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Future Tech

Grassroots AI: Driving Change in the Royal Navy with Workflow

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By Francis Heritage

Introduction

In September 2023, the Royal Navy (RN) advertised the launch of the Naval AI Cell (NAIC), designed to identify and advance AI capability across the RN. NAIC will act as a ‘transformation office’, supporting adoption of AI across RN use cases, until it becomes part of Business as Usual (BaU). NAIC aims to overcome a key issue with current deployment approaches. These usually deploy AI as one-off transformation projects, normally via innovation funding, and often result in project failure. The nature of an ‘Innovation Budget’ means that when the budget is spent, there is no ability to further develop, or deploy, any successful Proof of Concept (PoC) system that emerged.

AI is no longer an abstract innovation; it is fast becoming BaU, and it is right that the RN’s internal processes reflect this. Culturally, it embeds the idea that any AI deployment must be both value-adding and enduring. It forces any would-be AI purchaser to focus on Commercial Off The Shelf (COTS) solutions, significantly reducing the risk of project failure, and leveraging the significant investment already made by AI providers.

Nevertheless, NAIC’s sponsored projects still lack follow-on budgets and are limited in scope to just RN-focused issues. The danger still exists that NAIC’s AI projects fail to achieve widespread adoption, despite initial funding; a common technology-related barrier, known as the ‘Valley of Death.’ In theory, there is significant cross-Top Line Budget (TLB) support to ensure the ‘Valley’ can be bridged. The Defense AI and Autonomy Unit within MOD Main Building is mandated to provide policy and ethical guidance for AI projects, while the Defense Digital/Dstl Defense AI Centre (DAIC) acts as a repository for cross-MOD AI development and deployment advice. Defense Digital also provides most of the underlying infrastructure required for AI to deploy successfully. Known by Dstl as ‘AI Building Blocks’, this includes secure cloud compute and storage in the shape of MODCloud (in both Amazon Web Service and Microsoft Azure) and the Defense Data Analytics Portal (DDAP), a remote desktop of data analytics tools that lets contractors and uniformed teams collaborate at OFFICIAL SENSITIVE, and accessible via MODNet.

The NAIC therefore needs to combine its BaU approach with other interventions, if AI and data analytics deployment is to prove successful, namely:

  • Create cross-TLB teams of individuals around a particular workflow, thus ensuring a larger budget can be brought to bear against common issues.
  • Staff these teams from junior ranks and rates, and delegate them development and budget responsibilities.
  • Ensure these teams prioritize learning from experience and failing fast; predominantly by quickly and cheaply deploying existing COTS, or Crown-owned AI and data tools. Writing ‘Discovery Reports’ should be discouraged.
  • Enable reverse-mentoring, whereby these teams share their learnings with Flag Officer (one-star and above) sponsors.
  • Provide these teams with the means to seamlessly move their projects into Business as Usual (BaU) capabilities.

In theory this should greatly improve the odds of successful AI delivery. Cross-TLB teams should have approximately three times the budget to solve the same problem, when compared to an RN-only team. Furthermore, as the users of any developed solution, teams are more likely to buy and/or develop systems that work and deliver value for money. With hands-on experience and ever easier to deploy COTS AI and data tools, teams will be able to fail fast and cheaply, and usually at a lower cost than employing consultants. Flag Officers providing overarching sponsorship will receive valuable reverse-mentoring; specifically understanding first-hand the disruptive potential of AI systems, the effort involved in understanding use-cases and the need for underlying data and infrastructure. Finally, as projects will already be proven and part of BaU, projects may be cheaper and less likely to fail than current efforts.

Naval AI Cell: Initial Projects

The first four tenders from the NAIC were released via the Home Office Accelerated Capability Environment (ACE) procurement framework in March 2024. Each tender aims to deliver a ‘Discovery’ phase, exploring how AI could be used to mitigate different RN-related problems.1 However, the nature of the work, the very short amount of time for contractors to respond, and relatively low funding available raises concern about the value for money they will deliver. Industry was given four days to provide responses to the tender, about a fifth of the usual time, perhaps a reflection of the need to complete projects before the end of the financial year. Additionally, the budget for each task was set at approximately a third of the level for equivalent Discovery work across the rest of Government.2 The tenders reflect a wide range of AI use-cases, including investigating JPA personnel records, monitoring pictures of rotary wing oil filter papers, and underwater sound datasets, each of which requires a completely different Machine Learning approach.

Figure 1: An example self-service AI Workflow, made by a frontline civilian team to automatically detect workers not wearing PPE. The team itself has labelled relevant data, trained and then deployed the model using COTS user interface. Source: V7 Labs.

Take the NAIC’s aircraft maintenance problem-set as an example. The exact problem (automating the examination of oil filter papers of rotary wing aircraft) is faced by all three Single Services. First, by joining forces with the RAF to solve this same problem, the Discovery budget could have been doubled, resulting in a higher likelihood of project success and ongoing savings. Second, by licensing a pre-existing, easy-to-use commercial system that already solves this problem, NAIC could have replaced a Discovery report, written by a contractor, with cheaper, live, hands-on experience of how useful AI was in solving the problem.3 This would have resulted in more money being available, and a cheaper approach being taken.

Had the experiment failed, uniformed personnel would have learnt significantly more from their hands-on experience than by reading a Discovery report, and at a fraction of the price. Had it succeeded, the lessons would have been shared across all three Services and improved the chance of success of any follow-on AI deployment across wider MOD. An example of a COTS system that achieves this is V7’s data labeling and model deployment system, available with a simple user interface; it is free for up to 3 people, or £722/month for more complex requirements.4 A low-level user experiment using this kind of platform is unlikely to have developed sufficient sensitive data to have gone beyond OFFICIAL.

Introducing ‘Workflow Automation Guilds’

Focusing on painful, AI-solvable problems, shared across Defense, is a good driver to overcome these stovepipes. Identification of these workflows has been completed by the DAIC and listed in the Defence AI Playbook.5 It lists 15 areas where AI has potential to deliver a step-change capability. Removing the problems that are likely to be solved by Large Language Models (where a separate Defence Digital procurement is already underway), leaves 10 workflows (e.g. Spare Parts Failure Prediction, Optimizing Helicopter Training etc) where AI automation could be valuably deployed.

However, up to four different organizations are deploying AI to automate these 10 workflows, resulting in budgets that are too small to result in impactful, recurring work; or at least, preventing this work from happening quickly. Cross-Front Line Command (FLC) teams could be created, enabling budgets to be combined to solve the same work problem they collectively face. In the AI industry, these teams are known as ‘Guilds’; and given the aim is to automate Workflows, the term Workflow Automation Guilds (WAGs) neatly sums up the role of these potential x-FLC teams.

Focus on Junior Personnel

The best way to populate these Guilds is to follow the lead of the US Navy (USN) and US Air Force (USAF), who independently decided the best way to make progress with AI and data is to empower their most junior people, giving them responsibility for deploying this technology to solve difficult problems. In exactly the same way that the RN would not allow a Warfare Officer to take Sea Command if they are unable to draw a propulsion shaft-line diagram, so an AI or data deployment program should not be the responsibility of someone senior who does not know or understand the basics of Kubernetes or Docker.6 For example, when the USAF created their ‘Platform One’ AI development platform, roles were disproportionately populated by lower ranks and rates. As Nic Chaillan, then USAF Chief Software Officer, noted:

“When we started picking people for Platform One, you know who we picked? A lot of Enlisted, Majors, Captains… people who get the job done. The leadership was not used to that… but they couldn’t say anything when they started seeing the results.”7

The USN takes the same approach with regards to Task Force Hopper and Task Group 59.1.8 TF Hopper exists within the US Surface Fleet to enable rapid AI/ML adoption. This includes enabling access to clean, labeled data and providing the underpinning infrastructure and standards required for generating and hosting AI models. TG 59.1 focuses on the operational deployment of uncrewed systems, teamed with human operators, to bolster maritime security across the Middle East. Unusually, both are led by USN Lieutenants, who the the USN Chief of Naval Operations called ‘…leaders who are ready to take the initiative and to be bold; we’re experimenting with new concepts and tactics.’9

Delegation of Budget Spend and Reverse Mentoring

Across the Single Services, relatively junior individuals, from OR4 to OF3, could be formed on a cross-FLC basis to solve the Defence AI Playbook issues they collectively face, and free to choose which elements of their Workflow to focus on. Importantly, they should deploy Systems Thinking (i.e. an holistic, big picture approach that takes into account the relationship between otherwise discrete elements) to develop a deep understanding of the Workflow in question and prioritize deployment of the fastest, cheapest data analytics method; this will not always be an AI solution. These Guilds would need budgetary approval to spend funds, collected into a single pot from up to three separate UINs from across the Single Services; this could potentially be overseen by an OF5 or one-star. The one-star’s role would be less about providing oversight, and more to do with ensuring funds were released and, vitally, receiving reverse mentoring from the WAG members themselves about the viability and value of deploying AI for that use case.

The RN’s traditional approach to digital and cultural transformation – namely a top-down, directed approach – has benefits, but these are increasingly being rendered ineffective as the pace of technological change increases. Only those working with this technology day-to-day, and using it to solve real-world challenges, will be able to drive the cultural change the RN requires. Currently, much of this work is completed by contractors who take the experience with them when projects close. By deploying this reverse-mentoring approach, WAG’s not only cheaply create a cadre of uniformed, experienced AI practitioners, but also a senior team of Flag Officers who have seen first-hand where AI does (or does not) work and have an understanding of the infrastructure needed to make it happen.

Remote working and collaboration tools mean that teams need not be working from the same bases, vital if different FLCs are to collaborate. These individuals should be empowered to spend multi-year budgets of up to circa. £125k. As of 2024, this is sufficient to allow meaningful Discovery, Alpha, Beta and Live AI project phases to be undertaken; allow the use of COTS products; and small enough to not result in a huge loss if the project (which is spread among all three Services to mitigate risk) fails.

Figure 2: An example AI Figure 2: An example AI Opportunity Mapping exercise, where multiple AI capabilities (represented by colored cards) are mapped onto different stages of an existing workflow, to understand where, if anywhere, use of AI could enable or improve workflow automation. Source: 33A AI.

WAG Workflow Example

An example of how WAGs could work is as follows, using the oil sample contamination example. Members of the RN and RAF Wildcat and Merlin maintenance teams collectively identify the amount of manpower effort that could be saved if the physical checking of lube oil samples could be automated. With an outline knowledge of AI and Systems Thinking already held, WAG members know that full automation of this workflow is not possible; but they have identified one key step in the workflow that could be improved, speeding up the entire workflow of regular helicopter maintenance. The fact that a human still needs to manually check oil samples is not necessarily an issue, as they identify that the ability to quickly prioritize and categorize samples will not cause bottlenecks elsewhere in the workflow and thus provides a return on investment.

Members of the WAG would create a set of User Stories, an informal, general description of the AI benefits and features, written from a users’ perspective. With the advice from the DAIC, NAIC, RAF Rapid Capability Office (RCO) / RAF Digital or Army AI Centre, other members of the team would ensure that data is in a fit state for AI model training. In this use-case, this would involve labeling overall images of contamination, or the individual contaminants within an image, depending on the AI approach to be used (image recognition or object detection, respectively). Again, the use of the Defence Data Analytics Portal (DDAP), or a cheap, third-party licensed product, provides remote access to the tools that enable this. The team now holds a number of advantages over traditional, contractor-led approaches to AI deployment, potentially sufficient to cross the Valley of Death:

  • They are likely to know colleagues across the three Services facing the same problem, so can check that a solution has not already been developed elsewhere.10
  • With success metrics, labelled data and user requirements all held, the team has already overcome the key blockers to success, reducing the risk that expensive contractors, if subsequently used, will begin project delivery without these key building blocks in place.
  • They have a key understanding of how much value will be generated by a successful project, and so can quickly ‘pull the plug’ if insufficient benefits arise. There is also no financial incentive to push on if the approach clearly isn’t working.
  • Alternatively, they have the best understanding of how much value is derived if the project is successful.
  • As junior Front-Line operators, they benefit directly from any service improvement, so are not only invested in the project’s success, but can sponsor the need for BaU funding to be released to sustain the project in the long term, if required.
  • If working with contractors, they can provide immediate user feedback, speeding up development time and enabling a true Agile process to take place. Currently, contractors struggle to access users to close this feedback loop when working with MOD.

Again, Flag Officer sponsorship of such an endeavor is vital. This individual can ensure that proper recognition is awarded to individuals and make deeper connections across FLCs, as required.

Figure 3: Defense Digital’s Defense Data Analytics Portal (DDAP) is tailor-made for small, Front-Line teams to clean and label their data and deploy AI services and products, either standalone or via existing, call-off contract contractor support.

Prioritizing Quick, Hands-on Problem Solving

WAGs provide an incentive for more entrepreneurial, digitally minded individuals to remain in Service, as it creates an outlet for those who wish to learn to code and solve problems quickly, especially if the problems faced are ones they wrestle with daily. A good example of where the RN has successfully harnessed this energy is with Project KRAKEN, the RN’s in-house deployment of the Palantir Foundry platform. Foundry is a low-code way of collecting disparate data from multiple areas, allowing it to be cleaned and presented in a format that speeds up analytical tasks. It also contains a low-level AI capability. Multiple users across the RN have taken it upon themselves to learn Foundry and deploy it to solve their own workflow problems, often in their spare time, with the result that they can get more done, faster than before. With AI tools becoming equally straightforward to use and deploy, the same is possible for a far broader range of applications, provided that cross-TLB resources can be concentrated at a junior level to enable meaningful projects to start.

Figure 4: Pre-existing Data/AI products or APIs, bought from commercial providers, or shared from elsewhere in Government, are likely to provide the fastest, cheapest route to improving workflows.11

Deploying COTS Products Over Tailored Services

Figure 4 shows the two main options available for WAGs when deploying AI or data science capabilities: Products or Services. Products are standalone capabilities created by industry to solve particular problems, usually made available relatively cheaply. Typically, COTS, they are sold on a per-use, or time-period basis, but cannot be easily tailored or refined if the user has a different requirement.

By contrast, Services are akin to a consulting model where a team of AI and Machine Learning engineers build an entirely new, bespoke system. This is much more expensive and slower than deploying a Product but means users should get exactly what they want. Occasionally, once a Service has been created, other users realize they have similar requirements as the original user. At this point, the Service evolves to become a Product. New users can take advantage of the fact that software is essentially free to either replicate or connect with and gain vast economies of scale from the initial Service investment.

WAGs aim to enable these economies of scale; either by leveraging the investment and speed benefits inherent in pre-existing Products or ensuring that the benefits of any home-made Services are replicated across the whole of the MOD, rather than remaining stove-piped or siloed within Single Services.

Commercial/HMG Off the Shelf Product. The most straightforward approach is for WAGs to deploy a pre-existing product, licensed either from a commercial provider, or from another part of the Government that has already built a Product in-house. Examples include the RAF’s in-house Project Drake, which has developed complex Bayesian Hierarchical models to assist with identifying and removing training pipeline bottlenecks; these are Crown IP, presumably available to the RN at little-to-no cost, and their capabilities have been briefed to DAIC (and presumably briefed onwards to the NAIC).

Although straightforward to procure, it may not be possible to deploy COTS products on MOD or Government systems, and so may be restricted up to OFFICIAL or OFFICIAL SENSITIVE only. Clearly, products developed or deployed by other parts of MOD or National Security may go to higher classifications and be accessible from MODNet or higher systems. COTS products are usually available on a pay-as-you-go, monthly, or user basis, usually in the realm of circa £200 per user, per month, providing a fast, risk-free way to understand whether they are valuable enough to keep using longer-term.

Contractor-supported Product. In this scenario, deployment is more complex; for example, the product needs to deploy onto MOD infrastructure to allow sensitive data to be accessed. In this case, some expense is required, but as pre-existing COTS, the product should be relatively cheap to deploy as most of the investment has already been made by the supplier. This option should allow use up to SECRET but, again, users are limited to those use-cases offered by the commercial market. These are likely to be focused on improving maintenance and the analysis of written or financial information. The DAIC’s upcoming ‘LLMs for MOD’ project is likely to be an example of a Contractor-supported Product; MOD users will be able to apply for API access to different Large Language Model (LLM) products, hosted on MOD infrastructure, to solve their use-cases. Contractors will process underlying data to allow LLMs to access it, create the API, and provide ongoing API connectivity support.

Service built in-house. If no product exists, then there is an opportunity to build a low-code solution in DDAP or MODCloud and make it accessible through an internal app. Some contractor support may be required, particularly to provide unique expertise the team cannot provide themselves (noting that all three Services may have Digital expertise available via upcoming specialist Reserve branches, with specialist individuals available at a fraction of the cost of their civilian equivalent day rates).12 Defense Digital’s ‘Enhanced Data Teams’ service provides a call-off option for contractors to do precisely this for a short period of time. It is likely that these will not, initially, deploy sophisticated data analysis or AI techniques, but sufficient value may be created with basic data analytics. In any event, the lessons learnt from building a small, relatively unsophisticated in-house service will provide sufficient evidence to ascertain whether a full, contractor-built AI service will provide value for money, if built. Project Kraken is a good example of this; while Foundry is itself a product and bought under license, it is hosted in MOD systems and allows RN personnel to build their own data services within it.

Service built by contractors. These problems are so complex, or unique to Defense, that no COTS product exists. Additionally, the degree of work is so demanding that Service Personnel could not undertake this work themselves. In this case, WAGs should not be deployed. Instead, these £100k+ programs should remain the purview of Defense Digital or the DAIC and aim to instead provide AI Building Blocks that empower WAGs to do AI work. In many cases, these large service programs provide cheap, reproducible products that the rest of Defense can leverage. For example, the ‘LLMs for MOD’ Service will result in relatively cheap API Products, as explained above. Additionally, the British Army is currently tendering for an AI-enabled system that can read the multiple hand-and-type-written text within the Army archives. This negates the need for human researchers to spend days searching for legally required records that can now be found in seconds. Once complete, this system could offer itself as a Product that can ingest complex documents from the rest of the MOD at relatively low cost. This should negate the need for the RN to pay their own 7-figure sums to create standalone archive scanning services. To enable this kind of economy of scale, NAIC could act as a liaison with these wider organizations. Equipped with a ‘shopping list’ of RN use cases, it could quickly deploy tools purchased by the Army, RAF or Defense Digital across the RN.

Finding the Time

How can WAG members find the time to do the above? By delegating budget control down to the lowest level, and focusing predominantly on buying COTS products, the amount of time required should be relatively minimal; in essence, it should take the same amount of time as buying something online. Some work will be required to understand user stories and workflow design, but much of this will already be in the heads of WAG members. Imminent widespread MOD LLM adoption should, in theory, imminently reduce the amount of time spent across Defense on complex, routine written work (reviewing reports, personnel appraisals, post-exercise or deployment reports or other regular reporting).13 This time could be used to enable WAGs to do their work. Indeed, identifying where best to deploy LLMs across workflows are likely to be the first roles of WAGs, as soon as the ‘LLMs for MOD’ program reaches IOC. Counter-intuitively, by restricting the amount of time available to do this work, it automatically focuses attention on solutions that are clearly valuable; solutions that save no time are, by default, less likely to be worked on, or have money spent on them.

Conclusions

The RN runs the risk of spreading the NAIC’s budget too thinly, in its attempt to ‘jumpstart’ use of AI across Business as Usual disciplines. By contrast, users should be encouraged to form Workflow Automation Guilds across FLCs. Supported by a senior sponsor, knowledgeable members of the Reserves, the NAIC and one-on-one time with the DAIC, WAGs could instead focus on the COTS solution, or pre-existing Crown IP, that will best solve their problem. Budget responsibilities should be delegated down too, thereby enabling access to existing, centralized pools of support, such as the Enhanced Data Teams program, DDAP, or the upcoming ‘LLMs for MOD’ API Service. In this way, projects are more likely to succeed, as they will have demonstrated value from the very start and will have been co-developed by the very users that deploy them. The speed at which AI and data services are becoming easier to use is reflected by the RN’s Kraken team, while the need to trust low-level officers and junior rates is borne out by the success currently being enjoyed by both the USAF and USN with their own complex AI deployments.

Prior to leaving full-time service, Lieutenant Commander Francis Heritage, Royal Navy Reserve, was a Principal Warfare Officer and Fighter Controller. Currently an RNR GW officer, he works at the Defence arm of Faculty, the UK’s largest independent AI company. LtCdr Francis is now deployed augmenting Commander United Kingdom Strike Force (CSF).

The views expressed in this paper are the author’s, and do not necessarily represent the official views of the MOD, the Royal Navy, RNSSC, or any other institution.

References

1. Discovery’ is the first of 5 stages in the UK Government Agile Project Delivery framework, and is followed by Alpha, Beta, Live and Retirement. Each stage is designed to allow the overall program to ‘fail fast’ if it is discovered that benefits will not deliver sufficient value.

2. Author’s observations.

3. Volvo and the US commodities group Bureau Veritas both have Commercial off the Shelf products available for solving this particular problem.

4. Source: https://www.v7labs.com/pricing accessed 10 Apr 2024.

5. Source: https://assets.publishing.service.gov.uk/media/65bb75fa21f73f0014e0ba51/Defence_AI_Playbook.pdf

6. AI systems rely on machine learning frameworks and libraries; Docker packages these components together into reproducible ‘containers’, simplifying deployment. Kubernetes builds on Docker, providing an orchestration layer for automating deployment and management of containers over many machines.

7. Defence Unicorns podcast, 5 Mar 2024.

8. Source: Navy’s new ‘Project OpenShip’ aims to swiftly apply AI to data captured by vessels at sea | DefenseScoop.

9. https://www.afcea.org/signal-media/navys-junior-officers-lead-way-innovation.

10. The author knows of at least 3 AI projects across MOD aimed at automating operational planning and another 3 aiming to automate satellite imagery analysis.

11. API stands for Application Programming Interface, a documented way for software to communicate with other software. By purchasing access to an API (usually on a ‘per call’ or unlimited basis) a user can take information delivered by an API and combine it with other information before presenting it to a user. Examples include open-source intelligence, commercial satellite imagery, meteorological data, etc. 

12. Army Reserve Special Group Information Service, RNR Information Exploitation Branch and RAF Digital Reserves Consultancy. RNR IX and RAFDRC are both TBC.

13. Worldwide, Oliver Wyman estimates Generative AI will save an average of 2 hours per person per week; likely to be higher for office-based roles: https://www.oliverwymanforum.com/content/dam/oliver-wyman/ow-forum/gcs/2023/AI-Report-2024-Davos.pdf p.17.

Featured Image: The Operations Room of the carrier HMS Queen Elizabeth during an exercise in 2018. (Royal Navy photo)

Emerging Tech Week

A Roadmap to Successful Sonar AI

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Emerging Technologies Topic Week

By LT Andrew Pfau

Even as the private sector and academia have made rapid progress in the field of Artificial Intelligence (AI) and Machine Learning (ML), the Department of Defense (DoD) remains hamstrung by significant technical and policy challenges. Only a fraction of this civilian-driven progress can be applied to the AI and ML models and systems needed by the DoD; the uniquely military operational environments and modes of employment create unique development challenges for these potentially dominant systems. In order for ML systems to be successful once fielded, these issues must be considered now. The problems of dataset curation, data scarcity, updating models, and trust between humans and machines will challenge engineers in their efforts to create accurate, reliable, and relevant AI/ML systems.

Recent studies recognize these structural challenges. A GAO report found that only 38 percent of private sector research and development projects were aligned with DoD needs, while only 12 percent of projects could be categorized as AI or autonomy research.1 The National Security Commission on Artificial Intelligence’s Final Report also recognizes this gap, recommending more federal R&D funding for areas critical to advance technology, especially those that may not receive private sector investment.2 The sea services face particular challenges in adopting AI/ML technologies to their domains because private sector interest and investment in AI and autonomy at sea has been especially limited. One particular area that needs Navy-specific investment is that of ML systems for passive sonar systems, though the approach certainly has application to other ML systems.

Why Sonar is in Particular Need of Investment

Passive sonar systems are a critical component on many naval platforms today. Passive sonar listens for sounds emitted by ships or submarines and is the preferred tool of anti-submarine warfare, particularly for localizing and tracking targets. In contrast to active sonar, no signal is emitted, making it more covert and the method of choice for submarines to locate other vessels at sea. Passive sonar systems are used across the Navy in submarine, surface, and naval air assets, and in constant use during peace and war to locate and track adversary submarines. Because of this widespread use, any ML model for passive sonar systems would have a significant impact across the fleet and use on both manned and unmanned platforms. These models could easily integrate into traditional manned platforms to ease the cognitive load on human operators. They could also increase the autonomy of unmanned platforms, either surfaced or submerged, by giving these platforms the same abilities that manned platforms have to detect, track, and classify targets in passive sonar data.

Passive sonar, unlike technologies such as radar or LIDAR, lacks the dual use appeal that would spur high levels of private sector investment. While radar systems are used across the military and private sector for ground, naval, air, and space platforms, and active sonar has lucrative applications in the oil and gas industry, passive sonar is used almost exclusively by naval assets. This lack of incentive to invest in ML technologies related to sonar systems epitomizes the gap referred to by the NSC AI report. Recently, NORTHCOM has tested AI/ML systems to search through radar data for targets, a project that has received interest and participation from all 11 combatant commands and the DoD as a whole.3 Due to its niche uses, however, passive sonar ML systems cannot match this level of department wide investment and so demands strong advocacy within the Navy.

Dataset Curation

Artificial Intelligence and Machine Learning are often conflated and used interchangeably. Artificial Intelligence refers a field of computer science interested in creating machines that can behave with human-like abilities and can make decisions based on input data. In contrast, Machine Learning, a subset of the AI filed, refers to computer programs and algorithms that learn from repeated exposure to many examples, often millions, instead of operating based on explicit rules programmed by humans.4 The focus in this article is on topics specific to ML models and systems, which will be included as parts in a larger AI or autonomous system. For example, an ML model could classify ships from passive sonar data, this model would then feed information about those ships into an AI system that operates an Unmanned Underwater Vehicle (UUV). The AI would make decisions about how to steer the UUV based on data from the sonar ML model in addition to information about mission objectives, navigation, and other data.

Machine learning models must train on large volumes of data to produce accurate predictions. This data must be collected, labeled, and prepared for processing by the model. Data curation is a labor- and time-intensive task that is often viewed as an extra cost on ML projects since it must occur before any model can be trained, but this process should be seen as an integral part of ML model success. Researchers recently found that one of the most commonly used datasets in computer vision research, ImageNet, has approximately 6 percent of their images mislabeled 5. Another dataset, QuickDraw, had 10 percent of images mislabeled. Once the errors were corrected, model performance on the ImageNet dataset improved by 6 percent over a model trained on the original, uncorrected, dataset.5

For academic researchers, where the stakes of an error in a model are relatively low, this could be called a nuisance. However, ML models deployed on warships face greater consequences than those in research labs. A similar error, of 6 percent, in an ML model to classify warships would be far more consequential. The time and labor costs needed to correctly label data for use in ML model training needs to be factored into ML projects early. In order to make the creation of these datasets cost effective, automatic methods will be required to label data, and methods of expert human verification must ensure quality. Once a large enough dataset has been built up, costs will decrease. However, new data will still have to be continuously added to training datasets to ensure up to date examples are present in the training of models.

A passive acoustic dataset is much more than an audio recording: Where and when the data is collected, along with many other discrete factors, are also important and should be integrated into the dataset. Sonar data collected in one part of the ocean, or during a particular time of year, could be very different than other parts of the ocean or the same point in the ocean at a different time of year. Both the types of vessels encountered and the ocean environment will vary. Researchers at Brigham Young University demonstrated how variations in sound speed profiles can affect machine learning systems that operate on underwater acoustic data. They showed the effects of changing environmental conditions when attempting to classify seabed bottom type from a moving sound source, with variations in the ability of their ML model to provide correct classifications by up to 20 percent.6 Collecting data from all possible operating environments, at various times of the year, and labeling them appropriately will be critical to building robust datasets from which accurate ML models can be trained. Metadata, in the form of environmental conditions, sensor performance, sound propagation, and more must be incorporated during the data collection process. Engineers and researchers will be able to analyze metadata to understand where the data came from and what sensor or environmental conditions could be underrepresented or completely missing.

These challenges must be overcome in a cost-effective way to build datasets representative of real world operating environments and conditions.

Data Scarcity

Another challenge in the field of ML that has salience for sonar data are the challenges associated with very small, but important datasets. For an academic researcher, data scarcity may come about due to the prohibitive cost of experiments or rarity of events to collect data on, such as astronomical observations. For the DoN, these same challenges will occur in addition to DoN specific challenges. Unlike academia or the private sectors, stringent restrictions on classified data will limit who can use this data to train and develop models. How will an ML model be trained to recognize an adversary’s newest ship when there are only a few minutes of acoustic recording? Since machine learning models require large quantities of data, traditional training methods will not work or result in less effective models.

Data augmentation, replicating and modifying original data may be one answer to this problem. In computer vision research, data is augmented by rotating, flipping, or changing the color balance of an image. Since a car is still a car, even if the image of the car is rotated or inverted, a model will learn to recognize a car from many angles and in many environments. In acoustics research, data is augmented by adding in other sounds or changing the time scale or pitch of the original audio. From a few initial examples, a much larger dataset to train on can be created. However, these methods have not been extensively researched on passive sonar data. It is still unknown which methods of data augmentation will produce the best results for sonar models, and which could produce worse models. Further research into the best methods for data augmentation for underwater acoustics is required.

Another method used to generate training data is the use of models to create synthetic data. This method is used to create datasets to train voice recognition models. By using physical models, audio recordings can be simulated in rooms of various dimensions and materials, instead of trying to make recordings in every possible room configuration. Generating synthetic data for underwater audio is not as simple and will require more complex models and more compute power than models used for voice recognition. Researchers have experimented with generated synthetic underwater sounds using the ORCA sound propagation model.6 However, this research only simulated five discrete frequencies used in seabed classification work. A ship model for passive sonar data will require more frequencies, both discrete and broadband, to be simulated in order to produce synthetic acoustic data with enough fidelity to use in model training. The generation of realistic synthetic data will give system designers the ability to add targets with very few examples to a dataset.

The ability to augment existing data and create new data from synthetic models will create larger and more diverse datasets, leading to more robust and accurate ML models.

Building Trust between Humans and Machines

Machine learning models are good at telling a human what they know, which comes from the data they were trained on. They are not good at telling humans that they do not recognize an input or have never seen anything like it in training. This will be an issue if human operators are to develop trust in the ML models they will use. Telling an operator that it does not know, or the degree of confidence a model has in its answer, will be vital to building reliable human-machine teams. One method to building models with the ability to tell human operators that a sample is unknown is the use of Bayesian Neural Networks. Bayesian models can tell an operator how confident they are in a classification and even when the model does not know the answer. This falls under the field of explainable AI, AI systems that can tell a human how the system arrived at the classification or decision that is produced. In order to build trust between human operators and ML systems, a human will need some insight into how and why an ML system arrived at its output.

Ships at sea will encounter new ships, or ships that were not part of the model’s original training dataset. This will be a problem early in the use of these models, as datasets will initially be small and grow with the collection of more data. These models cannot fail easily and quickly, they must be able to distinguish between what is known and what is unknown. The DoN must consider how human operators will interact with these ML models at sea, not just model performance.

Model Updates

To build a great ML system, the models will have to be updated. New data will be collected and added to the training dataset to re-train the model so that it stays relevant. In these models, only certain model parameters are updated, not the design or structure of the model. These updates, like any other digital file can be measured in bytes. An important question for system designers to consider is how these updates will be distributed to fleet units and how often. One established model for this is the Acoustic- Rapid COTS Insertion (ARCI) program used in the US Navy’s Submarine Force. In the ARCI program, new hardware and software for sonar and fire control is built, tested, and deployed on a regular, two-year cycle.7 But two years may be too infrequent for ML systems that are capable of incorporating new data and models rapidly. The software industry employs a system of continuous deployment, in which engineers can push the latest model updates to their cloud-based systems instantly. This may work for some fleet units that have the network bandwidth to support over the air updates or that can return to base for physical transfer. Recognizing this gap, the Navy is currently seeking a system that can simultaneously refuel and transfer data, up to 2 terabytes, from a USV.8 This research proposal highlights the large volume of data will need to be moved, both on and off unmanned vessels. Other units, particularly submarines and UUVs, have far less communications bandwidth. If over-the-air updates to submarines or UUVs are desired, then more restrictions will be placed on model sizes to accommodate limited bandwidth. If models cannot be made small enough, updates will have to be brought to a unit in port and updated from a hard drive or other physical device.

Creating a good system for when and how to update these models will drive other system requirements. Engineers will need these requirements, such as size limitations on the model, ingestible data type, frequency of updates needed by the fleet, and how new data will be incorporated into model training before they start designing ML systems.

Conclusion

As recommended in the NSC AI report, the DoN must be ready to invest in technologies that are critical to future AI systems, but that are currently lacking in private sector interest. ML models for passive sonar, lacking both dual use appeal and broad uses across the DoD, clearly fits into this need. Specific investment is required to address several problems facing sonar ML systems, including dataset curation, data scarcity, model updates, and building trust between operators and systems. These challenges will require a combination of technical and policy solutions to solve them, and they must be solved in order to create successful ML systems. Addressing these challenges now, while projects are in a nascent stage, will lead to the development of more robust systems. These sonar ML systems will be a critical tool across a manned and unmanned fleet in anti-submarine warfare and the hunt for near-peer adversary submarines.

Lieutenant Andrew Pfau, USN, is a submariner serving as an instructor at the U.S. Naval Academy. He is a graduate of the Naval Postgraduate School and a recipient of the Rear Admiral Grace Murray Hopper Computer Science Award. The views and opinions expressed here are his own.

Endnotes

1. DiNapoli, T. J. (2020). Opportunities to Better Integrate Industry Independent Research and Development into DOD Planning. (GAO-20-578). Government Accountability Office.

2. National Security Commission on Artificial Intelligence (2021), Final Report.

3. Hitchens, T. (2021, July 15) NORTHCOM Head To Press DoD Leaders For AI Tools, Breaking Defense, https://breakingdefense.com/2021/07/exclusive-northcom-head-to-press-dod-leaders-for-ai-tools/

4. Denning, P., Lewis, T. Intelligence May Not be Computable. American Scientist. Nov-Dec 2019. http://denninginstitute.com/pjd/PUBS/amsci-2019-ai-hierachy.pdf

5. Hao, K. (2021, April 1) Error-riddled data sets are warping our sense of how good AI really is. MIT Technology Review. https://www.technologyreview.com/2021/04/01/1021619/ai-data-errors-warp-machine-learning-progress/

6. Neilsen et al (2021). Learning location and seabed type from a moving mid-frequency source. Journal of the Acoustical Society of America. (149). 692-705. https://doi.org/10.1121/10.0003361

7. DeLuca, P., Predd, J. B., Nixon, M., Blickstein, I., Button, R. W., Kallimani J. G., and Tierney, S. (2013) Lessons Learned from ARCI and SSDS in Assessing Aegis Program Transition to an Open-Architecture Model, (pp 79-84) RAND Corperation, https://www.jstor.org/stable/pdf/10.7249/j.ctt5hhsmj.15.pdf

8. Office of Naval Research, Automated Offboard Refueling and Data Transfer for Unmanned Surface Vehicles, BAA Announcement # N00014-16-S-BA09, https://www.globalsecurity.org/military/systems/ship/systems/oradts.htm

Featured Image: Sonar Technician (Surface) Seaman Daniel Kline performs passive acoustic analysis in the sonar control room aboard the guided-missile destroyer USS Ramage (DDG 61). (U.S. Navy photo by Mass Communication Specialist 2nd Class Jacob D. Moore/Released)

Future Tech

Winning The AI-Enabled War-at-Sea

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By Dr. Peter Layton

Artificial intelligence (AI) technology is suddenly important to military forces. Not yet an arms race, today’s competition is more in terms of an experimentation race with many AI systems being tested and new research centers established. There may be a considerable first-mover advantage to the country that first understands AI adequately enough to change its existing human-centered force structures and embrace AI warfighting.

In a new Joint Studies Paper, I explore sea, land and air operational concepts appropriate to fighting near-to-medium term future AI-enabled wars. With much of the underlying narrow AI technology already developed in the commercial sector, this is less of a speculative exercise than might be assumed. Moreover, the contemporary AI’s general-purpose nature means its initial employment will be within existing operational level constructs, not wholly new ones.

Here, the focus is the sea domain. The operational concepts mooted are simply meant to stimulate thought about the future and how to prepare for it. In being so aimed, the concepts are deliberately constrained; crucially they are not joint or combined. In all this, it is important to remember that AI enlivens other technologies. AI is not a stand-alone actor, rather it works in the combination with numerous other digital technologies. It provides a form of cognition to these.

AI Overview

In the near-to-medium term, AI’s principal attraction is its ability to quickly identify patterns and detect items hidden within very large data troves. The principal consequence of this is that AI will make it much easier to detect, localize and identity objects across the battlespace. Hiding will become increasingly difficult. However, AI is not perfect. It has well known problems in being able to be fooled, in being brittle, being unable to transfer knowledge gained in one task to another and being dependent on data.

AI’s warfighting principal utility then becomes ‘find and fool’. AI with its machine learning is excellent at finding items hidden within a high clutter background. In this role AI is better than humans and tremendously faster. On the other hand, AI can be fooled through various means. AI’s great finding capabilities lack robustness.

A broad generic overview is useful to set the scene. The ‘find’ starting point is placing a large number of low cost Internet of Things (IoT) sensors in the optimum land, sea, air, space and cyber locations in the areas across which hostile forces may transit. From these sensors, a deep understanding can be gained of the undersea terrain, sea conditions, physical environment and local virtual milieu. Having this background data accelerates AI’s detection of any changes and, in particular, of the movement of military forces across it.

The fixed and mobile IoT edge-computing sensors are connected into a robust cloud to reliably feed data back into remote command support systems. The command system’s well-trained AI could then very rapidly filter out the important information from the background clutter. Using this, AI can then forecast adversary actions and predict optimum own force employment and its combat effectiveness. Hostile forces geolocated by AI can, after approval by human commanders, be quickly engaged using indirect fire including long-range missiles. Such an approach can engage close or deep targets; the key issues being data on the targets and the availability of suitable range firepower. The result is that the defended area quickly becomes a no-go zone.

To support the ‘fool’ function, Uncrewed Vehicles (UV) could be deployed across the battlespace equipped with a variety of electronic systems suitable for the Counter Intelligence Surveillance And Reconnaissance And Targeting (C-ISRT) task. The intent is to defeat the adversary’s AI ‘find’ capabilities. Made mobile through AI, these UVs will be harder for an enemy to destroy than fixed jammers would be. Moreover, mobile UVs can be risked and sent close in to approaching hostile forces to maximize jamming effectiveness. Such vehicles could also play a key role in deception, creating a false and misleading impression of the battlefield to the adversary. Imagine a battlespace where there are a thousand ‘valid’ targets, only a few of which are real.

A War-at-Sea Defense Concept

Defense is the more difficult tactical problem during a war-at-sea. Its intent is solely to gain tactical time for an effective attack or counterattack. Wayne Hughes goes as far in his seminal work to declare that: “All fleet operations based on defensive tactics…are conceptually deficient.”1  The AI-enabled battlefield may soften this assertion.

Accurately determining where hostile ships are in the vast ocean battlefields has traditionally been difficult. A great constant of such reconnaissance is that there never seems to be enough. However, against this, a great trend since the early 20th century is that maritime surveillance and reconnaissance technology is steadily improving. The focus is now not on collecting information but on improving the processing of the large troves of surveillance and reconnaissance data collected.2 Finding the warship ‘needle’ in the sea ‘haystack’ is becoming easier. 

The earlier generic ‘find’ concept envisaged a large distributed IoT sensor field. Such a concept is becoming possible in the maritime domain given AI and associated technology developments.

DARPA’s Ocean of Things (OoT) program aims to achieve maritime situational awareness over large ocean areas through deploying thousands of small, low-cost floats that form a distributed sensor network. Each smart float will have a suite of commercially available sensors to collect environmental and activity data; the later function involves automatically detecting, tracking and identifying nearby ships and – potentially – close aircraft traffic. The floats use edge processing with detection algorithms and then transmit the semi-processed data periodically via the Iridium satellite constellation to a cloud network for on-shore storage. AI machine learning then combs through this sparse data in real time to uncover hidden insights. The floats are environmentally friendly, have a life of around a year and in buys of 50,000 have a unit cost of about US$500 each. DARPA’s OoT shows what is feasible using AI.

In addition to floats, there are numerous other low-cost AI-enabled mobile devices that could noticeably expand maritime situational awareness including: the EMILY Hurricane Trackers, Ocean Aero Intelligent Autonomous Marine Vehicles, Seaglider Autonomous Underwater Vehicles, Liquid Robotics Wave Gliders and Australia’s Ocius Technology Bluebottles.

In addition to mobile low-cost autonomous devices plying the seas there is an increasing number of smallsats being launched by governments and commercial companies into low earth orbit to form large constellations. Most of these will use AI and edge computing; some will have sensors able to detect naval vessels visually or electronically.

All this data from new sources can be combined with that from the existing large array of traditional maritime surveillance systems. The latest system into service is the long-endurance MQ-4C Triton uncrewed aerial vehicle with detection capabilities able to be enhanced through retrofitting AI. The next advance may be the USN’s proposed 8000km range, AI-enabled Medium Unmanned Surface Vessel (MUSV) which could cruise autonomously at sea for two months with a surveillance payload.

With so many current and emerging maritime surveillance systems, the idea of a digital ocean is becoming practical. This concept envisages the data from thousands of persistent and mobile sensors being processed by AI, analyzed though machine learning and then fused into a detailed ocean-spanning three-dimensional comprehensive picture. Oceans remain large expanses making this a difficult challenge. However, a detailed near-real time digital model of smaller spaces such as enclosed waters like the South China Sea, national littoral zones or limited ocean areas of specific import appears practical using current and near-term technology.

Being able to create a digital ocean model may prove revolutionary. William Williamson of the USN Naval Postgraduate School declares: “On the ‘observable ocean’, the Navy must assume that every combatant will be trackable, with position updates occurring many times per day. …the Navy will have lost the advantages of invisibility, uncertainty, and surprise. …Vessels will be observable in port…[with] the time of departure known to within hours or even minutes. This is true for submarines as well as for surface ships.”3

This means that in a future major conflict, the default assessment by each warship’s captain might be that the adversary probably knows the ship’s location. Defense then moves from being “conceptually deficient” to being the foundation of all naval tactics in an AI-enabled battlespace. The emerging AI-enabled maritime surveillance system of systems will potentially radically change traditional war-at-sea thinking. The ‘attack effectively first’ mantra may need to be rewritten to ‘defend effectively first.’

The digital, ‘observable ocean’ will ensure warships are aware of approaching hostile warships and a consequent increasing risk of attack. In this addressing this, three broad alternative ways for the point defense of a naval task group might be considered.

Firstly, warships might cluster together, so as to concentrate their defensive capabilities and avoid any single ship being overwhelmed by a large multi-axis, multi-missile attack. In this, AI-enabled ship-borne radars and sensors will be able to better track incoming missiles amongst the background clutter. Moreover, AI-enabled command systems will be able to much more rapidly prioritize and undertake missile engagements. In addition, nearby AI-enabled uncrewed surface vessels may switch on active illuminator radars, allowing crewed surface combatants to receive reflections to create fire control-quality tracks. The speed and complexity of the attacks will probably mean that human-on-the-loop is the generally preferred AI-enabled ship weapon system control, switching to human-out-of-the-loop as numbers of incoming missiles rise or hypersonic missiles are faced.

Secondly, instead of clustering, warships might scatter so that an attack against one will not endanger others. Crucially, modern technology now allows dispersed ships to fight together as a single package. The ‘distributed lethality’ concept envisages distant warships sharing precise radar tracking data across a digital network, although there are issues of data latency that limit how far apart the ships sharing data for this purpose can be. An important driver of the ‘distributed lethality’ concept is to make adversary targeting more difficult. With the digital ocean, this driver may be becoming moot.

Thirdly, the defense in depth construct offers new potential through becoming AI-enabled, particularly when defending against submarines although the basic ideas also have value against surface warship threats. In areas submarines may transit through, stationary relocatable sensors like the USN’s Transformational Reliable Acoustic Path System could be employed backed up by unpowered, long endurance gliders towing passive arrays. These passive sonars would use automated target recognition algorithms supported by AI machine learning to identify specific underwater or surface contacts.

Closer to the friendly fleet, autonomous MUSVs could use low-frequency active variable depth sonars supplemented by medium-sized uncrewed underwater vehicles (UUV) with passive sonar arrays. Surface warships or the MUSVs could further deploy small UUVs carrying active multistatic acoustic coherent sensors already fielded in expendable sonobuoys. Warships could employ passive sonars to avoid counter-detection and take advantage of multistatic returns from the active variable depth sonars deployed by MUSVs.

Fool Function. The “digital ocean” significantly increases the importance of deception and confusion operations. This ‘fool’ function of AI may become as vital as the ‘find’ function, especially in the defense. In the war-at-sea, the multiple AI-enabled systems deployed across the battlespace offer numerous possibilities for fooling the adversary.

Deception involves reinforcing the perceptions or expectations of an adversary commander and then doing something else. In this, multiple false cues will need seeding as some clues will be missed by the adversary and having more than one will only add to the deception’s credibility. For example, a number of uncrewed surface vessels could set sail as the warship leaves port, all actively transmitting a noisy facsimile of the warships electronic or acoustic signature. The digital ocean may then suggest to the commander multiple identical warships are at sea, creating some uncertainty as to which is real or not.

In terms of confusion, the intent might be not to avoid detection as this might be very difficult but instead prevent an adversary from classifying vessels detected as warships or identifying them as a specific class of warship. This might be done using some of the large array of AI-enabled floaters, gliders, autonomous devices, underwater vehicles and uncrewed surface vessels to considerably confuse the digital ocean picture. The aim would be to change the empty oceans – or at least the operational area – into a seemingly crowded, cluttered, confusing environment where detecting and tracking the real sought-after warships was problematic and at best fleeting. If AI can find targets, AI can also obscure them.

A War-at-Sea Offense Concept

In a conflict where both sides are employing AI-enabled ‘fool’ systems, targeting adversary warships may become problematic. The ‘attack effectively first’ mantra may evolve to simply ‘attack effectively.’ Missiles that miss represent a significant loss of the task group’s or fleet’s net combat power, and take a considerable time to be replaced. Several alternatives may be viable.

In a coordinated attack, the offence might use a mix of crewed and uncrewed vessels. One option is to use three ship types: a large, well-defended crewed ship that carries considerable numbers of various types of long-range missiles but which remains remote to the high-threat areas; a smaller crewed warship pushed forward into the area where adversary ships are believed to be both for reconnaissance and to provide targeting for the larger ship’s long-range missiles; and an uncrewed stealthy ship operating still further forward in the highest risk area primarily collecting crucial time-sensitive intelligence and passing this back through the smaller crewed warship onto the larger ship in the rear.

The intermediate small crewed vessel can employ elevated or tethered systems and uncrewed communications relay vehicles to receive the information from the forward uncrewed vessel and act as a robust gateway to the fleet tactical grid using resilient communications systems and networks. Moreover, the intermediate smaller crewed vessel in being closer to the uncrewed vessel will be able to control it as the tactical situation requires and, if the context changes, adjust the uncrewed vessel’s mission.

This intermediate ship will probably also have small numbers of missiles available to use in extremis if the backward link to the larger missile ship fails. Assuming communications to all elements of the force will be available in all situations may be unwise. The group of three ships should be network enabled, not network dependent, and this could be achieved by allowing the intermediate ship to be capable of limited independent action.

The coordinated attack option is not a variant of the distributed lethality concept noted earlier. The data being passed from the stealthy uncrewed ship and the intermediate crewed vessel is targeting, not fire control, quality data. The coordinated attack option has only loose integration that is both less technically demanding and more appropriate to operations in an intense electronic warfare environment.

An alternative concept is to have a large crewed vessel at the center of a networked constellation of small and medium-sized uncrewed air, surface and subsurface systems. A large ship offers potential advantages in being able to incorporate advanced power generation to support emerging defensive systems like high energy lasers or rail guns. In this, the large crewed ship would need good survivability features, suitable defensive systems, an excellent command and control system to operate its multitude of diverse uncrewed systems and a high bandwidth communication system linking back to shore-based facilities and data storage services.

The crewed ship could employ mosaic warfare techniques to set up extended kinetic and non-kinetic kill webs through the uncrewed systems to reach the adversary warships. The ship’s combat power is not then in the crewed vessel but principally in its uncrewed systems with their varying levels of autonomy, AI application and edge computing.

The large ship and its associated constellation would effectively be a naval version of the Soviet reconnaissance-strike complex.  An AI-enabled war at sea then might involve dueling constellations, each seeking relative advantage.

Conclusion

The AI-enabled battlespace creates a different war-at-sea. Most obvious are the autonomous systems and vessels made possible by AI and edge computing. The bigger change though may be to finally take the steady scouting improvements of the last 100 years or so to their final conclusion. The age of AI, machine learning, big data, IoT and cloud computing appear set to create the “observable ocean.” From combining these technologies, near-real digital models of the ocean environment can be made that highlight the man-made artefacts present.

The digital ocean means warships could become the prey as much as the hunters. Such a perspective brings a shift in thinking about what the capital ship of the future might be. A recent study noted: “Navy’s next capital ship will not be a ship. It will be the Network of Humans and Machines, the Navy’s new center of gravity, embodying a superior source of combat power.” Tomorrow’s capital ship looks set to be the human-machine teams operating on an AI-enabled battlefield.

Dr. Peter Layton is a Visiting Fellow at the Griffith Asia Institute, Griffith University and an Associate Fellow at the Royal United Services Institute. He has extensive aviation and defense experience and, for his work at the Pentagon on force structure matters, was awarded the US Secretary of Defense’s Exceptional Public Service Medal. He has a doctorate from the University of New South Wales on grand strategy and has taught on the topic at the Eisenhower School. His research interests include grand strategy, national security policies particularly relating to middle powers, defense force structure concepts and the impacts of emerging technology. The author of ‘Grand Strategy’, his posts, articles and papers may be read at: https://peterlayton.academia.edu/research.

Endnotes

1. Wayne P. Hughes and Robert Girrier, Fleet tactics and naval operations, 3rd edn., (Annapolis: Naval Institute Press, 2018), p. 33.

2. Ibid., pp.132, 198.

3. William Williamson, ‘From Battleship to Chess’, USNI Proceedings, Vol. 146/7/1,409, July 2020, https://www.usni.org/magazines/proceedings/2020/july/battleship-chess

Featured image: Graphic highlighting Fleet Cyber Command Watch Floor of the U.S. Navy. (U.S. Navy graphic by Oliver Elijah Wood and PO2 William Sykes/Released)

Force Development

Virtual Training: Preparing Future Naval Officers for 21st Century Warfare

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By Joseph Bunyard

Introduction

“[We must] embrace the urgency of the moment: our maritime supremacy is being challenged.” —CNO NAVPLAN 2021

The fundamental character of war is changing.1 Distributed networks, next generation threats, and artificial intelligence will change “the face of conflict” by compressing and accelerating the Observe, Orient, Decide, Act (OODA) loop, streamlining the closure of kill chains.2 American security depends on the Navy’s ability to control the seas and project power ashore.3 Preparing future naval officers for 21st century warfare must begin at the US Naval Academy (USNA), where Virtual Training Environments (VTEs) could provide education and training opportunities once exclusive to the Fleet.4

21st century warfare requires data producers and smart data consumers. Although the Department of Defense recognizes the need for an “AI ready force,” the 2018 National Defense Strategy claims that professional military education “has stagnated at the expense of lethality and ingenuity.”5 To address this charge, the Navy’s 2020 Education for Seapower Strategy calls for the creation of a “continuum of learning” through the Naval University System.6 While the Naval Postgraduate School conducts innovative technical research—and the Naval War College endows senior leaders with a strategic outlook on the future of warfare—the US Naval Academy does not feature AI, unmanned systems, tactics, or strategy in its core curriculum.7

Figure 1 – Aviation Officer Career Progression. Above: aviation officers require 2.5 years of training before deployment. 8

New technology often means new qualification requirements for junior officers. Added training extends the length of time before an officer is ready to deploy, a worrying trend at which Type Commanders are taking aim (see Figure 1).9 VTEs could offer Midshipmen exposure to the naval applications of disruptive technologies, the chance to accomplish existing Fleet training prior to commissioning, and Artificial Intelligence (AI)/ Machine Learning (ML) tools that they could take to the Fleet. To realize these objectives, the Naval Academy must leverage three types of VTEs—low-cost, commercial-off-the-shelf (COTS), and Fleet-integrated—to expand training opportunities and reinforce its core curriculum.

E-learning in the COVID-19 era provides the Naval Academy a chance to update its operating system (OS). Instead of using new media, such as Zoom, to present the same PowerPoints Midshipmen would receive in-person, USNA should update its curriculum to take advantage of VTEs with proven training and educational outcomes. Incorporating new media into existing curricula requires an OS update that expands USNA’s “leadership laboratory” into a 21st century warfare laboratory, where smart data producers and consumers are forged. 10

Integrating Low-Cost Virtual Training Environments (VTEs)

“To maintain naval power in an era of great power competition and technological change, the Navy and Marine Corps need to strengthen and expand their educational efforts.”—Education for Seapower Strategy 2020

The Navy and Marine Corps increasingly rely on VTEs to “expand watch team proficiency and combat readiness” across the Fleet.11 Unlike traditional simulators, virtual reality trainers are highly mobile and often rely on commercial-off-the-shelf (COTS) hardware. The Chief of Naval Air Training’s Project Avenger simulator, for example, uses gaming computers and virtual reality headsets to qualify students for solo flights in half of the traditional number of flight hours.12 The Marine Corps’ tactical decision kits use similar technology to train infantry battalions on weapon systems and tactics.13 Mixed reality glasses, which overlay a user’s vision with digital information, help crews across the Fleet complete complex maintenance.14

Expanding access to existing virtual reality trainers at the Naval Academy could enable Midshipmen to complete portions of Naval Introductory Flight Evaluation (NIFE), The Basic School (TBS), and Basic Division Officer Course (BDOC) syllabi prior to commissioning. “Future multi-domain combat will be so complex and long-ranged that the military will rely heavily on simulations to train for it.”15 More access to VTE trainers means more familiarization with the technology and interfaces that junior officers are increasingly likely to encounter in the Fleet.

Figure 2 – A Project Avenger Simulator. U.S. Navy photo. 16

Accessing the Navy Continuous Training Environment (NCTE)

“Winning in contested seas also means fielding and equipping teams that are masters of all-domain fleet operations.” —CNO NAVPLAN 2021

VTEs allow users to conceptualize next generation threats. While the Naval Academy provides Midshipmen the technical foundation to understand Anti-Access/ Area-Denial (A2/AD) bubbles and contested communications zones, it offers few means for Midshipmen to visualize these abstract threats in an operational context.17 NAVAIR’s Joint Simulation Environment (JSE) and INDOPACCOM’s Pacific Multi-Domain Training and Experimentation Capability simulate next generation threats for operations analysis and platform research design testing and evaluation (RDT&E).18 The Navy Continuous Training Environment (NCTE) enables cross-platform integration of these platforms, and many more, which allows warfighters around the world to take part in scalable multi-domain battle problems.19

Figure 3 – NAVAIR’s JSE 20

To meet the Fleet’s growing need for diversified data, the Navy should leverage the informed and available, yet inexperienced, potential of the Academy’s more than 4,000 Midshipmen. Providing the Naval Academy with NCTE access could generate data for the Fleet and the operational context of classroom lessons for Midshipmen. Data is the new oil; improving predictive AI/ML models, concepts of operation, and training interfaces requires mass amounts of quality data from a range of problem-solving approaches.21 Installing an NCTE node in Hopper Hall’s new Sensitive Compartmented Information Facilities (SCIFs) would not only allow Midshipmen to observe Fleet training events but also to perform their own operations analysis on platforms, capabilities, and strategies developed during their capstone research.22

Leveraging Commercial-Off-The-Shelf (COTS) VTEs

“Advances in artificial intelligence and machine learning have increased the importance of achieving decision superiority in combat.” —CNO NAVPLAN 2021

For the cost of a video game, the Naval Academy could use the same software as defense industry leaders to improve the decision-making ability of Midshipmen, reinforce classroom concepts, and introduce next generation threats and platforms. The Defense Advanced Research Projects Agency (DARPA) uses popular videogames like Command: Modern Operations ($79.99 on Steam) to search for “asymmetrical conditions” within “hyper-realistic theater-wide combat simulators” that could be exploited in real-world scenarios.23 Many titles offer open Application Programming Interfaces (APIs) that allow users to change the decision-making logic of AI opponents and load custom platforms and capabilities into the game, such as squadrons of future unmanned systems.24 Modern concepts of operation—like Expeditionary Advanced Basing Operations and Joint All-Domain Command Control—often undergo “virtual sea trials” in such simulations.25

Figure 4 – Simulated Theater-Level Conflict in the South China Sea

The user-friendly, scalable, and unclassified nature of wargame simulators like Command: Modern Operations make them suitable for inter-academy use. Allies such as the United Kingdom already use commercial titles to host “Fight Clubs” among military and civilian personnel across all roles and ranks of their armed forces.26 By leveraging its cadre of foreign exchange officers and multilateral relationships, the Naval Academy could form an international “fight club” in the style of the growing “e-sports” industry. Competing with and against international Midshipmen and officers would allow Naval Academy Midshipmen to forge relationships with allies and learn from their approaches to tactics, strategies, and decision-making across a variety of simulated scenarios.

COTS Artificial Intelligence (AI) & Machine Learning (ML) VTEs

“Adopting AI capabilities at speed and scale is essential to maintain military advantage.”—2020 Department of Defense AI Education Strategy

Virtual machines provide users with access to advanced AI and ML tools, as well as the computing power necessary to use them at scale, anywhere there is an internet connection.27 Maintaining the Navy’s military advantage requires an “AI ready force” of smart data producers and consumers.28 Applying AI to operations and processes across the Fleet will likely make open-source ML software the Excel of the future, requiring both smart data producers and consumers. Not every officer is an Excel “wizard,” but most understand how it works, the problems it can solve, and the type of data it needs to function. In order to build an “AI ready force” across all roles and ranks, the Naval Academy should join the growing field of leading research universities incorporating introductory AI and ML courses in their core curricula.29

Just as seamanship and navigation are the cornerstone of maritime competence, AI-literacy will be the core of digital competence. Incorporating AI and ML into the Naval Academy’s core curriculum would create smart data producers and consumers, accelerating the Fleet’s exposure to AI through the bottom up approach envisioned in the Department of Defense AI Education Strategy.30 According to a 2019 study by IBM, “model interoperability,” understanding how a model arrives at a given decision is the single factor that most influences users’ trust in AI.31 Naval Academy graduates literate in AI and ML could better lead enlisted sailors as increasingly complex systems join the Fleet.

Towards a 21st Century Warfare Laboratory

“Transforming our learning model for the 21st century will enable us to adapt and achieve decisive advantage in complex, rapidly changing operating environments.” —2020 Triservice Maritime Strategy 32

The Naval Academy must return to the warfighting mentality of its past.33 In 2007, the Naval Academy not only removed its only tactics and strategy course from the Midshipmen core curriculum, it stopped offering it altogether.34 Until recently, this decision signaled the end of a rich history of wargaming at USNA, which included Academy-wide games held at varying levels of classification.35 VTEs offer the Naval Academy an opportunity to reprioritize warfighting by providing the “ready, relevant learning” future naval officers will need to conduct 21st century warfare.36

New concepts of operation require learning and experimentation that 21st century warfare-literate junior officers could accelerate. The Navy and Marine Corps continue to outline ambitious plans that leverage AI, unmanned platforms, and next generation networks in new concepts of operation. Consequently, the Navy aims to equip sailors with “a high degree of confidence and skill operating alongside” unmanned platforms and AI by “the end of this decade.”37 Creating a true “learning continuum” to prepare the Fleet for the future of warfare must start at the US Naval Academy, where the COVID-19 distance-learning environment offers an opportunity for the Naval Academy to update its operating system using VTEs.

Ensign Bunyard is a 2020 graduate of the U.S. Naval Academy. Upon completing his Master’s in Information Technology Strategy at Carnegie Mellon University, he will report to Pensacola for training as a student naval aviator.

Endnotes

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4. Wilson, Clay. Network Centric Warfare: Background and Oversight Issues for Congress. CRS Report for Congress § (2005).
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6. Gilday, 4.
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20. “Joint Simulation Environment.”
21. Graham, Karen. “AI Systems Are ‘Only as Good as the Data We Put into Them’.” Digital Journal: A Global Digital Media Network, September 5, 2018. http://www.digitaljournal.com/tech-and-science/technology/a-i-systems-are-only-as-good-as-the-data-we-put-into-them/article/531246. Also, Nilekani, Nandan. “Data to the People.” Foreign Affairs. Council on Foreign Relations, July 29, 2020. https://www.foreignaffairs.com/articles/asia/2018-08-13/data-people.
22. Tortora, Paul. “Center for Cyber Security Studies – 2018-2019 Stewardship Report.” Cyber Studies, March 14, 2020. http://1970.usnaclasses.com/Classprojects/Center%20for%20Cyber%20Studies.html.
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24. Atherton, Kelsey.
25. Atherton, Kelsey.
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29. “2020 Department of Defense Artificial Intelligence Education Strategy.”
30. “2020 Department of Defense Artificial Intelligence Education Strategy.”
31. Ashoori, Maryam, Weisz, Justin.” “In AI We Trust? Factors that Influence Trustworthiness of AI-Infused Decision-Making Processes.” IBM. December 5, 2019. https://arxiv.org/pdf/1912.02675.pdf., 2.
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33. McKinney, Michael. “Warfighting First? Not so Much.” U.S. Naval Institute. May 2019. https://www.usni.org/magazines/proceedings/2019/may/warfighting-first-not-so-much
34. “Initial Report of the Dean’s Cyber Warfare Ad Hoc Committee.” The US Naval Academy. August 21, 2009. https://www.usna.edu/Users/cs/needham/CyberSecurityInitiative/USNACyberInitiativeInitialReport_USNA-CS-TR-2011-02.pdf#search=ns310., 76.
“Core Curriculum Review.” USNA Division of Seamanship and Navigation. March 2, 2005. https://www.usna.edu/Academics/_files/documents/sapr/ProDev_Core.pdf., slide 13.
35. “Wargaming at the Naval Academy.” Shipmate. The United States Naval Academy Alumni Foundation. February 2021., 25-26.
36. “Ready, Relevant Learning.” Naval Education and Training Command. Accessed March 19, https://www.netc.navy.mil/RRL/.
37. Gilday, 11.

Feature photo: A U.S. Naval Academy Midshipman conducts a simulated T-6B Texan II flight on a newly installed virtual reality trainer device at the U.S. Naval Academy during Aviation Selection Night at Dahlgren Hall. (U.S. Navy photo by Lt. Cmdr. Rick Healey/Released)