Physicists are the professionals with the lowest risk of being replaced in the near future by machines, while butchers and meat packers face the highest risk, according to a method that measures the risk of a profession being robotized.
The method has been developed by a team of Swiss scientists, which reveals that artificial intelligence is not the only threat to human employment, as physical jobs may also be carried out more and more by robots.
In a study published today by the team, made up of robotics specialists from the Federal Polytechnic School of Lausanne (EPFL) and the University of Lausanne, a new way of looking at the impact of robotization on employment is proposed by moving away from classic analyses, which focus on robotic programs based on artificial intelligence.
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These include voice and image recognition, financial consulting or chatbots, which are applications that are often used in customer service to offer automated answers to frequently asked questions.
“Instead, we not only consider computer applications, but also robots that can perform physical work,” explained the director of the Intelligent Systems Laboratory at EPFL, Dario Floreano.
For their prediction, the researchers took into consideration documentation from the European Commission that describes dozens of skills that are required of current and future robots, such as manipulation, perception, sensation and interaction with humans.
Likewise, the patents and descriptions of the latest generation robots were analyzed.
When this information was crossed with the knowledge and skills to perform around 1,000 jobs, it was determined that jobs that require a person to work making movements with a precision level of one millimeter can be performed without difficulty by a robot.
“If the job requires that skill, it is more likely to be automated than professions that require critical and creative thinking,” the study concludes.
The authors created an algorithm that predicts the level of automation risk for hundreds of jobs and suggests transition alternatives with minimal training effort, which is accessible, in English only, at http://lis2.epfl.ch/resiliencetorobots . (I)
Source: Eluniverso
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