Robots are coming for our jobs
Technical unemployment is the term Jon Maynard Keynes used to describe the loss of human jobs to machines. His prediction in 1930 was that by 100 years later we may have just 15 hours a week employment.
Technical unemployment is the term Jon Maynard Keynes used to describe the loss of human jobs to machines. His prediction in 1930 was that by 100 years later we may have just 15 hours a week employment. This has not yet come to fruition as certain human traits cannot be replicated. The current figure shows that this is getting closer, as McKinsey has analysed things and determined that 45% of the current workload could be carried out as a result of technology that already exists. Artificial intelligence should soon move onto human skills such as covering clear ground, communication and the processing of information.
Examples of the vulnerable roles are truck drivers and call centre workers. Within the following 10 to 20 years around 80% of jobs at the lower end of the wage scale could be lost to robots and the level of programming will not be so intense and require skilled engineers.
“Many of the tasks that simply had to be done by humans in the past will soon fall within their growing capabilities — as we already see with jobs as diverse as security guards, call center workers and truck drivers.“
Machine learning is the process by which computer algorithms can detect patterns and analyse digital data to refine their actions. Trial and error allow the robot to develop quickly and adapt to additional new tasks.
3D printing and drones lead the way, and could take tens of thousands of jobs. Machines have taken jobs in the past, but it is harder to find an area from which they can be excluded. Calls are made for a universal income allowing all to enjoy life without having to labor by Andrew Ng an AI researcher. Labor will always be needed, but humans don’t have to provide it.
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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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