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Spies and technology
间谍和技术
Machine intelligence
机器智能
Intelligence agencies are grappling with the promise—and pitfalls—of AI
情报机构正在努力应对人工智能带来的希望和困难
When it comes to using artificial intelligence (AI), intelligence agencies have been at it longer than most. In the coldwarAmerica's National Security Agency (NSA) and Britain's Government Communications Headquarters (GCHQ) explored early AI to help transcribe and translate the enormous volumes of Soviet phone-intercepts they began hoovering up in the 1960s and 1970s.
说到使用人工智能(AI)，情报机构比大多数其他机构使用的时间都要长。在冷战时期，美国国家安全局(NSA)和英国政府通信总部(GCHQ)探索了早期人工智能技术，以帮助转录和翻译他们在20世纪60年代和70年代开始收集的大量苏联的窃听电话。
Yet the technology was immature. One former European intelligence officer says his service did not use automatic transcription or translation in Afghanistan in the 2000s, relying on native speakers instead. Now the spooks are hoping to do better. The trends that have made AI attractive for business in recent years—more data, better algorithms, and more processing power to make it all hum—are giving spy agencies big ideas, too.
然而，这项技术还不成熟。一名前欧洲情报官员表示，本世纪头十年，他的部门在阿富汗没有使用自动转录或翻译，而是依赖母语人士。现在，间谍们希望能做得更好。近年来，使人工智能对商业具有吸引力的趋势——更多的数据、更好的算法和更强大的处理能力，让一切都变得活跃起来——也给了间谍机构一些大胆的想法。
On February 24th GCHQ published a paper on how AI might change its work. "Machine-assisted fact-checking" could help spot faked images, check disinformation against trusted sources and identify social-media bots that spread it. AI might block cyber-attacks by "analysing patterns of activity on networks and devices", and fight organised crime by spotting suspicious chains of financial transactions.
2月24日，国家通信情报局(GCHQ)发表了一篇关于人工智能可能如何改变其工作的论文。“机器辅助的事实核查”可以帮助识别虚假图像，检查不可信来源的虚假信息，并识别传播这些信息的社交媒体机器人。人工智能可能通过“分析网络和设备上的活动模式”来阻止网络攻击，也可能通过发现可疑的金融交易链来打击有组织犯罪。
Other, less well-resourced organisations have already shown what is possible. The Nuclear Threat Initiative, an American ngo, recently showed that applying machine learning to publicly available trade data could spot previously unknown companies and organisations suspected of involvement in the illicit trade in materials for nuclear weapons. But spy agencies are not restricted to publicly available data.
其他资源不那么丰富的组织已经证明了什么是可能的。美国非政府组织“核威胁倡议”最近表示，将机器学习应用于公开的贸易数据，可以发现以前不为人知的公司和组织，这些公司和组织涉嫌参与用于核武器的材料的非法贸易。但间谍机构并不局限于公开的数据。
Some hope that, aided by their ability to snoop on private information, such modest applications could pave the way to an AI-fuelled juggernaut. "AI will revolutionise the practice of intelligence," gushed a report published on March 1st by America's National Security Commission on Artificial Intelligence, a high-powered study group co-chaired by Eric Schmidt, a former executive chairman of Alphabet, Google's parent company, and Bob Work, a former deputy defence secretary.
一些人希望，在它们窥探私人信息的能力的帮助下，这种不起眼的应用程序可能会为由人工智能驱动的巨大力量铺平道路。3月1日，美国国家安全委员会发布了一份关于人工智能的报告，报告称：“人工智能将给情报界带来一场革命。”美国国家安全委员会是一个强大的研究小组，由谷歌母公司Alphabet前执行董事长埃里克·施密特和前国防部副部长鲍勃·沃克共同担任其董事长。
The report does not lack ambition. It says that by 2030 America's 17 or so spy agencies ought to have built a "federated architecture of continually learning analytic engines" capable of crunching everything from human intelligence to satellite imagery in order to to foresee looming threats. The commission points approvingly to the Pentagon's response to covid-19, which integrated dozens of data sets to identify covid-19 hotspots and manage demand for supplies.
这份报告并不缺乏雄心壮志。报告称，到2030年，美国的大概17家间谍机构应该已经建立起了一个“持续学习分析引擎的联合架构”，能够分析从人类情报到卫星图像的一切，以便预测逐渐逼近的威胁。该委员会赞同五角大楼(美国政府)应对新冠的方式，它整合了数十组数据以确定新冠热点地区和管理供应需求。
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