How To Compete With Robots – Artificial Intelligence and Robotics News

When it comes to the future of intelligent robots, the first question people ask is often: how many jobs will they kill? Whatever the answer, the second question will probably be: how can I ensure that my work is not part of it?

In a study just published in Scientific robotics, a team of roboticists from EPFL and economists from the University of Lausanne offers answers to these two questions. By combining the scientific and technical literature on robotic capabilities with employment and wage statistics, they developed a method to calculate which of the currently existing jobs are most likely to be performed by machines in the near future. In addition, they have developed a method to suggest career transitions to jobs that are less risky and require less retraining effort.

“There are several studies predicting how many jobs will be automated by bots, but they all focus on software bots, such as speech and image recognition, financial robo-advisors, chatbots, etc. Moreover, these predictions fluctuate wildly depending on how job requirements and software capabilities are assessed. Here, we consider not only artificial intelligence software, but also real intelligent robots that perform physical work and we have developed a method for a systematic comparison of human and robotic abilities used in hundreds of jobs”, explains the Professor Dario Floreano, director of EPFL’s Intelligent Systems Laboratory, who led the study at EPFL.

The key innovation of the study is a new mapping of robot capabilities to job demands. The team looked at the H2020 European Robotics Multi-Annual Roadmap (MAR), a strategic document from the European Commission that is periodically reviewed by robotics experts. The MAR describes dozens of capabilities that are required of the current robot or that may be required by future ones, ranging, organized into categories such as manipulation, perception, sensing, interaction with humans. The researchers scoured research papers, patents and robotic product descriptions to assess the level of maturity of robotic capabilities, using a well-known scale to measure the level of technological development, the “Technology Readiness Level” (TRL ).

For human abilities, they relied on the O*net database, a database of resources widely used in the US labor market, which ranks about 1,000 occupations and breaks down the skills and knowledge most crucial to each of them.

After selectively matching human abilities from the O*net list to robotic abilities from the MAR document, the team was able to calculate the probability that every existing trade was performed by a robot. Say, for example, that a job requires a human to work with millimeter precision of movement. Bots are very good at this, so the TRL of the corresponding ability is the highest. If a job requires enough such skills, it will be more likely to be automated than a job that requires skills such as critical thinking or creativity.

The result is a ranking of the 1,000 jobs, with ‘physicists’ at the lowest risk of being replaced by machinery, and ‘slaughterhouses and meat packers’ at the highest risk. In general, jobs in food processing, construction and maintenance, construction and extraction seem to present the highest risks.

“The main challenge for society today is how to become resilient to automation,” says Professor Rafael Lalive. who co-directed the study at the University of Lausanne. “Our work provides in-depth career advice to workers facing high risks of automation, enabling them to take on more secure jobs while reusing many of the skills learned in the old job. With these tips, governments can help society become more resistant to automation. »

The authors then created a method to find, for a given job, alternative jobs that have a significantly lower risk of automation and are reasonably close to the original in terms of the skills and knowledge they need — thus minimizing the retraining effort and making career transition possible. To test how this method performed in real life, they used data from the US workforce and simulated thousands of career moves based on the algorithm’s suggestions, finding that it would indeed allow workers in higher-risk occupations moving to medium-risk jobs. trades, while undergoing a relatively low retraining effort.

The method could be used by governments to measure how many workers might face automation risks and adjust retraining policies, by companies to assess the costs of increasing automation, by robotics manufacturers to better adapt their products to market needs; and by the public to identify the easiest way to reposition oneself in the labor market.

Finally, the authors translated the new methods and data into an algorithm that predicts automation risk for hundreds of jobs and suggests resilient career transitions with minimal retraining effort, publicly available on

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How To Compete With Robots – Artificial Intelligence and Robotics News

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