代写Artificial intelligence The impact on jobs
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代写Artificial intelligence The impact on jobs
Artificial intelligence
The impact on jobs
Will smarter machines cause mass unemployment?
Jun 25th 2016 | From the print edition
SITTING IN AN office in San Francisco, Igor
Barani calls up some medical scans on his
screen. He is the chief executive of Enlitic, one of
a host of startups applying deep learning to
medicine, starting with the analysis of images
such as X-rays and CT scans. It is an obvious use
of the technology. Deep learning is renowned for
its superhuman prowess at certain forms of
image recognition; there are large sets of labelled
training data to crunch; and there is tremendous
potential to make health care more accurate and
efficient.
Dr Barani (who used to be an oncologist) points
to some CT scans of a patient’s lungs, taken from
three different angles. Red blobs flicker on the
screen as Enlitic’s deep-learning system
examines and compares them to see if they are
blood vessels, harmless imaging artefacts or malignant lung nodules. The system ends up
highlighting a particular feature for further investigation. In a test against three expert
human radiologists working together, Enlitic’s system was 50% better at classifying malignant
tumours and had a false-negative rate (where a cancer is missed) of zero, compared with 7%
for the humans. Another of Enlitic’s systems, which examines X-rays to detect wrist fractures,
also handily outperformed human experts. The firm’s technology is currently being tested in
40 clinics across Australia.
A computer that dispenses expert radiology advice is just one example of how jobs currently
done by highly trained white-collar workers can be automated, thanks to the advance of deep
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learning and other forms of artificial intelligence. The idea that manual work can be carried
out by machines is already familiar; now ever-smarter machines can perform tasks done by
information workers, too. What determines vulnerability to automation, experts say, is not so
much whether the work concerned is manual or white-collar but whether or not it is routine.
Machines can already do many forms of routine manual labour, and are now able to perform
some routine cognitive tasks too. As a result, says Andrew Ng, a highly trained and specialised
radiologist may now be in greater danger of being replaced by a machine than his own
executive assistant: “She does so many different things that I don’t see a machine being able
to automate everything she does any time soon.”
So which jobs are most vulnerable? In a widely noted study published in 2013, Carl Benedikt
Frey and Michael Osborne examined the probability of computerisation for 702 occupations
and found that 47% of workers in America had jobs at high risk of potential automation. In
particular, they warned that most workers in transport and logistics (such as taxi and delivery
drivers) and office support (such as receptionists and security guards) “are likely to be
substituted by computer capital”, and that many workers in sales and services (such as
cashiers, counter and rental clerks, telemarketers and accountants) also faced a high risk of
computerisation. They concluded that “recent developments in machine learning will put a
substantial share of employment, across a wide range of occupations, at risk in the near
future.” Subsequent studies put the equivalent figure at 35% of the workforce for Britain
(where more people work in creative fields less susceptible to automation) and 49% for Japan.
What determines vulnerability to automation is not so much whether the work concerned is
manual or white-collar but whether or not it is routine
Economists are already worrying about “job polarisation”, where middle-skill jobs (such as
those in manufacturing) are declining but both low-skill and high-skill jobs are expanding. In
effect, the workforce bifurcates into two groups doing non-routine work: highly paid, skilled
workers (such as architects and senior managers) on the one hand and low-paid, unskilled
workers (such as cleaners and burger-flippers) on the other. The stagnation of median wages
in many Western countries is cited as evidence that automation is already having an effect
—though it is hard to disentangle the impact of offshoring, which has also moved many
routine jobs (including manufacturing and call-centre work) to low-wage countries in the
developing world. Figures published by the Federal Reserve Bank of St Louis show that in
America, employment in non-routine cognitive and non-routine manual jobs has grown
steadily since the 1980s, whereas employment in routine jobs has been broadly flat (see
chart). As more jobs are automated, this trend seems likely to continue.
And this is only the start. “We are just seeing the tip of the iceberg. No office job is safe,” says
Sebastian Thrun, an AI professor at Stanford known for his work on self-driving cars.
Automation is now “blind to the colour of your collar”, declares Jerry Kaplan, another
Stanford academic and author of “Humans Need Not Apply”, a book that predicts upheaval in
the labour market. Gloomiest of all is Martin Ford, a software entrepreneur and the
bestselling author of “Rise of the Robots”. He warns of the threat of a “jobless future”,
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pointing out that most jobs can be broken down into a series of routine tasks, more and more
of which can be done by machines.
In previous waves of automation, workers had the option of moving from routine jobs in one
industry to routine jobs in another; but now the same “big data” techniques that allow
companies to improve their marketing and customer-service operations also give them the raw
material to train machine-learning systems to perform the jobs of more and more people.
“E-discovery” software can search mountains of legal documents much more quickly than
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human clerks or paralegals can. Some forms of journalism, such as writing market reports and
sports summaries, are also being automated.
Predictions that automation will make humans redundant have been made before, however,
going back to the Industrial Revolution, when textile workers, most famously the Luddites,
protested that machines and steam engines would destroy their livelihoods. “Never until now
did human invention devise such expedients for dispensing with the labour of the poor,” said
a pamphlet at the time. Subsequent outbreaks of concern occurred in the 1920s (“March of the
machine makes idle hands”, declared a New York Times headline in 1928), the 1930s (when
John Maynard Keynes coined the term “technological unemployment”) and 1940s, when the
New York Times referred to the revival of such worries as the renewal of an “old argument”.
As computers began to appear in offices and robots on factory floors, President John F.
Kennedy declared that the major domestic challenge of the 1960s was to “maintain full
employment at a time when automation…is replacing men”. In 1964 a group of Nobel
prizewinners, known as the Ad Hoc Committee on the Triple Revolution, sent President
Lyndon Johnson a memo alerting him to the danger of a revolution triggered by “the
combination of the computer and the automated self-regulating machine”. This, they said,
was leading to a new era of production “which requires progressively less human labour” and
threatened to divide society into a skilled elite and an unskilled underclass. The advent of
personal computers in the 1980s provoked further hand-wringing over potential job losses.
Yet in the past technology has always ended up creating more jobs than it destroys. That is
because of the way automation works in practice, explains David Autor, an economist at the
Massachusetts Institute of Technology. Automating a particular task, so that it can be done
more quickly or cheaply, increases the demand for human workers to do the other tasks
around it that have not been automated.
There are many historical examples of this in weaving, says James Bessen, an economist at
the Boston University School of Law. During the Industrial Revolution more and more tasks
in the weaving process were automated, prompting workers to focus on the things machines
could not do, such as operating a machine, and then tending multiple machines to keep them
running smoothly. This caused output to grow explosively. In America during the 19th century
the amount of coarse cloth a single weaver could produce in an hour increased by a factor of
50, and the amount of labour required per yard of cloth fell by 98%. This made cloth cheaper
and increased demand for it, which in turn created more jobs for weavers: their numbers
quadrupled between 1830 and 1900. In other words, technology gradually changed the nature
of the weaver’s job, and the skills required to do it, rather than replacing it altogether.
In a more recent example, automated teller machines (ATMs) might have been expected to
spell doom for bank tellers by taking over some of their routine tasks, and indeed in America
their average number fell from 20 per branch in 1988 to 13 in 2004, Mr Bessen notes. But
that reduced the cost of running a bank branch, allowing banks to open more branches in
response to customer demand. The number of urban bank branches rose by 43% over the
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same
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period, so the total number of employees increased. Rather than destroying jobs, ATMs
changed bank employees’ work mix, away from routine tasks and towards things like sales
and customer service that machines could not do.
The same pattern can be seen in industry after industry after the introduction of computers,
says Mr Bessen: rather than destroying jobs, automation redefines them, and in ways that
reduce costs and boost demand. In a recent analysis of the American workforce between 1982
and 2012, he found that employment grew significantly faster in occupations (for example,
graphic design) that made more use of computers, as automation sped up one aspect of a job,
enabling workers to do the other parts better. The net effect was that more computer-intensive
jobs within an industry displaced less computer-intensive ones. Computers thus reallocate
rather than displace jobs, requiring workers to learn new skills. This is true of a wide range of
occupations, Mr Bessen found, not just in computer-related fields such as software
development but also in administrative work, health care and many other areas. Only
manufacturing jobs expanded more slowly than the workforce did over the period of study, but
that had more to do with business cycles and offshoring to China than with technology, he
says.
So far, the same seems to be true of fields where AI is being deployed. For example, the
introduction of software capable of analysing large volumes of legal documents might have
been expected to reduce the number of legal clerks and paralegals, who act as human search
engines during the “discovery” phase of a case; in fact automation has reduced the cost of
discovery and increased demand for it. “Judges are more willing to allow discovery now,
because it’s cheaper and easier,” says Mr Bessen. The number of legal clerks in America
increased by 1.1% a year between 2000 and 2013. Similarly, the automation of shopping
through e-commerce, along with more accurate recommendations, encourages people to buy
more and has increased overall employment in retailing. In radiology, says Dr Barani,
Enlitic’s technology empowers practitioners, making average ones into experts. Rather than
putting them out of work, the technology increases capacity, which may help in the developing
world, where there is a shortage of specialists.
And while it is easy to see fields in which automation might do away with the need for human
labour, it is less obvious where technology might create new jobs. “We can’t predict what jobs
will be created in the future, but it’s always been like that,” says Joel Mokyr, an economic
historian at Northwestern University. Imagine trying to tell someone a century ago that her
great-grandchildren would be video-game designers or cybersecurity specialists, he suggests.
“These are jobs that nobody in the past would have predicted.”
Similarly, just as people worry about the potential impact of self-driving vehicles today, a
century ago there was much concern about the impact of the switch from horses to cars, notes
Mr Autor. Horse-related jobs declined, but entirely new jobs were created in the motel and
fast-food industries that arose to serve motorists and truck drivers. As those industries decline,
new ones will emerge. Self-driving vehicles will give people more time to consume goods and
services, increasing demand elsewhere in the economy; and autonomous vehicles might
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greatly expand demand for products (such as food) delivered locally.
Only humans need apply
There will also be some new jobs created in the field of AI itself. Self-driving vehicles may
need remote operators to cope with emergencies, or ride-along concierges who knock on doors
and manhandle packages. Corporate chatbot and customer-service AIs will need to be built
and trained and have dialogue written for them (AI firms are said to be busy hiring poets);
they will have to be constantly updated and maintained, just as websites are today. And no
matter how advanced artificial intelligence becomes, some jobs are always likely to be better
done by humans, notably those involving empathy or social interaction. Doctors, therapists,
hairdressers and personal trainers fall into that category. An analysis of the British workforce
by Deloitte, a consultancy, highlighted a profound shift over the past two decades towards
“caring” jobs: the number of nursing assistants increased by 909%, teaching assistants by
580% and careworkers by 168%.
Focusing only on what is lost misses “a central economic mechanism by which automation
affects the demand for labour”, notes Mr Autor: that it raises the value of the tasks that can be
done only by humans. Ultimately, he says, those worried that automation will cause mass
unemployment are succumbing to what economists call the “lump of labour” fallacy. “This
notion that there’s only a finite amount of work to do, and therefore that if you automate some
of it there’s less for people to do, is just totally wrong,” he says. Those sounding warnings
about technological unemployment “basically ignore the issue of the economic response to
automation”, says Mr Bessen.
But couldn’t this time be different? As Mr Ford points out in “Rise of the Robots”, the impact
of automation this time around is broader-based: not every industry was affected two
centuries ago, but every industry uses computers today. During previous waves of automation,
he argues, workers could switch from one kind of routine work to another; but this time many
workers will have to switch from routine, unskilled jobs to non-routine, skilled jobs to stay
ahead of automation. That makes it more important than ever to help workers acquire new
skills quickly. But so far, says Mr Autor, there is “zero evidence” that AI is having a new and
significantly different impact on employment. And while everyone worries about AI, says Mr
Mokyr, far more labour is being replaced by cheap workers overseas.
Another difference is that whereas the shift from agriculture to industry typically took
decades, software can be deployed much more rapidly. Google can invent something like
Smart Reply and have millions of people using it just a few months later. Even so, most firms
tend to implement new technology more slowly, not least for non-technological reasons.
Enlitic and other companies developing AI for use in medicine, for example, must grapple
with complex regulations and a fragmented marketplace, particularly in America (which is
why many startups are testing their technology elsewhere). It takes time for processes to
change, standards to emerge and people to learn new skills. “The distinction between
invention and implementation is critical, and too often ignored,” observes Mr Bessen.
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What of the worry that new, high-tech industries are less labour-intensive than earlier ones?
Mr Frey cites a paper he co-wrote last year showing that only 0.5% of American workers are
employed in industries that have emerged since 2000. “Technology might create fewer and
fewer jobs, while exposing a growing share of them to automation,” he says. An oft-cited
example is that of Instagram, a photo-sharing app. When it was bought by Facebook in 2012
for $1 billion, it had tens of millions of users, but only 13 employees. Kodak, which once
employed 145,000 people making photographic products, went into bankruptcy at around the
same time. But such comparisons are misleading, says Marc Andreessen. It was smartphones,
not Instagram, that undermined Kodak, and far more people are employed by the smartphone
industry and its surrounding ecosystems than ever worked for Kodak or the traditional
photography industry.
Is this time different?
So who is right: the pessimists (many of them techie types), who say this time is different and
machines really will take all the jobs, or the optimists (mostly economists and historians),
who insist that in the end technology always creates more jobs than it destroys? The truth
probably lies somewhere in between. AI will not cause mass unemployment, but it will speed
up the existing trend of computer-related automation, disrupting labour markets just as
technological change has done before, and requiring workers to learn new skills more quickly
than in the past. Mr Bessen predicts a “difficult transition” rather than a “sharp break with
history”. But despite the wide range of views expressed, pretty much everyone agrees on the
prescription: that companies and governments will need to make it easier for workers to
acquire new skills and switch jobs as needed. That would provide the best defence in the event
that the pessimists are right and the impact of artificial intelligence proves to be more rapid
and more dramatic than the optimists expect.
Read on: Implications for education and policy >> (http://www.economist.com
/news/special-report/21700760-artificial-intelligence-will-have-implications-policymakerseducation-
代写Artificial intelligence The impact on jobs
