请教此句翻译

这个补偿方应支付任何产生的定居点,判断或法令,然而,提供,接受赔偿者应在任何时候也有正确的充分参与国防在接受赔偿者唯一的成本和费用,只要这种参与发生没有阻碍或损害国防的补偿方承担;

经济学人2018-02-06

YOU have multiple jobs, whether you know it or not. Most begin first thing in the morning, when you pick up your phone and begin generating the data that make up Silicon Valley’s most important resource. That, at least, is how we ought to think about the role of data-creation in the economy, according to a fascinating new economics paper. We are all digital labourers, helping make possible the fortunes generated by firms like Google and Facebook, the authors argue. If the economy is to function properly in the future—and if a crisis of technological unemployment is to be avoided—we must take account of this, and change the relationship between big internet companies and their users.

无论你是否知道，其实你身兼数职。大部分开始于早晨你做的第一件事——拿起手机，开始产生数据，而这是硅谷最重要的资源。一篇新发表的 有趣的 经济学论文表示，至少我们应该这样来衡量创造数据对经济所起的作用。文章作者认为，我们都是数字化的劳工，帮助谷歌和脸书这样的大企业创造财富。如果未来经济要 正常运转 ，并且避免技术性失业危机的发生，我们必须要考虑这一点，改变大型互联网企业和用户之间的关系。

Artificial intelligence (AI) is getting better all the time, and stands poised to transform a host of industries , say the authors (Imanol Arrieta Ibarra and Diego Jiménez Hernández, of Stanford University, Leonard Goff, of Columbia University, and Jaron Lanier and Glen Weyl, of Microsoft). But, in order to learn to drive a car or recognisea face, the algorithms that make clever machines tick must usually be trained on massive amounts of data. Internet firms gather these data from users every time they click on a Google search result, say, or issue a command to Alexa. They also hoover up valuable data from users through the use of tools like reCAPTCHA, which ask visitors to solve problems that are easy for humans but hard for AIs, such as deciphering text from books that machines are unable to parse. That does not just screen out malicious bots, but also helps digitise books. People “pay” for useful free services by providing firms with the data they crave .

文章作者们(斯坦福大学的伊马诺尔·阿列塔·伊巴拉和迭戈希·门尼斯·埃尔南德斯、哥伦比亚大学的伦纳德·勒戈夫以及微软公司的杰伦•拉尼尔和格伦•外尔)一致认为， 人工智能（AI）技术日趋完善，势必将为多个行业带来变革 。但是，为了学习驾驶汽车或是进行面部识别，计算机必须通过算法来进行训练，而训练中就要使用大量的数据。每当用户点击谷歌搜索结果或是在Alexa上下达指令的时候，互联网公司都会收集这些数据。互联网公司同样使用工具，如使用验证码，从用户获取有价值的数据，要求访问者解决对于人类来说轻而易举但对于AI来说却非常困难的问题，例如解析书上的文字，这对于机器来说是不可能的。这不仅能够筛选出恶意的网络机器人，还能帮助将图书转为数字形式。人们向互联网公司提供了他们渴望的数据，以此来换取的免费网络服务。

These data become part of the firms’ capital, and, as such, a fearsome source of competitive advantage. Would-be startups that might challenge internet giants cannot train their AIs without access to the data only those giants possess. Their best hope is often to be acquired by those very same titans, adding to the problem of uncompetitive markets.

这些数据成为企业资本的一部分，因此也是竞争优势的可怕来源。那些有可能挑战互联网巨头的初创公司，由于无法获得这些被垄断的数据，因而无法训练他们的人工智能。他们最大的希望就是被同业互联网巨头收购，加重了市场的垄断程度。

That, for now, AI’s contributions to productivity growth are **all, the authors say, is partly because of the free-data model, which limits the quality of data gathered. Firms trying to develop useful applications for AI must hope that the data they have are sufficient, or come up with ways to coax users into providing them with better information at no cost. For example, they must **pester **random people—like those blur-deciphering visitors to websites—into labelling data, and hope that in their annoyance and haste they do not make mistakes.

文章作者认为，目前人工智能对于生产率增长的贡献还很小，部分原因在于免费数据的模式限制了数据的质量。如果互联网公司想要开发更有用的人工智能程序，他们一定希望能取得足够的数据，或者想方设法哄骗用户，免费获得更好的用户信息。例如，他们会随意骚扰随机用户——比如识别模糊验证码的网站访问者，进行数据标注，希望他们在不胜其扰的情况下也不会犯错误。

Even so, as AI improves, the amount of work made vulnerable to displacement by technology grows, and ever more of the value generated in the economy accrues to profitable firms rather than workers. As the authors point out, the share of GDP paid out to workers in wages and salaries—once thought to be relatively stable—has already been declining over the past few decades.

即便如此，随着人工智能技术的进步，越来越多的工作岗位将更容易被取代，而在经济发展中产生的价值则会越来越多地落到公司的口袋，而非惠及普通员工。正如作者所指出，在过去的几十年里，工人的工资和薪金占GDP的份额——一度被认为是相对稳定的，在逐年下降。

To tackle these problems, they have a radical proposal. Rather than being regarded as capital, data should be treated as labour—and, more specifically, regarded as the property of those who generate such information, unless they agree to provide it to firms in exchange for payment. In such a world, user data might be sold multiple times, to multiple firms, reducing the extent to which data sets serve as barriers to entry. Payments to users for their data would help spread the wealth generated by AI. Firms could also potentially generate better data by paying. Rather than guess what a person is up to as they wander around a shopping centre, for example, firms could ask individuals to share information on which shops were visited and which items were viewed, in exchange for payment. Perhaps most ambitiously, the authors muse that data labour could come to be seen as useful work, conferring the same sort of dignity as paid employment: a desirable side-effect in a possible future of mass automation.

为了解决这些问题，他们提出了一个非常激进的建议。 数据不应被视为一种资本，而应当作为一种劳动，更确切地说，应该作为创造这些信息的人的财产，除非他们同意向企业有偿提供这些信息。在这种情况下，用户的数据可能会被多次出售给不同的公司，同时也能够降低作为入门门槛的数据收集难度。对用户的数据付费有助于扩散人工智能创造的财富。企业也将有可能通过付费获得质量更好的数据。 例如，当一个人在商场闲逛时，商家可以不用去猜测这个人在干什么，而可以通过付费的方式要求个人分享诸如他逛了哪些商店，看了哪些商品之类的信息。而作者大胆地设想，在不久的将来，数据劳动也能够被当成是一种有价值的工作，并且能够获得与有薪工作同样的尊严，而这正是未来可能的大规模自动化带来的一种好的副作用。

The authors’ ideas need fleshing out; their paper, thought-provoking though it is, runs to only five pages. Parts of the envisioned scheme seem impractical. Would people really be interested in taking the time to describe their morning routine or office habits without a substantial monetary **inducement **(and would their data be valuable enough for firms to pay a substantial amount)? Might not such systems attract data mercenaries, spamming firms with useless junk data simply to make a quick buck?

从目前来看，作者的想法需要充实，他们的文章尽管 发人深省 ，但是全文仅仅只有5页。部分设想看上去不太实际。在没有足够大金钱利益驱动下，人们真的会有兴趣去花时间记录自己的日常习惯或者是办公习惯吗（或是用户数据的价值是否足以使企业去支付大量的费用）？这样的体系下是不是会吸引大批唯利是图，企图通过垃圾邮件或者无用数据来赚快钱的公司？

Still, the paper contains essential insights which should frame discussion of data’s role in the economy. One concerns the imbalance of power in the market for data. That stems partly from concentration among big internet firms. But it is also because, though data may be extremely valuable in aggregate, an individual’s personal data typically are not. For one Facebook user to threaten to deprive Facebook of his data is no threat at all. So effective negotiation with internet firms might require collective action: and the formation, perhaps, of a “data-labour union”.

尽管如此，论文提出了数据在经济中的作用的 基本见解 。其中一点是目前数据市场权力的不平衡，这源于大部分的资源都集中在大型互联网公司手里。但是这也是因为数据只有在成规模的时候才有价值，单个的数据信息并没有那么有价值。比如说一个脸书的用户威胁他将从脸书上自己的数据，而这根本无法构成威胁。因此，同互联网公司进行有效谈判可能也得采取抱团的方式，也许通过‘数据劳工工会’这种形式比较合适。

This might have drawbacks. A union might demand too much in compensation for data, for example, impairing the development of useful AIs. It might make all user data freely available and extract compensation by demanding a share of firms’ profits; that would rule out the pay-for-data labour model the authors see as vital to improving data quality. Still, a data union holds potential as a way of solidifying worker power at a time when conventional unions struggle to remain relevant.

但是工会的模式可能也有问题。例如，它可能会对于数据要价太高从而影响那些非常实用的人工智能技术的发展。它可能让所有用户的数据可以自由获取，但用户通过从企业收益中获取分成从而获得补偿。如果这样，无疑会毁掉数据付费使用这一劳动模式，而这种模式正是作者希望用来提升数据质量至关重要的一环。尽管如此，当传统工会难以保持利益相关性之时，数据工会依然可能成为保护劳动者权利的一种方式。

Most important, the authors’ proposal puts front and centre the collective nature of value in an AI world. Each person becomes something like an oil well, pumping out the fuel that makes the digital economy run. Both fairness and efficiency demand that the distribution of income generated by that fuel should be shared more evenly, according to our contributions. The tricky part is working out how.

最重要的是，作者的建议将人工智能世界中价值的集体性质置于超前和中心的位置。 每个人都变成了一个“油井”，喷发出让数字经济运行的“石油”。公平和效率都要求根据贡献来更加均等地分配由我们生产的“石油”所产生的回报。理想很美好，但难的是如何将一切付诸行动。

References: [取经号]（ ）

impair什么意思及同义词

impair 英[ɪmˈpeə(r)] 美[ɪmˈper]

vt. 损害，削弱;

[例句]Consumption of alcohol impairs your ability to drive a car or operate machinery

饮酒会削弱你驾驶汽车或操控机器的能力。

[其他] 第三人称单数：impairs 现在分词：impairing 过去式：impaired过去分词：impaired

impaired可以做非谓语动词吗？

一般it is no use doing sth结构。

用不定式作主语建议用：It is useless simply to blame digital devices

因为impair是及物动词，后面有宾语，所以，应该用impairing。impaired是过去分词，表示"adj. 受损的;损坏的"，有被动含义。

所以这个句子应该改为

It is useless simply to blame digital devices impairing kids' mental health.

简单地指责损害孩子的心理健康的数字设备是没有用的。

impaired可以作非谓语动词。

adj. 受损的;损坏的;出毛病的;有(身体或智力)缺陷的;有…障碍的（形容词性过去分词）

v.损害;削弱

impair的过去分词和过去式

There are 1.7 million blind and visually impaired people in Britain. impaired作定语，过去分词。

英国有170万失明和视力受损人口。

《柯林斯高阶英汉双解学习词典》

抹不开的意思

题库内容：

抹不开的解释

(1) [feel embarrassed]

∶觉得 困窘 ,因受批评而不好意思 (2) [afraid of impairing personal relations]∶ 害怕 伤了私人关系 他有 错误 ,就该批评,有什么抹不开的? 详细解释 (1).脸面上下不来。 柳青 《创业史》 第一部第三十章：“人，光光是一时的面皮抹不开罢了。” (2).不好意思。 高凤阁 《垫道》 ：“ 刘队长 、 老雷头 都嘿嘿着说不用，叫 大家 这么 一关心反有点抹不开了。” (3).方言。想不通。 艾明之 《无言歌》 ：“说他是一个什么外国 权威 的 孝子贤孙 ，是个反 革命 。老教师心里抹不开，就在自己房门背后，用一根绳子结束了自己的 生命 。”

词语分解

抹的解释 抹 ǒ 涂：涂抹。抹粉（喻美化或掩饰）。抹黑（喻丑化）。抹子（瓦工用来抹灰泥的器具。亦称“抹刀”）。 揩，擦：抹拭。哭天抹泪。 除去，勾掉，不计在内：抹煞。 轻微的痕迹：“林梢一抹青如画”。一抹 余晖 。

MARS这个英文怎么读

一、mars的读音英 [mɑːz] 　 　 美 [mɑːrz]

二、释义

abbr. master attitude reference system 主要态度参考系统

n. 毁坏，损坏，玷污( mar的名词复数 );  

v. 毁坏，损坏，玷污( mar的第三人称单数 );  

三、例句

The similarities between Mars and Earth were enough to keep alive hopes of some form of Martian life.

火星和地球之间的诸多相似处足以让人们对火星上存在某种生物抱以希望。

In thirty years from now the United States should have a manned spacecrafton Mars.

未来30年内，美国有可能会把载人航天器送上火星。

四、原型： mar  

扩展资料

近义词：impair、damage 

1、impair  

英[ɪmˈpeə(r)]    美[ɪmˈper]  

vt.    损害，削弱;  

Consumption of alcohol impairs your ability to drive a car or operate machinery

饮酒会削弱你驾驶汽车或操控机器的能力。

第三人称单数：impairs

现在分词：impairing

过去式：impaired

过去分词：impaired  

2、damage  

英[ˈdæmɪdʒ]    美[ˈdæmɪdʒ] 

vt.    损害，毁坏;  

n.    损害，损毁; 赔偿金;  

He maliciously damaged a car with a baseball bat

他用棒球棒恶意损毁了一辆汽车。

第三人称单数：damages

复数：damages

现在分词：damaging

过去式：damaged

过去分词：damaged