Summary of What Can Machine Learning Do? Workforce Implications

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Machine learning (ML) will be a game changer. Soon, humans and technology will be so interconnected that life without intelligent systems will be difficult to imagine. Yet what are the practical uses of ML, and how will it transform the workplace? MIT economist Erik Brynjolfsson and artificial intelligence expert Tom Mitchell from Carnegie Mellon University seek to answer some of these questions in a recent issue of Science magazine. getAbstract recommends their article to professionals, HR specialists and economic policy makers who want to catch a glimpse of the not-so-distant future.  

In this summary, you will learn

  • How machines learn,
  • How machine learning will transform the workplace and
  • What the current limitations of machine learning are.
 

About the Authors

Erik Brynjolfsson is director of the Center for Digital Business at the MIT Sloan School of Management. Tom Mitchell teaches computer science at Carnegie Mellon University.

 

Summary

Machine learning allows systems to improve from experience and make independent decisions.

To train a machine, scientists must first define the parameters and goals of the task in the ML algorithm. Next, a scientist will have to “feed” the machine with data from which the machine can learn. One way to do this is through a “learning apprentice” approach, in which ML systems learn by assisting, observing and imitating human workers. AI systems may draw on additional data to augment their performance. ML is a “general purpose technology” with a broad range of possible applications.


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