With the rapid development of artificial intelligence technology, generative AI such as ChatGPT and Sora has demonstrated its powerful capabilities in various fields. The year 2022 is referred to as the beginning of generative AI; in 2023, GPT-4 gained popularity due to its human-like intelligence; and in early 2024, the emergence of Sora shocked the world once again. Generative AI can significantly enhance the military’s capabilities in intelligent information acquisition, decision support, and human-machine collaborative combat, expanding its applications in the military domain.
Generative AI integrates achievements from artificial intelligence technologies such as natural language processing, computer vision, speech recognition, and deep learning. It can generate new content based on existing data and knowledge through algorithms, pushing intelligent warfare into a new phase of “fully autonomous combat”.
The Basic Concept of Generative AI The National Internet Information Office defines generative AI (AIGC) in the “Measures for the Management of Generative Artificial Intelligence Services (Draft for Comments)” as a technology that generates text, images, sounds, videos, codes, and other content based on algorithms, models, and rules. There are mainly five categories: first, generative text models. By learning from a large amount of text data, they can generate articles, stories, comments, and poetry. The most representative examples include OpenAI’s ChatGPT, Google’s BERT, PaLM, and Microsoft’s Turing-NLG, all of which exhibit characteristics such as “strong interaction,” “strong understanding,” and “strong generation.” Second, generative image models. By learning from a vast amount of image data, they can create new paintings and illustrations. The most notable is OpenAI’s DALL-E2/E3 model, which can quickly generate realistic and creative images based on descriptions. Third, generative audio models. By learning from a large amount of audio data, they can generate new music and speech. The most representative is OpenAI’s Voice Engine, which can clone a person’s multilingual audio from just a 15-second voice sample. The popular video translation software HeyGen uses this engine. Fourth, generative video models. By learning from a large amount of video data, they can generate new video clips or complete video content. The most notable is OpenAI’s newly launched text-to-video model Sora, which can create realistic, vivid, and complex high-quality videos based on text instructions, achieving smooth transitions between multiple angles, with production quality close to that of commercial films. Fifth, generative code models. They can automatically write or assist in writing code to improve development efficiency and quality. The most representative examples are OpenAI’s CodeX and DeepMind’s AlphaCode.
Generative AI is significantly different from traditional AI. First, from the perspective of core goals and functions, the core goal of generative AI is innovation, capable of generating new content. This creativity transcends the traditional AI category, which mainly establishes connections between known inputs and outputs for prediction. Second, regarding learning mechanisms and technologies, generative AI widely uses technologies such as generative adversarial networks and Transformers, requiring a large amount of sample data for training, and can evolve by learning from its generated content, surpassing the traditional AI approach that relies solely on machine learning. Third, from the perspective of application scenarios and advantages, generative AI has broad application prospects and can be applied in creative or assistive creative fields, while traditional AI is more applied in scenarios requiring precise recognition and classification or predicting future trends based on historical data.
The Development History of Generative AI From a technical perspective, the development of generative AI is a process of continuous breakthroughs and innovations. First, the rise of expert systems. From the 1980s to the early 1990s, expert systems emerged, relying on rules and predefined knowledge bases to perform reasoning and decision-making in specific fields, laying the foundation for the development of generative AI in logical reasoning and knowledge representation. Second, the rise of deep learning. In the late 1990s, due to algorithmic limitations and insufficient computing power, the development of generative AI encountered bottlenecks. Entering the 21st century, the rise of deep learning technology brought revolutionary changes to generative AI. It constructs deep neural network models to automatically learn feature representations and generation rules from large-scale data, greatly enhancing the quality and diversity of generated content. Third, the development of multimodal models. Since 2022, thanks to continuous optimization of algorithms and improvements in computing power, generative AI has begun to develop from single-modal to multimodal cross-domain generation, enabling AI to simultaneously process and understand various forms of information such as text, images, audio, and video, providing broader space for the application of generative AI in various fields. GPT-4 is a large multimodal model that not only possesses advanced reasoning and complex instruction processing capabilities but also exhibits high creativity. This marks the entry of generative AI into a fast-track development phase. Fourth, the goal of general artificial intelligence. The ultimate development goal of generative AI is to achieve general artificial intelligence (AGI), which is machine intelligence similar to human intelligence, with broad adaptability. The founding purpose of OpenAI was to ultimately achieve AGI. Currently, achieving AGI still faces many challenges, including how to enable machines to understand and process natural language, how to allow machines to learn and create like humans, and how to make reasonable decisions in the face of unknown situations. In addition, the development of AGI also requires breakthroughs in existing algorithmic and technological limitations, and finding a universal theory of intelligence.
The Technical Features of Generative AI Generative AI is a powerful artificial intelligence technology that exhibits great potential in imitation and creation. Its technical features are mainly reflected in several aspects. First, data-driven. Generative AI relies on a large amount of data for training and learning to obtain sufficient information and knowledge to generate content and can process large-scale, high-dimensional data to extract useful information and features. Second, scalability. The model architecture typically has modular and hierarchical characteristics, making it easy to expand and customize, allowing for widespread application in different fields and scenarios; the algorithm design usually considers computational efficiency and resource utilization, and performance and generation capabilities can be improved with the continuous development of technology and accumulation of data. Third, interactivity. Generative AI can understand and respond to users’ natural language inputs, dynamically adjusting and optimizing output content based on user feedback and needs, enabling more intelligent and flexible interactions with users, providing more personalized and precise services. Fourth, creativity. It no longer relies solely on rules and can mimic human creativity, drawing inspiration from multiple data sources, merging different styles, cultures, and knowledge to generate innovative and diverse content, providing infinite possibilities for its application in various fields.
The relationship between military and technology has always been close. The explosion of generative AI signifies that weak artificial intelligence is evolving towards strong artificial intelligence and general artificial intelligence, providing an increasing number of application scenarios for future intelligent warfare.
Intelligent Situation Analysis Generative AI has enormous potential and value in battlefield situational intelligence analysis. First, multi-domain data fusion analysis. It can leverage its strong information processing and output capabilities to break through the “fog of war,” deeply mining and fusing vast amounts of multi-source, complex, heterogeneous, and rapidly growing battlefield intelligence data, providing comprehensive and accurate intelligence support for command decision-making. Second, joint battlefield situational awareness. Faced with rapidly changing battlefield data and information, it can achieve comprehensive awareness of the land, sea, air, space, electronic, and network multi-dimensional battlefield situation through multi-modal information fusion, helping combat personnel understand the battlefield environment, identify strike targets, and provide early warnings for operational threats, improving the accuracy and timeliness of command decisions. Third, situation prediction and threat assessment. By deeply learning from a large amount of historical and real-time battlefield data, it can identify patterns and trends in battlefield changes, construct complex battlefield models, simulate and predict the evolution trends of battlefield situations, and forecast possible enemy actions, attack methods, and targets, providing early warnings and response suggestions for friendly forces. Huawei’s “Pangu Meteorological Model” does not use traditional numerical forecasting methods; instead, it employs a three-dimensional neural network adapted to Earth’s coordinates and a hierarchical temporal aggregation strategy, completing high-resolution numerical weather forecasts for seven days globally in just 10 seconds, with a calculation speed improvement of over ten thousand times and a reduction in computational power consumption to one hundred thousandths, which can be used for battlefield environmental support in the future.
Command Intelligent Decision-Making Generative AI can provide strong support and assistance for command decision-making, improving the accuracy and efficiency of decisions and enhancing the flexibility and adaptability of actions. First, assisting task planning. It can use multi-source intelligence analysis tools and decision support models to automatically extract key information, generate situational images, data charts, deployment strategies, and resource allocation documents, assisting in task planning and improving operational planning efficiency. Second, assisting scenario simulation. It can accurately analyze battlefield situations and operational needs, automatically draft operational assumptions, generate multiple operational plans, and predict the outcomes of each plan through simulation, providing reference for commanders to select optimized plans. Third, assisting combat decision-making. It can be integrated into an integrated joint combat command system, assess battlefield situations, predict the course of the war, and continuously iterate and optimize through human-machine interaction, forming combat suggestions that assist command decision-making, with faster response times, higher decision levels, and more prominent effectiveness compared to traditional command methods. To enhance the speed and effectiveness of command decision-making, the US military has included generative AI in its “Technology Watch List,” initiating the “Artificial Intelligence Forward Plan” and the “From Data to Decision” project, reshaping the command decision-making process and building a capability system that transforms vast intelligence data into effective autonomous decision outputs.
Operational Intelligent Collaboration Generative AI can significantly enhance the collaborative efficiency between different combat units, strengthening overall combat capability. First, enhancing collaborative combat effectiveness. By real-time perception and analysis of battlefield situations, it can quickly identify the actions and intentions of both friendly and enemy forces, automatically allocating and scheduling tasks based on the capabilities, positions, and mission needs of each combat unit, improving the efficiency and accuracy of collaborative combat. Second, shortening the operational process. It can decompose combat tasks, use built-in algorithms to optimize plans, and rely on its rapid response speed to accelerate the “OODA” (Observe-Orient-Decide-Act) combat loop, shortening decision cycles and forming an advantage of “winning by speed.” Third, supporting human-machine collaboration. Generative AI, being a product of machines, can understand human language, making it an ideal human-machine interaction interface that can be integrated into unmanned combat platforms. Through intelligent algorithms and autonomous decision-making capabilities, it supports cross-platform and cross-domain collaborative combat between manned and unmanned systems, fully supporting new operational styles such as “loyal wingman” and “swarm” combat, significantly enhancing combat effectiveness. In future intelligent warfare, generative AI can accurately connect with unmanned combat platforms, plan combat tasks in an “order” style, assign tasks in a “dispatch” style, and lock onto targets in a “take order” style, potentially achieving dimensional strikes against traditional combat opponents.
Intelligent Support for Logistics Generative AI can be comprehensively integrated into various support systems, with people, equipment, and supplies interconnected and organically integrated, greatly enhancing the efficiency and accuracy of operational support. First, material management and distribution. It can analyze battlefield needs and material inventory in real-time, optimizing material distribution and scheduling; it can predict material consumption trends, replenish in advance to avoid shortages; and it can comprehensively analyze factors such as transportation routes, traffic conditions, and material needs to optimize transportation plans, reducing costs and improving efficiency. Second, equipment maintenance and repair. It can monitor and analyze equipment operation data in real-time, predict possible failures, formulate maintenance plans in advance, optimize maintenance resource allocation, and conduct timely equipment maintenance and repair, reducing maintenance costs and extending equipment lifespan, improving equipment availability. Third, medical support and care. By analyzing medical data, it can quickly and accurately identify potential disease signs and injuries, predict treatment outcomes, providing more efficient and intelligent medical support for military personnel; it can assist in remote diagnosis, provide optimal treatment plans, and improve the efficiency and accuracy of battlefield medical rescue; it can match the nearest rescue personnel and resources, quickly organizing the evacuation and treatment of casualties, enhancing battlefield rescue efficiency. The US Army’s Telemedicine and Advanced Technology Research Center collaborates with Johns Hopkins University to utilize artificial intelligence, augmented reality, and robotics to provide virtual assistance for medical personnel and soldiers on the battlefield.
Artificial intelligence is not a single discrete technology but a universal enabling technology. With its powerful learning and innovation capabilities, the widespread application of generative AI in the military field will bring revolutionary changes to warfare, producing comprehensive and profound impacts.
Changing the Nature of Warfare A disruptive technology group centered around AI is rapidly entering the military domain, with “intelligent” elements replacing “information” elements, triggering significant changes in the nature of warfare. First, the combat system is shifting towards a human-machine mixed composition. Generative AI will completely overturn the main combat forces on the battlefield, with intelligent payloads, intelligent platforms, and intelligent systems becoming new trends in equipment development. Human-machine mixed formations and unmanned autonomous formations will become new forms of combat force composition. Second, the combat style is transitioning to intelligent unmanned systems. Driven by generative AI, human-led human-machine collaborative combat, algorithm-based intelligent cognitive combat, cloud-centered data open-source combat, and intelligence-driven all-domain distributed combat will become the main combat styles in the future. The “kill chain” will transform into a “kill network,” exhibiting characteristics of low risk, low cost, and low threshold. Third, the winning mechanism is evolving towards intelligence-driven victory. The development of war control rights aligns with the evolution of the nature of warfare, with “intelligence control rights” becoming the core of the struggle for control in intelligent warfare, concentrated in the comprehensive competition of “algorithms + data + computing power,” highlighting victory through intelligence, speed, collaboration, and calculation, with intelligence dominating, autonomy steering, and intelligence strategizing becoming the basic laws of warfare.
Lowering the Threshold of Combat Generative AI can effectively lower the threshold for actions such as intelligence collection, cyber attacks, and cognitive warfare, significantly increasing the range of participants in warfare. First, it lowers the threshold for intelligence collection. Without the need for high-level professional skills and experience, it can real-time fuse and process massive amounts of multi-source heterogeneous battlefield data, providing combat personnel with real-time battlefield situations and strike targets, and early warnings of threat information. During the Ukraine crisis, the Ukrainian army used “intelligence crowdfunding” apps, calling on the public to upload real-time information on Russian military movements, utilizing generative AI technology to analyze and process collected data, images, videos, and other information, enhancing its situational awareness and data processing capabilities in a complex environment. Second, it lowers the threshold for cyber attacks. It can quickly achieve functions such as discovering network vulnerabilities, writing malicious code, forging phishing emails, and spreading and executing malware, significantly reducing the entry requirements for cyber attacks, potentially achieving zero thresholds. A former SpaceX software engineer used GPT-3 to review code from a Git repository, discovering 213 security vulnerabilities, while the best commercial tools could only find 99. Recent studies have found that GPT-4 can generate malicious code exploiting vulnerabilities by reading security vulnerability announcements. Third, it lowers the threshold for cognitive warfare. Cognitive domain warfare involves multiple fields such as religion, language, culture, psychology, and thought, requiring a large number of specialized talents for support. Generative AI can innovatively generate high-quality propaganda and psychological attack information based on combat needs, can be deployed in bulk and operated continuously, lowering the production cost of “cognitive munitions” and the threshold for cognitive domain warfare. In 2017, a report from Harvard University stated that AI technology is more accessible than other military technologies (such as nuclear technology) and has a lower usage threshold.
Enhancing Combat Effectiveness Generative AI can automatically process vast amounts of data and information, assisting command decision-making to be more scientific and rational, strengthening human-machine collaboration to complete reconnaissance and defensive tasks, and enhancing overall combat effectiveness. First, it enhances battlefield situational awareness. It can conduct automatic recognition and fusion processing based on multi-source data, efficiently identify abnormal behaviors and potential threats based on directive associations, and rapidly mine action patterns based on a large amount of enemy data, autonomously judging target values, combat intentions, action plans, and force comparisons, providing a basis and plan for battlefield situation assessment. Second, it assists in intelligent decision-making for command. In the face of increasingly complex battlefield scenarios and exponentially growing battlefield information, it can efficiently analyze the operational needs of commanders, providing risk assessments and decision suggestions through large model processing, combat simulations, and war gaming, helping commanders reduce subjective misjudgment interference, objectively assess the situation, and make correct decisions, enhancing the efficiency and level of command decision-making, transitioning from computer-aided decision-making to human-machine integrated intelligent decision-making, moving from “humans in the loop” to “humans above the loop.” Third, it promotes the autonomy of combat actions. The “open interface” mode and platform attributes of generative AI provide possibilities for the autonomy and intelligence of traditional or automated weapons. The US military has transformed traditional fighter jets into AI-controlled X-62A drones by adding data tracking computing systems and flight automatic control systems. In April 2024, this type of aircraft conducted the world’s first aerial combat test with a manned aircraft, engaging in aerial combat at a speed of up to 1.58 Mach, with the closest distance being about 610 meters. Fourth, it diversifies innovative combat styles. The creativity of generative AI grants it infinite possibilities and broad application prospects in warfare. The Ukraine crisis is considered the first AI-enabled war, with various parties exploring the use of artificial intelligence technology and continuously innovating combat actions. The Ukrainian army used AI facial recognition technology to search over 2 billion photos on Russian social media, establishing a facial recognition database for Russian soldiers, quickly confirming target identities through information comparison, and verifying the identities of Russian agents infiltrating Ukraine. The Russian military utilized AI self-learning algorithms to quickly adapt to changes in network defense strategies, achieving intelligent and automated cyber attacks, with the frequency of large-scale cyber attacks rising from 15 times per month to 125 times.
Expanding the Risks of War Generative AI presents a comprehensive, three-dimensional, and multi-faceted participation in warfare, iterating and evolving at an astonishing speed, potentially becoming a “terrifying fusion” that changes the course of war and even affects the progress of human civilization. First, it easily leads to the proliferation of false information. Generative AI can rapidly and massively produce false information, fake news, and fog of war surrounding specific topics aimed at specific targets. The knowledge of generative AI is defined by its training datasets, and when questions fall outside the training set, it may “speak nonsense with a straight face.” This phenomenon is academically termed “hallucination.” If combat command heavily relies on generative AI, it may easily result in false intelligence, guiding erroneous decisions and leading to “dehumanization of fatal decision-making,” challenging human dominance over warfare. Second, it may impact fundamental principles of warfare. Generative AI possesses “human-like consciousness” and may completely alter warfare modes and rules of engagement, causing disruptive impacts on the fundamental principles of the laws of war. Generative AI’s fatal dependence on massive data training, coupled with the complex and variable battlefield environment and the enemy’s “deception tactics,” may render the principles of distinction difficult to apply; generative AI can only make “purely rational” judgments that are most favorable for achieving military objectives, lacking the capacity for value judgments, making it difficult to adhere to the principle of proportionality; generative AI lacks pain perception, a natural flaw that makes it difficult to comply with the principle of avoiding unnecessary suffering. Third, the inability to predict capabilities raises fears. Generative AI accelerates the development of general artificial intelligence, intensifying human concerns and fears regarding the weaponization of AI. Currently, human fears regarding AI largely stem from imagination, which is quite different from nuclear weapons. While the destructive power of nuclear weapons is sufficient to annihilate humanity, humans can “find ways” to control them. However, in controlling AI, humans cannot “outthink” machines, leading to fear. People worry that general artificial intelligence will become a nightmare for all of humanity. Elon Musk likened the development of AI to “summoning demons,” stating that “every wizard claims to control demons, but none succeed in the end.”
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