考研

　　2022考研英语复习要抓紧，尤其是英语阅读占得分值会比较多。考研英语阅读能力想提升，还需要考生不断地日积月累阅读文章才能进步！文都网校考研小编为大家分享2022考研英语经济学人：情报机构与人工智能。希望能帮助到大家~【文末领资料】

　　2022考研英语经济学人：情报机构与人工智能

　　When it comes to using artificial intelligence (AI), intelligence agencies have been at it longer than most. In the cold war America’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.

　　说到人工智能(AI)，情报机构对其探索的历史比其他任何机构都要长。冷战时期，美国国家安全局(NSA)和英国政府通信总部(GCHQ)探索了早期的人工智能技术，从而对他们在二十世纪六七十年代开始截获的大量苏联电话进行转录和翻译。

　　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.

　　不过，当时这项技术还不成熟。一名前欧洲情报官员表示，在2000年代，他所在的机构并未在阿富汗使用自动转录或翻译的技术，而仍然依靠母语人士。

　　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.

　　如今，特工们希望人工智能技术能变得更强大。近年来，使人工智能对商业更具吸引力的那些趋势(更多的数据、更好的算法以及更强大的数据处理能力)也给情报机构带来了巨大的灵感。

　　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.

　　2月24日，英国政府通信总部发表了一篇关于人工智能或将如何改变其工作方式的论文。“机器辅助的事实核查”可以帮助其识别虚假图像，对比可信来源核查虚假信息，并识别出传播该虚假信息的社交媒体机器人。

　　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.

　　人工智能甚至可以通过“分析网络和设备上的活动模式”来阻止网络攻击，并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.

　　其他一些资源匮乏的组织已经证明了人工智能的潜力。美国非政府组织“核威胁倡议”最近发现，将机器学习应用到公开的贸易数据中，便可发现一些过去不为人知的涉嫌参与核武器材料非法贸易的公司和组织。而情报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，借助于其对私密信息的窥探能力，这类不起眼的应用程序可以为人工智能驱动的巨头铺平道路。3月1日，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·沃克共同担任联合主席的美国国家安全委员会人工智能研究委员会发布了一份报告称：“人工智能将彻底改变情报的获取方式。”

　　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.

　　这份报告并不乏雄心壮志。报告称，到2030年，美国大约17家情报机构将建立起一个“持续学习分析引擎的联合架构”，该架构能够分析从人类情报到卫星图像的一切信息，从而预见到迫在眉睫的威胁。

　　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.

　　该委员会对五角大楼应对新冠肺炎疫情的方式表示赞同，五角大楼整合了数十组数据，以识别疫情热点并管理供应需求。

　　Yet what is possible in public health is not always so easy in national security. Western intelligence agencies must contend with laws governing how private data may be gathered and used.

　　然而，在公共卫生领域可行的事情不一定在国家安全领域同样行得通。西方情报机构必须应对有关如何收集和使用私人数据的法律。

　　In any case, the constraints facing AI in intelligence are as much practical as ethical. Machine learning is good at spotting patterns—such as distinctive patterns of mobile-phone use—but poor at predicting individual behaviour.

　　无论如何，人工智能在情报领域所面临的实践上的限制不亚于道德约束。机器学习擅长识别模式(比如手机使用的独特模式)，但并不善于预测个人行为。

　　That is especially true when data are scarce, as in counter-terrorism. Predictive-policing models can crunch data from thousands of burglaries each year. Terrorist attacks are much rarer, and therefore harder to learn from.

　　在缺乏数据的情况下尤其如此，比如在反恐方面。预测性警务模型每年可以处理数千起入室盗窃案的数据。但恐怖袭击实践却少见得多，因此也更难进行分析学习。

　　重难点词汇：

　　transcribe [trænˈskraɪb] v. 转录;抄写

　　intercept [ˌɪntərˈsept] v. 拦截;截断 n. 截获的情报

　　spook [spuːk] n. 鬼;间谍;特工

　　involvement [ɪnˈvɑːlvmənt] n. 参与;牵连

　　burglary [ˈbɜːrɡləri] n. 入室偷窃

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