In the tony northern suburbs of New York City, IBM Research is pushing super-smart computing into the realms of such professions as medicine, finance, and customer service. IBM’s efforts have resulted in Watson, a computer system best known for beating human champions on the game show Jeopardy! in 2011. That version of Watson now sits in a corner of a large data center at the research facility in Yorktown Heights, marked with a glowing plaque commemorating its glory days. Meanwhile, researchers there are already testing new generations of Watson in medicine, where the technology could help physicians diagnose diseases like cancer, evaluate patients, and prescribe treatments.
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Getting a first job is an important rite of passage in many cultures. The youth may start by doing household work, odd jobs, or working for a family business. In many countries, school children get summer jobs during the longer summer vacation. Students enrolled in higher education can apply for internships or coops to further enhance the probability of securing an entry level job upon graduation.
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Now, it’s possible that some of the productivity slowdown is the result of humans shifting out of factories into service jobs (which have historically been less productive than factory jobs). But even productivity growth in manufacturing, where automation and robotics have been well-established for decades, has been especially paltry of late. “I’m sure there are factories here and there where automation is making a difference,” says Dean Baker, an economist at the Center for Economic and Policy Research. “But you can’t see it in the aggregate numbers.”
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The expression day job is often used for a job one works in order to make ends meet while performing low-paying (or non-paying) work in their preferred vocation. Archetypal examples of this are the woman who works as a waitress (her day job) while she tries to become an actress, and the professional athlete who works as a laborer in the off season because he is currently only able to make the roster of a semi-professional team.
Noma Bar (Illustration); Data from Bureau of Labor Statistics (Productivity, Output, GDP Per Capita); International Federation of Robotics; CIA World Factbook (GDP by Sector); Bureau of Labor Statistics (Job Growth, Manufacturing Employment); D. Autor and D. Dorn, U.S. Census, American Community Survey, and Department of Labor (Change in Employment and Wages by Skill, Routine Jobs); Bureau of Labor Statistics (Productivity, Output, GDP Per Capita); International Federation of Robotics; CIA World Factbook (GDP by Sector)
In December of last year, Indeed also provided CoinDesk with data regarding blockchain jobs posted. The report indicated that the number of blockchain jobs posted in the U.S. had increased by 207 percent since 2016, and by 631 percent since November 2015.
An activity that requires a person’s mental or physical effort is work (as in “a day’s work”). If a person is trained for a certain type of job, they may have a profession. Typically, a job would be a subset of someone’s career. The two may differ in that one usually retires from their career, versus resignation or termination from a job.
Despite the system’s remarkable ability to make sense of all that data, it’s still early days for Dr. Watson. While it has rudimentary abilities to “learn” from specific patterns and evaluate different possibilities, it is far from having the type of judgment and intuition a physician often needs. But IBM has also announced it will begin selling Watson’s services to customer-support call centers, which rarely require human judgment that’s quite so sophisticated. IBM says companies will rent an updated version of Watson for use as a “customer service agent” that responds to questions from consumers; it has already signed on several banks. Automation is nothing new in call centers, of course, but Watson’s improved capacity for natural-language processing and its ability to tap into a large amount of data suggest that this system could speak plainly with callers, offering them specific advice on even technical and complex questions. It’s easy to see it replacing many human holdouts in its new field.
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That robots, automation, and software can replace people might seem obvious to anyone who’s worked in automotive manufacturing or as a travel agent. But Brynjolfsson and McAfee’s claim is more troubling and controversial. They believe that rapid technological change has been destroying jobs faster than it is creating them, contributing to the stagnation of median income and the growth of inequality in the United States. And, they suspect, something similar is happening in other technologically advanced countries.
Most people spend up to forty or more hours each week in paid employment. Some exceptions are children, retirees, and people with disabilities; However, within these groups, many will work part-time, volunteer, or work as a homemaker. From the age of 5 or so, many children’s primary role in society(and therefore their ‘job’) is to learn and study as a student.
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But are these new technologies really responsible for a decade of lackluster job growth? Many labor economists say the data are, at best, far from conclusive. Several other plausible explanations, including events related to global trade and the financial crises of the early and late 2000s, could account for the relative slowness of job creation since the turn of the century. “No one really knows,” says Richard Freeman, a labor economist at Harvard University. That’s because it’s very difficult to “extricate” the effects of technology from other macroeconomic effects, he says. But he’s skeptical that technology would change a wide range of business sectors fast enough to explain recent job numbers.
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Last year, the Japanese company SoftBank opened a cell phone store in Tokyo and staffed it entirely with sales associates named Pepper. This wasn’t as hard as it sounds, since all the Peppers were robots.
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Brynjolfsson himself says he’s not ready to conclude that economic progress and employment have diverged for good. “I don’t know whether we can recover, but I hope we can,” he says. But that, he suggests, will depend on recognizing the problem and taking steps such as investing more in the training and education of workers.
McAfee points to both retail and transportation as areas where automation is likely to have a major impact. Yet even in those industries, the job-loss numbers are less scary than many headlines suggest. Goldman Sachs just released a report predicting that autonomous cars could ultimately eat away 300,000 driving jobs a year. But that won’t happen, the firm argues, for another 25 years, which is more than enough time for the economy to adapt. A recent study by the Organization for Economic Cooperation and Development, meanwhile, predicts that 9 percent of jobs across 21 different countries are under serious threat from automation. That’s a significant number, but not an apocalyptic one.
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