An executive’s guide to machine learning

June, 2015. Mckinsey.

Machine learning (ML) is important as ever more of the analog world gets digitized. Our ability to learn from data by developing and testing ML algorithms will only become more important for what are now seen as traditional businesses.

This short article is not so much a guide as a good introduction to machine learning.

Machine learning (ML) is based on algorithms that can learn from data without relying on rules-based programming. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the need for more machine learning. The authors give a good illustration of what makes ML so radically different from non-ML programming:

In 2007 Fei-Fei Li, the head of Stanford’s Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital

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