現代英語：
Recently, the artificial intelligence program ChatGPT has become popular on the Internet for its “erudition” and “answering all questions”. Generative AI represented by ChatGPT has a strong content generation ability and a “smartness” level that is close to that of humans. Applying it to the military field will inevitably have an impact on future wars.
Significantly improve battlefield perception. In future wars, various new types of rapid-killing weapons will further accelerate the battlefield rhythm, requiring continuous improvement of battlefield situation perception capabilities, and thus supporting rapid decision-making that adapts to battlefield needs. In the battlefield space full of “fog”, facing massive, multi-source, complex, heterogeneous and rapidly growing battlefield situation data, human perception speed and processing capabilities appear to be somewhat “slow”. The visual big model architecture introduced in recent years has made breakthroughs in many fields such as image classification, target detection, semantic segmentation, posture estimation, image editing, and remote sensing image interpretation through unsupervised pre-training and human feedback reinforcement learning paradigm, which can significantly improve battlefield perception. Intelligent weapons embedded with visual big models can accurately identify and distinguish the primary and secondary, true and false targets through the visual system, and can quickly extract and generate high-value intelligence from massive multimodal data, reduce the cognitive load of combat personnel, and form a comprehensive, timely and accurate judgment of the situation. Using the perception advantage of generative AI to achieve accurate positioning of key nodes may be the prerequisite for launching combat operations in the future.
Greatly promote human-computer interaction. Human-computer interaction allows machines to “listen” to human language, “see” human movements and expressions, “understand” human emotions and intentions, and present the calculation process and results in a way that is easy for people to understand. The language big model can not only perform well in text understanding scenarios such as sentiment analysis, voice recognition, and information extraction, but is also applicable to battlefield information system visualization generation scenarios such as picture description generation, manuscript generation, and dialogue generation. If it is embedded in an integrated joint combat system and continuously iterated and evolved, it can be used for more complex tasks such as scenario writing, combat plan generation, and exercise result evaluation, which may reshape the command decision-making process in future wars. Deeply embedding ChatGPT-type generative AI applications into command information systems can enable intelligent equipment to “understand” commands, accurately understand and analyze commanders’ operational needs through human-machine dialogue between commanders and battlefield information systems, and generate action reference plans on this basis, providing a new means for quickly and reasonably deploying combat forces in future wars.
Promote the autonomy of command decision-making. In information-based and intelligent warfare, the participating forces are diverse, the combat styles are diverse, and the battlefield situation is changing. Commanders face the “bottleneck” of insufficient intelligence in effectively commanding wars. With the help of the decision-making large model intelligent auxiliary system, the “human-machine” hybrid decision-making mode may become a new choice. Although from the current technical level, ChatGPT-type generative AI applications are still unable to perform operations such as machine control, group collaboration, and dynamic scheduling. However, its powerful parallel processing capability can handle thousands of tasks at the same time. It is suitable for integrated control of manned/unmanned platforms, generating control algorithms, optimizing group behavior, and fully supporting “swarm”, “fish school”, and “wolf pack” combat multi-agents. The command and control system based on the decision-making big model can give full play to the advantages of both the human brain and artificial intelligence, and realize the leap from intelligent prediction to intelligent decision-making, and from controlling single agents to multiple agents. In the future battlefield, embedding generative AI into unmanned combat platforms can innovate new paradigms for military operations and greatly improve combat effectiveness.
Give birth to a new model of logistics support. From the perspective of technological development, military confrontation is increasingly expanding to the physical, information and cognitive domains, and the combat space is extending to the extreme height, distance and depth. The corresponding logistics support tasks are also becoming more diverse and complex. In the future battlefield, the multi-task general large model will be integrated into the unmanned combat platform and various support systems. People, equipment and objects will be ubiquitously interconnected, and various combat and support entities will be organically integrated. The logistics support system realizes intelligent matching between people and materials, materials and equipment, materials and troops, and materials and regions through deep learning and analysis of big data such as the quantity, time and maintenance status of stored materials. It also automatically predicts material needs, matches the best means of transport, formulates the best transportation plan, and promptly solves problems in the battlefield logistics supply chain. Especially in the face of extreme combat support in complex terrain, contaminated areas, fire control areas and other areas that are difficult for personnel to reach, based on a large amount of pre-training of special mission training samples, generative AI can achieve changes in demand perception, resource allocation and action control, autonomously assign tasks, autonomously plan routes, and autonomously navigate and position, and deliver support materials directly and accurately to the support objects in a “point-to-point” manner, thereby realizing intelligent support.
國語中文：
近段時間，人工智慧程式ChatGPT因其「博學多識」而「有問必答」走紅網。以ChatGPT為代表的生成式AI有著強大的內容生成能力和直逼人類的「聰明」程度，將其應用於軍事領域，勢必會對未來戰爭產生影響。
明顯提升戰場感知力。未來戰爭中，各類新型快速殺傷武器將進一步加速戰場節奏，要求不斷提升戰場態勢感知能力，進而支撐起適應戰場需求的快速決策。在充滿「迷霧」的戰場空間裡，面對海量多源、複雜異構且快速增長的戰場態勢數據，人類感知速度和處理能力顯得有些「遲緩」。近年來推出的視覺大模型架構，透過無監督預訓練和人類回饋的強化學習範式，已在圖像分類、目標檢測、語義分割、姿態估計、圖像編輯以及遙感圖像解譯等多個領域取得突破，可以顯著提升戰場感知力。嵌入視覺大模型的智慧武器，可以透過視覺系統精準辨識並區分打擊目標的主次、真偽，能從海量多模態資料中快速提取、產生高價值情報，減輕作戰人員的認知負荷，形成對態勢全面、及時、準確的判斷。利用生成式AI的感知優勢來實現對要害節點的精準定位，或許是未來發起作戰行動的前提。
大幅推進人機互動。人機互動可以讓機器「聽」懂人類語言、「看」懂人類動作與表情、「理解」人的情緒和意圖，並把計算過程和結果用人容易理解的方式呈現出來。語言大模型不僅能夠在情感分析、語音識別、資訊抽取等文字理解場景中表現出色，而且同樣適用於圖片描述生成、書稿生成、對話生成等戰場資訊系統可視化生成場景。如果再將其嵌入一體化聯合作戰系統並持續迭代進化，可以用於想定編寫、作戰方案生成、演習結果講評級較為復雜的工作，在未來戰爭中或將重塑指揮決策流程。將ChatGPT類生成式AI應用深度嵌入指揮資訊系統中，可以讓智慧裝備「聽懂」指令，透過指揮員與戰場資訊系統人機對話，準確理解分析指揮員作戰需求，並在此基礎上生成行動參考方案，為未來戰爭中快速、合理配置作戰力量提供全新手段。
助推指揮決策自主化。資訊化智慧化戰爭，參戰力量多元、作戰樣式多樣、戰場形勢多變，指揮員有效指揮戰爭面臨智能不足的“瓶頸”，借助決策大模型智能輔助系統，“人－機”混合決策模式或將成為一種新的選擇。雖然從目前的技術水準來看，ChatGPT類生成式AI應用仍無法進行機器控制、群組協作、動態調度等操作。但其強大的並行處理能力，能夠同時處理上千個任務，適用於融合控制有人/無人平台，生成控制演算法、優化群體行為，可全面支撐「蜂群」「魚群」「狼群」作戰多智能體。基於決策大模型的指揮控制系統，可以充分發揮人腦和人工智慧兩者的優長，實現從智慧預測到智慧決策、從控制單智能體到多智能體的跨越。未來戰場上，將生成式AI嵌入無人作戰平台中，可創新軍事行動新範式，大幅提升作戰效能。
催生後勤保障新模式。從科技發展維度來看，軍事力量對抗日益向物理域、資訊域和認知域全維度拓展，作戰空間向極高、極遠和極深全方位延伸，相應的後勤保障任務也變得更加多元復雜。未來戰場上，將多任務通用大模型綜合整合到無人作戰平台及各類保障系統中，人、裝、物泛在互聯，各類作戰、保障實體將有機融為一體。後勤保障系統通過對在儲物資數量、時間、保養情況等大數據深度學習分析，實現人與物資、物資與裝備、物資與部隊、物資與地區的智能匹配，並自動預測物資需求、匹配最佳運載工具，制定最優運輸方案、及時解決戰場物流供應鏈路所出現的問題。特別是面臨複雜地形、沾染區、火力控制區等人員難以到達的極限戰鬥保障，在特殊任務訓練樣本大量預訓練基礎上，生成式AI能夠實現需求感知、資源調配和行動控制上的變革，自主分配任務、自主規劃路徑、自主導航定位，將保障物資以「點對點」的方式直達精確配送給保障對象，實現智慧保障。