用大模型自动分析军事情报，提升国家战略竞争力。
Governing Automated Strategic Intelligence
- 用多模态大模型融合卫星、定位、社交媒体等数据
- 实验证明可自动化完成复杂战略分析任务
- 适合关注国防与智能决策的政策制定者
国家间军事与经济战略竞争将越来越取决于前沿人工智能模型的能力与成本。首个由这类系统带来的地缘政治优势将体现在军事情报自动化上。尽管关于自主武器或战略决策的讨论很多，但“数据中心里的中情局分析师”如何规模化整合多元数据并产生洞见，仍缺乏研究。多模态基础模型正有望自动化以往由人类完成的战略分析。它们可将当前丰富的卫星影像、手机定位轨迹、社交媒体记录和文书资料融合为一个可查询系统。本文开展初步提升研究以实证评估这些能力，提出此类系统可回答的底层事实问题分类体系，给出影响其智能水平的关键因素框架，并为国家提供在新型自动化情报范式中保持战略竞争力的建议。
原文摘要 · Abstract (English)
Military and economic strategic competitiveness between nation-states will increasingly be defined by the capability and cost of their frontier artificial intelligence models. Among the first areas of geopolitical advantage granted by such systems will be in automating military intelligence. Much discussion has been devoted to AI systems enabling new military modalities, such as lethal autonomous weapons, or making strategic decisions. However, the ability of a country of "CIA analysts in a data-center" to synthesize diverse data at scale, and its implications, have been underexplored. Multimodal foundation models appear on track to automate strategic analysis previously done by humans. They will be able to fuse today's abundant satellite imagery, phone-location traces, social media records, and written documents into a single queryable system. We conduct a preliminary uplift study to empirically evaluate these capabilities, then propose a taxonomy of the kinds of ground truth questions these systems will answer, present a high-level model of the determinants of this system's AI capabilities, and provide recommendations for nation-states to remain strategically competitive within the new paradigm of automated intelligence.
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