劍橋雅思16Test1Passage3閱讀原文翻譯 The future of work

劍橋雅思16Test1Passage3閱讀原文翻譯 The future of work 具體的內(nèi)容和中國教育在線平臺一起來看看吧

劍橋雅思16 Test1 Passage3閱讀原文翻譯

第1段

According to a leading business consultancy, 3-14% of the global workforce will need to switch to a different occupation within the next 10-15 years, and all workers will need to adapt as their occupations evolve alongside increasingly capable machines. Automation – or ’embodied artificial intelligence’ (AI) – is one aspect of the disruptive effects of technology on the labour market.’Disembodied AI’, like the algorithms running in our smartphones, is another.

根據(jù)一家一流商業(yè)咨詢機構(gòu)的預(yù)測，全球3%到15%的勞動力需要在10到15年內(nèi)更換自己的工作。所有勞動者都需要適應(yīng)由越來越先進的機器所帶來的工作內(nèi)容的變化。自動化，或者“看得見的人工智能”，是技術(shù)對勞動力市場破壞性影響的一個方面。而“看不見的人工智能”，如我們手機中運行的算法，是其影響的另一方面。

第2段

Dr Stella Pachidi from Cambridge Judge Business School believes that some of the most fundamental changes are happening as a result of the ‘a(chǎn)lgorithmication’ of jobs that are dependent on data rather than on production – the so-called knowledge economy. Algorithms are capable of learning from data to undertake tasks that previously needed human judgement, such as reading legal contracts, analysing medical scans and gathering market intelligence.

劍橋大學(xué)賈吉商學(xué)院Stella Pachidi博士認為一些最根本的改變是工作內(nèi)容“算法化”的結(jié)果，即工作依賴于數(shù)據(jù)而非生產(chǎn)-所謂的知識經(jīng)濟。算法可以從數(shù)據(jù)中學(xué)習(xí)，進而執(zhí)行之前需要人類判斷的任務(wù)，比如閱讀法律合同，分析醫(yī)療檢查結(jié)果，并收集市場情報。

第3段

‘In many cases, they can outperform humans,’ says Pachidi, ‘Organisations are attracted to using algorithms because they want to make choices based on what they consider is “perfect information”, as well as to reduce costs and enhance productivity.’

“許多情況下，它們的表現(xiàn)都超過人類”，Pachidi說，“組織機構(gòu)傾向于使用算法，因為他們想要將決策建立在他們所認為的‘完全信息’上，減少成本，并提升生產(chǎn)效率”。

第4段

‘But these enhancements are not without consequences,’ says Pachidi. ‘If routine cognitive tasks are taken over by AI, how do professions develop their future experts?’ she asks. ‘One way of learning about a job is “l(fā)egitimate peripheral participation” – a novice stands next to experts and learns by observation. If this isn’t happening, then you need to find new ways to learn.’

“但是，這些提升并非沒有代價”，Pachidi說，“如果日常認知任務(wù)都由人工智能進行，那么各行各業(yè)如何培養(yǎng)自己未來的專家？”她問道?！傲私饽稠椆ぷ鞯囊环N方法是‘合法的邊緣性參與’，即新手站在專家旁，通過觀察學(xué)習(xí)。如果這種方式消失了的話，那么你就得尋找新的學(xué)習(xí)方法”。

第5段

Another issue is the extent to which the technology influences or even controls the workforce. For over two years, Pachidi monitored a telecommunications company. ‘The way telecoms salespeople work is through personal and frequent contact with clients, this article is from Laokaoya website. using the benefit of experience to assess a situation and reach a decision. However, the company had started using a[n]…algorithm that defined when account managers should contact certain customers about which kinds of campaigns and what to offer them.’

另外一項問題是，技術(shù)在多大程度上影響甚至控制員工。Pachidi觀察一家電信公司兩年多的時間?！半娦殴句N售人員的工作通過與客戶私下而頻繁的接觸展開，利用他們的經(jīng)驗優(yōu)勢評估狀況并促成決定。然而，公司已經(jīng)開始使用算法決定客戶經(jīng)理應(yīng)該在什么時候、就哪種活動與產(chǎn)品聯(lián)系特定的顧客”。

第6段

The algorithm – usually built by external designers – often becomes the keeper of knowledge, she explains. In cases like this, Pachidi believes, a short-sighted view begins to creep into working practices whereby workers learn through the ‘a(chǎn)lgorithm’s eyes’ and become dependent on its instructions. Alternative explorations – where experimentation and human instinct lead to progress and new ideas -are effectively discouraged.

她解釋道，這種通常由外部設(shè)計師搭建的算法成為知識的保管員。Pachidi認為，在諸如此類的情況下，一種較為短視的看法開始影響工作實踐。員工通過算法的視角進行學(xué)習(xí)，并依賴于它的指示。替代性探索，即實驗與人類直覺催生進步和新觀點，遭到打擊。

第7段

Pachidi and colleagues even observed people developing strategies to make the algorithm work to their own advantage.’We are seeing cases where workers feed the algorithm with false data to reach their targets,’ she reports.

Pachidi和她的同事甚至觀察到，人們開發(fā)出相應(yīng)的策略，讓算法為滿足他們自己的利益而工作。“我們觀察到如下案例，員工將錯誤的數(shù)據(jù)提供給算法以達到自己的目的”，她指出。

第8段

It’s scenarios like these that many researchers are working to avoid. Their objective is to make AI technologies more trustworthy and transparent, so that organisations and individuals understand how AI decisions are made. In the meantime, says Pachidi,’ We need to make sure we fully understand the dilemmas that this new world raises regarding expertise, occupational boundaries and control.’

這正是許多研究者努力避免的情景。他們的目標(biāo)是讓人工智能技術(shù)變得更加可信、更加透明，以便組織機構(gòu)和個人文章來自老烤鴨雅思能夠理解人工智能如何做出決策。與此同時，Pachidi說道，“我們需要確保自己充分理解這一新世界在專業(yè)技能、職業(yè)邊界和控制方面發(fā)引發(fā)的困境”。

第9段

Economist Professor Hamish Low believes that the future of work will involve major transitions across the whole life course for everyone: ‘The traditional trajectory of full-time education followed by full-time work followed by a pensioned retirement is a thing of the past,’ says Low. Instead, he envisages a multistage employment life: one where retraining happens across the life course, and where multiple jobs and no job happen by choice at different stages.

經(jīng)濟學(xué)教授Hamish Low認為，未來的工作會給所有人生活的方方面面帶來重大轉(zhuǎn)變?！皞鹘y(tǒng)的接受全日制教育之后從事全日制工作，隨后再領(lǐng)取津貼退休的路徑已經(jīng)是過去時”。他反而展望一種多階段的職業(yè)生活：整個生命過程中都會進行再培訓(xùn)，不同階段選擇從事多種工作或者不工作。

第10段

On the subject of job losses, Low believes the predictions are founded on a fallacy: “It assumes that the number of jobs is fixed. If in 30 years, half of 100 jobs are being carried out by robots, that doesn’t mean we are left with just 50 jobs for humans. The number of jobs will increase: we would expect there to be 150 jobs.’

至于工作流失的問題，Low認為這一預(yù)測建立在謬誤之上：“它假定工作的數(shù)量是固定的。如果在未來30年里，100個工作崗位中有一半由機器人承擔(dān)，這并不意味著只剩下50個崗位給人類。工作數(shù)量會上升。我們能夠期望會有150個工作機會”。

第11段

Dr Ewan McGaughey, at Cambridge’s Centre for Business Research and King’s College London, agrees that ‘a(chǎn)pocalyptic’ views about the future of work are misguided. ‘It’s the laws that restrict the supply of capital to the job market, not the advent of new technologies that causes unemployment.

劍橋大學(xué)商業(yè)研究中心與倫敦國王學(xué)院的Ewan McGaughey博士同樣認為有關(guān)未來工作的“末日論”充滿誤導(dǎo)?！跋拗魄舐毷袌鲑Y本供給的法律才是引發(fā)失業(yè)的原因，而非新技術(shù)的出現(xiàn)”。

第12段

His recently published research answers the question of whether automation, AI and robotics will mean a ‘jobless future’ by looking at the causes of unemployment. ‘History is clear that change can mean redundancies. But social policies can tackle this through retraining and redeployment.’

他最近發(fā)表的研究通過探詢失業(yè)的起因回答了自動化，人工智能以及機器人是否會引發(fā)“無工作的未來”這一問題?！皻v史表明，改變可能意味著裁員。但社會政策能夠通過再培訓(xùn)以及再分配解決這一問題”。

第13段

He adds: ‘If there is going to be change to jobs as a result of AI and robotics then I’d like to see governments seizing the opportunity to improve policy to enforce good job security. We can “reprogramme” the law to prepare for a fairer future of work and leisure.’ McGaughey’s findings are a call to arms to leaders of organisations, governments and banks to pre-empt the coming changes with bold new policies that guarantee full employment, fair incomes and a thriving economic democracy.

他補充到：“如果人工智能和機器人引發(fā)工作上的改變，那么我想看到政府抓住機會提升政策以確保良好的工作安全。我們可以重新編排法律以迎接工作和休閑更為公平的未來”。McGaughey的發(fā)現(xiàn)呼吁組織機構(gòu)、政府和銀行的領(lǐng)導(dǎo)制定大膽的新政策，以提前應(yīng)對即將到來的改變，確保就業(yè)率、公平收入、以及繁榮的經(jīng)濟民主。

第14段

‘The promises of these new technologies are astounding. They deliver humankind the capacity to live in a way that nobody could have once imagined,’ he adds. ‘Just as the industrial revolution brought people past subsistence agriculture, and the corporate revolution enabled mass production, a third revolution has been pronounced. But it will not only be one of technology. The next revolution will be social.’

“這些新技術(shù)帶來的前景讓人震驚。他們賦予人類以一種前人無法想象的方式生活的能力”，他補充到，“正如工業(yè)革命讓人們脫離勉強糊口的農(nóng)業(yè)生活，企業(yè)革命讓大規(guī)模生產(chǎn)成為可能，第三次革命已經(jīng)出現(xiàn)。但它絕不僅僅是技術(shù)革命。下一次革命一定是社會性的”。

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