The nexus of robotics and autonomous systems (RAS) and artificial intelligence (AI) has the potential to change the nature of warfare. RAS offers the possibility of a wide range of platforms—not just weapon systems—that can perform "dull, dangerous, and dirty" tasks—potentially reducing the risks to soldiers and Marines. Regarding AI, one report suggests One of the promises of AI in the military that seems to guarantee its adoption is its broad applicability. AI can be used to increase effectiveness and efficiency for more than just combat operations. AI can improve supply lines, enhance the training of new soldiers, and increase the effectiveness and efficiency of intelligence gathering and processing. But their effectiveness in combat operations seems especially promising. AI is not a wholly revolutionary idea to be applied to the military domain, and it is merely the next logical step in the digitization and mechanization of the modern battlefield. As a stated imperative in the National Defense Strategy, the Department of Defense (DOD) and the Services are pursuing RAS and AI for a wide variety of applications. Aside from the programmatic and budgetary considerations for Congress, another key aspect of these technologies that may merit consideration by Congress is articulated in the following passage from a U.S. Air Force document: Authorizing a machine to make lethal combat decisions is contingent on political and military leaders resolving legal and ethical questions. These include the appropriateness of machines having this ability, under what circumstances should it be employed, where responsibility for mistakes lies, and what limitations should be placed on the autonomy of such systems.... Ethical discussions and policy decisions must take place in the near term in order to guide the development of future [unmanned aircraft systems] capabilities, rather than allowing the development to take its own path apart from this critical guidance. Apart from the U.S. military's pursuit of RAS and AI, there has been a proliferation of RAS and AI internationally, ranging from foreign militaries, to violent nonstate groups to criminal organizations. These technologies have already been used to a limited degree against U.S. military forces. How the United States will address further foreign advances in these realms may be of interest to Congress. There are a variety of definitions for the following terms and, for the purposes of this report, the following definitions will be used: The level of independence that humans grant a system to execute a given task. It is the condition or quality of being self-governing to achieve an assigned task based on the system's own situational awareness (integrated sensing, perceiving, analyzing), planning, and decisionmaking. Autonomy refers to a spectrum of automation in which independent decisionmaking can be tailored for a specific mission, level of risk, and degree of human-machine teaming. A powered machine capable of executing a set of actions by direct human control, computer control, or both. It is composed minimally of a platform, software, and a power source. RAS is an accepted term in academia and the science and technology (S&T) community and highlights the physical (robotic) and cognitive (autonomous) aspects of these systems. For the purposes of this concept, RAS is a framework to describe systems with a robotic element, an autonomous element, or more commonly, both. The capability of a computer system to perform tasks that normally require human intelligence such as visual perception, speech recognition, and decisionmaking. In the 115 th Congress, multiple bills included definitions for AI and incorporated an often-cited classification scheme that categorizes AI systems as designed to think rationally, act rationally, think like humans, or act like humans. These classifications were broadly incorporated into the first definition of AI in statute, included in the John S. McCain National Defense Authorization Act for Fiscal Year 2019 ( P.L. 115-232 ), which states that the term AI includes (1) Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets. (2) An artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action. (3) An artificial system designed to think or act like a human, including cognitive architectures and neural networks. (4) A set of techniques, including machine learning that is designed to approximate a cognitive task. (5) An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision-making, and acting. While not specified in these definitions, a distinction between narrow and general AI is important when discussing the current and future abilities of AI systems. The term "narrow AI" describes technologies tailored to particular, narrowly defined tasks; the AI systems in use today fall within this category. While narrow AI systems can exceed human capabilities in their specific task set, they cannot understand context or apply what the systems have learned to related tasks. In contrast, "general AI" refers to systems that demonstrate intelligent behavior across a range of cognitive tasks, which is unlikely to occur for decades or longer, according to most analysts. Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that are provided by humans. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. A weapon system that, once activated, can select and engage targets without further intervention by a human operator. This includes human-supervised autonomous weapon systems that are designed to allow human operators to override operation of the weapon system, but can select and engage targets without further human input after activation. Much has been written about how RAS and AI have affected society and, in particular, the workplace. A comprehensive 2017 study by the International Bar Association's (IBA) Global Employment Institute offers some interesting insights on both society and the workplace: Modern information technologies and the advent of machines powered by artificial intelligence (AI) have already strongly influenced the world of work in the 21 st century. Computers, algorithms and software simplify everyday tasks, and it is impossible to imagine how most of our life could be managed without them. However, is it also impossible to imagine how most process steps could be managed without human force? The information economy characterized by exponential growth replaces the mass production industry based on economy of scales. When we transfer the experience of the past to the future, disturbing questions arise: what will the future world of work look like and how long will it take to get there? Will the future world of work be a world where humans spend less time earning their livelihood? Alternatively, are mass unemployment, mass poverty and social distortions also a possible scenario for the new world, a world where robots, intelligent systems and algorithms play an increasingly central role? What is the future role of a legal framework that is mainly based on a 20 th century industry setting? What is already clear and certain is that new technical developments will have a fundamental impact on the global labor market within the next few years, not just on industrial jobs but on the core of human tasks in the service sector that are considered 'untouchable.' Economic structures, working relationships, job profiles and well-established working time and remuneration models will undergo major changes. In addition to companies, employees and societies, education systems and legislators are also facing the task of meeting the new challenges resulting from constantly advancing technology. Legislators are already lagging behind and the gap between reality and legal framework is growing. The study further suggests that because of RAS and AI, society has entered a "Fourth Industrial Revolution," described as [t]he technical integration of cyber physical systems (CPS) into production and logistics and the use of the 'internet of things'(connection between everyday objects) and services in (industrial) processes—including the consequences for a new creation of value, business models as well as downstream services and work organization. CPS refers to the network connections between humans, machines, products, objects and ICT (information and communication technology) systems. Within the next five years, it is expected that over 50 billion connected machines will exist throughout the world. The introduction of AI in the service sector distinguishes the fourth industrial revolution from the third. The analysis also provides examples of Fourth Industrial Revolution robotics and artificial intelligence: Well-known examples from the field of robotics and AI are the so-called 'smart factories', driverless cars, delivery drones or 3D printers, which, based on an individual template, can produce highly complex things without changes in the production process or human action in any form being necessary. Well-known service models are, for example, networking platforms like Facebook or Amazon Mechanical Turk, the economy-on-demand providers Uber and Airbnb, or sharing services, such as car sharing, Spotify and Netflix. Studies show that merely due to sharing services the turnover of the sector will grow twentyfold within the next ten years. If, as some suggest, society is in a "Fourth Industrial Revolution" what are the implications for the U.S. military as a whole and, in particular, U.S. ground forces, namely the Army and Marine Corps? The U.S. 2018 National Defense Strategy of the United States of America, in describing DOD's strategic approach, states The Department will invest broadly in military application of autonomy, artificial intelligence, and machine learning, including rapid application of commercial breakthroughs, to gain competitive military advantages. In this regard, the Army and Marines are directed to pursue RAS and AI in support of the National Defense Strategy, but there are also more practical reasons why the Army and Marines might emphasize the development of RAS and AI.