How AI can help choose your next career and stay ahead of automation

 

PRI ESPL INT .SYDNEY FES20 ARTIFICIAL-INTELLIGENCE-CAREER How AI can help choose your next career and stay ahead of automation By Nik Dawson, University of Technology Sydney, Marian-Andrei Rizoiu, University of Technology Sydney and Mary-Anne Williams, The University of New South Wales Sydney, Aug 6 (The Conversation) The typical Australian will change careers five to seven times during their professional lifetime, by some estimates. And this is likely to increase as new technologies automate labour, production is moved abroad, and economic crises unfold. Jobs disappearing is not a new phenomenon have you seen an elevator operator recently? but the pace of change is picking up, threatening to leave large numbers of workers unemployed and unemployable. New technologies also create new jobs, but the skills they require do not always match the old jobs. Successfully moving between jobs requires making the most of your current skills and acquiring new ones, but these transitions can falter if the gap between old and new skills is too large. We have built a system to recommend career transitions, using machine learning to analyse more than 8 million online job ads to see what moves are likely to be successful. The details are published in PLOS ONE. Our system starts by measuring similarities between the skills required by each occupation. For example, an accountant could become a financial analyst because the required skills are similar, but a speech therapist might find it harder to become a financial analyst as the skill sets are quite different. Next, we looked at a large set of real-world career transitions to see which way around these transitions usually go: accountants are more likely to become financial analysts than vice versa. Finally, our system can recommend a career change that's likely to succeed and tell you what skills you may need to make it work. Measure the similarity of occupations Our system uses a measure economists call revealed comparative advantage (RCA) to identify how important an individual skill is to a job, using online job ads from 2018. The map below visualises the similarity of the top 500 skills. Each marker represents an individual skill, coloured according to one of 13 clusters of highly similar skills. Once we know how similar different skills are, we can estimate how similar different professions are based on the skills required. The figure below visualises the similarity between Australian occupations in 2018. Each marker shows an individual occupation, and the colours depict the risk each occupation faces from automation over the next two decades (blue shows low risk and red shows high risk). Visibly similar occupations are grouped closely together, with medical and highly skilled occupations facing the lowest automation risk. Mapping transitions We then took our measure of similarity between occupations and combined it with a range of other labour market variables, such as employment levels and education requirements, to build our job transition recommender system. Our system uses machine learning techniques to learn from real job transitions in the past and predict job movements in the future.

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