Implication of Artificial Intelligance aided Mil Campaigns by Maj Rohit Pandey



  1. In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Computer science defines Al research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is used to describe machines that mimic “cognitive” functions that humans associate with other human minds, such as “learning” and “problem-solving”.
  2. Artificial intelligence can be classified into three different types of systems:

(a)       Analytical. Analytical Al has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions.

(b)       Human-inspired. Human-inspired Al has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making.

(c)       Humanized artificial intelligence. Humanized Al shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.

  1. Artificial intelligence was founded as an academic discipline in 1956. The traditional problems (or goals) of Al research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic Al. Many tools are used in Al, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The Al field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields. The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider Al to be a danger to humanity if it progresses unabated. Others believe that Al, unlike previous technological revolutions, will create a risk of mass unemployment. In the twenty-first century, Al techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and Al techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.


  1. The Aim of the paper is to bring out the implications of Artificial Intelligence aided Military Campaigns.


  1. The Scope of the paper cover the following

(a)       What is Al

(b)       Military Applications

(C)      Impact on Strat/Tacts

(d)       Way Ahead


  1. A typical Al analyses its environment and takes actions that maximize its chance of success. An Al’s intended utility function (or goal) can be simple (“1 if the Al wins a game of Go, 0 otherwise”) or complex (“Do mathematically similar actions to the ones succeeded in the past”). Goals can be explicitly defined or induced. If the Al is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behaviour or punishing others. Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring Al systems, similarly to how animals evolved to innately desire certain goals such as finding food. Some Al systems, such as nearest-neighbour. instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose “goal” is to successfully accomplish its narrow classification task.
  2. Al often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A complex algorithm is often built on top of other, simpler, algorithms. Many Al algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbour, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could, therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of Al research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial.
  3. The earliest (and easiest to understand) approach to Al was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business Al applications, are analogies such as SVM and nearest-neighbour: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other Al and non-Al algorithms; the best approach is often different depending on the problem.


  1. Artificial Intelligence (AI) is becoming a critical part of modern warfare. Compared with conventional systems, military systems equipped with Al are capable of handling larger volumes of data more efficiently. Additionally, Al improves the self-control, self-regulation, and self-actuation of combat systems due to its inherent computing and decision-making capabilities.
  2. Al is deployed in almost every military application, and increased research and development funding from military research agencies to develop new and advanced applications of artificial intelligence is projected to drive the increased adoption of Al- driven systems in the military sector. Following are eight major military applications where Al will prove its importance in the years to come.
  3. Warfare Platforms. Defence forces from different countries across the globe are embedding Al into weapons and other systems used on land, naval, airborne, and space platforms. Using Al in systems based on these platforms has enabled the development of efficient warfare systems, which are less reliant on human input. It has also led to increased synergy and enhanced performance of warfare systems while requiring less maintenance. Al is also expected to empower autonomous and high-speed weapons to carry out collaborative attacks.
  4. Cybersecurity. Military systems are often vulnerable to cyber attacks, which can lead to loss of classified military information and damage to military systems. However, systems equipped with Al can autonomously protect networks, computers, programs, and data from any kind of unauthorized access. In addition, Al-enabled web security systems can record the pattern of cyber attacks and develop counter-attack tools to tackle them.
  5. Logistics & Transportation. Al is expected to play a crucial role in military logistics and transport. The effective transportation of goods, ammunition, armaments, and troops is an essential component of successful military operations. Integrating Al with military transportation can lower transportation costs and reduce human operational efforts. It also enables military fleets to easily detect anomalies and quickly predict component failures. Recently, the US Army collaborated with IBM to use its Watson artificial intelligence platform to help pre-identify maintenance problems in Stryker combat vehicles.
  6. Target Recognition. Al techniques are being developed to enhance the accuracy of target recognition in complex combat environments. These techniques allow defence forces to gain an in-depth understanding of potential operation areas by analysing reports, documents, news feeds, and other forms of unstructured information. Additionally, Al in target recognition systems improves the ability of these systems to identify the position of their targets. Capabilities of Al- enabled target recognition systems include probability-based forecasts of enemy behaviour, aggregation of weather and environmental conditions, anticipation and flagging of potential supply line bottlenecks or vulnerabilities, assessments of mission approaches, and suggested mitigation strategies. Machine learning is also used to learn, track, and discover targets from the data obtained.
  7. Battlefield Healthcare. In war zones, Al can be integrated with Robotic Surgical Systems (RSS) and Robotic Ground Platforms (RGPs) to provide remote surgical support and evacuation activities. The US in particular is involved in the development of RSS, RGPs, and various other systems for battlefield healthcare. Under difficult conditions, systems equipped with Al can mine soldiers’ medical records and assist in complex diagnoses. For instance, IBM’s Watson research team partnered with the US Veterans Administration to develop a clinical reasoning prototype known as the Electronic Medical Record Analyzer (EMRA). This preliminary technology is designed to use machine learning techniques to process patients’ electronic medical records and automatically identify and rank their most critical health problems.
  8. Combat Simulation & Training. Simulation & training is a multidisciplinary field that pairs system engineering, software engineering, and computer science to construct computerized models that acquaint soldiers with the various combat systems deployed during military operations. The US is investing increasingly in simulation & training applications. The US Navy and Army have each been conducting warfare analysis, which has led to the initiation of several sensor simulation programs. The US Navy has enlisted such companies such as Leidos, SAIC, AECOM, and Orbital ATK to support their programs, while the US Army’s programs are supported by firms including SAIC, CACI, Torch Technologies, and Millennium Engineering.
  9. Threat Monitoring & Situational Awareness. Threat monitoring & situational awareness rely heavily on Intelligence, Surveillance, and Reconnaissance (ISR) operations. ISR operations are used to acquire and process information to support a range of military activities. Unmanned systems used to carry out ISR missions can either be remotely operated or sent on a pre-defined route. Equipping these systems with Al assists defence personnel in threat monitoring, thereby enhancing their situational awareness. Unmanned aerial vehicles (UAVs) – also known as drones – with integrated Al can patrol border areas, identify potential threats, and transmit information about these threats to response teams. Using UAVs can thus strengthen the security of military bases, as well as increase the safety and efficacy of military personnel in battle or at remote locations.
  10. Al & Data Information Processing. Al is particularly useful for quickly and efficiently processing large volumes of data in order to obtain valuable information. Al can assist in culling and aggregating information from different datasets, as well as acquire and sum supersets of information from various sources. This advanced analysis enables military personnel to then recognize patterns and derive correlations.


  1. Formulating national security strategy. The national security strategy composed of ends, ways, and means is a useful framework for understanding security objectives, how they will be fulfilled, and the resources available for doing so. This framework is iterative, as shifts along any one of its vertices influences the entire effort. At a more granular level, national security strategy formulation has three primary thrusts: diagnosis, decision-making, and assessment. Whether setting the broad vision of India’s “interests, goals, and objectives” or considering specific near-term efforts to harness the “political, economic, military, and other elements of India’s national power,” as the Government requires of every new administration, setting national security strategy nevertheless involve some degree of energy and effort in this vein.
  2. Diagnosis focuses on understanding the strategic landscape as it exists and considering the trajectories it might take in the future. It requires deep and textured knowledge of global and regional trends. For example, how has power shifted between the United States and China over the last decade and in what ways might it do so in the near- to mid-term? How does the Iranian Supreme Leader view his country’s opportunities and challenges in the Middle East?
  3. Decision making requires answering the colossal strategic questions of employing national power in support of national interests and values. Who should the India fight? Why should it do so? Over which issues? How should it fight? What constitutes victory and what constitutes defeat?
  4. Artificial intelligence can influence national security strategy formulation in any number of ways. It provides both opportunities and challenges to decision-makers many of which remain unknown. Going forward, artificial intelligence could influence who joins and succeeds in the national security profession, how familiar they are with what machines can and cannot tell us, and how responsible oversight is conducted. One analogy worth considering is how the national security profession has dealt with nuclear weapons. In that area, a small cohort of experts, composed of what is teasingly referred to as the priesthood, has helped foment the belief that policymakers must become deep experts and scale massive barriers to entry in order to meaningfully contribute to decisions on this topic.
  5. Given the expected overwhelming influence of artificial intelligence in broader national security affairs, this analogy portends real problems if it comes to fruition. It portends these problems not only because having a select cohort who has scaled similar barriers engaging on issues invariably provides a limited perspective. Artificial intelligence is not just the object of a decision, but it may also assist in making decisions. Simply put, artificial intelligence may help national security policymakers decide whether and if it should even be employed in the decision at hand. And, given the private sector’s involvement in digital technology, the Defence Department’s consideration of, and ability to execute, its decisions on “Who should the India fight? Why should it do so? Over which issues? How should it fight?” may largely depend on its relationship with the private sector.
  6. It is easy to say that humans and not machines that will make the important decisions in an artificial intelligence infused national security world, Perhaps it is also lazy. Artificial intelligence will influence the management, employment, and development of military force; that goes beyond swarms of weapons to better targeting of adversaries to offering decisionmakers new and different options in conflict. While the defence Department may have pledged humans will always make the ultimate decision about killing another human being, there are nevertheless serious questions about what that means if artificial intelligence can enable a weapons system that can “independently compose and select among alternative courses of action to accomplish goals based on its knowledge and understanding of the world, of itself, and of the local, dynamic context.”
  7. Moreover, that last issue context is particularly relevant since policymakers will be more inclined to empower machines to make decisions under some circumstances rather than others. For example, when speed plays an outsized role, such as in a missile defence scenario, or when connectivity is limited so a system cannot consult a human for additional guidance, decision making authority may be further devolved down. Detaching operational level employment of artificial intelligence capabilities from the strategic level dangerously dismisses how operators will formulate military options intertwined with these tools. Above all, national security policymakers must be cautious of satisfying themselves with the false antidote of ultimate control.
  8. The increasing capability of artificial intelligence will influence all three phases of national security strategy formulation; diagnosis, decision-making, and assessment. Indeed, it likely will both facilitate and impede them. By unearthing and filtering through a surfeit of information, decision-makers will have more detail than ever before imagined on a wide range of subjects, ranging from permutations in the security environment to shifting adversary military capabilities and perceptions. There is a powerful inertia that plagues national security decision-making in India, no doubt tied to the distribution of power in a system deliberately designed to constrain action, and an abundance of information will not necessarily overcome that dynamic. Rather, it could lead to further indecision, micromanagement, or analysis by paralysis.
  9. Bias is the modus operandi of national security strategy in all three phases. To be sure, “data analytics and algorithms are developed by and for human consumption and can only be as useful as humans make them.” And, as any experienced national security policymaker knows well, narratives develop around how to understand various dilemmas and breaking through them can be exceedingly difficult. If artificial intelligence can help policymakers see patterns that they were unable or unwilling to grasp, then it will be particularly valuable.


  1. Although it is not in doubt that Al is going to be part of the future of militaries around the world, the landscape is changing quickly and in potentially disruptive ways. Al is advancing but given the current struggle to imbue computers with true knowledge and expert-based behaviours, as well as limitations in perception sensors, it will be many years before Al will be able to approximate human intelligence in high- uncertainty settings -as epitomized by the fog of war. Given the present inability of Al to reason in such high-stakes settings, it is understandable that many people want to ban autonomous weapons, but the complexity of the field means that prohibition must be carefully scoped. Fundamentally, for instance, does the term autonomous weapon describe the actual weapon i.e. a missile on a drone or the drone itself? Autonomous guidance systems for missiles on drones will likely be strikingly similar to those that deliver packages, so banning one could affect the other. And how will technologies be treated that emerge from the growing commercial market, which is expected to leapfrog some aspects of military capability and possibly change public perception? The impact of the rapid expansion of the commercial market on autonomous.
  2. systems development cannot be overstated, and an even bigger problem in the short term is how to fully understand the global implications of the discernible shift in the power base of Al expertise from the military to commercial enterprises. Machines, computers and robots are getting ‘smarter’ primarily because roboticists and related engineers are getting smarter, so this relatively small group of expert humans is becoming a critical commodity. Universities have been slow to respond to this demand, and governments and industry have also lagged behind in providing scholarship mechanisms to incentivize students in the field of Al. Ultimately, the growth in the commercial information technology and automotive sectors, in terms of both attracting top talent and expanding autonomous systems capabilities in everyday commercial products, could be a double-edged sword that will undoubtedly affect militaries around the world in as yet imagined ways.


  1. Today the case of India’s scenario is different from China, USA, Germany, Japan and Russia for deployment of Al. Al may be considered as 6th Dimension of warfare. The application and scope of Al go beyond as discussed in the paper. We need a team of brains for the implementation of the vision from strategic and tactical points of view.