Technological Unemployment: A New Wave
BENVGAAD: Design as a Knowledge-Based Process
Charles Fried (UCQBLCF)
MSc Adaptive Architecture & Computation The Bartlett School of Architecture January 2016
Abstract
I
n an age of exponential technological progress, developments come by at such a rate that they can have unpredictable consequences. This paper analyses empirical quantitative evidence in order to formulate an understanding of the impact of future technological unemployment. The automation of routine task by the means of robotics is today widely used and accepted, lowering labour cost and increasing productivity by surpassing the limitations of our muscles. This change left many unemployed, thankfully only for a short period of re-adaptation. However, with the increase of computational power as described in Moore’s law, we are seeing the emergence of artificial intelligence which enables many cognitive technologies. The second machine age, as Andrew Mcafee calls it, is arguably the most important development in history, emulating mental power, the main factor for progress and development (Are droids taking our jobs?, 2012). The question is, therefore: to what extent will this cause technological unemployment? Keywords: Technology, Robotics, Employment, Automation, Artificial Intelligence
Contents Introduction Muscles to Machines Brains to Artificial Intelligence GDP and Beyond Conclusion References
6 7 9 14 15 18
This research project emerged from the DKBP group debate around the following topic: “Is technology essentially value neutral?�. University College London
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Introduction
I
n the past, every human had to gather, farm or hunt for their food in order to survive. As we got smarter this soon changed, creating tools to make the process easier and more efficient. This evolved from a simple stick, to a plough and today, tractors which help mass produce our food. As a result of this, the agriculture workforce dropped dramatically. We always seek new ways to surpass the limitations of our muscles in order to achieve higher output than ever before. This allows people to move up in to higher skilled jobs which benefits the economy and in turn increases the standards of living. Even though this drastic fall in the workforce did not leave many unemployed, it seems that this revolution observed over 160 years cannot be simply compared with the one ahead. Through technological progress, especially artificial intelligence, we are reaching new levels of efficiency which could cause new employment crises. Today, in order for companies to thrive they must seek optimum efficiency by continuously reviewing the structure of their business. In many cases efficiency is synonymous to automation, needless to say that if a task can be replaced cost-efficiently by the machine, a step closer has been made towards optimality.
Figure 1. Percentage of American workforce in agriculture (1840-2000)
(USDA, 2000)
Although very beneficial for the company involved, this can prove to have a significant impact on future employments as described in 1930 by English economist John Maynard Keynes: “We are being afflicted with a new disease of which some readers may not have heard the name, but of which they will hear a great deal in the years to come – namely, technological unemployment.” (KEYNES, John Maynard, 1930) The shift observed in the agriculture industry (Figure 1) was driven by the quest to overcome our physical limitations. However, we are now entering a completely new phase of automation which seeks to replicate the actions of our minds. If this is successful, the number of affected jobs would be much greater than previously in the manufacturing and agriculture industry. The question is therefore whether or not we will be ready for this radical shift in employment? If so, can we assume this is a beneficial change for both the economy and the workers? Alternatively, if this shift in employment isn’t equal, will computerization lead to a situation of mass-unemployment? This paper aims to analyze the past effects of automation on jobs in order to predict the future impacts of artificial intelligence on the global
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Muscle to Machine
A
hundred years after Henry Ford introduced the assembly line, the factory floor is no longer populated by humans but rather robots, twenty thousand of them in fact (Figure 2) (MCKENZIE, Sheena, 2015). Fitted with lasers as eyes and hydraulic actuators as muscles they largely surpass human capabilities, completing repetitive tasks and lifting high loads with outstanding accuracy (Figure 2). This chapter aims to cover automation without any cognitive abilities, those that execute simple routine tasks, a technology otherwise known as robotics, coined by science fiction writer Isaac Asimov in 1942. Who went on to devise “The Three Rules of Robotics”, although these may seem superficial, science fiction writers have always played a large role in increasing the cultural adoption of emerging technologies.
Figure 2. Ford model T assembly line
(FORD, 1913) Figure 3. Ford Focus assembly line
(AUTOALLIANCE, 2015)
Isaac Asimov’s “Three Laws of Robotics” (ASIMOV, Isaac, 1942) 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 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.
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By 2025, 25% of all tasks will be automated through robotics, saving in average 16% in labor cost
44% of the firms surveyed by the McKinsey Global Institute who had reduced their headcount since the 2008 financial crisis had done so through the means of automation (MANYIKA, James et al., June). However, the unemployment rate in the US took only two years before reaching the highest levels since 1983 (U.S. BUREAU OF LABOR STATISTICS , 2015). Even though this describes the national level, it also shows that non-cognitive automation may have a short term imUnfortunately, this doesn’t come pact on employment, which could without the negatives, it seems a be described as a re-adaptation pecorrelation can be seen between riod. the implementation of autonomous systems and higher unemployment rates. From 2007 industrial robots accounted for only 2.25% of the capital stock in the affected industries (GUY MICHAELS, Georg Graetz, 2015). This is most probably due to the relatively high unit price, prohibiting its access to smaller businesses. However, according to a report by the Boston Consulting Group by 2025, 25% of all tasks will be automated through robotics, saving in average 16% in labor cost (Figure 4).
Figure 4. Labor cost savings from adoption of advance industrial robots (% 2025)
(BCG, 2015)
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Brains to Artificial Intelligence
M
eet Watson, developed by IBM he’s arguably the most advanced example of artificial intelligence most notoriously known for beating the two winners on TV show Jeopardy. However gimmicky this might be, the analytical and cognitive system it uses has a huge potential. Just to emphasize the increasing viability of AI, the company now claims that Watson is 2,400% smarter than on the day of its triumph. So much so that it is currently investing one billion dollars towards its commercialisation, most of which is in the medical sector (DAVID, Schatsky et al., 2014).
Even though this example is by no means comparable to the Turing test it clearly shows that computerization is no longer confined to routine manufacturing tasks. According to Moore’s law, we’re witnessing the emergence of several cognitive abilities which are all a product of AI. The exponentially increasing computational power enables the creation of smarter systems which have the ability conduct many tasks which were previously inconceivable by the machine (Figure 4). This was made very apparent in the stock market, which is no longer operated by people but instead by self learning machines which trade with one another.
Figure 5. The of artificial intelligence has produced a number of cognitive technologies.
(DAVID, Schatsky et al., 2014)
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Perception and Manipulation, Creativity and Social Intelligence are skills notoriously hard for the machine.
In order to discover which jobs are most prone to replacement, it’s important to understand which abilities are most complex for the computer to emulate human behavior. Frey and Osborn were able to identify the three following skills which are also used in the UK Standard Classification of Occupations (ISCO-08): The skill of perception and manipulation, creativity and social intelligence. The more these skills are needed for a job, the lower the risk of computerisation. Perception and manipulation In the case of the manipulation of irregular objects, which requires iterations in-between tasks, the change is very dependent upon technological progress. This is due to the high level of complexity in pattern recognition to gain a perception of the object, which us humans execute flawlessly. Creativity, the musical intelligence experiment from David Cope (COPE, David, 1981) can be used to argue that computers are creative. This example demonstrates its use
within one very specific scenario going against the definition of creativity by Margaret Ann Boden, who claims that creative ideas are new ideas, Cope’s experiment samples only samples existing music. Furthermore, the key of creativity resides in finding a reliable system that produces an idea which “makes sense”. For example, for the computer to design a building which fits the brief it would need a rich knowledge of Architecture which would allow it to combine them in new ways, just like an architect would. Social intelligence, the computing aspect mostly consists of affective computing which is under active research at MIT. It aims to bridge the gap between human emotions and technology (MIT, 2015). This ability can be tested with the Turing test, which hasn’t yet been passed and is proving to be a very difficult task for computers. This factor places jobs such as healthcare, legal other services in the low risk section, as opposed to a factory worker who don’t need great social abilities.
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An analogy can be made between the industrial revolution and the computerization we’re seeing, the developments in mechanical engineering, metallurgy and chemistry, all constructed the building blocks which enabled the revolution. Similarly, artificial intelligence, big data, and computing power can be seen as the foundation for our digital revolution. With the catalyst now in place, it would be easy to assume that we are heading to what some
would call a jobless utopian future. However, this doesn’t seem the case, at least not for the next thirty years. According to a report from Carl Benedikt Frey and Michael A. Osborne called: “The future of employment: how susceptible are jobs to computerization?” there is a correlation between the probability of computerization, educational attainment and wages. It can also be said that these factors also have a direct relationship with the skill level.
Figure 6. Current Jobs at risk from computerization: London*
(CARL, Benedikt and Dr Michael, Osborn, 2014)
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This correlation demonstrates the importance of education to supply the demand for high skilled jobs
Additionally, as suggested earlier it has also been proven that there is a strong correlation between educational attainment and technological unemployment, as illustrated below. This correlation demonstrates the importance of education to supply the demand for high skilled jobs which fuels the growth of the economy.
the “Bureau of Labor Statistics” with the three skill factors listed above: “Teach classes about information resources” or “Help library patrons conduct research and find the information they need” and “Organize library materials so they are easy to find, and maintain collections” (STATISTICS BUREAU OF LABOR, 2015). It is clear that none of these duties require As a final robustness check, the creativity, social intelligence, or and manipulation. observations made above can be Perception compared with the data obtained Automation in this case, mostly with the two tables below (Figure 8 likely took the shape of an interactive & 9) which classify ten jobs which booth which is connected to the were the most lost in London, libraries database, technologies either replaced by automation or which are currently widely used. moved out of the city. At the top of This seems to match up with figure list are librarians, to eliminate any 6, where “Office and administrative subjectivity on this comparison support” accounts for the majority of we can analyze their roles from the jobs at high risk.
Figure 7. Wage and Education level as a function of the probability of computerisation; note that both plots share a legend
(CARL BENEDIKT, Frey and Michael A., Osborne, 2013)
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Figure 8. Lost Jobs from London, 2001 – 2013
(OFFICE FOR NATIONAL STATISTICS, 2014)
In figure 9 are listed the ten most popular jobs which didn’t exist five years ago, except from the Zumba instructor which we can assume to be a fitness trend most others are highly skilled, tech-savvy positions. This is self-explanatory, however its noteworthy to see IOS and Android programmers at the top. Now that we’ve analyzed the affected sectors, to conclude this chapter it is worth discussing the speed at which this change will take place. According to London’s Economic Outlook Forecast (LEOF) by the Greater London Authority (GLA), the employment rate forecast is increasing. However,
Figure 9. Top ten jobs that didn’t exist 5 years ago
(MURPHY, Sohan, 2014)
even though it includes historical technological developments, it does not take into account the speed at which this revolution may take place. Moreover, as previously mentioned, cultural adoption will be a large driver on the rate of change. The jobs at medium risk, which Carl and Frey refer to as the plateau, are more dependent on the rate of technological innovation. As opposed to the high risks jobs for which the technology is already existing.
The jobs at medium risk are more dependent on the rate of technological innovation.
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GDP and Beyond
T
echnology has a huge impact on the economic structure of a country. Whilst some of the GDP growth is made through the extraction of resources, much of it is due to being able to attain higher labour productivity from input. As an interesting fact, it would take the average American only eleven hours of labor per week to produce as much as he or she produced in forty hours in 1950 (BRYNJOLFSSON, Erik and McAfee, Andrew, 2014). In a paper by economist Chad Syverson, he demonstrates the link between labor productivity and electrification. As the technology became more available and reliable the productivity increased at the same rate.
Figure 10. Labor productivity growth during the electrification era (18901940) and the IT Era (1970-2012) in the United States (1915 = 100 and 1995 = 100)
(SYVERSON, Chad, 2013)
On the other hand, technology can also have a negative effective impact on the GDP. With the abundance of free services and content. Using Skype as opposed to a mobile, WhatsApp instead of texts or selling an item on Gumtree as opposed to an auction house. Together, these shifts can cause a significant decrease in GDP. However, the added value and productivity input into creating these services is not reflected. In this new digital realm, it is therefore crucial that we include the net value gained from the internet in order to accurately assess a country’s wellbeing. Another worrying aspect which is becoming more apparent is wealth inequality. An all too familiar factor is the amount of wealth owned by the top 1% of the population, accounting for 45% of the world’s fortune (BENTLEY, Daniel, 2015). One can certainly wonder to what extent will the increase in automation and efficiency reinforce this inequality.
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Conclusion
T
o contextualize the conclusion of this paper, a brief look at the relationship between man and progress is taken. Technology is man’s attempt at making life easier: it increases the standard of living, increases leisure time, helps eliminate poverty and results in the creation of many products. This progress allows people to spend more time on subjects of a higher level of concern, such as family, love and personal development. We can therefore conclude that resisting this change would imply a level of satisfaction with the current situation of poverty, famine and disease. Subsequently, an existence free from change would be dull and boring, instead we are creatures fulfilled by novelty, surprise, innovation and creativity. Fortunately, with a large proportion of the employment in the creative sector, the future of the job market in London doesn’t seem too bleak. The city accounts for the highest percentage of graduate’s talent worldwide and boasts 5 of the top 20 universities. This will provide sufficient supply to feed the growing demand for highskilled jobs (Figure 9). However, if technological innovation, as
observed in chapter three, grows following Moore’s law the number of jobs affected by automation could quickly surpass the ones being created. Furthermore, increasing efficiency levels could reduce the quantity of labor needed to fulfill a task, ultimately leading to higher unemployment. The importance therefore lays in understanding the changes ahead and for policy makers to act accordingly as described by Angus Knowles-Cutler. “Unless these changes coming in the next two decades are fully understood and anticipated by businesses, policy makers and educators, there will be a risk of avoidable unemployment and under-employment.” Angus Knowles-Cutler, London senior partner at Deloitte (KNOWLES-CUTLER, Angus, 2014)
Technology increases labour productivity, leisure time and helps eliminate poverty .
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It’s highly unlikely to cause mass unemployment, but rather skill biased technical change
To understand the impact on a global scale it is worth pointing out that the implementation of automated systems must be cost efficient. In a Ford factory robots are hugely beneficial, however this is not the case for a shoe factory in India. Therefore, until costs drop dramatically, only the medium and low skilled workers will be most affected. However, it’s highly unlikely to cause mass-unemployment, but rather skill biased technical change, as described by Daron Acemoglu and David Autor, in the late 20th century in early computerisation (DARON, Acemoglu and David, Autor, 2011). Workers with medium and low skilled jobs as well as low educational attainment will be most prone to this change. For the high skilled portion, these will see intelligent systems being implemented as a powerful tool to assist them on daily tasks. The increase in productivity by replacing labour with automation will increase income and wealth. In turn this will generate demand for new products and service, creating jobs for the displaced workers, at least
according to historical trends. Just as mechanisation freed workers from manual dexterity, cognitive technologies could lead labor towards highly social occupations such as therapist or Zumba instructors. Finally, we should consider the enormity of the achievements surrounding artificial intelligence, when doing so we can only doubt whether the previous developments even came close. Consequently, the impacts they might bring could be far greater than expected, turning workers into luddites*. Finally, we should consider the enormity of the achievements surrounding artificial intelligence, when doing so we can only doubt whether the previous developments even came close. Consequently, the impacts they might bring could be far greater than expected, turning workers into luddites*. * Luddite: A member of any of the bands of English workers who destroyed machinery, especially in cotton and woollen mills, which they believed was threatening their jobs (1811–16) (DICTIONARY, Oxford, 2011)
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References
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