Opportunities and Challenges of Applying AI/ML & Deep Learning Technologies in Military

Rajesh Uppal December 8, 2024 AI & IT, Cyber, Military Comments Off on Opportunities and Challenges of Applying AI/ML & Deep Learning Technologies in Military 477 Views

In recent years, the integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies has revolutionized various sectors, and the military is no exception. These advanced technologies offer unprecedented opportunities to enhance operational capabilities, improve decision-making processes, and transform traditional warfare strategies. However, their adoption also presents significant challenges and ethical considerations that must be carefully navigated.

Many nations are competing fervently to secure a leading edge in artificial intelligence (AI), recognizing its pivotal role in enhancing competitiveness, driving productivity gains, safeguarding national security, and addressing societal challenges. Those failing to cultivate successful AI innovations risk losing global market influence. Andrew Moore, former Carnegie Mellon University computer science dean and current Google Cloud AI leader, underscores the stakes, noting that this race will determine the future tech giants akin to Google, Amazon, and Apple by 2030. Nations slow to adopt AI risk reduced market presence across critical sectors like finance, manufacturing, and mining, while insufficient investment in AI R&D, particularly for military applications, jeopardizes national defense capabilities. Consequently, falling behind in AI can lead to economic setbacks and diminished geopolitical influence.

AI technology

AI is further divided into two categories: narrow AI and general AI. Narrow AI systems can perform only the specific task that they were trained to perform, while general AI systems would be capable of performing a broad range of tasks, including those for which they were not specifically trained. General AI systems do not yet exist. Narrow AI is currently being incorporated into a number of military applications by both the United States and its competitors. This was made possible by the advancement in Big Data, Deep Learning, and the exponential increase of chip processing capabilities, especially GPGPUs. Big Data is a term used to signify the exponential growth of data taking place, as 90% of the data in the world today has been created in the last two years alone.

Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text, or sound. Deep learning is usually implemented using a neural network architecture consisting of multiple layers of nonlinear processing units. In this context, a neuron refers to a single computation unit where the output is a weighted sum of inputs that passed a (nonlinear) activation function (e.g., a function that passes the signal only if it is positive).

A deep neural network refers to systems with large virtual networks that combine multiple nonlinear processing layers of neurons operating in parallel and inspired by biological nervous systems. It consists of an input layer, several hidden layers, and an output layer. The layers are interconnected via nodes, or neurons, with each hidden layer using the output of the previous layer as its input.

The term “deep” refers to the number of layers in the network—the more layers, the deeper the network. Traditional neural networks contain only 2 or 3 layers, while deep networks can have hundreds. Each layer in the network takes in data from the previous layer, transforms it, and passes it on. The network increases the complexity and detail of what it is learning from layer to layer. Therefore Deep learning, consume often a very large amount of raw input data. They process this data through many layers of nonlinear transformations of the input data in order to calculate a target output. The deep learning (DL) algorithms allow high-level abstraction from the data, and this is helpful for automatic features extraction and for pattern analysis/classification. Representation learning is one of the main reasons for the high performance of DNNs. Using DL and DNNs it is no longer necessary to manually craft the features required to learn a specific task. Instead, discriminating features are automatically learned during the training of a DNN.

Over the past decade, DNNs have become the state-of-the-art algorithms of Machine Learning in object recognition, face recognition, text translation, speech recognition, computer vision, natural language processing, and and advanced driver assistance systems, including, lane classification and traffic sign recognition.

Opportunities in Military Applications

Artificial Intelligence (AI) is becoming a critical part of modern warfare. In military and security applications, AI is pivotal across all domains—land, sea, air, space, and information—and at every level of warfare—political, strategic, operational, and tactical. At the political and strategic levels, AI can disrupt adversaries by generating and disseminating large volumes of deceptive information, necessitating AI defenses against such attacks. Operationally, AI enhances semi-autonomous control in unmanned systems, enabling more efficient human operation and greater battlefield impact.

Compared with conventional systems, military systems equipped with AI are capable of handling larger volumes of data more efficiently. Additionally, AI improves self-control, self-regulation, and self-actuation of combat systems due to its inherent computing and decision-making capabilities. A new Harvard Kennedy School study concludes AI could revolutionize war as much as nuclear weapons have done.

The promise of AI—including its ability to improve the speed and accuracy of everything from logistics and battlefield planning to human decision making—is driving militaries around the world to accelerate research and development. AI race has ensued between countries like US, China and Russia to take a lead in this strategic technology.

Enhanced Decision Making: AI and ML algorithms can analyze vast amounts of data with speed and precision that surpass human capabilities. The Department of Defense (DoD) faces a burgeoning volume of intelligence, surveillance, and reconnaissance (ISR) data, particularly from UAVs, producing petabytes of video annually. Harnessing this data fully is challenging due to human limitations in analysis and training costs.

AI, including deep learning, proves essential for on-platform processing of streaming data, facilitating real-time signal and target detection critical for decision support.

In military operations, this translates to more informed decision-making processes. For instance, AI can sift through intelligence data to identify patterns, predict enemy movements, and recommend optimal strategies in real-time, thereby improving mission outcomes and reducing risks to personnel.

Deep learning excels in pattern recognition tasks like natural language processing and image feature detection, enhancing geospatial analysis to extract actionable intelligence from ISR assets. Integrating AI, computer vision, and IoT supports target identification and classification, streamlining military logistics with reduced effort and error.

Autonomous Systems: Autonomous systems powered by AI can perform a range of tasks traditionally carried out by humans, such as reconnaissance, surveillance, and logistics. Unmanned aerial vehicles (UAVs) and ground vehicles equipped with AI can navigate complex environments, gather intelligence, and even engage in combat operations autonomously, thereby reducing human exposure to danger. Advancements in technology, such as large field-programmable gate arrays (FPGAs) and efficient GPUs, enable real-time deep learning applications previously impractical. Military initiatives integrating AI include neural networks in tanks for diagnostics, automated target recognition on aircraft, and fire detection systems using AI-enhanced video analysis. Autonomous weapons with embedded AI can independently analyze and engage targets, augmenting military capabilities with unprecedented precision and speed.

Cybersecurity and Defense: AI-based cybersecurity systems can detect and respond to cyber threats more effectively than traditional methods. ML algorithms can analyze network traffic patterns to identify anomalies indicative of cyber attacks, while DL techniques can enhance encryption and secure communication channels crucial for military operations.

Predictive Maintenance: AI-enabled predictive maintenance can optimize the lifecycle management of military equipment and vehicles. By analyzing sensor data and performance metrics in real-time, AI can predict potential failures before they occur, schedule maintenance proactively, and minimize downtime on critical assets.

Training and Simulation: AI and ML technologies are transforming military training through realistic simulations and virtual environments. AI-driven simulations can replicate complex battlefield scenarios, allowing soldiers to train in realistic conditions and adapt to various combat situations without the need for live exercises, thereby reducing costs and risks.

Modeling and simulation has been used extensively by the military for training, decision support, studies, etc. As a result, there are lots of already validated models that have been developed over long periods of time that could also potentially be used to generate synthetic data for ML-applications. As an example, a flight-simulator could be used to generate synthetic images of aircrafts placed in different environmental settings. Labeling is in this case automatic since the aircraft type is known prior to generating the synthetic image. However, not surprisingly, using synthetic images may result in poor performance when applying the model to real-world images. One approach that is currently being explored is to enhance the synthetic image using GANs to make it photo-realistic.

MIlitary AI in action

The integration of artificial intelligence into the battlefield is no longer a concept of the future. It’s a present reality. Recent revelations confirm the US military’s increased reliance on AI-powered systems to identify and neutralize threats.

Following the October 7th Hamas attacks on Israel, the US Central Command significantly ramped up its use of artificial intelligence. According to the command’s chief technology officer, Schuyler Moore, machine learning algorithms played a pivotal role in pinpointing targets for over 85 air strikes across Iraq and Syria this month.

These algorithms, honed through projects like the Pentagon’s Maven initiative, are capable of analyzing vast amounts of data, including satellite imagery and drone footage, to identify potential threats with remarkable speed and accuracy. While humans maintain ultimate decision-making authority, AI serves as a potent tool in accelerating the target identification process.

The deployment of AI in this capacity marks a significant shift in modern warfare. It underscores the growing importance of technology in military operations and raises critical questions about the ethical implications of AI-driven warfare. As AI continues to evolve, so too will its role in shaping future conflicts.

Russian Advance

Russian scientists have reportedly developed advanced technology known as NAKA, described as a neural network, specifically designed to identify and distinguish enemy installations and equipment, including European and US systems such as the Leopard main battle tank and the Bradley infantry fighting vehicle. This technology operates using UAV cameras to analyze video feeds and accurately pinpoint targets, displaying results with high probability percentages and exact coordinates. It represents part of Russia’s accelerated efforts in indigenous drone technology since 2022, following vulnerabilities exposed during conflicts. The NAKA technology also holds potential for civilian applications like agriculture and search-and-rescue operations in the future, showcasing its versatility beyond military uses.

Solving deep learning challenges with HPEC

Advances in high-performance embedded computing (HPEC) are crucial as data continues to grow in velocity, variety, veracity, and volume, posing challenges for traditional enterprise-class servers and storage. HPEC platforms have evolved significantly, not only accommodating deep learning algorithms but also meeting stringent size, weight, power, and cost (SWaP) constraints essential for military applications.

Key technologies like high-speed switched serial links and rugged standardized form factors, supported by HPEC middleware, have been refined to address specific military needs such as synthetic aperture radar (SAR) and signal intelligence (SIGINT). For system integrators, the task lies in effectively applying deep learning algorithms to solve unique military challenges.

HPEC systems offer military forces robust solutions for intensive data processing required by deep learning, especially for real-time evaluation of critical information. This capability enhances situational awareness, accelerates decision-making processes, and supports dynamic military operations. Applications range from intelligence analysis to rapid situational assessment in diverse operational environments, empowering military personnel with actionable insights into threats and adversaries.

Deploying modular HPEC systems optimized for deep learning, such as Kontron’s VX3058 3U VPX board, equipped with advanced Intel Xeon Processor D architecture, exemplifies the potential to integrate server-class computing into ruggedized environments. Platforms like the Kontron StarVX HPEC system leverage these capabilities, facilitating footprint reduction and enhancing operational efficiency through server virtualization. These advancements underscore HPEC’s pivotal role in enhancing military capabilities by enabling scalable, efficient, and adaptive deep learning solutions tailored to the demands of modern warfare scenarios.

Challenges and Advancements

In the realm of military applications, artificial intelligence (AI) and machine learning (ML) face several critical challenges that must be addressed for effective deployment and operational reliability. Transparency stands out as a paramount concern, as military systems utilizing AI must ensure understandable decision-making processes to maintain accountability and trust. The inherent vulnerabilities of AI systems, such as susceptibility to adversarial attacks or unintended biases in data, pose significant risks in military contexts where security and reliability are paramount. Moreover, the ability of AI models to learn effectively with limited training data is crucial, given the often restricted availability of labeled datasets in military environments.

Enhancing user trust and transparency

Many applications require, in addition to high performance, high transparency, high safety, and user trust or understanding. Such requirements are typical in safety-critical systems, surveillance systems, autonomous agents, medicine, and other similar applications. The required transparency of AI depends on the end-users needs. Transparency may concern user need for Trust in situations where it is difficult for users to question system recommendations. However, it may be unclear whether user trust is based on system performance or robustness, performance relative to the user, or how comfortable the user is with system recommendations.

There are in principle, two ways to make AI systems transparent. Firstly, some types of models are perceived as more interpretable than others, such as linear models, rule-based systems, or decision trees. Inspection of such models gives an understanding of their composition and computation. Lipton describes how the interpretability depends on whether users can predict system recommendations, understand model parameters, and understand the training algorithm.

Secondly, the system may explain its recommendations. Such explanations may be textual or visual. For example, by indicating what aspects of an image mostly contributes to its classification.

Bias and Reliability: Another aspect is fairness to avoid systematic biases that may result in unequal treatment for some cases. For example, evaluation of credit applications should not be based on personal attributes, such as sex or ethnicity, although such attributes may distinguish population groups on an overall statistical level.

AI algorithms are susceptible to biases embedded in training data, which can lead to discriminatory outcomes or erroneous decisions. In military applications, biased algorithms could impact targeting decisions, threat assessments, and personnel evaluations, highlighting the need for transparency, accountability, and diversity in AI development and deployment.

Beyond these primary challenges, optimizing AI algorithms for specific military tasks, ensuring robust generalization across diverse operational scenarios, and designing architectures that balance performance and resource constraints are additional complexities. Moreover, fine-tuning hyperparameters and achieving production-grade deployment of AI systems to operate reliably in real-world conditions are essential but less critical challenges that require careful consideration in military applications. Addressing these challenges is crucial for harnessing the full potential of AI and ML technologies to enhance military capabilities while mitigating risks and ensuring operational effectiveness.

Military AI Advancements

Developing machine learning (ML) applications within military contexts presents unique challenges due to the nature of data collection in such environments. Military data—from operations, sensor networks, and platforms—often lacks the volume and quality necessary for effective ML training. This scarcity hinders the ability to derive meaningful insights and build robust models.

Transfer learning emerges as a pivotal technique in mitigating these challenges, particularly when datasets are limited and computational resources are constrained. In transfer learning, pre-trained models—typically deep neural networks (DNNs)—are adapted for new tasks by either relearning the output layer or fine-tuning the entire model. Relearning the output layer involves replacing the final layer of a pre-trained model with a new output layer tailored to the target task, updating only its weights during training while keeping others fixed. On the other hand, fine-tuning allows for adjustments across the entire DNN, which enhances adaptability but demands more extensive training data.

Moreover, transfer learning not only facilitates model adaptation but also enhances generalization capabilities. However, its effectiveness diminishes as the dissimilarity between the original and target tasks increases.

Examples of feature visualization

Although DNNs offer high performance in many applications, their sub-symbolic computations with perhaps millions of parameters makes it difficult to understand exactly how input features contribute to system recommendations. Since DNNs high performance is critical for many applications, there is a considerable interest in how to make them more interpretable . Many algorithms for interpreting DNNs transform the DNN-processing into the original input space in order to visualize discriminating features. Typically, two general approaches are used for feature visualization, activation maximation and DNN explanation.

Activation maximation computes which inputs features that will maximally activate possible system recommendations. For image classification, this represents the ideal images that show discriminating and recognizable features for each class. However, the images often look unnatural since the classes may use many aspects of the same object and the semantic information in images is often spread out. Some examples of methods for activation maximation are gradient ascent, better regularization to increase generalizability, and synthesizing preferred images

Addressing the ‘black box’ problem

One of the major unresolved issue in ML/DNN networks is that when things go wrong, scientists are often at a loss to explain why. This is due to a lack of understanding of the decision-making within the AI systems. This issue is called the ‘black box’ problem.

As cognitive psychologist Gary Marcus writes at the New Yorker, the methods that are currently popular “lack ways of representing causal relationships (such as between diseases and their symptoms), and are likely to face challenges in acquiring abstract ideas like ‘sibling’ or ‘identical to.’ They have no obvious ways of performing logical inferences, and they are also still a long way from integrating abstract knowledge, such as information about what objects are, what they are for, and how they are typically used.” In other words, they don’t have any common sense. This implies DNNs are still not ready for use in real world applications such driverless cars.

DNN explanation explains system recommendations by highlighting discriminating input features. In image classification, such visualizations may highlight areas that provide evidence for or against a certain class or only show regions that contain discriminating features. One approach for calculating discriminating features is sensitivity analysis using local gradients or another measure of variation

Generative adversarial networks (GANs)

Another promising advancement in ML is generative adversarial networks (GANs), introduced by Goodfellow et al. GANs consist of two competing DNNs: a generator that creates synthetic data and a discriminator that distinguishes between real and generated data. Through adversarial training, GANs achieve equilibrium where improvements in one network enhance the performance of the other. This capability makes GANs valuable for tasks beyond semi-supervised learning, including image reconstruction, super-resolution, and image-to-image translation.

For military applications, GANs could be pivotal in transforming night-vision images into daylight scenarios or enhancing image quality in reconnaissance missions. These applications illustrate GANs’ potential to augment operational capabilities by generating synthetic data and improving overall model robustness in diverse military environments.

Data Security and Privacy:

The reliance on AI necessitates the collection and analysis of vast amounts of sensitive data, including personal information and classified intelligence. Protecting this data from cyber threats and unauthorized access is paramount to maintaining operational security and safeguarding national interests.

Cyberthreats and Cyversecurity

Artificial intelligence (AI) and machine learning (ML) introduce unique cybersecurity challenges alongside their transformative potential. These technologies are susceptible to vulnerabilities and misconfigurations, similar to earlier technological advancements, but they also present novel risks.

Two primary concerns hindering AI development are data and algorithm insecurity, both vulnerable to exploitation by known hackers. Deep neural networks (DNNs) face specific vulnerabilities: manipulation of input and model manipulation, where slight modifications can deceive systems designed to process vast amounts of data more effectively than humans. Furthermore, the integrity of training and testing data is critical, as they often contain sensitive information that, if compromised, can undermine system reliability and trust.

Incorporating AI requires handling massive data volumes, often processed in cloud platforms, adding complexity and potential vulnerabilities. Cybersecurity remains a top concern for AI adopters, with many organizations noting insufficient readiness to mitigate associated risks.

Moreover, AI’s deployment in applications like facial recognition and object detection reveals vulnerabilities. Studies demonstrate how subtle changes to inputs can mislead AI systems, such as misclassifying images imperceptible to humans or misinterpreting contextual cues due to training biases or environmental factors.

Cybersecurity

Addressing these challenges demands rigorous cybersecurity practices, including data anonymization, tokenization, and encryption, to safeguard AI and ML systems. Additionally, strategies against adversarial attacks, such as data poisoning and model manipulation, are crucial to maintain system robustness and reliability.

When designing deep neural networks (DNNs) with limited training data, leveraging pre-trained models through transfer learning is a common practice to achieve robust performance. This involves adapting models trained on large datasets by replacing and customizing the final layers to suit specific tasks, followed by fine-tuning using available data. Numerous pre-trained models are readily accessible online, facilitating rapid deployment in various applications.

However, the use of pre-trained models introduces potential vulnerabilities, particularly through hidden backdoors inserted into them. These backdoors can be maliciously implanted during model training, posing security risks when the model encounters manipulated inputs in real-world scenarios. For instance, researchers have demonstrated vulnerabilities by inserting backdoors into models trained to recognize US traffic signs, where alterations like stickers on stop signs can cause misclassifications in systems intended for different traffic sign types.

To defend against such threats, several strategies have been proposed. One approach involves integrating adversarial examples—manipulated inputs designed to deceive the model—during the training phase. By exposing the model to adversarial scenarios during training, it becomes more resilient to similar attacks during deployment.

Another method is defense distillation, which modifies how the model interprets class probabilities. Initially, the model is trained conventionally, and then a new model is trained using soft class labels derived from the first model’s output probabilities. This approach helps mitigate vulnerabilities by introducing flexibility in how the model assigns probabilities, thereby reducing the impact of adversarial inputs.

Additional defense techniques include feature squeezing, which modifies input data representations to reduce the effectiveness of adversarial perturbations. Techniques like mean or median filtering and non-linear pixel encodings aim to distort input signals in ways that are less susceptible to manipulation.

Despite these defenses, complete mitigation of vulnerabilities remains challenging, especially when attackers possess detailed knowledge of the model and its defense mechanisms. Ongoing research focuses on enhancing model robustness and developing more sophisticated defenses to safeguard against evolving cybersecurity threats in deep learning systems.

As AI continues to evolve across military and civilian domains, mitigating these cybersecurity risks is essential for maximizing its transformative potential while ensuring operational integrity and trustworthiness in critical applications.

Ethical Considerations

The use of AI in military applications raises ethical concerns, such as the implications of autonomous weapons systems (AWS). Questions about accountability, compliance with international humanitarian law (IHL), and the ethical use of lethal force by AI-driven systems remain contentious issues that require global dialogue and regulation.

Technological Dependence: Overreliance on AI and automation in military operations may pose risks if systems malfunction, are hacked, or fail to adapt to unforeseen circumstances. Maintaining human oversight and control over AI-driven systems is crucial to mitigating risks and ensuring operational effectiveness in dynamic and unpredictable environments.

International Competition and Regulation: The rapid advancement of AI technologies has sparked a global race for military superiority, raising concerns about arms races, proliferation, and strategic stability. International cooperation and regulations are essential to establish norms for the ethical use of AI in warfare, prevent misuse, and promote transparency among nations.

Conclusion

The application of AI, ML, and deep learning technologies in military contexts offers profound opportunities to enhance operational efficiency, improve strategic decision-making, and protect personnel. However, these advancements must be accompanied by robust ethical frameworks, stringent data security measures, and careful consideration of the implications for international security and human rights. By addressing challenges proactively and fostering responsible innovation, nations can harness the full potential of AI while upholding ethical standards and ensuring global stability in an AI-driven future.