[Future / Now] — AI Disrupts the Global Job Market
AI+News: stories written by journalists empowered by AI
The advancement of technology and Artificial Intelligence generate fear that machines will replace humans in the workplace. The real issue, however, is that technology and AI redefine the dynamics of labor; the big risk, as it often happens with tech-related issues, is to stay dormant. Savvy employers soon understand that, in order to take advantage of AI, their workforce has to be retrained. Employers who do not retrain their workforce and who do not invest in AI are left behind and contribute to a crisis in certain sectors in the global job market.
But for all the disruption that AI brings and the jobs it evaporates, it also creates opportunities in industries like healthcare, education, communication, and other sectors that require a human touch. A report by PwC estimates that automation and AI will make about 7 million existing jobs disappear in the United Kingdom by year 2037; at the same time, it will create the need of new jobs in industries like the ones mentioned above, mostly compensating for the lost positions.
It is a matter of governments and industries being proactive in generating the workforce they will need in the right industries. That will be the great economic challenge of the upcoming years.
This story was written by a journalist empowered by AI.
The journalist is Giomar Silva (@G_SV), founder of Migrante21 (@Migrante21). Giomar has an extensive background as a reporter and editor in Peru and Washington, D.C. After covering stories about human rights, culture, technology and politics in Peru, he focused on immigrant and minorities issues as a web editor at Washington Hispanic, the largest Spanish-language newspaper in the D.C. area. His interest in these topics led him to found Migrante21, a bilingual website that aims to document the immigrant experience in America.
Minerva leverages news data collections available in the Web and uses Artificial Intelligence based on Machine Learning (AI/ML) to discover the multiple relations among global risks, a data-driven approach that is more appealing in terms of timeliness and efficient discovery of such relations than current methodologies based on opinion surveys.