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?
A team of EPFL roboticists and economists from the University of Lausanne just published a study in Science Robotics that answers both questions. They developed a method to calculate which of the already existing jobs are more likely to be performed by machines in the near future by merging scientific and technical literature on robotic abilities with employment and income statistics. They’ve also devised a technique for recommending career transitions to jobs that are less vulnerable and require the least amount of retraining.
“There are several studies predicting how many jobs will be automated by robots, but they all focus on software robots, such as speech and image recognition, financial robo-advisers, chatbots, and so forth. Furthermore, those predictions wildly oscillate depending on how job requirements and software abilities are assessed. Here, we consider not only artificial intelligence software, but also real intelligent robots that perform physical work and we developed a method for a systematic comparison of human and robotic abilities used in hundreds of jobs,” says Prof. Dario Floreano, Director of EPFL’s Laboratory of Intelligent System, who led the study at EPFL
The study’s most important contribution is a new mapping of robot capabilities to job requirements. The team looked at the European H2020 Robotic Multi-Annual Roadmap (MAR), a European Commission policy document that is updated on a regular basis by robotics experts. The MAR lists dozens of abilities that are currently required in robots or may be necessary in the future, organised into categories such as manipulation, perception, sensing, and human interaction. The researchers used a well-known scale for gauging the amount of technology development, “technology readiness level,” to measure the maturity level of robotic abilities by reading research articles, patents, and product descriptions (TRL).
They used the O*net database to classify human abilities, which is a widely used resource database on the US job market that defines about 1,000 jobs and breaks down the skills and knowledge that are most important for each of them.
The team was able to calculate how probable each present work vocation is to be done by a robot by selecting comparing human abilities from the O*net list to robotic abilities from the MAR document. Assume that a job requires a human to perform actions with millimetre-level precision. Because robots excel at this, the TRL of the relevant ability is the highest.
If a job necessitates a large number of such skills, it is more likely to be automated than one that requires critical thinking or creativity.
The outcome is a ranking of 1,000 jobs, with “Physicists” facing the least chance of being replaced by a machine and “Slaughterers and Meat Packers” facing the worst risk. Food processing, building and maintenance, construction, and extraction tend to be the most dangerous jobs.
“The key challenge for society today is how to become resilient against automation” says Prof. Rafael Lalive. who co-led the study at the University of Lausanne. “Our work provides detailed career advice for workers who face high risks of automation, which allows them to take on more secure jobs while re-using many of the skills acquired on the old job. Through this advice, governments can support society in becoming more resilient against automation.”
The authors then created a method to find, for any given job, alternative jobs that have a significantly lower automation risk and are reasonably close to the original one in terms of the abilities and knowledge they require — thus keeping the retraining effort minimal and making the career transition feasible. To test how that method would perform 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 the occupations with the highest risk to shift towards medium-risk occupations, while undergoing a relatively low retraining effort.
The method could be used by governments to determine how many workers is at risk of automation and adjust retraining policies, by business owners to assess the costs of increased automation, by robotics manufacturers to better tailor their products to market needs, and by the general public to determine the most efficient way to reposition themselves on the job market.
Finally, the authors created an algorithm that forecasts the risk of automation for hundreds of jobs and suggests robust career transfers with low retraining effort, which is publicly available online.! https://lis2.epfl.ch/resiliencetorobots
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