Data from drones in Ukraine is fueling a new Wild West marketplace

乌克兰无人机数据正在催生一个狂野的西部式新市场

Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part of the AI architecture that shapes even civilian life. 乌克兰的战场上散落着无人机的残骸，这些无人机现已确立了其作为现代战争关键武器的地位。但在这些残骸背后，国防工业正挖掘出一座新的金矿。无人机产生的数据将远比它们所参与的战争存在得更久，并日益成为塑造民用生活的人工智能架构的一部分。

For every flight, unmanned systems collect thousands of points of data, from images and video to controller inputs. Together, those records show how a machine and a person responded to constantly shifting circumstances. Ukraine has now begun converting that experience into a resource. Its Ministry of Defense announced in January that it would make millions of data points gathered during tens of thousands of drone flights available to both military contractors and commercial companies, and since then more than 100 companies and the UK government have gained access. 在每一次飞行中，无人系统都会收集数以千计的数据点，从图像、视频到控制器输入。这些记录共同展示了机器和人类如何应对不断变化的环境。乌克兰现在已开始将这些经验转化为资源。其国防部在一月份宣布，将把在数万次无人机飞行中收集的数百万个数据点提供给军事承包商和商业公司。自那时起，已有超过 100 家公司和英国政府获得了访问权限。

For a country at war, it’s a quick way to attract funding and partnerships. But this step turns the front line into an active site of model training, taking advantage of how the chaos of war creates conditions that AI companies struggle to reproduce on their own. Other countries and battlefields are likely to follow Ukraine’s lead, but the responsibility for governing this new industry cannot fall solely on a country fighting for its survival. That legal vacuum has to be filled together by the countries and companies involved in this industry’s development. 对于一个处于战争状态的国家来说，这是一种吸引资金和合作伙伴的快捷方式。但这一举措将前线变成了活跃的模型训练场，利用战争的混乱创造出人工智能公司难以自行模拟的条件。其他国家和战场很可能会效仿乌克兰，但监管这一新兴行业的责任不能仅仅落在正为生存而战的国家身上。这一法律真空必须由参与该行业发展的国家和公司共同填补。

Explosive growth

爆炸式增长

Ukraine’s battlefields are not the first to produce records used to train and develop models: American drones over Syria and Yemen collected data that informed the first generation of semiautonomous military hardware in the late 2010s. The difference now is that access to that data is being used to develop a wider ecosystem. And the financial value to defense firms is immense: Battlefield data offers large volumes of machine experience gathered under conditions that no laboratory can produce. 乌克兰战场并不是第一个产生用于训练和开发模型记录的地方：2010 年代末，美国在叙利亚和也门上空的无人机所收集的数据，为第一代半自主军事硬件提供了支持。现在的区别在于，对这些数据的访问正被用于开发一个更广泛的生态系统。对于国防公司而言，其经济价值是巨大的：战场数据提供了在任何实验室都无法模拟的条件下收集的大量机器经验。

That’s because the data that’s most valuable for training AI models comes from exceptions: the moment visibility disappears, a signal jams, or a human operator improvises. AI companies spend years and enormous sums trying to capture enough of these moments to make their models more robust. But war produces them at a frequency controlled testing cannot match. This constantly changing terrain is what makes drone data valuable far beyond the battlefield. 这是因为对于训练人工智能模型最有价值的数据往往来自“异常情况”：比如能见度突然消失、信号受到干扰，或人类操作员进行即兴操作的时刻。人工智能公司花费数年时间和巨额资金，试图捕捉足够多的此类时刻，以使模型更加稳健。但战争产生的这些时刻的频率是受控测试无法比拟的。这种不断变化的战场环境，使得无人机数据的价值远远超出了战场本身。

A commercial drone used for delivery or remote sensing may never encounter artillery fire, but it must still operate with incomplete information in a world where people behave unpredictably. The same problem is compressed by war into a much shorter timeline. Processed and matched against records of what its operator was doing, that data turns operational records into training sets. Combat becomes a commercial asset. 用于配送或遥感的商用无人机可能永远不会遭遇炮火，但它仍然必须在一个人类行为不可预测的世界中，在信息不完整的情况下运行。战争将同样的问题压缩到了更短的时间轴内。通过处理并将其与操作员的行为记录进行匹配，这些数据将作战记录转化为了训练集。战斗由此成为了一种商业资产。

Many conflicts have already seen this training loop happen as drone footage feeds subsequent generations of military technology, and the market is set to grow. Enabled Intelligence, an American company that specializes in processing data to become usable in AI training, says it has already made more than half a million hours of Ukrainian drone footage available to feed into the next round of models, advertising possible uses in both military and commercial systems. 许多冲突中已经出现了这种训练循环，无人机拍摄的画面为后续几代的军事技术提供了养料，且该市场注定会继续增长。专门从事数据处理以使其可用于人工智能训练的美国公司 Enabled Intelligence 表示，它已经提供了超过 50 万小时的乌克兰无人机影像，用于喂养下一轮模型，并宣传其在军事和商业系统中的潜在用途。

Closing the data loop

闭合数据循环

Many of the drones that now define our modern age of warfare began as civilian technology. But they’ve recently been turbocharged by new, commercially available AI systems, which allow cheap machines to operate autonomously—either individually or as a flock—as the environment changes around them. Each flight then creates a record of what the system encountered. The resulting data is critical. The controlled lab environments usually developed to train these autonomous systems can approximate failure but are no match for the live conditions of a battlefield with very real risks. 许多定义了我们现代战争时代的无人机最初都是民用技术。但最近，它们被新型的商用人工智能系统“涡轮增压”，使廉价机器能够在环境变化时自主运行——无论是单独行动还是成群结队。每一次飞行都会记录下系统所遇到的情况。由此产生的数据至关重要。通常用于训练这些自主系统的受控实验室环境可以模拟故障，但无法与充满真实风险的战场实况相提并论。

Military intelligence programs have held data generated by sensor-heavy systems like Predator and Reaper drones for nearly a decade through programs like Project Maven, but access remained entirely within the defense world. The data generated was available only through restricted, classified channels for the sole purpose of developing new weapons systems that would feed back into the same military that produced the data in the first place. 通过“Maven 项目”（Project Maven）等计划，军事情报部门近十年来一直掌握着由“捕食者”（Predator）和“死神”（Reaper）等传感器密集型无人机系统生成的数据，但访问权限完全局限于国防领域。所生成的数据仅通过受限的机密渠道提供，其唯一目的是开发新的武器系统，并回馈给最初产生这些数据的军队。

That experience is now being shared to a much broader development network. The loop now closes. Commercial technologies adapted for the battlefield are generating data that can flow back into the industries from which they came, becoming part of the data infrastructure relied on by governments and the private sector alike. Drones that were trained in the signal-jammed airspace over Ukraine are now being deployed in the agricultural sector to help farmers map and survey their fields in places lacking the cell signal necessary for previous generations of technology. 现在，这些经验正被分享给一个更广泛的开发网络。循环正在闭合。为战场改造的商业技术正在生成数据，这些数据可以回流到它们最初所属的行业，成为政府和私营部门共同依赖的数据基础设施的一部分。在乌克兰信号受干扰的空域中训练过的无人机，现在正被部署到农业领域，帮助农民在缺乏前几代技术所需蜂窝信号的地方绘制和勘测农田。

Other countries are likely to follow Ukraine in selling their battlefield data, and we are not ready for the new marketplace this will create. Bad actors could acquire the data, but purchase controls already mitigate that risk. Intelligence operatives scrutinize potential customers’ infrastructure for ways that data could reach enemies or nefarious actors. Training data creates a new tracing problem, though. Whereas the movement of commercial datasets can be followed when planted contact details appear two steps from the original buyer, the provenance of AI training data vanishes in a manner embedded in the technology itself. 其他国家很可能会效仿乌克兰出售其战场数据，而我们尚未为这一新市场做好准备。恶意行为者可能会获取这些数据，但购买控制措施已经缓解了这一风险。情报人员会审查潜在客户的基础设施，以防数据流向敌人或不法分子。然而，训练数据带来了一个新的追踪难题。虽然商业数据集的流向可以通过在原始买家之后两步出现的植入联系信息进行追踪，但人工智能训练数据的来源却以一种嵌入技术本身的方式消失了。

Another risk is that this use of the data creates an extractive economy in which wealthier countries far from danger benefit from the mortal threat borne by frontline states, potentially creating a market incentive for war to continue as an unending mine for digital gold. A fraught new frontier. Existing laws regulate how militaries may conduct war. But they say al 另一个风险是，这种数据使用方式创造了一种“掠夺性经济”，即远离危险的富裕国家从前线国家所承受的致命威胁中获益，这可能会产生一种市场激励，使战争作为数字黄金的无尽矿藏而持续下去。这是一个充满忧虑的新前沿。现行法律规定了军队如何进行战争，但它们……（原文在此处中断）