranks in order to develop the necessary tools for AI prototyping.

The current research and development (R&D) approach utilised by the ADF is a top-down affair. It reflects a view of AI as a high risk technology reserved for scientists, requiring sophisticated tools only for the development of drones, robots, and the weapons of tomorrow. The result is AI innovation and an R&D strategy primarily centred on the external sourcing of ideas with the use of personnel outside of Defence to solve problems only within a few major projects. The foundations for a culture of AI literacy are therefore not being built within the ADF itself.

While the development of cutting-edge AI is certainly justified for large autonomous systems like the Loyal Wingman and AIR6500, the ADF’s top-down approach runs the risk of a sluggish response to this technological revolution [1]. The consequences of underestimating the impact of AI will be serious if potential adversaries are more agile in its integration [2]. However, the addition of a bottom-up approach will negate this risk and encourage rapid integration of AI technology throughout the organisation. So what is required for the ADF to support the bottom-up use of AI?

The Bottom-Up Approach: More Projects - Smaller in Scope

Current ADF culture suggests AI is a technology of tomorrow – not today. It is easy to think of AI projects as those focused only on advancing sophisticated capability for the future force, such as for autonomous weapons systems and robotics. These projects, however, are mammoth in scope, come with significant cost, and require many years to implement due to their complexity. The result is the confining of innovation to high-risk projects that inevitably necessitate top-down managerial modus operandi. This approach does not exploit the benefits of machine learning (ML) that are available now [3]. ML does not need to be the preserve of multi-million dollar projects; sufficient off-the-shelf tools and ML algorithms have already emerged to render the implementation of AI projects exceedingly accessible to all ADF personnel. This is reflected in the fact that AI is a hobby to many. One only needs to peruse r/learnmachinelearning to find AI hobbyists showing off complex algorithms they have built using basic tools at home. The ADF could use these same tools, accessible to all personnel, within an AI developer environment on Defence networks and look to smaller AI projects with lower risk.

Figure 1: Project management constraints [4]

Why smaller projects? The success of a project is determined by the balance of its triple constraints: time, cost and scope (Fig.1). The disproportionate dominance of one constraint, such as the large scope of AI projects within Defence, necessitates the compensatory increase of the others as a form of risk management. However, it is this form of retrospective risk management that leads to the top-down directives seen in Defence capability acquisition.

Implicit within Figure 1 is the recognition that project scope neither corresponds to, nor predicts project quality. On the contrary – the smaller the project, the less time and cost required to produce a quality result, therefore reducing the consequence of failure. This is an important distinction for the pursuit of bottom-up innovation within Defence for AI, as it is the projects of smaller scope that can accelerate a wider proliferation of R&D. These are not the exciting projects involving drones and robots, but can be as simple as maximising administrative efficiency – such as automating the processes for the filling of forms - rendering the task less menial and time-consuming. Embracing small projects enables improvements in efficiency for the ADF organisation, and allow it to keep pace with AI technological development.

Figure 2: AI-Search testing on-board a C-27J Spartan [5]

Indeed, a case for fostering smaller AI projects within Defence is made with the ‘AI-Search’ detection system. The search and rescue (SAR) portable AI system was designed to rapidly locate vessels at sea through deep learning (DL) algorithms. A junior officer, LEUT Hubbert, developed the visual search algorithm through the use of common off-the-shelf ML tools in a mere two weeks [6]. This effort was supported by the Plan Jericho innovation hub, which helped fund and test the low-cost AI capability on a C-27 Spartan in 2019 [7]. This type of small scale, improvised R&D activity demonstrates exactly what bottom-up AI innovation can rapidly achieve. The ADF needs to professionally develop more personnel like LEUT Hubbert, whose prior knowledge of ML rendered possible his initiative. Personnel need to be given opportunities to acquire this niche skill.

How can the ADF develop personnel that can deliver innovative bottom-up AI projects? Two initiatives are needed: Opportunities for AI Professional Military Education (PME), and a dedicated AI developer environment. The latter of these requirements is being established through the Defence Artificial Intelligence Centre (DAIC), but the former is currently very limited and must change.

The Importance of Education for Developing AI Innovation

Established under the Joint Capabilities Group in 2019, the DAIC serves as a centralised authority for the deployment of new AI capabilities within the ADF. Innovation is fostered through unclassified collaboration between the ADF and a network of industry, academia and allies. R&D efforts are to be focused within a collection of laboratories under the Defence Technology Acceleration ColLab (DTAC), where project proposals will be vetted by a dedicated management team to ensure non-repetition elsewhere. The DTAC plans to enable development of bottom-up AI innovation within the ADF through providing ML development tools on an unclassified network. However, one issue remains: a lack of AI-educated ADF personnel.

In order for ADF personnel to effectively contribute to the DTAC, an AI education program needs to be developed which enables uniformed personnel to build the skills to develop prototype AI applications. Specifically, AI literate Defence members need: an understanding of ML techniques, fluency in the Python computer coding language, experience in data wrangling, and practical knowledge of ML tools provided within the DTAC developer environment. These four requirements enable familiarity with the various data structures available for training algorithms, the different solutions that are achievable for a specific problem, and the level of effort required to complete a given undertaking.

Armed with such knowledge, uniformed personnel will be able to uncover niche solutions in practical project ideas of smaller scope. More importantly however, it will allow for the horizontal integration of ML algorithms within the ADF through combining the specialist knowledge found in intelligence, aviation, logistics, engineering and other ADF roles with AI configured for each respective domain. Building an ADF workforce that is AI literate will therefore be essential for the DTAC to exploit siloed specialist Defence knowledge within its ranks, without which bottom-up innovation becomes unachievable.

The Missing Piece - Education Options for ADF Personnel

The skills required to develop AI solutions can be very difficult to learn independently, and there are few options for formal education for Defence personnel. The first step in generating an AI literate workforce is the provision of short courses. These courses should offer a higher-level understanding of what AI actually is, and how Defence could exploit its benefits. Short courses could be provided by DAIC, and be aimed at mid-level and senior officers. General online courses could also be made available to all interested personnel wishing to understand the technology as part of their PME.

While this is a first, and interim step, short courses are not enough to develop the skills required to utilise AI development tools and contribute to the DTAC. To produce specialist personnel who can contribute, Defence needs to implement formal education options in AI/Data Science in collaboration with UNSW Canberra. As of writing, there are no AI-focused qualifications available for study through the ADFA postgraduate program. Only one class introduced in 2019, ‘Big Data Analytics for Security’, teaches ML techniques for the purpose of cyber defence. In my personal experience, it was this class that got me interested in the subject of AI and inspired me to pursue a deeper understanding of the technology. However, I have not been able to find another ADF supported program in AI that can realistically be completed part-time.

Another option open to personnel is to study outside UNSW Canberra through distance learning. There are currently three Masters of Data Science degrees with online classes on ML available through the DASS program. However, this still makes a postgraduate degree prohibitively expensive for many junior military professionals.

The preferred option is a selection courses that offer increasingly complex understanding to those who are interested in specialising. A DAIC led education program open to all roles in the RAN, Army, and RAAF can offer short courses, diplomas and masters via coursework to personnel wishing to specialise. DAIC could follow the education model used by Defence Science & Technology Group (DSTG) within the Joint and Operations Analysis Division for developing military personnel with Decision Analytics skills. DSTG held an annual short course which gave the option for participants to gain a diploma in Decision Analytics upon completion or continue to study via enrolling at UNSW Canberra for the masters via coursework. An equivalent approach for AI could allow members to have flexibility, developing general AI awareness amongst the ADF as well as producing AI specialists within the ADF capable of contributing to the DTAC.

Figure 3: Example of AI Education Pathway

Postgraduate education will also support innovation through the option for a research project in the final year of the degree for high performers.This enables personnel to explore options for AI within their own fields; once completed, these projects could directly be supported for implementation by the DTAC. UNSW Canberra already has PhD options available for AI, so ADF personnel could even continue these projects at the PhD level if desired.


A lack of investment in AI education for ADF personnel will result in DTAC project collaboration being weighted towards industry and academia; thereby ensuring very little innovation will originate from within the organisation. This limits bottom-up innovation; instead, efforts will be disproportionately focused upon problems deemed by industry and academia to be relevant – that is, projects ambitiously scaled for profitability, complexity and academic research interests. That approach risks a continued focus on large projects, or worse, the stove-piping of AI projects with a failure to consider the needs of the end user [8]. To fully mobilise AI development, the ADF will need project ideas that originate from within the organisation – not only outside it.

Education is the first step in fostering a bottom-up AI innovation movement and setting the foundations for a bottom-up innovation culture within the ADF. PME focused on AI will allow for personnel to understand the strengths and weaknesses of this technology with a realistic appreciation of how it can be implemented. Knowledge in AI will inspire personnel to find solutions within their own siloed roles across the ADF, leading to new opportunities for AI projects that are smaller in scope and immediately achievable. If the ADF is to rapidly exploit AI, top-down innovation cannot be the only R&D strategy. AI has matured enough to allow for rapid development of tools to support the ADF of today, not just the ADF of tomorrow.

Jacob Simpson is a Flight Lieutenant in the Royal Australian Air Force. He holds a Masters in Strategic Studies from the Australian National University and is currently undertaking a Masters in Decision Analytics at the University of New South Wales.


[1] ADBR (2019). Accenture report highlights impact of Artificial Intelligence. [online] ADBR. Available at:

[2] Shoebridge, M. (2019). AI and autonomous systems are urgent priorities for today’s defence force. [online] The Strategist. Available at:

[3] Moy, G., Shekh, S., Oxenham, M. and Ellis-Steinborner, S. (2020). Recent Advances in Artificial Intelligence and their Impact on Defence. Defence Science and Technology Group.

[4] Hulett, D. (2013). Integrated cost-schedule risk analysis. Farnham: Gower.

[5] Kuper, S. (2019). Artificial intelligence to enhance Aussie search and rescue capabilities. [online] Available at:

[6] Royal Australian Air Force (2019). AI-Search to Transform Search & Rescue | Royal Australian Air Force. [online] Available at:

[7] Milne, S. (2020). AI-Search enters ‘second phase of development.’ [online] Available at:

[8] ADM (2020). University of Queensland partners with ADF on AI - Australian Defence Magazine. [online] Available at:

[9] Middlebrooks, S.E. (2003). The COMPASS Paradigm For The Systematic Evaluation Of U.S. Army Command And Control Systems Using Neural Network And Discrete Event Computer Simulation. [online] Available at:

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