How can Humans Compete with Robots?

When we think of robots, the first thing that comes to our mind is – how will these robots replace us in our job roles?

When we think of robots, the first thing that comes to our mind is – how will these robots replace us in our job roles? Whatever the answer, the second question is almost certainly: How can I ensure that my job is not threatened? A team of researchers from EPFL and economists from the University of Lausanne has released research in Science Robotics that provides answers to both issues. They created a method to assess which of the already existing tasks are more likely to be done by machines in the coming years by merging scientific and technical publications on robotic capabilities with employment and income information.

They have also developed a mechanism for proposing career transfers to professions that are less vulnerable and need the least amount of retraining.

Prof. Dario Floreano, Director of EPFL's Laboratory of Intelligent System says that numerous studies have been conducted to estimate how many professions would be mechanised by robots, but they all shine a spotlight on software robots, like voice and picture recognition, chatbots, financial adviser robots, and many more.  Moreover, based on the kind of work needs and software skills that are measured, such projections might vary dramatically. We take into account, not just artificial intelligence technology, but also genuine intelligent robots that execute physical tasks, and we devised a mechanism for comparing human and robotic capacities in lots of vocations.

The study's main novelty is a new modelling of robot capabilities to work needs. The team investigated the European H2020 Robotic Multi-Annual Roadmap (MAR), a European Commission policy document that is constantly reviewed by robotics specialists. The MAR covers dozens of skills necessary for present robots or that may be needed for future ones, arranged in categories like manipulation, vision, sensing, and human interaction. The researchers used a well-known measure for determining the degree of technical advances TRL (technology readiness level) to review research publications, patents, and product specifications to estimate the sophistication level of robotic skills.

They relied on the ontonline.org catalog of human capabilities, an extensively used resource compilation on the US labour market that categorises roughly 1,000 jobs and disaggregates the most relevant talents and knowledge for each of them.

The researchers could determine the likelihood of any existing work activity being done by a robot by selecting comparing human capabilities from the database to robotic capabilities from the MAR report. Assume that a task needs a human to operate with millimeter-level precision in movement. Robots excel at this, hence the TRL for the associated ability is the greatest. If a job needs enough of these talents, it is more likely to be mechanised than one that demands critical thinking or creativity.

As a consequence, the 1,000 positions are ranked, with "Physicists" facing the lowest danger of being substituted by a machine and "Slaughterers and Meat Packers" facing the worst risk. Jobs in food manufacturing, construction and operation, construction, and extraction tend to be the most dangerous in general.

Prof. Rafael Lalive said that the most pressing issue facing civilization now is how to become more resistant to automation. The research gives thorough career recommendations for people who are at high danger of being automated, allowing them to move on more secure positions while repurposing many of their previous competencies. Governments may help societies become more resistant to automation by following this advice.

The authors then devised a method for identifying alternative jobs for any given job that have a substantially lower automation uncertainty and are justifiably similar to the real one in aspects of the skill and abilities required, thus keeping the restructuring effort to a minimum and making the career shift feasible. To see how that technique would work in practice, they used data from the US labour force and modelled thousands of career changes based on the algorithm's recommendations, discovering that it'd also allow workers in high-risk occupations to switch to medium-risk jobs with a fairly low restructuring effort.

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