Although machine learning will continue to grow, CIOs and business leaders must tune into this technology and understand its disruptive capabilities.
It's tempting to think of artificial intelligence (AI), cognitive computing and deep learning capabilities as somewhat futuristic—even with companies such as IBM, Microsoft and Google introducing increasingly sophisticated features. Yet machine learning—which constantly sorts through incoming data and improves on its own over time—is already making waves across a wide swath of industries, including travel, pharmaceutical research and financial services.
Facebook and Google use machine learning to analyze users, click patterns and deliver personalized content and ads. Others are turning to machine learning and predictive analytics to understand everything from consumer buying and spending patterns to real estate and housing rental markets. Still others are putting it to use to improve cyber-security.
According to The Wall Street Journal's CIO Journal, office supply retailer Staples is introducing machine learning algorithms that not only allows customers to use natural language to build an order, but also to adjust the ordering system to better fit their needs, desires and patterns.
Things get even more interesting from there. Intel and IBM are reportedly using sentiment-analysis software—which incorporates natural language processing and a machine learning component—to gauge employee emotions on any given day. Apple is researching how to use machine learning to predict user's needs and desires.
As autonomous vehicles, robots and drones zoom into existence, expect the trend to accelerate further. There is no end to the possibilities.
At this point, it's safe to say that the future has arrived. Although machine learning capabilities will grow exponentially over the next decade and beyond, CIOs and other business leaders must tune into this technology and understand just how disruptive it is. Increasingly powerful neural networks, powerful cloud-based frameworks and data platforms such as Hadoop are changing the stakes.
Today, disruption is the new normal—and machine learning, however powerful, is only one cog in the giant wheel. Yet it's clear that, moving forward, organizations will require new and different thinking from leaders along with a vastly larger number of data scientists to understand risks and rewards, make new discoveries, improve processes, boost security and compete in a more agile and flexible way.
What's more, as the Internet of things takes hold and data flows in at even greater rates, it will be machines that slice and dice through it to provide insights and answers that have never before been possible.
The wild ride has just begun.
This article was originally published on 04-08-2016