Robotics And Employment

It seems to me….

Increasingly, the work we do is enabled more and more by new IT, including automation, robotics, and intelligent platforms.” ~ Pierre Nanterme[1].

During the Industrial Revolution, John Stuart Mill wrote “there cannot be a more legitimate object of the legislator’s care” than looking after those whose livelihoods are disrupted by machines. Currently, it is mostly wealthy countries that are concerned about the effects of automation on education, welfare, and development but policymakers in developing countries will increasingly also need to consider any possible resulting societal impact. Additional effort will be critical to ensure not only U.S. but worldwide middleclass prosperity and individual success in an era of ever intensifying globalization and technological upheaval.

The coming technological transformation won’t entail occupational shifts on the scale of the Industrial Revolution with its wholesale redistribution of labor between the agricultural and industrial sector – the vast majority of Americans already work in the service sector. It will, however, be more important than ever for people of all ages to continuously update their skills and renew their training given how their occupations will continue to be reshaped by technology.

The first articulated arm, the Unimate developed by George Devol and Joseph Engelberger, went into service at General Motors (GM) in 1961 and since then has been the image of the standard industrial robot: a stationary arm endlessly picking up objects, attaching them together, and making additional adjustments. The first actual industrial robot arrived on the GM company assembly line the following year in 1962 welding auto bodies.

Now, one of the biggest shifts over the next ten years will be a massive growth in mobile robots. This latest breed of robots will move people, deliver goods, spray and harvest crops, monitor streets, participate in military operations, and mine minerals in inhospitable environments such as the bottom of the ocean. Many robots are designed in humanoid form simply to better operate in an environment designed for humans: bipedal movement is actually less practical than wheels, four-legs, or other alternative methods of movement.

According to industry experts and a recent forecast[2], several emerging robotic market sectors will enable today’s $25bn market to grow to $123bn by 2026. Mobile robots promise to be the biggest driver in the robotics industry.

Much of the potential for this new wave of robots has come from advancements in so-called machine learning, the software that bestows robots with contextual intelligence. The technology has advanced to where it is going to subsume a significant percentage of routine predictable-type jobs. The future will depend upon constantly increasing automated processes.

Robots, machine learning, and artificial intelligence promise to fundamentally change the nature of work. Total factor productivity, the best summary measure of the pace of technical change, has been languishing since 2005 but technological advances should result in significant improvement once these developments become fully integrated into the production process.

This new wave of primarily software-driven innovation is changing manufacturing[3] including not only new production techniques, such as 3D and 4D printing, but also the ability to link every component on a factory floor through a “digital thread” connecting machines to workers and the factory to supply chains and distribution networks. Workers can be more productive and safer using electronic sensors and portable and wearable devices. These are new and better manufacturing jobs which require a new set of skills and made possible by innovation and high-quality infrastructure.

Robots are now beginning to do what until now have been considered human jobs. Innovations in robotics will continue to accelerate disrupting and changing the paradigm of business operations in many industries. A computer can substitute for a human in performing a particular task when two conditions are satisfied: (1) An Information Condition: all information necessary to carry out the task can be identified and acquired in a form that computers can process; (2) A Processing Condition: the actual information processing can be expressed in a set of rules.

Much currently computerized work involves complicated tasks that have been simplified by imposing structure. Computers are extremely good at executing rules; humans, on the hand, excel at the cognitive processing of information.

The human labor market will center on three kinds of work: solving unstructured problems, working with new and different types of information, and carrying out non-routine manual tasks. The human mind’s strength is its flexibility; the ability to process and integrate many kinds of information to perform a complex task.

The bulk of the rest of the work will be done by computers with some work reserved for low-wage workers abroad[4]. The computer’s strengths are speed and accuracy, not flexibility, and computers are best at performing tasks for which logical rules or a statistical model provide a path to a solution. Much computerized work involves complicated tasks that have been simplified by imposing structure.

Low wage work; such as personal care, personal services, food preparation, building and grounds cleaning… have all grown in importance and all involve non-routine physical work that is hard to computerize. Machine operation, production, craft and repair occupations, and office and administration contain significant amounts of routine work that can be expressed in deductive or inductive rules so are candidates for computer substitution and/or offshoring. Occupational projections indicate rapid growth in high-end jobs but also show comparable rapid growth in low paying jobs carrying out those non-routine manual tasks; for example, healthcare support occupations that require little formal education.

Routine cognitive or other repetitive tasks probably can be automated with relatively little difficulty. Some task categories such as solving unstructured problems or working with new information is considerably more difficult. Until recently non-routine manual tasks; e.g., driving; were considered extremely difficult to automate but progress in this area necessitates reevaluation of what is possible.

Without assistance, as increasing employment options are subsumed by computerization and automation, many displaced less-skilled workers will be forced into greater competition for available lower-level positions that no longer enable them to maintain their previous standard of living. Currently, automation pushes people out of jobs making the people who own the machines wealthy and everyone else poorer. Resulting widespread unemployment could result in a negative economic spiral as those unemployed do not create product demand leading to still further lower wage and employment demand and lower investment in human capital and equipment.

All jobs, even those of doctors, lawyers, and professors, are being transformed. But transformed is not the same as threatened; jobs are changing, not disappearing. Concerns about AI and automation have led to calls for a stronger safety net to protect people from labor-market disruption and help them switch to new types of employment.

The greatest opportunity of technological advances is unleashing of the potential of human ingenuity. While not always apparent, we now live in a time of diminishing poverty and hunger, greater freedom and social justice, less violence and suffering, and more opportunity and potential for personal actualization than ever before in history. While the path is not always obvious, there is sufficient justification for optimism for continued future progress. There never can be some upward bound on achievement, as similar to poverty which always is defined as some percentage of the least wealthy regardless of income level, the same is also true of progress and advancement in every other area of social endeavor.

That’s what I think, what about you?

[1] Pierre Nanterme is a French business executive currently the chairman and CEO of Accenture.

[2] Global Industrial Robotics Market Forecast and Opportunities 2020, Research and Markets,, March 2015.

[3] Annunziata, Marco. Chief Economist, General Electric Company, Quora,, 6 April 2016.

[4] Levy, Frank, and Richard J. Munane. Dancing With Robots,, 13 July 2013.

About lewbornmann

Lewis J. Bornmann has his doctorate in Computer Science. He became a volunteer for the American Red Cross following his retirement from teaching Computer Science, Mathematics, and Information Systems, at Mesa State College in Grand Junction, CO. He previously was on the staff at the University of Wisconsin-Madison campus, Stanford University, and several other universities. Dr. Bornmann has provided emergency assistance in areas devastated by hurricanes, floods, and wildfires. He has responded to emergencies on local Disaster Action Teams (DAT), assisted with Services to Armed Forces (SAF), and taught Disaster Services classes and Health & Safety classes. He and his wife, Barb, are certified operators of the American Red Cross Emergency Communications Response Vehicle (ECRV), a self-contained unit capable of providing satellite-based communications and technology-related assistance at disaster sites. He served on the governing board of a large international professional organization (ACM), was chair of a committee overseeing several hundred worldwide volunteer chapters, helped organize large international conferences, served on numerous technical committees, and presented technical papers at numerous symposiums and conferences. He has numerous Who’s Who citations for his technical and professional contributions and many years of management experience with major corporations including General Electric, Boeing, and as an independent contractor. He was a principal contributor on numerous large technology-related development projects, including having written the Systems Concepts for NASA’s largest supercomputing system at the Ames Research Center in Silicon Valley. With over 40 years of experience in scientific and commercial computer systems management and development, he worked on a wide variety of computer-related systems from small single embedded microprocessor based applications to some of the largest distributed heterogeneous supercomputing systems ever planned.
This entry was posted in AI, AI, Artificial Intelligence, Artificial Intelligence, Automation, Automation, Automation, Automation, Computer, Computerization, Education, Education, Employment, Employment, General Motors, George Devol, GM, Industrial Revolution, Industrial Revolution, Jobs, John Stuart Mill, Joseph Engelberger, Labor, Low-Skill, Machine Learning, Manufacturing, Productivity, Robotics, Robotics, Robotics, Robotics, Robots, Technology, Training, Training, Unimate, Wages, Workers and tagged , , , , , , , , , , , , , , , , , , , , , , , , , , . Bookmark the permalink.

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