Jump up ^ John Reid Blackwell (October 18, 2011). “Snagajob snags top place to work award – Richmond Times Dispatch: Metro-Richmond’s Latest Business & Economic News”. .timesdispatch.com. Retrieved June 4, 2013.
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“Over the last year interest in cryptocurrency jobs on Indeed has risen strongly. However, in recent months the prices of bitcoin and other cryptocurrencies have been volatile and (in some cases) declining,” the company said. “Job seeker interest on Indeed for bitcoin and cryptocurrency jobs has fallen, too.”
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The third interview was an onsite interview where I was able to meet with 4 separate individuals in a few different departments. Snagajob and the whole team was great on working with my schedule and did more than I can say most companies would typically do. Each conversation I had was different and covered different areas on how I would work within their team. A lot of culture fit came into this, but in the end was a lot of good conversation!
This anxiety about automation is understandable in light of the hair-raising progress that tech companies have made lately in robotics and artificial intelligence, which is now capable of, among other things, defeating Go masters, outbluffing champs in Texas Hold’em, and safely driving a car. And the notion that we’re on the verge of a radical leap forward in the scale and scope of automation certainly jibes with the pervasive feeling in Silicon Valley that we’re living in a time of unprecedented, accelerating innovation. Some tech leaders, including Y Combinator’s Sam Altman and Tesla’s Elon Musk, are so sure this jobless future is imminent—and, perhaps, so wary of torches and pitchforks—that they’re busy contemplating how to build a social safety net for a world with less work. Hence the sudden enthusiasm in Silicon Valley for a so-called universal basic income, a stipend that would be paid automatically to every citizen, so that people can have something to live on after their jobs are gone.
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Granted, there are much scarier forecasts out there, like that University of Oxford study. But on closer examination, those predictions tend to assume that if a job can be automated, it will be fully automated soon—which overestimates both the pace and the completeness of how automation actually gets adopted in the wild. History suggests that the process is much more uneven than that. The ATM, for example, is a textbook example of a machine that was designed to replace human labor. First introduced around 1970, ATMs hit widespread adoption in the late 1990s. Today, there are more than 400,000 ATMs in the US. But, as economist James Bessen has shown, the number of bank tellers actually rose between 2000 and 2010. That’s because even though the average number of tellers per branch fell, ATMs made it cheaper to open branches, so banks opened more of them. True, the Department of Labor does now predict that the number of tellers will decline by 8 percent over the next decade. But that’s 8 percent—not 50 percent. And it’s 45 years after the robot that was supposed to replace them made its debut. (Taking a wider view, Bessen found that of the 271 occupations listed on the 1950 census only one—elevator operator—had been rendered obsolete by automation by 2010.)
Even our fears about automation and computerization aren’t new; they closely echo the anxieties of the late 1950s and early 1960s. Observers then too were convinced that automation would lead to permanent unemployment. The Ad Hoc Committee on the Triple Revolution—a group of scientists and thinkers concerned about the impact of what was then called cybernation—argued that “the capability of machines is rising more rapidly than the capacity of many human beings to keep pace.” Cybernation “has broken the link between jobs and income, exiling from the economy an ever-­widening pool of men and women,” wrote W. H. Ferry, of the Center for the Study of Democratic Institutions, in 1965. Change “cybernation” to “automation” or “AI,” and all that could have been written today.
What’s more, even if today’s digital technologies are holding down job creation, history suggests that it is most likely a temporary, albeit painful, shock; as workers adjust their skills and entrepreneurs create opportunities based on the new technologies, the number of jobs will rebound. That, at least, has always been the pattern. The question, then, is whether today’s computing technologies will be different, creating long-term involuntary unemployment.
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However, Indeed reports that interest in blockchain-related roles has endured, with the search term garnering 47 searches per million at the time of the report – only slightly lower than during its February high.
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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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A job, or occupation, is a person’s role in society. More specifically, a job is an activity, often regular and often performed in exchange for payment (“for a living”). Many people have multiple jobs (e.g., parent, homemaker, and employee). A person can begin a job by becoming an employee, volunteering, starting a business, or becoming a parent. The duration of a job may range from temporary (e.g., hourly odd jobs) to a lifetime (e.g., judges).
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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.
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The talking bot can supposedly identify joy, sadness, anger, and surprise and determine whether a person is in a good or bad mood—abilities that Pepper’s engineers figured would make “him” an ideal personal assistant or salesperson. And sure enough, there are more than 10,000 Peppers now at work in SoftBank stores, Pizza Huts, cruise ships, homes, and elsewhere.
David Autor, an economist at MIT who has extensively studied the connections between jobs and technology, also doubts that technology could account for such an abrupt change in total employment. “There was a great sag in employment beginning in 2000. Something did change,” he says. “But no one knows the cause.” Moreover, he doubts that productivity has, in fact, risen robustly in the United States in the past decade (economists can disagree about that statistic because there are different ways of measuring and weighing economic inputs and outputs). If he’s right, it raises the possibility that poor job growth could be simply a result of a sluggish economy. The sudden slowdown in job creation “is a big puzzle,” he says, “but there’s not a lot of evidence it’s linked to computers.”
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