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.

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 ‘algorithmication’ 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.

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.’

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 “legitimate 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.’

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.’

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 ‘algorithm’s eyes’ and become dependent on its instructions. Alternative explorations – where experimentation and human instinct lead to progress and new ideas -are effectively discouraged.

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.

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.’

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.

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.’

第11段

Dr Ewan McGaughey, at Cambridge’s Centre for Business Research and King’s College London, agrees that ‘apocalyptic’ 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.

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.’

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.

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.’

据一家领先的商业咨询公司称，全球劳动力中的3-14%将需要在未来10-15年内转换到不同的职业，并且所有的工人都需要适应他们的职业随着越来越强大的机器的发展而演变的情况。自动化或称为“具身人工智能”是技术对劳动力市场影响的一方面。而像我们智能手机中运行的算法一样的“非具身人工智能”则是另一方面。

来自剑桥大学商学院的Stella Pachidi博士认为，一些最根本的变化是由于依赖数据而不是生产的工作的“算法化”而发生的，即所谓的知识经济。算法能够从数据中学习并执行以往需要人类判断的任务，比如阅读法律合同，分析医学扫描和收集市场情报。
Pachidi说：“在许多情况下，它们可以胜过人类。组织之所以倾向于使用算法，是因为他们希望基于他们认为是‘完美信息’来进行选择，并且降低成本和提高生产力。”

Pachidi表示：“但是这些改进并非没有后果。如果AI接管了例行认知任务，那么职业如何培养未来的专家？”她问道，“了解工作的一种方式是通过‘合法周边参与’，新手站在专家旁边通过观察学习。如果这种情况不存在，那么你需要找到新的学习方式。”

另一个问题是技术对劳动力的影响程度，甚至控制程度。Pachidi在一个电信公司进行了两年多的监测。她解释道：“电信销售人员的工作方式是通过与客户进行个人和频繁的接触，利用经验来评估情况并做出决策。然而，该公司开始使用了一种算法，规定了客户经理应该何时与特定客户联系，以及关于哪种活动和提供什么服务。”

她解释说，通常由外部设计师构建的算法往往成为知识的守护者。在这种情况下，Pachidi认为，一种近视的观点开始渗入工作实践中，工人们通过“算法的眼睛”学习，并依赖它的指示。探索其他可能性，即通过实验和人类直觉推动进步和新思路的方式被有效地阻止。

Pachidi和同事们甚至观察到人们制定策略以使算法为自己服务。她报告说：“我们看到有些工人向算法输入虚假数据以达到他们的目标。”

许多研究人员正在努力避免这些场景。他们的目标是使人工智能技术更加可靠和透明，以便组织和个人了解人工智能决策的制定过程。在此期间，Pachidi表示：“我们需要确保充分理解这个新世界在专业知识、职业边界和控制方面引发的困境。”

经学家Hamish Low教授认为，未来的工作将对每个人的整个生命周期进行重大转变：“传统的全日制教育后全日制工作再到退休领养金的轨迹已经成为过去，”Low说道。相反，他设想了一个多阶段的就业生涯，其中在整个生命周期中进行再培训，并且在不同阶段选择多份工作或没有工作。

关于工作流失的问题，Low认为这些预测是基于谬误的：“它假设就业岗位数是固定的。如果在30年后，有一半的100个工作岗位由机器人完成，这并不意味着我们只剩下50个工作岗位供人类从事。就业岗位的数量将增加：我们预计会有150个工作岗位。”

剑桥大学商业研究中心和伦敦国王学院的Ewan McGaughey博士同意关于工作未来的“末日”观点是错误的。“导致失业的不是新技术的出现，而是限制资本进入就业市场的法律。”

他最近发表的研究回答了自动化、人工智能和机器人是否意味着“无工作的未来”的问题，通过研究失业的原因。他说：“历史清楚地表明，变革可能意味着人员裁减。但是社会政策可以通过再培训和再就业来解决这个问题。”

他补充道：“如果由于人工智能和机器人的出现而导致工作发生变化，我希望政府能抓住机遇，改进政策以确保良好的工作安全。我们可以‘重新编程’法律为一个更加公平的工作和休闲的未来做准备。”

McGaughey的研究结果对组织、政府和银行的领导者发出了一声呼吁，要以大胆的新政策来预防即将到来的变化，以保证充分就业、公平收入和繁荣的经济民主。

他补充道：“这些新技术的承诺是令人震惊的。它们给人类带来了一种无法想象的生活方式，”他补充道。“正如工业革命使人们摆脱了务农的生活方式，企业革命实现了大规模生产，第三次革命已经宣告。但它不仅仅是技术的革命，下一次革命将是社会的。”