Category: Jobs and Automation

Background

This article is one of a series of articles on how certain industries and products are upended by new entrants outside of the existing industries. This article will focus on vacuum cleaners which are now replaced by robots.

History of Vacuum Cleaners

Every house will have a vacuum cleaner which cleans dirt and dust from every corner and surface. Vacuuming a house is a major chore that my wife would like to avoid as we have seven cats living together with us.

Vacuum cleaners are electrical appliances that use an air pump to suck up dirt and dust from floors and other surfaces. The dust and dirt are collected in a bag that can be emptied later. As electrical appliances, vacuum cleaners are sold by well known companies such as Samsung, Philips, Panasonic and Electrolux.

The first technology that led to the development of vacuum cleaners occurred in Chicago in 1868 by Ives W. McGaffey. His first -hand held vacuum cleaner was manually-powered by cranking it while it was being pushed along. The first vacuum cleaner that resembled today’s vacuum cleaners was created in 1905 by Walter Griffiths. It was still a manual appliance. However, it was much smaller and portable, which made it easier for one person to operate it. The appliance consisted of a bellow that would suck dust into a removable pipe. The pipe would be cleaned for next use. It also had differently-shaped attachments so that a housewife could reach other areas of the house that needed cleaning.

In 1908, James Murray Sprangler was awarded a patent for his vacuum technology that involved a rotating brush coupled with an electric vacuuming machine. He sold his idea to the Hoover Harness and Leather Factory, a company based in North Canton, Ohio. It made several improvements and models on the idea.

The Story of Hoover Vacuum Cleaners

Like Colgate Palmolive in toothpaste, Hoover was synonymous with vacuum cleaners.The emerging car business was seriously threatening the future of horse collars. The owner of Hoover Harness and Leather Factory, William Henry Hoover, was looking to expand his company. On a hot summer day in 1908, Hoover met James Murray Sprangler on his front porch to discuss a cleaning contraption that Sprangler had sold to his cousin, who was also his wife.

Vacuum cleaners were a boon to sanitation and health in the early 1900s but they were cumbersome and required two people to operate. Sprangler was an aging, sometime inventor working as a janitor to clear his debts. He developed a portable cleaning device to minimize dust that rose from the carpets he cleaned every night.

Sprangler attached an electric fan motor atop a soap box and sealed the cracks with adhesive tape. A pillow case billowing out the back served as a dust bag. Hoover and his wife were both impressed with the new machine but not many homes then had electricity in 1908.

Hoover bought the patents anyway and started the Electric Suction Sweeper Company. He set aside a corner of his leather goods factory for the production of suction sweepers, turning six cleaners a day. James Sprangler, with his debts relieved, became Hoover’s superintendent of production.

The first Hoover advertisement appeared in the newspaper, The Saturday Evening Post, on 5th, December 1908. The ad described the simple premise of the suction sweeper: “A rapidly evolving brush loosens the dust which is sucked back into the dirt bag.” The ad went on to further state that “Repairs and adjustments are not necessary.” Finally, readers were offered a free ten-day trial at home.

Hundreds of housewives took Hoover up his offer. He shipped the suction sweepers through local dealers who received a commission if the cleaner was purchased. If not, the dealer could keep the vacuum cleaner for in-store demonstrations. Thus, he began the national network of loyal Hoover dealers in the US.

Hoover then organized an army of door-to-door demonstrators. The sales power of the skilled demonstration was Hoover’s secret weapon. No one could deny that his portable vacuum cleaner was effective and time-saving. Research and innovation followed. In 1926, Hoover patented an agitator bar which beat the carpet before brushing it. When he died in 1932, Hoover vacuum cleaners were established as the American Standard for cleaning. In 1952, the company introduced the “Hoover Constellation”. This model hovered above the floor as it cleaned the floor. This model is still found in many American.

Today this famous company is part of the appliance giant, Maytag Corporation.

The Entry of Robot into Household Cleaning

Cleaning a house of dirt and dust is such a chore that inventors are developing robots to replace housewives to operate vacuum cleaners. Enter the robotic vacuum cleaner, which was introduced in 2002. These robotic vacuum cleaners were small and roamed around the house, sucking up dust and dirt. Detectors helped these robotic vacuum cleaners avoid bumping into things. The most popular robotic vacuum cleaner is the Roomba, which is marketed by iRobot Corporation, a company based in Boston, USA. It was founded by three MIT graduates who designed robots for space exploration and military defense. The initial Roomba has been updated with new features to allow it to become an advanced robot that cleans the floor efficiently.

iRobot Corporation is late entrant to the vacuum cleaner industry, and it has overtaken the traditional appliance companies in the household cleaning sector. Although the robotic vacuum cleaners are more expensive than the conventional hand-held vacuum cleaners, they have innovative features that make floor cleaning a hassle-free activity. The robotic cleaners are expected to become cheaper and having more features as technical advances are made in mapping and navigation.

The robotic cleaning industry is upending a sector that has been dominated by the appliance industry. The traditional appliance companies are also introducing their own robotic cleaners. However, they need to acquire new know-how in advanced technologies such as navigation, mapping and artificial intelligence. At the same time, more robotic companies would be entering the household cleaning sector with more intelligent robots.

Acknowledgement:

We would like to thank my DBA students, Dr Tamil, Dr Pang and Dr Justin, for providing initial information for this blog. Continue reading “Robots versus Conventional Vacuum Cleaners”

Automation and Jobs

A report by Oxford Martin School, University of Oxford (The Report), has examined the susceptibility of jobs to computerization. The impact of computerisation on jobs (labour market) is well-established.  It is documented that there will be a decline in routine -intensive occupations, that is, occupations mainly consisting of task following well-defined procedures that can easily be performed by sophisticated algorithms.

At the same time, with falling prices of computers, problem-solving skills are becoming productive, which explains substantial employment growth in occupations involving cognitive tasks where skilled labour has a comparative advantage. According to Brynjolfsson and McAfee (2011), technological innovation is still increasing with more sophisticated technologies disrupting labour by making workers and employees redundant.

According to Autor, et al. (2003) workplace tasks can be categorized as follows:

1. Routine versus non-routines tasks, and
2. Manual versus cognitive tasks.

In short, routine tasks are defined as tasks that follow explicit rules that can be accomplished by machines while, while non-routine tasks are not sufficiently well understood in computer codes. Each of these task categories can, in turn, be of either manual or cognitive in nature, that is, they relate to physical labour or knowledge work.

Perception and Manipulation Tasks

Robots are still unable to match the depth and breadth of human perception. While basic geometric identification is reasonably mature, enabled by the rapid development of sophisticated sensors and lasers, significant challenges remain for more complex perception tasks, such as identifying objects and their properties in a cluttered field of view. As such, tasks that relate to an unstructured work environment can make jobs less susceptible to computerisation.  The difficulty of perception has ramifications for manipulation tasks. This is, in particular, the handling of irregular objects, for which robots are yet to reach human level of aptitude.

A related challenge is failure recovery, that is, identifying and rectifying the mistakes of the robot when it has, for example, dropped an object. Manipulation is also limited by the difficulties of planning out the sequence of actions required to move an object form one place to another.

The main challenges to robotic computerization, perception and manipulation, thus largely remain and are unlikely to be fully resolved in the next decade or two.

Creative and Intelligence Tasks

The psychological processes underlying human creativity are difficult to specify. According to Borden (2003), creativity is the ability to come up with ideas or artifacts that are novel and valuable. Ideas, in a broader sense, include concepts, poems, musical compositions, scientific theories, cooking recipes and jokes, whereas artifacts are objects such as paintings, sculptures, machinery and pottery. One process of creating ideas (and similarly artifacts) involves making unfamiliar combinations of familiar ideas, requiring a rich store of knowledge. The challenge here is to find some reliable means of arriving at combinations that “make sense.”

It seems unlikely that occupations requiring a high degree of creative intelligence will be automated in the next decades.

Social Intelligence Tasks

Human social intelligence is important in a wide range of work tasks, such as those involving negotiations, persuasion and care. While algorithms and robots can reproduce some aspects of human social interaction, the real-time recognition of natural human emotion remains a challenging problem, and the ability to respond intelligently to such inputs is even more difficult. Even simplified versions of typical social tasks prove difficult for computers, as is the case in which social interaction is reduced to pure text.

The authors of the Oxford Martin School’s report noted that while sophisticated algorithms and development in MR, building upon big data now allow many non-routine tasks to be automated, occupations that involve complex perception and manipulation tasks, creative intelligence tasks, and social intelligence tasks are unlikely to be substituted by computer capital over the next decades or two.

The probability of an occupation being automated can thus be described as a function of these task characteristics.

Measuring Impact of Computerisation

The Report, using 702 detailed occupation information of the US Labour Department’s Standard Occupation Classification (SOC), has developed a model to measure the impact of computerization of various types of occupations.

Table 1 shows the top 20 occupations that are least-computerisable , while Table 2 shows the top 20 occupations that are most-computerisable.

Table 1: Top 20 Least-Computerisable

Rank	Probability	SOC Code	Occupation
1	0.0028	29-1125	Recreational Therapists
2	0.003	49-1011	First-Line Supervisors of Mechanics, Installers and Repairers
3	0.003	11-9161	Emergency Management Directors
4	0.0031	21-1023	Mental Health and Substance Abuse Social Workers
5	0.0033	29-1181	Audiologists
6	0.0035	29-1122	Occupational Therapists
7	0.0035	29-2091	Orthotists and Prosthetists
8	0.0035	21-1022	Healthcare Social Workers
9	0.0036	29-1022	Oral and Maxillofacial Surgeons
10	0.0036	33-1011	First-Line Supervisors of Fire Fighting and Prevention Workers
11	0.0039	29-2031	Dietitians and Nutritionists
12	0.0039	11-9081	Lodging Managers
13	0.004	27-2032	Choreographers
14	0.0041	41-9031	Sales Engineers
15	0.0042	29-1060	Physicians and Surgeons
16	0.0042	25-9031	Instructional Coordinators
17	0.0043	19-3039	Psychologists and, All Others
18	0.0044	33-1012	First-Line Supervisors of Police and Detectives
19	0.0044	29-1021	Dentists, General
20	0.0044	25-2021	Elementary School Teachers

 

Table 2: Top 20 Most-Computerisable

 

Rank	Probability	SOC Code	Occupation
1	0.99	41-9041	Telemarketers
2	0.99	23-2093	Title Examiners, Abstractors and Searchers
3	0.99	51-6051	Sewers Hand
4	0.99	15-2091	Mathematical Technicians
5	0.99	13-2053	Insurance Underwriters
6	0.99	49-9064	Watch Repairers
7	0.99	43-5011	Cargo and Freight Agents
8	0.99	13-2082	Tax Preparers
9	0.99	51-9151	Photographic Process Workers
10	0.99	43-4141	New Account Clerks
11	0.99	25-4031	Library Technicians
12	0.99	43-9021	Data Entry Keyers
13	0.98	51-2093	Timing Device Assemblers and Adjusters
14	0.98	43-9041	Insurance Claims and Policy Processing Clerks
15	0.98	43-4011	Brokerage Clerks
16	0.98	43-4151	Order Clerks
17	0.98	13-2072	Loan Officers
18	0.98	27-2023	Umpires, Referees and Other Sport Officials
19	0.98	43-3071	Tellers
20	0.98	51-9194	Etchers and Engravers

 

Please see the whole list of 702 occupations in Appendix of Oxford Martin School’s Report.

Highlights 

The Report’s main conclusions are as follows:

1. It distinguishes high, medium and low risk occupations, depending on their probability of computerisation. It makes no attempt to estimate the number of jobs that will actually be automated, and focus on potential job automatability over some unspecified number of years.
2. It predicts that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labour in production occupations, are at risk.
3. It provides evidence that wages and educational attainment exhibit a strong negative relationship with the probability of computerization.
4. It implies that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerization, that is, tasks requiring creative and social intelligence.                                                                    For workers to win the race, however, they will have to acquire creative and social skills.

 

Reference:

1. Carl Benedict Frey, and Micheal A. Osborne (20130, The future employment: How susceptible are jobs to computerisation. Working Paper, Oxford Martin School, University of Oxford.
   1. Brynjolfsson and E. McAffe (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibility transforming employment and economy. Digital Frontiers Press, Lexington, MA.
2. A. Boden (2003). The creative mind: Myths and mechanisms. Routledge.
3. Autor, F. Levy and R. J. Murnane (2003). The skill content of recent technological change: Am empirical exploration. The Quarterly Journal of Economics. Vol. 118, no.4, pp. 1279-1333.