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Bank of England’s Andy Haldane warns Smart machines could take 15 million UK jobs and 80 million in the US
In an important new paper based on a speech at the trade union congress in London, Andy Haldane Chief Economist at the Bank of England and Executive Director of Monetary Analysis and Statistics has examined the history of technological unemployment in which he gave a thorough review of the literature and implications for public policy. The media will likely focus on the number of jobs that can be displaced (as I did in the title) and not necessarily Haldane’s points on new jobs being created – both of which are highly important as is ‘skilling-up’.
Andy notes that arguments about “technological unemployment” – the idea that technological advance puts people out of work and bears down on wages – have been raging for centuries. According to Andy, most evidence shows that over the broad sweep of history technological progress has not damaged jobs but rather boosted wages: “Technology has enriched labour, not immiserated it.”
However, he also notes that this broad pattern obscures the fact that there has an increasing skills premium has emerged with each passing wave of technological progress. This was especially the case in the late 20th century, as new machines such as computers began replacing not only physical but cognitive labour. He finds that each phase has eventually resulted in a “growing tree of rising skills, wages and productivity”. But they have also been associated with a “hollowing out of this tree”. Indeed, this hollowing-out of jobs has “widened and deepened with each new technological wave”. This has resulted in a widening income gap between high- and low- skilled workers.
Andy states: “By itself, a widening distribution of incomes need not imply any change in labour’s share of national income: in the past, technology’s impact on the labour share appears to have been broadly neutral. But this time could be different.”
Skipping the history parts I’ve highlighted some key points of robot and automation that will displace jobs and I agree with:
Viewed over the sweep of history, then, there is essentially no evidence to suggest technology has damaged jobs and plenty to suggest it has boosted wages. Technology has enriched labour, not immiserated it. Mill was right; Ricardo was wrong. Labour is not dead wood to be carved up between tasks. It is a tree whose trunk and branches have lengthened and thickened with time. The “lump of labour” fallacy is just that.
Or is it?
Looking more closely at past phases of rapid technological change paints a more nuanced picture. Each phase has eventually resulted in a growing tree of rising skills, wages and productivity. But they have also been associated with a “hollowing out” of this tree. Indeed, this hollowing-out has widened and deepened with each new technological wave.
Further going on to indicate:
Based on past patterns, it is argued that information technology may be poised for exponential growth, as its full fruits are harvested. Indeed, we may be on the cusp of a fourth Industrial Revolution or Second Machine Age (Brynjolfsson and McAfee (2014), Ford (2015)).
Its defining feature would be that new-age machines will be thinking as well as doing, sensing as well as sifting, adapting as well as enacting. They will thus span a much wider part of the skill distribution than ever previously. As robots extend their skill-reach, “hollowing-out” may thus be set to become ever-faster, ever-wider and ever-deeper. Or that, at least, is the picture some have painted.
How much wider and deeper? Research by Carl Benedikt Frey and Michael Osborne has tried to quantify this hollowing-out, by assigning probabilities to certain classes of job being automated over the course of the next few decades. Their work was initially done for the US, but has recently been extended to the UK (Frey and Osbourne (2013), Deloitte (2015b)).
Using this methodology, the Bank has recently done its own exercise for the UK and US. Table 3 classifies jobs three ways in the US and UK – high (greater than 66%), medium (33-66%) and low (less than 33%) probability of automation. It also shows the fraction of employment these jobs represent. Chart 27 provides a more granular breakdown of these jobs.
For the UK, roughly a third of jobs by employment fall into each category, with those occupations most at risk including administrative, clerical and production tasks. Taking the probabilities of automation, and multiplying them by the numbers employed, gives a broad brush estimate of the number of jobs potentially automatable. For the UK, that would suggest up to 15 million jobs could be at risk of automation. In the US, the corresponding figure would be 80 million jobs.
Will we have robot hairdressers and elder care robots?
No-one anytime soon is I think going to choose a robot to cut their hair – I told you the hairdressers were safe. Nor are they likely to choose a robot to look after their young children or elderly parents (tempting as that can sometimes sound). When it comes to forecasting the economy, I can quite believe a thinking machine might over time displace me. But it is less likely an “Andy Robot” will be giving this lecture to the TUC even a decade from now.
However he does clarify his vision:
Even if this diagnosis is right, it nonetheless may suggest a fundamental reorientation in the nature of work could be underway. We may already be seeing early signs of that in the move towards more flexible working, with an increased incidence of part-time working, temporary contracts and, in particular, self-employment. Some have speculated that these seismic shifts could result in the emergence of a “new artisan” class : micro-businesses offering individually-tailored products and services, personalised to the needs of customers, from healthcare and social care, to leisure products and luxuries. This really will be Back to the Future.
Yet the smarter machines become, the greater the likelihood that the space remaining for uniquely-human skills could shrink further. Machines are already undertaking tasks which were unthinkable – if not unimaginable – a decade ago. The driverless car was science fiction no more than a decade ago. Today, it is scientific fact. Algorithms are rapidly learning not just to process and problem-solve, but to perceive and even emote (Pratt (2015)).
As digital replaced analogue, perhaps artificial intelligence will one day surpass the brain’s cognitive capacity, a tipping point referred to as the “singularity” (Stanislaw (1958))). Brad Delong has speculated that, just as “peak horse” was reached in the early part of the 20th century, perhaps “peak human” could be reached during this century (Delong (2014)).
Whilst the word ‘robot’ generally conjures up visions of humanoids with superior intelligence, this science fiction image tends to forget the other type of robots: machines that carry out complicated motions and tasks, such as automated software processes (1), industrial robots, unmanned vehicles (driverless cars, drones) or even prosthetics. And it is principally the programmable machine robots that are among the robotic advances being acquired by major companies across the globe (2). These are also the robotic technologies that are disrupting commercial production and employment, and will likely continue to do so over the remainder of this decade.
Many economists and technophobes claim automation and technological progress has broad implications for the shape of the production function, inequality, and macroeconomic dynamics. However, robotics is also adding hundreds of thousands of jobs to the payroll across the globe, and it may just be that people have not yet acclimatized to the new jobs and skills required to do them.
Job displacement and skill gaps
In his magical science fiction classic, The Hitchhiker’s Guide to the Galaxy, Douglas Adams wrote about the ‘B’ Ark.
“The ‘B’ Ark was one of three giant space ships built to take people off the ‘doomed’ planet and relocate them on a new one. The inhabitants of ‘B’ Ark included: “tired TV producers, insurance salesmen, personnel officers, security guards, public relations executives, management consultants, account executives, and countless others.”
These were essentially people displaced from the workplace by automation.
Douglas Adams explained that there were three space ships, each designated for a different type of person: “the idea was that into the first ship, the ‘A’ ship, would go all the brilliant leaders, the scientists, the great artists, you know, all the achievers; and into the third, or ‘C’ ship, would go all the people who did the actual work, who made things and did things; and then into the `B’ ship – that’s us – would go everyone else, the middlemen.”
We later discover the planet was not in fact doomed, nor did the other two giant spaceships, ‘A’ Ark and ‘C’ Ark depart the planet.
MIT Economist David Autor and his co-authors echo Adams point that technology is displacing the ‘middle-class,’writing that automation has:
“Fostered a polarization of employment, with job growth concentrated in both the highest and lowest-paid occupations, while jobs in the middle have declined.”
This job polarization has in fact contributed significantly to income inequality.
Research by Lawrence Katz Professor of Economics at Harvard also shows the ‘hollowing out’ of middle skilled jobs due to technological advances. A recent paper by Carl Frey and Michael Osborne of Oxford University concludes that 47 per cent of US jobs are at high risk from automation.
It’s not all doom and gloom for those with ‘middle skills’ and the MIT and Harvard researchers do allude to an increase in jobs and income for the ‘new artisans,’ a term coined by Professor Katz to refer to those who ‘virtuously combine technical and interpersonal tasks.’
Expanding upon this, Professor Autor expects that ”a significant stratum of middle skill, non-college jobs combining specific vocational skills with foundational middle skills – literacy, numeracy, adaptability, problem-solving and common sense – will persist in coming decades.”
Those skills according to Autor will provide employment for:
“Licensed practical nurses and medical assistants; teachers, tutors and learning guides at all educational levels; kitchen designers, construction supervisors and skilled tradespeople of every variety; expert repair and support technicians; and the many people who offer personal training and assistance, like physical therapists, personal trainers, coaches and guides. These workers will adeptly combine technical skills with interpersonal interaction, flexibility and adaptability to offer services that are uniquely human.”
Skill-biased technological change is not a new phenomenon. Joseph Schumpeter termed it Creative Destruction. Writing at the time of the Great Depression in the 1930’s, he said the prime cause of economic development was entrepreneurial spirit: “Without innovations, no entrepreneurs; without entrepreneurial achievement, no capitalist returns and no capitalist propulsion.”
Many smart people of that time believed that technology had reached its limits and capitalism had passed its peak. Schumpeter believed the exact opposite, and of course he was right. Technology changes, economic principles do not. As demand for one set of labor skills declines, demand for a new set of skills grows, often with better pay.
Why are big corporations buying robotic companies?
Major corporations, and creative destructors, such as Google, Amazon, Apple, Inc. have made headlines recently with their acquisitions of Robot and Deep Learning companies, the use of Machine Learning technology and their Artificial Intelligence aspirations.
What exactly do these corporations want with robots and Artificial Intelligence, and how does it impact society?
Andrew Ng, a Professor at Stanford University and Google fellow who teaches a popular Coursera (online free education) class in Machine Learning, says:
“In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.”
Machine learning technology helps the machine to learn and remember things, or to act ‘without being explicitly programmed.’ It is the science (or art) of building algorithms that can recognize patterns in data and improve as they learn. For example, it may use your last search queries and current location to improve new search results, effectively providing enhanced search results.
Whilst machine learning is used extensively across companies such as Facebook, Google, LinkedIn, Netflix, Twitter, Apple, Adobe, Microsoft and many more, it’s not just the tech companies that are seeing the benefits. Machine learning technology is much in demand across industry with proven results at Wall Street investment banks, insurance companies and motor manufacturers such as Toyota and Tesla Motors.
Machine learning and health
IBM’s Watson is possibly the most famous example of a system using machine learning through its triumph at the popular TV gameshow Jeopardy. Watson is now aiding researchers and medical practitioners, and is (or will soon be) the world’s best diagnostician for cancer related ailments.
Having machines assist medical practitioners and researchers could significantly improve diagnoses and treatments for patients. Additionally these technologies will become more pervasive through wearable devices, such as Google Glass, Android phones, Apple’s iPhone or maybe a new Apple ‘iHealth’ gadget using its M7 motion sensing technology to monitor our health on the go.
I personally believe that significant improvements will be made in people’s health and wellbeing through improved technology advances, robotic treatments in hospitals, such as the operating theater and prescription services, improvement of assisted devices and prosthetics for those with disabilities and on a very large scale wearable technology. Machine learning and robotic technology will be central to this health revolution.
Machine learning is a game changer for those companies that implement its technologies successfully. Jobs for people with machine learning technology skills are and will continue be much in demand in the coming decade, particularly in industries where ‘Big Data’ factors heavily.
In March 2012, Amazon announced the $775 million cash acquisition of Kiva Systems, a warehouse automation robot, and some seventeen months later, in October 2013, Amazon CEO Jeff Bezos noted that they had “deployed 1,382 Kiva robots in three Fulfillment Centers.” Amazon has approximately 52 fulfillment centers spread across 8 countries with at least another 12 announced to be open in the next 9 months.
The rollout of Kiva robots across these fulfillment centers will have a significant strategic benefit to Amazon as it moves towards its goal of becoming the world’s largest retailer. So far this rollout has not reduced the number of employees at Amazon. In fact, Amazon continues to significantly grow its number of employees: last year Amazon added 20,000 full-time employees to its US fulfillment centers alone and this week announced a further recruitment drive of an additional 2,500 full time US fulfillment staff, indicating a 30 percent pay premium over traditional retail jobs. At the end of December 2013 Amazon employed 117,300 full and part-time employees globally (excluding contractors and temporary personnel). This is more than four times the 28,300 employees it reported on June 30th 2010, just three and half years ago. An increase of 89,000 jobs.
Kiva, together with the right qualified employees, provides Amazon the ability to cut its fulfillment costs, double its productivity, and increase its service levels.
Industrial robot manufacturers are reporting between 18 percent and 25 percent growth in orders and revenue year on year. Whilst some jobs will be displaced due to the increased rollout of robots in the manufacturing sector, many will also be created as robot manufactures recruit to meet their growing demand and jobs. Furthermore, jobs that were previously sent offshore are now being brought back to developed countries (for example, Apple manufacturing its Mac Pro in America and spending approximately US$ 10.5 billion in assembly robotics and machinery).
Cognitive machine assistants
There have been recent press speculations that Google intends to enter the industrial robot market after the acquisition of 8 robot companies at the end of 2013. Reports indicate that Andy Rubin, former head of Google’s Android platform, and new head of their robot development, met with Foxconn Chairman Terry Gou to discuss Foxconn’s robot initiatives (replacing 1 million employees with robots).
Whilst I think it highly unlikely Google will become a manufacturer of industrial robots, I do think it could use Mr. Rubin’s experience of creating a telecom industry-standard platform in order to develop a standard for an industrial robot platform, and that Google could lead and license this to other industrial robot manufacturers. If Google does go this route, expect them to announce the collaboration – as it has done with the Android development and the driverless car standard framework.
What does Google want with the robot companies it has acquired?
The immediate need is likely to be related to Google’s localization and mapping strategy. Google spends billions of dollars per year on its mapping program, and due to sophisticated new search technologies (especially mobile-related improvements in cognitive assistants, such as Google Now and Siri), Google must seek ways to stabilize the costs and ward off the threat of competition.
A big part of this requires that the search giant provides the best mapping experience and localization services. Remember that maps are not set in stone, but are constantly evolving; biannual updates of Street View and other associated solutions add considerably to the costs; and Google also wants to map inside major buildings, such as shopping malls, airports, etc, and indeed it has already started. Imagine the advertising opportunities and revenues available through improved localization!
Google knows search is becoming local and whilst its algorithms are some of the most advanced, within the next three years it expects Google Now (its voice-activated ‘cognitive assistant’) to deliver far more useful and relevant data to consumers. Without significant improvements in localization, including traffic data (which Google can deliver through its Waze purchase and integration) Google’s search advertisement revenue will falter. I’ve written more extensively on this here, and recently Google’s Director of Research Peter Norvig mentioned “the global localization and mapping problem” when he was asked a question relating to Google and robots interest (video around the 50 minutes mark). It makes more sense to send robots with high visualization and recording capabilities on mapping expeditions across the world than platoons of people carrying expensive and heavy equipment.
The real value produced by an information provider comes in locating, filtering, and communicating what is useful to the consumer. Google does that better than others, and its robot acquisitions – coupled with its machine learning and AI expertise – are designed to keep it at the forefront.
The fact that major corporations are buying into robotics, artificial intelligence and related technologies is helping to not only preserve but to increase their market share. Yes, jobs will be displaced, but many more will be created in the process. Great opportunities will be available to those with skills to complement and work with the machines.
[i] Automation and robotics are often considered the same in many languages and it is this automation, through advances in machine learning and artificial intelligence that is driving rapid development in our workplaces.
[ii] Of course these programmable machine robots, and their advanced technologies in software and hardware, will eventually lead to the successful development of humanoid robots.
“The future is forseeable. Unless, as Orwell cautioned, we anticipate future problems, begin the search for alternative solutions, and stake a claim on our long-term future, we may lose what it has to offer.” From 1994: The World of Tomorrow.
Couldn’t agree more…
Laws of Robotics by Isaac Asimov in I, Robot (1950)
1. A robot may not injure a human being or, through inaction, allow a
human being to come to harm
2. A robot must obey orders given to it by human beings, except
where such orders would conflict with the First Law
3. A robot must protect its own existence as long as such protection
does not conflict with the First or Second Law