Why are people talking about the "tipping point" for machine learning?


Why are people talking about the "tipping point" for machine learning?


A significant number of experts are alerting others to the idea that machine learning is really due to explode within the next few years as an emerging industry. As a specific element of artificial intelligence work, machine learning relies on sophisticated algorithms and data training sets to develop complex probabilistic responses that can be applied to nearly any situation or industry. With that in mind, machine learning adoption in the enterprise community is now growing as companies try to be the first among their competitors to really apply machine learning in specific ways.

The business applications are only one side of the potential growth of machine learning. Companies are also finding that smarter technologies and smarter products are going to unlock a new generation of more functional consumer goods and services.

People talk about the "tipping point" of machine learning as a perfect storm of advancement in hardware, algorithms and data. The Harvard Business Review mentions all three of these in a July piece discussing the pending explosion of machine learning. Of course, big data is perhaps the most trumpeted in the tech press; of these three elements, big data has already exploded over the last 10 years. However, the algorithms themselves have also developed quite significantly.

Another component that so many people are talking about is the hardware that is driving more widespread machine learning applications.

Essentially, companies are moving toward a process of developing application-specific circuit boards and processor chips that are made to handle machine learning, rather than outfitting traditional circuit board technologies to handle the large number of inputs and computations involved in probabilistic decision-making. Some reference technologies such as Google's Tensor Processing Unit or TPU and other products that are built specifically to enable machine learning computation, for example, through the use of programmable logic gate arrays.

All of these trends come together to present a growing demand for machine learning systems and skills that executives and others are paying a lot of attention to as they contemplate the future of business technology in 2018 and beyond.

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Written by Justin Stoltzfus
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Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.
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