When it comes to the future of intelligent robots, the first question people ask is often: how many jobs will they make disappear? Whatever the answer, the second question is likely to be: how can I make sure that my job is not among them? In a study just published in Science Robotics, a team of roboticists from EPFL and economists from the University of Lausanne offers answers to both questions. By combining the scientific and technical literature on robotic abilities with employment and wage statistics, they have developed a method to calculate which of the currently existing jobs are more at risk of being performed by machines in the near future. Additionally, they have devised a method for suggesting career transitions to jobs that are less at risk and require smallest retraining efforts.

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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Most studies introducing AI models for COVID-19 diagnosis and prognostication exhibit systematic errors that make them unusable in most clinical settings. However, there remain opportunities for machine learning to assist front-line workers during the COVID-19 pandemic, and the steps we take now will leave us better in the future.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The work of B. Zieliński was supported by the National Science Centre (Poland) under grant agreement no 2015/19/D/ST6/01215; 2016-2019. The work of P. Spurek was supported by the National Science Centre (Poland) under grant agreement no. The work of K. Misztal was supported by the National Science Centre (Poland) under grant agreement no. Competing interests: The authors have declared that no competing interests exist.