What to expect from Machine learning Premium

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With its capacity to enhance talent management, optimize operations, and drive connections with customers, machine learning is an increasingly critical business asset. The first step? Taking a closer look at these technologies and their implications for organizations.

Machine learning is not new. Learning algorithms and approach¬es have been used since the 1990s to perform tasks such as filtering spam from email inboxes, executing trades on Wall Street, and making product recommendations on Amazon. So why the (relatively) recent buzz around machine learning? Why are many experts now heralding this technology, in the words of Pedro Domingos, a computer scientist and winner of the SIGKDD Innovation Award (the highest honor in data science), as “pervasive and game changing”? The answer has to do with the explosion in the volume of data in recent years, coupled with ongoing increases in scalable computing power, which is raising both the potential of machine learning and the need for it to new heights.

Traditional statistical analysis methods were not designed for the huge datasets to which companies now have access. With learning algorithms, however, the more data that is available to them, the more powerful they become, making them a valuable asset in the digital era.

Scaling up data analysis
“The more data we have, the more we can learn. No data? Nothing to learn. Big data? Lots to learn. That’s why machine learning has been turning up everywhere, driven by exponentially growing mountains of data,” explains Pedro Domingos, who goes on to note: “In retrospect, we can see that the progression from computers to the Internet to machine learning was inevitable: computers enable the Internet, which creates a flood of data and the problem of limitless choice; and machine learning uses the flood of data to help solve the limitless choice problem.” Machine learners have the capacity to sort through and analyze huge volumes of data at speeds beyond human comprehension. “Machine learning is the scientific method on steroids,” explains Pedro. “It follows the same process of generating, testing, and discarding or refining hypotheses. But while a scientist may spend his or her whole life coming up with and testing a few hundred hypotheses, a machine-learning system can do the same in a fraction of a second.”

Expanding the scope of prediction
Machine learning is based in classical statistics and probabilities but is unrestrained by the same assumptions of these fields, which were designed to work with much smaller datasets than are now available. “Big data and machine learning greatly expand (the) scope (of prediction),” writes Pedro Domingos. “Some everyday things can be predicted by the unaided mind, from catching a ball to carrying on a conversation. Some things, try as we might, are just unpredictable…

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What to expect from Machine learning

encadré USBD26101Based on The master algorithm: How the quest for the ultimate learning machine will remake our world by Pedro Domingos (Basic Books, October 2015), “Power to the new people analytics” by Bruce Fecheyr-Lippens, Bill Schaninger and Karen Tanner (McKinsey Quarterly, March 2015) and “An executive’s guide to machine learning” by Dorian Pyle and Cristina San Jose (McKinsey Quarterly, June 2015) and an interview with George JOHN, Chairman and Founder, Rocket Fuel Inc.