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A short primer on the effects that job automation (computerization) will have on the job market, and the fastest growing jobs in the next ten years.

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This solution briefly explains the concept of job automation, including a review of a leading 2013 study by Frey & Osborne, who first quantified the probability of job automation in the US economy. Further, this solution reviews the jobs that are in the highest and low risk categories for job automation, with a glance at the US Bureau of Labour Statistics' recent (October 2017) ten-year forecast on the fastest growing jobs of the future.

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This solution briefly explains the concept of job automation, summarizing a leading 2013 study by Frey & Osborne, who first quantified the probability of job automation in the US economy. Further, this solution highlights the jobs that have the lowest and highest risk in being automated. In addition, the solution provides the top ten fastest growing jobs (the three C's) provided by the US Bureau of Labour Statistics in their ten-year forecast, which has linkages to Frey & Osborne's study.

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A Short Primer on Job Automation and the Three C's of Fastest Growing Jobs

According to current studies, approximately 47% of all US jobs are at risk of being automated away through computerization (Frey & Osborne, 2013). Similarly, 42% is a commonly cited figure for the Canadian context (Lamb, 2016). Regardless of the precise percentage, job automation is a significant phenomenon for the current labor force, job seekers and future job seekers to be aware of- particularly in North America.

In the recent study by Frey and Osborne (2013), what drove their quantitative finding of 47% was an analysis of routine versus non-routine (tasks that are not easily conveyed for computers to display in computer code) and manual (physical work) versus cognitive (knowledge work) tasks of 702 occupations. Interestingly, automation is now beginning to impact tasks that are classified as non-routine and cognitive. This is primarily a result of the emergence of big data (complex and large datasets), and machine learning algorithms to detect patterns, manage large calculations, avoid human biases, and, ...

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