Phân Tích Bài Đọc Cambridge IELTS 16 Academic: The Future of Work

Reading Passage 3: The Future of Work. Source: Cambridge IELTS 16 Academic (page 24)

*Trau dồi Anh ngữ học thuật và kĩ năng đọc viết tư duy với Cambridge IELTS 16

Việc làm trong tương lai sẽ ra sao khi tự động hóa với trí tuệ nhân tạo và thuật toán ẩn trú đang kiểm soát, thăm dò, thay thế tất cả. Và cuộc ‘cách mạng’ nào đang diễn ra có lợi cho con người?

The closer language exposure and awareness, the greater acquisition and skill building!

*Topic-specific vocabulary:

> Artificial intelligence and underlying algorithm (blue)

Trí tuệ nhân tạo và thuật toán ẩn mình

*Sentence connectors and linkers within a paragraph:

> Linkers of cause and effect, addition, contrast, conditions, examples, … (red)

Tập hợp từ nối các loại trong phạm vi một bài báo

> Relative / Appositive / Exemplification / Adverbial clauses (red)

Liên từ nối kết mệnh đề liên hệ, đồng vị, minh họa và tính trạng

A. Let’s Start with Skim-reading and Compare with the Learning Notes that Follows:

Get the general idea from paragraph 1 and follow the line of thought throughout the whole passage from (1) to (5).

*Cambridge IELTS 16 Academic | Test 1 | Reading Passage 3:

The future of work

(1) 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.

*American Speaker, Dana Ebbett, from Reflective English:

(2) 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.

‘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.’

(3) ‘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.’

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, 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.’

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.

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.

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.’

(4) 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.

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.’

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.’

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.’

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.

(5) ‘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.’

Adapted from “Future of work” by the University of Cambridge.

After skimming for gist, you may get these points:

(1a) Future problems: Workforce will have to adapt or switch to a different occupation.

(1b) Background causes: AI and Algorithm – increasingly capable machines – take control.

(2) How artificial intelligence and algorithm work and take effect.

(3) Further related issues or ramifications of the rise of AI and AL.

(4) Solutions and measures to tackle problems.

(5) Conclusion: New technologies are great, soon comes a third revolution, technological and social.

B. Learning Notes on the Reading Skill and Language Awareness:

*American Speaker, Dana Ebbett, from Reflective English:

And you may notice these key words on the topic that gradually unfolds the story line.

1. Problematic social backgrounds:

Trường từ liên quan đến bối cảnh xã hội

+ embodied AI, automation, cognitive tasks taken over by AI and robotics

+ algorithms, algorithmication, dependent on data, knowledge economy, learning from data, gathering market intelligence, perfect information

+ technology influences or even controls the workforce, no future human experts, algorithm, the keeper of knowledge, algorithm’s eyes, strategies to make the algorithm work to their own advantage, false data

2. Suggested solutions:

Trường từ liên quan đến giải pháp

+ new technologies are astounding, make AI technologies more trustworthy and transparent, how AI decisions are made, multistage employment life

+ retraining happens across the life course, multiple jobs and no job, social policies can tackle this through retraining and redeployment, policy to enforce good job security

+ reprogramme the law, fairer future of work and leisure, pre-empt the coming changes with bold new policies, the next revolution will be social

3. Future changes, predictions and assumptions:

Trường từ liên quan đến thay đổi, dự đoán và võ đoán sai lệch

+ switch to sth, adapt to sth, evolve alongside AI and Algorithm

+ outperform humans, enhance productivity, lead to progress

+ improve policy to enforce good job security

+ assess a situation, reach a decision

+ begins to creep into …, become dependent on …

+ involve major transitions, envisage/predict a multistage employment life

+ predictions / assumptions are founded/based/grounded on a fallacy

+ assume that …, expect many more jobs created

+ pre-empt/foresee/anticipate the coming changes

C. Learning Notes on the Paragraph-level Writing Skill:

*American Speaker, Dana Ebbett, from Reflective English:

Notice a broad range of trigger words, sentence connectors and transition words can be found in bold alongside the passage.

“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, using the benefit of experience to assess a situation and reach a decision. However, the company had started using an ‘unseen’ algorithm that defined when account managers should contact certain customers about which kinds of campaigns and what to offer them.”

Reading Passage 3: The Future of Work. Source: Cambridge IELTS 16 Academic (page 25)

1. More ‘advanced’ connectors:

+ … in a way that / … in a certain manner

They deliver humankind the capacity to live in a way that nobody could have once imagined.

+ … to the extent that / … to a certain degree

Technologies have influenced human life to the extent that what will be in just 5 years’ time is an unknown.

+ instead, … / In place of that, …

The traditional stereotype is a one-stage working life followed by old-age retirement. Instead, he envisages a multistage employment life.

+ regarding… / relating to…

Make sure we fully understand the dilemmas that this new world raises regarding expertise, occupational boundaries and control.

+ rather than… / different or opposite to the idea that you have stated previously…

The ‘algorithmication’ of jobs is dependent on data rather than on production.

The ‘algorithmication’ of jobs is dependent on data, not (much) on production.

Rather than on production, the ‘algorithmication’ of jobs is dependent on data.

+ just as …, / in the same way as…

Just as the industrial revolution brought people past subsistence agriculture, and the corporate revolution enabled mass production, a third revolution has been pronounced.

+ …, one where … / …, a situation in which…

He envisages a multistage life: one where retraining happens across the life course.

+ whereby / wherein … / by which…, in that situation…

Pachidi believes a short-sighted view begins to creep into working practices whereby workers learn through the ‘algorithm’s eyes.

2. Appositive clauses/phrases:

(A noun phrase immediately after or before another noun phrase that refers to the same person or thing.)

+ … ‘algorithmication’ of jobs that are dependent on data rather than on production – the so-called knowledge economy.

+ Automation – or ‘embodied artificial intelligence’ (AI) – is one aspect of the disruptive effects of technology on the labour market.

+ ‘One way of learning about a job is “legitimate peripheral participation” – a novice stands next to experts and learns by observation.

+ 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.

+ It’s the laws that restrict the supply of capital to the job market, not the advent of new technologies that causes unemployment.

3. Exemplification clauses:

+ ‘Disembodied AI’, like the algorithms running in our smartphones, is another.

+ … that previously needed human judgement, such as reading legal contracts, analysing medical scans and gathering market intelligence.

4. Relative clauses:

+ The algorithm – usually built by external designers – often becomes the keeper of knowledge…

+ Alternative explorations – where experimentation and human instinct lead to progress and new ideas – are effectively discouraged.

+ It’s the laws that restrict the supply of capital to the job market, not the advent of new technologies that causes unemployment.

+ It’s scenarios like these that many researchers are working to avoid.

+ … with bold new policies that guarantee full employment, fair incomes and a thriving economic democracy.

+ We fully understand the dilemmas that this new world raises regarding expertise, occupational boundaries and control.

5. Adverbial clauses:

+ Another issue is the extent to which the technology influences or even controls the workforce.

+ The way telecoms salespeople work is through personal and frequent contact with clients.

+ … working practices whereby workers learn through the ‘algorithm’s eyes.

+ They deliver humankind the capacity to live in a way that nobody could have once imagined.

+ Just as the industrial revolution brought people past subsistence agriculture, and the corporate revolution enabled mass production, a third revolution has been pronounced.

6. Noun clauses:

+ History is clear that change can mean redundancies.

+ It was algorithm that defined when account managers should contact certain customers about which kinds of campaigns and what to offer them.

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