 "Human factors in the automation debate")
Thomas Davenport and Julia Kirby's book comes with a whiff of optimism and plenty of specific, practical advice based on real-life cases for professionals - scientists, radiologists, teachers, actuaries, financial analysts, lawyers and all "knowledge workers" who fear that they will lose their jobs to automation. This is where it is different from the scarily sensational non-fiction on machines versus humans. The title is an inversion of Jerry Kaplan's memorable book, Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence, published in 2015.
Yes, computers could be coming after your job. And yes, machines are very smart and are becoming smarter. So ignoring them or trying to compete against them is a zero-sum game - the machine will win and you will lose. John Henry, a West Virginia driller, learnt that in 1870. He competed against a steam- powered drill. He won - only to die from over exertion soon after.
Dirty, dangerous, physically demanding and highly structured jobs, like those on an industrial production line, have been doomed since the 1990s. The United States lost more jobs to automation at home than from outsourcing to India. This trend will worsen. Even the proportion of "knowledge workers", highly educated professionals - who comprise 25 to 50 per cent of the workforce in advanced economies - will get flooded out via automation by 2040. McKinsey estimates automated systems will replace the equivalent of between 110 million and 140 million human jobs by as early as 2025.
The way out is "augmentation" - keeping humans at the centre whilst farming out work that machines can do better - as opposed to "autonomy" - progressively substituting humans with machines. Steve Jobs illustrates: a human, "augmented" with a bicycle, becomes far more energy-efficient that even a condor, the most energy-efficient of all species. Augmentation is more than mere "complementarity" or co-existence with machines. It means actively collaborating with automation and artificial intelligence to sharpen our skills in areas where humans are most competitive.
Take apart your job into two components - structured tasks that can be codified and tasks that cannot, or at least not just yet. Focus on honing the latter. The former will be automated. You have five options to adapt to the future.
You can "step in" by learning how machines can offload your "dodo", or routinised, tasks thereby freeing up space for your core "human" skills. This presents the largest opportunities to partner machines, oversee them, point out errors they have made or help improve them.
You could also "step forward" by acquiring the highly specialised quant skills, engineering knowledge and coding expertise needed to create newer and better machines. But the skill requirements would be of a very high level with the need for continuous upgrades.
"Step up" options involve honing the expertise to take unstructured decisions by integrating information from multiple sources. Warren Buffet defines one such option that defies codification: what should a car driver do if the choice is either to mow down a child who has strayed onto the road or to plough into a car with four adult passengers? Complex, corporate "trade-offs" in business strategy are no different.
Others may prefer to "step aside" or take up jobs machines cannot do, like explaining in plain language to an irate Ben Bernanke, the erstwhile chairman of the US Federal Reserve, why a computer assessed him as too risky for mortgage refinance in 2014.
"Stepping narrow" is the fifth option. These are jobs so specialised and so restricted - such as dealing with special kids or translating lost languages - that they lack the scale required to make automation efficient. In 1997, film maker Errol Morris featured four such narrow specialisations - a topiary gardener (that is, someone who clips shrubs and trees into ornamental shapes), a lion tamer, an authority on the colony behaviour of naked mole rats and, ironically, Rodney Brooks, inventor of autonomous robots.
Is automation Keynes' leisure-filled utopia or a jobless dystopia scarred by rising inequality and violence? Elon Musk thinks artificial intelligence is "our biggest existential threat". Stephen Hawking warns that it "could spell the end of the human race". Bill Gates wonders why "some people are not concerned". The authors are clearly not concerned. Nor are 52 per cent of nearly 2,000 experts polled by Pew Research. But the near-term problem of managing the transition, particularly in poor countries like India, remains a public concern. The authors rightly debunk the option of universal income transfers as short-term palliatives - like the National Rural Employment Guarantee programme in India - with potentially negative fiscal and work-ethic related unintended consequences.
To cope with the rise of artificial intelligence, governments need to reorient education. The focus on science, technology and quant skills is good for those who step in, up or forward. But one half of workers will be stepping aside or stepping narrowly. Education policy does little to encourage these skills. Corporations should get incentives for generating "humans-only" work, as Innovation for Jobs (i4j) is doing. International regulation of autonomous machines and artificial intelligence is critical but absent. We need to collectively "trade off" the benefits from automation against the social cost of increasing joblessness and inequality. Such complex decisions should be a humans-only skill. Unfortunately, we have rarely made wise public choices. This skill needs to be augmented. A first step could be for all those concerned to read this book.
The reviewer is Advisor, Observer Research Foundation
Yes, computers could be coming after your job. And yes, machines are very smart and are becoming smarter. So ignoring them or trying to compete against them is a zero-sum game - the machine will win and you will lose. John Henry, a West Virginia driller, learnt that in 1870. He competed against a steam- powered drill. He won - only to die from over exertion soon after.
Dirty, dangerous, physically demanding and highly structured jobs, like those on an industrial production line, have been doomed since the 1990s. The United States lost more jobs to automation at home than from outsourcing to India. This trend will worsen. Even the proportion of "knowledge workers", highly educated professionals - who comprise 25 to 50 per cent of the workforce in advanced economies - will get flooded out via automation by 2040. McKinsey estimates automated systems will replace the equivalent of between 110 million and 140 million human jobs by as early as 2025.
Also Read
Take apart your job into two components - structured tasks that can be codified and tasks that cannot, or at least not just yet. Focus on honing the latter. The former will be automated. You have five options to adapt to the future.
You can "step in" by learning how machines can offload your "dodo", or routinised, tasks thereby freeing up space for your core "human" skills. This presents the largest opportunities to partner machines, oversee them, point out errors they have made or help improve them.
You could also "step forward" by acquiring the highly specialised quant skills, engineering knowledge and coding expertise needed to create newer and better machines. But the skill requirements would be of a very high level with the need for continuous upgrades.
"Step up" options involve honing the expertise to take unstructured decisions by integrating information from multiple sources. Warren Buffet defines one such option that defies codification: what should a car driver do if the choice is either to mow down a child who has strayed onto the road or to plough into a car with four adult passengers? Complex, corporate "trade-offs" in business strategy are no different.
Others may prefer to "step aside" or take up jobs machines cannot do, like explaining in plain language to an irate Ben Bernanke, the erstwhile chairman of the US Federal Reserve, why a computer assessed him as too risky for mortgage refinance in 2014.
"Stepping narrow" is the fifth option. These are jobs so specialised and so restricted - such as dealing with special kids or translating lost languages - that they lack the scale required to make automation efficient. In 1997, film maker Errol Morris featured four such narrow specialisations - a topiary gardener (that is, someone who clips shrubs and trees into ornamental shapes), a lion tamer, an authority on the colony behaviour of naked mole rats and, ironically, Rodney Brooks, inventor of autonomous robots.
Is automation Keynes' leisure-filled utopia or a jobless dystopia scarred by rising inequality and violence? Elon Musk thinks artificial intelligence is "our biggest existential threat". Stephen Hawking warns that it "could spell the end of the human race". Bill Gates wonders why "some people are not concerned". The authors are clearly not concerned. Nor are 52 per cent of nearly 2,000 experts polled by Pew Research. But the near-term problem of managing the transition, particularly in poor countries like India, remains a public concern. The authors rightly debunk the option of universal income transfers as short-term palliatives - like the National Rural Employment Guarantee programme in India - with potentially negative fiscal and work-ethic related unintended consequences.
To cope with the rise of artificial intelligence, governments need to reorient education. The focus on science, technology and quant skills is good for those who step in, up or forward. But one half of workers will be stepping aside or stepping narrowly. Education policy does little to encourage these skills. Corporations should get incentives for generating "humans-only" work, as Innovation for Jobs (i4j) is doing. International regulation of autonomous machines and artificial intelligence is critical but absent. We need to collectively "trade off" the benefits from automation against the social cost of increasing joblessness and inequality. Such complex decisions should be a humans-only skill. Unfortunately, we have rarely made wise public choices. This skill needs to be augmented. A first step could be for all those concerned to read this book.
The reviewer is Advisor, Observer Research Foundation