This is an excerpt from Erik Brynjolfsson and Tom Mitchell's 2017 Science article, "What can machine learning do? Workforce implications". The authors explore the capabilities and limitations of machine learning (ML), focusing on its impact on the workforce. They identify tasks well-suited for ML and those less amenable to automation, highlighting that ML's effects on employment are multifaceted, extending beyond simple job replacement. The article examines six key economic factors influencing ML's impact, including substitution, complementarities, and elasticity. Finally, it concludes that while ML will automate some tasks, it will also create new opportunities and necessitate complementary investments in skills and infrastructure. Hosted on Acast. See acast.com/privacy for more information.

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