Most of the academic literature begins by focusing on

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help to both make some existing jobs more productive and create new jobs. Most of the academic literature begins by focusing on the concept of “task” – recognizing that jobs involve the combination of a number of different activities. A first strand of the literature divides task in “routine” and “non-routine”. Routine tasks are defined as activities that can be well described by a codified set of instructions. By their nature, these are more likely to be successfully executed by a computer—in the case of “cognitive” tasks—or by a robot—in the case of “manual” tasks. Autor et al. (2003) show how a decline in jobs involving routine manual and cognitive tasks became apparent in the 1980s and had continued since. They also note a stabilization in the number of jobs involving non-routine manual tasks, that is jobs that require interpersonal interaction, situational awareness and the need to respond flexibly and rapidly to changes in the environment. Autor and Price (2013) confirm an increase in these jobs starting about 2000. Both studies also show a stabilization in the number of non-routine cognitive tasks, those requiring
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Economics: The Open-Access, Open-Assessment E-Journal 11 (2018–42) Global Solutions Papers 5 managerial or analytical skills; though Autor and Price note a marginal decline between 2000 and 2006, followed by a modest rebound through 2009. These studies and others confirm an intuitive insight: computers and other machines are usually better than humans at repetitive tasks in a controlled environment; when we can, we tend to delegate those tasks to the machines. Frey and Osborne (2017) take the argument one step further. They note that thanks to advances in Artificial Intelligence (AI) and Machine Learning (ML), machines no longer need to be given step-by-step instructions in order to perform a task—they can learn by themselves just by absorbing enormous amounts of data on how a task is performed. Frey and Osborne cite self-driving cars as a case in point. The task of driving a car in varying conditions is way too complex to be described by a set of instructions, and self-driving cars were once considered science fiction. Today they are a reality. Artificial Intelligence could therefore greatly expand the range of tasks that will be taken over by machines. Frey and Osborne take a detailed look at the universe of existing jobs in the U.S., based on the Bureau of Labor Statistics classification. They assume that advances in AI and ML will proceed at the same pace as in the recent past, and from there infer which tasks are likely to be automated in the coming years (Frey and Osborne do not give a definite time scale, but a couple of decades is seen as a plausible horizon). They conclude that 47% of existing U.S. jobs are at high probability of becoming automated (where high is defined as 70% or higher).
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