Embrace the Robots as Part of the Workforce – They Create Jobs!
Many people these days are concerned that with the introduction of so many robots into the business world that there will be fewer jobs for us, humans, to do, but this is simply not the case.
Many people these days are concerned that with the introduction of so many robots into the business world that there will be fewer jobs for us, humans, to do, but this is simply not the case. Studies have shown that with every single industrial robot that is created and used as intended, three million human jobs are created.
So, why are all of these human jobs going to be created you may ask? Well, although robots can be marvelous tools, they are not foolproof and when they do need to be fixed, who needs to fix them? Humans! Robots can not ‘self-repair’ themselves when they go wrong, nor can they perform complex tasks such as developing new ideas or negotiating, so there will always be those types of roles for humans to do. Engineers will be needed for developing, upgrading and continually maintaining the robots just as a mechanic will always be necessary to fix and maintain cars.
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Our society is changing at a very past pace, with technology being right at the front, so we need to learn to accept that robot are going to be a big part of our future. With the aid of robots taking over certain roles, time and money can be saved in these areas, and used someplace else where people are more highly needed. Robots are not a bad thing and are not out destroy the human race as we know it. They are simply technologically advanced tools developed to make our lives a little easier, so do not fear the robots but welcome them and all that they can do for you!
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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