Rapid advances in artificial intelligence now threaten the
jobs of educated white-collar workers
If Daniel Nadler is right, a generation of college graduates with well-paid
positions as junior researchers and analysts in the banking industry should be
worried about their jobs. Very worried.
Mr Nadler’s start-up, staffed with ex-
Google engineers
and backed partly by money from Google’s venture capital arm, is trying to put
them out of work.
Its algorithms assess how different securities are likely to react after the
release of a market-moving piece of information, such as a monthly employment
report. That is the kind of work usually done by well-educated junior analysts,
who pull data from terminals, fill in spreadsheets and crunch numbers. “There
are several hundred thousand people employed in that capacity. We do it with
machines,” says Mr Nadler. “We’re not competing with other [tech] providers.
We’re competing with people.”
Warren
– the name given to the system, in homage to investor Warren Buffett – is part
of a new army of “smart” machines that are threatening to invade office life.
These computers do not just collect and process information; they draw
inferences, answer questions and recommend actions, too.
The threat to jobs stretches beyond the white-collar
world. Advances in
artificial
intelligence (AI) also make possible more versatile robots capable of taking
over many types of manual work. “It’s going to decimate jobs at the low end,”
predicts Jerry Kaplan, a Silicon Valley entrepreneur who teaches a class about
AI at Stanford University. Like others working in the field, he says he is
surprised by the speed at which the new technologies are moving out of the
research labs.
“People don’t understand it, they don’t get what it’s going to mean,” adds Mr
Kaplan, who says he was “radicalised” by a sudden awareness of the
job-destroying capabilities of the new technology. “I feel like one of the early
guys warning about global warming.”
It’s going to decimate jobs at
the low end. I feel like one of the early guys warning about global warming
The impact of IT and automation on the world of work – and dire warnings
about the job destruction they might cause – are as old as the technology
itself. But the convergence of a number of tech trends has made the threat more
immediate.
As a result, 47 per cent of jobs in the US are now at risk from
computerisation, according to a prediction last year from Carl Benedikt Frey and
Michael Osborne from Oxford university. McKinsey, the management consultancy,
has estimated that by 2025, productivity gains in fields of “knowledge work”,
ranging from clerical to professional services, could account for 40 per cent of
all the current jobs in those areas.
One long-familiar tech trend is the relentless fall in the
cost of computing power. According to
Erik
Brynjolfsson and Andrew McAfee, academics at the Massachusetts Institute of
Technology, these incremental advances in computing have combined to make great
leaps. Their book,
The Second Machine
Age , has stirred up angst this year about what the coming smart
machines will do to job levels.
A second factor is the availability of vast bodies of digital data. Feeding
off that information, advanced pattern-recognition systems – using a technique
known as machine learning – are able to draw deductions that earlier machines
could not attempt.
This has given rise to a field known as “cognitive computing”. IBM has been
the acknowledged leader since its Watson machine won a television quiz in the US
three years ago, in the process overcoming the notoriously difficult challenge
for a computer of mastering “natural” language.
New ways of interacting with computers, making it easier for non-technical
humans to work with machines on complex tasks, are a third part of the AI
revolution. As with many other aspects of technology, smartphones have been the
forerunner.
Services such as the Siri question-and-answer feature on Apple’s iPhone and
the Google Now service that tries to anticipate a user’s information needs, may
be the forerunners of similar user-friendly systems that will come to dominate
the office.
This has fed two visions of the future of work. In one, the machines take on
many of the boring parts of a job, setting humans free to supply the more
advanced – and satisfying – brain work. The other vision is less harmonious: the
machines leave many human workers on the scrap heap altogether.
The incursions being made by this new generation of technology can be hard to
trace, although that does not diminish its potential impact.
A San Diego-based company called SmartAction, for instance, uses machine
learning and natural language recognition in its automated response software for
call centres. The better it can “understand” what callers want, the more likely
the technology can deal with their queries successfully and prevent a human
call-centre worker getting involved, says Tom Lewis, chief executive.
“It makes it so you need fewer agents,” he says. And while many baby boomers
may still yearn for a human voice, he adds, a younger generation brought up on
digital technology feels perfectly at ease dealing with automated systems.
In other fields, the displacement of humans can be easier
to spot. With technology from a Chicago company called Narrative Science, Len
Welter, an entrepreneur in London, used machines to write reports based on
financial data. His start-up, which was sold last year to British financial
information firm
Markit,
produced a “newswire” of automated reports to round out deeper research being
done by human analysts.
Despite churning out 40 reports a day, he claims the robo-writer, called
Quill, was good at disguising its non-human origins: “They changed the grammar
and language – you couldn’t tell it was from a computer.” He admits the human
writers at his company “freaked out” when they heard he was planning to use the
system.
. . .
The workers that machines threaten to displace cover a wide range of office
work. Smart digital assistants, for instance, could stand in for many types of
support staff – or, by making the ones who remain more productive, greatly
reduce their numbers.
The jobs of many analysts and researchers could also be in the line of fire.
Advances in machine learning and natural language systems make it easier to
interrogate large amounts of data and to derive smarter answers in more
intelligible forms.
Even highly paid professionals with specialist expertise are not immune. In
fields such as law and medicine, machines are likely to produce “generally
better answers” than humans, who struggle to keep up with the latest knowledge
in their fields, says James Manyika, a director at McKinsey Global
Institute.
IBM recently began selling its most advanced cognitive computing technology
as an “ingredient” to be used in other business software applications – a kind
of “Watson Inside” approach. Moves like this could see deeper intelligence seep
into a wide range of everyday office technologies.
Perhaps not surprisingly, most of the technologists working in the field – as
well as the companies buying the new systems – predict that the upshot of moves
such as this will be to enhance human workers rather than replace them
altogether. The new systems are promoted as opportunities to jettison the most
dull and onerous aspects of work.
Companies involved in digital advertising, for instance, engage in big number
crunching to improve their campaigns, making constant data-driven judgments
about which sites to use, at which times of day and in front of which types of
audience.
“It’s a real drag to have that job – it’s a constant, crushing load,” says
George John, chief executive of Rocket Fuel, a digital advertising company whose
technology instead automates the process.
Robotics companies trying to replace various types of manual labour make
similar claims for their products, notes Mr Kaplan. A weeding machine smart
enough to pick its way through a field without harming the crop, for instance,
is advertised by its manufacturer, Blue River, as an advance on the
“back-breaking job” it is replacing.
However, for the humans who risk being put out of work, Mr Kaplan adds, it
may be little consolation to be told that their jobs were undesirable in the
first place.
. . .
A second defence of the smart machines is that the slow speed of human labour
causes bottlenecks in an increasingly digital production chain. Even an army of
analysts with calculators and spreadsheets would be unable to process all the
data being churned out by some of today’s systems.
Kris Hammond, chief scientist of Narrative Science, says that many companies
“have spent a tremendous amount on collecting data, and they might have analysts
who only produce one or two reports a day”.
Companies with large numbers of branch managers or franchisees, insurance
companies with big sales forces and wealth management concerns with thousands of
customers are among the businesses his company is targeting for its automated
report-writing software.
This points to a technology dividend that often follows widespread
automation. Once humans are taken out of the equation and the cost per unit of
work collapses, volumes explode. Telephone switches, for instance, enabled a
volume of calls that the switchboard operators they replaced would never have
been able to handle.
Whether this is good or bad for individual workers will depend on where they
stand on the all-important hierarchy of “knowledge” work.
“Technology substitutes for lower- level people and frees up higher-level
people to engage in ever more cognitive, ever more cerebral activities,” says Mr
Nadler.
For workers worried about their jobs, the critical question will be where
that line is drawn – and whether there will be enough of the brainier,
higher-level jobs to go around. A second issue also looms large: whether new
types of work are invented at a fast enough pace to replace the jobs that are
lost.
The history of other technology transitions gives cause for optimism, says
Tom Malone, a management professor at MIT and author of
The Future of
Work. “In every single case where people have worried about that, in the
long run just as many jobs were created as destroyed.”
In the century or so leading up to 1910, for instance, the automation of
agriculture reduced the proportion of US workers engaged in the sector from 90
per cent to 2 per cent, says Mr Kaplan. While individual workers may have
suffered, new productive uses were found for the workforce as a whole.
Yet that does not guarantee the transition will be smooth, says Mr Malone.
The pace of digital change in many industries has been quickening. Twenty
years ago, Fidelity Investments, one of the largest US mutual funds groups,
conducted almost all of its business over the phone, says Sean Belka, head of
its advance technologies division. In particularly busy periods, even
headquarters staff were co-opted to answer the phones.
These days, 12,000 of Fidelity’s staff – or three workers out of every 10 –
are employed in IT roles and the company spends nearly $1bn a year on new
software projects such as smartphone apps. Fidelity is now among the companies
testing technologies including Warren and Quill as it tries to anticipate how
work will change next.
“Business will just shift to more technologically enabled companies,”
predicts Mr John at Rocket Fuel. His company’s staff more than doubled last year
to about 600 – a reflection of the wider upheaval that is transforming
advertising.
Like airline pilots, more and more people will come to find themselves, at
work, as “humans embedded in complex systems”, Mr John says.
If they still have a job to go to, that is.
-------------------------------------------
Robots: Blue-collar workers beware: Baxter is after your job
The robots are ready to move on past their jobs on the production line.
A $25,000 machine with a tablet computer for a face, Baxter – the product of
US company Rethink Robotics – is one of the first all-purpose robots designed to
move around and handle a range of tasks.
Recent breakthroughs in computer vision, long one of the
toughest challenges in artificial intelligence, lie behind the emergence of
machines such as this, says
Rodney
Brooks, the former AI professor who dreamt up Baxter. He also credits a
collapse in the price of sensors (another byproduct of the smartphone
revolution) and machine learning that supplies the “brains” of the machine.
For now, Baxter is most likely to be found moving bulky packages in a
warehouse or loading a truck. Warehouse workers are “nothing but hands and eyes:
go to this bin, pick it up, put it somewhere else”, says Jerry Kaplan, a Silicon
Valley entrepreneur, who predicts most will quickly be replaced by robots.
Amazon and Google last year bought two of the leading warehouse robotics
companies. The involvement of these companies could trigger a technology race
that brings rapid advances, says Mr Brooks. It could, for instance, lead to
robot “hands” with fine motor skills – another challenging area.
The arrival of robots such as Baxter threatens to upend a long-held belief
about work: that many types of low-skilled manual work are simply too hard to
automate and will remain the preserve of humans.
Full automation may not be practical, says Mr Kaplan, but when most aspects
of a job can be given to a robot, the work is likely to be restructured to let
the machines do what they do best.
It may be decades before Google’s pioneering driverless car leads to a fully
automated taxi service. But autonomous vehicles have already proved themselves
safe in highway driving. That could lead to drone trucks carrying goods on
transcontinental highways, handing off to human drivers to quickly negotiate the
last few miles, Mr Kaplan predicts.
Many jobs could be rearranged this way, cutting a swath through the
blue-collar workforce. “Digging ditches, laying pipes, directing traffic – it’s
just going to start knocking them off.”
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