The US military service is setting up a Digital Corps comprising Computer Scientists – creating a new breed of “civilian soldiers” who fight for their nation using data skills
In 2020, there were more than 430,000 open computer science jobs in the United States, while only 71,000 new computer scientists graduate from American universities each year. Assess the number of civilian personnel needed in software developer, software engineer, knowledge management, data scientist, and AI career fields for both management and specialist tracks. It is strategizing to increase the pool of home-grown talent and make up the shortfall by relaxing immigration rules for data scientists. These resources will form a Digital Corps modelled along the lines of a Medical Corps for the US defence forces.
If the military services create career fields for software developers and data scientists, this will almost inevitably change what it means to be a soldier, sailor, airman, or marine, much as the introduction of aviation did generations ago. The government would create civilian occupational series for software development, software engineering, knowledge management, data science, and AI. The military services would create career fields in software development, data science, and AI, with both management and specialist tracks. Digital Corps will need additional career fields as they develop, but these steps will establish a strong foundation.
AI will make the process of finding and hitting targets of military value faster and more efficient. It will also increase accuracy of target identification and minimize collateral damage. Currently, this process generally involves passing data in a serial fashion from a sensor, through a series of humans, to a platform that can shoot at the target. AI will help automate some of the intermediate stages of the decision process. AI will also create opportunities for more advanced processes that would operate more akin to a web, fusing multiple sensors and platforms to manage complex data flows and transmitting actionable information to human operators and machines across all domains. AI-enabled intelligence, surveillance, and reconnaissance platforms and AI-enabled indication and warning (I&W) systems will be critical for the kind of advanced warfighting capabilities.
Today, we have reached an inflection point. Global digital transformation has led to an overwhelming supply of data. Statistical ML algorithms, particularly deep neural networks, have matured as problem solvers—albeit with limitations. The powerful and networked computing that fuels ML capabilities has become widely available. The convergence of these factors now places this capable technology in the hands of the technical and nontechnical alike. The fundamental “question is no longer how this technology works, but what it can do for you.”
Biology and germs will be new weapons of this technology warfare. Biology is now programmable. New technologies such as the gene editing tool CRISPR ushered in an era where humans are able to edit DNA. Combined with massive computing power and AI, innovations in biotechnology may provide novel solutions for mankind’s most vexing challenges, including in health, food production, and environmental sustainability. Like other powerful technologies, however, applications of biotechnology can have a dark side.
The COVID-19 pandemic reminded the world of the dangers of a highly contagious pathogen. AI may enable a pathogen to be specifically engineered for lethality or to target a genetic profile—the ultimate range and reach weapon. Also, AI, when applied to biology, could optimize for the physiological enhancement of human beings, including intelligence and physical attributes. To the extent that brain waves can be represented as a machine vision challenge for AI, the mysteries of the brain may be unlocked and programmed.
Russia has plans to automate a substantial portion of its military systems. It has irresponsibly deployed autonomous systems in Syria for testing on the battlefield.9 China sees AI as the path to offset US conventional military superiority by “leapfrogging” to a new generation of technology. Its military has embraced “intelligentized war” investing, for example, in swarming drones to contest US naval supremacy. China’s military leaders talk openly about using AI systems for “reconnaissance, electromagnetic countermeasures and coordinated firepower strikes.” China is testing and training AI algorithms in military games designed around real-world scenarios. As these authoritarian states field new AI-enabled military systems, we are concerned that they will not be constrained by the same rigorous testing and ethical code that guide the U.S. military.
AI will revolutionize the practice of intelligence. There may be no national security function better suited for AI adoption than intelligence tradecraft and analysis. Machines will sift troves of data amassed from all sources, locate critical information, translate languages, fuse data sets from different domains, identify correlations and connections, redirect assets, and inform analysts and decision-makers. The most urgent and compelling reason to accelerate the use of AI for national security is the possibility that more advanced machine analysis could find and connect the dots before the next attack, when human analysis alone may not see the full picture as clearly.
AI will compress decision time frames from minutes to seconds, expand the scale of attacks, and demand responses that will tax the limits of human cognition. Human operators will not be able to defend against AI-enabled cyber or disinformation attacks, drone swarms, or missile attacks without the assistance of AI-enabled machines. The best human operator cannot defend against multiple machines making thousands of manoeuvres per second potentially moving at hypersonic speeds and orchestrated by AI across domains. Humans cannot be everywhere at once, but software can.
Successful development and fielding of AI technologies depends on a number of interrelated elements that can be envisioned as a stack. AI requires talent, data, hardware, algorithms, applications, and integration. We regard talent as the most essential requirement because it drives the creation and management of all the other elements.
Data is critical for most AI systems. Labelled and curated data enables much of current machine learning (ML) used to create new applications and improve the performance of existing AI applications. The underlying hardware provides the computing power to analyse ever-growing data pools and run applications.
This hardware layer includes cloud-based compute and storage, supported by a networking and communications backbone, instrumental for connecting smart sensors and devices at the network edge. Algorithms are the mathematical operations that tell the system how to navigate the data to provide answers in response to specific questions. An application makes the answers useful for specific tasks. Integration of these elements is critical to fielding a successful end-to-end AI system.
This requires significant engineering talent and investment to integrate existing data flows, decision pipelines, legacy equipment, testing designs, etc. This task of integration can be daunting and historically has been underestimated. This is where the US government is focusing its energies on right now, building, attracting, and retaining the best of talent for its Digital Corps. The US is taking steps to ensure it wins the competition for international talent for years to come. It is redefining its immigration system to attract students, technical experts, and entrepreneurs; grant stability while they continue to contribute to the American economy and research environment; and retain students, entrepreneurs, and experts rather than sending them home or to competing countries.