{"id":528,"date":"2017-09-28T09:59:54","date_gmt":"2017-09-28T09:59:54","guid":{"rendered":"http:\/\/kusuaks7\/?p=133"},"modified":"2025-03-27T12:23:23","modified_gmt":"2025-03-27T12:23:23","slug":"we-re-asking-all-the-wrong-questions-about-the-future-of-jobs","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/we-re-asking-all-the-wrong-questions-about-the-future-of-jobs\/","title":{"rendered":"We’re Asking All the Wrong Questions About the Future of Jobs"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tWork is not what it used to be. The very concept of work has evolved considerably over the centuries, as newer technologies have become integrated into how we function individually as well as a society.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn engineering, work is defined as the product of force and distance. Early machines amplified force and turbocharged human productivity, providing an early platform for industrialised society. They eliminated physical grunt work and created new kinds of work for humans, such as operating and fixing machines and using their capabilities in new ways. Computers have similarly reduced our informational grunt work, enabling more productive, creative, and rewarding work for humans.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut today\u2019s machines are different in that they are increasingly figuring things out for themselves. They have learned how to learn, a quality that has until now been exclusive to humans. Our ability to perceive the world and integrate unstructured information spontaneously serves as the bedrock of much of human work. With machines now performing this role, often better than us, how valuable will the human – or \u2018nonroutine\u2019 – aspects of work be in the future?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe current political preoccupation with job numbers ignores the quality distribution of such jobs. Of the\u00a050,000 people Amazon is looking to hire<\/a>immediately, for example, what proportion has the potential for real career and earnings growth?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIt isn\u2019t possible to answer this question based on past data, because nonroutine work does not lend itself well to scientific management-style observation, or the standard occupational descriptions as laid down by the\u00a0O*NET occupational classification system<\/a>. Technology has altered the composition of jobs and the associated skills required; the work of an accountant or lawyer today, for example, is very different from what it was a decade or two ago.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nThe skill requirements of jobs change as what was nonroutine becomes routine. The superior far-distance vision capability of professional drivers, for example, diminishes in value if a machine can see better than humans, and a superior ability to memorise precedents is less valuable for lawyers if a machine can find them faster and better than humans. In order to predict what impact artificial intelligence (AI) will have on human work, we need to understand at a detailed level the \u2018digital fingerprint\u2019 associated with work, and especially nonroutine work.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tPast research shows that automation has replaced routine tasks while complementing nonroutine ones. Furthermore, the share of the labour force employed in occupations that make intensive use of nonroutine tasks has increased substantially over the last few decades, while those involving mostly routine cognitive and manual tasks have declined substantially. If this is how the future will unfold, one might reasonably project that the nature of human employment will depend on the supply, and in particular the demand, for nonroutine task-oriented occupations.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut this routine\/nonroutine view of the world is blurry and breaks down when autonomous learning systems \u2013 as opposed to automated systems \u2013 become capable of performing tasks previously considered nonroutine. In 1972 the philosopher Hubert Dreyfus asserted that computers would never be able to perform simple tasks such as driving. As recently as 10 years ago, driving a vehicle was considered nonroutine and therefore inherently human. And yet autonomous driving vehicles are about to make navigation largely routine. More generally, machines are becoming better at other cognitive tasks long considered human, including investment decision making, creating tax returns, and more. Big data, especially perception-based data from systems that can see, hear, read, and touch, is providing machines with the ability to learn on their own.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA recent\u00a0report by the McKinsey Global Institute<\/a>\u00a0reported that 60% of occupations are 30% automatable. The report notes that such occupations make up almost $2.7 trillion in wages in the United States, slightly more than half the country\u2019s total economic activity. The report also envisions humans working alongside machines in new ways. Indeed, AI machines are already redefining theboundaries of physical as well as cognitive work<\/a>, helping humans with the time-consuming or error-prone components of work. But when machines become much better than us at making important nonroutine decisions involving our health, finance, navigation or entertainment, for example, what value will humans add?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAt universities, we teach students how to think in order to prepare them for the future workplace. This is largely about how to conduct inquiry scientifically, which begins by asking the right questions, formulating the problem, execution, analysis of outputs, and communication of results. Some researchers have proposed that we should encourage\u00a0computational thinking<\/a>, which considers these steps as a whole by \u201creformulating a seemingly difficult problem into one we know how to solve, perhaps by reduction, embedding, transformation, or simulation\u201d, according to Jeannette Wing, head of the computer science department at Carnegie Mellon University. Existing skills-based descriptions of work don\u2019t adequately address these increasingly important aspects of nonroutine knowledge work.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Have you read?<\/h3><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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