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Machine Learning Is Bringing More Intelligent Things

According to Gartner, AI and new machine learning techniques will enable a new class of intelligent apps and intelligent things -- along with the emergence of so-called digital twins.

Artificial intelligence (AI) looms large in Gartner's forecast of strategic technology trends for 2017.

The market watcher lists AI and what it calls "advanced" machine learning (ML) as a top strategic trend, along with intelligent apps, intelligent things -- and so-called digital twins. Gartner says all three technologies are examples of what it calls "Intelligence Everywhere."

The Growth of Advanced Machine Learning

Data scientists are starting to use AI and advanced ML technologies -- such as deep learning neural networks and natural language processing (NLP) -- according to Gartner vice president David Cearley. This allows for "the creation of intelligent physical and software-based systems that are programmed to learn and adapt," Cearley said in a statement.

Traditional ML is predominantly rule-driven, using decision trees, association rule learning, and other rule-based techniques to automate both decision making and learning, Cearley explained.

New technologies such as deep learning, neural networks, and NLP permit the development of a new class of apps that are -- figuratively speaking -- more intelligent. They have a greater capacity to learn, predict, and adapt, said Cearley, as well as (in some circumstances) to work autonomously.

"Applied AI and advanced machine learning give rise to a spectrum of intelligent implementations, including physical devices ... and apps and services," Cearley noted. "These implementations will be delivered as a new class of obviously intelligent apps and things as well as provide embedded intelligence for a wide range of ... devices and existing software and service solutions."

Examples of AI-Enabled Apps and Devices

Cearley cited virtual private assistants (VPA), such as Apple's Siri and Google Assistant (the new VPA included with Google's Pixel smartphone) as consumer-oriented examples. Another class of app, the virtual customer assistant, is a type of VPA for sales and customer service.

These and other intelligent apps have the capacity to fundamentally change our daily personal and work lives, Cearley argued. "Over the next 10 years, virtually every app, application, and service will incorporate some level of AI," he said. "This will form a long-term trend that will continually evolve and expand the application of AI and machine learning for apps and services."

Ditto for intelligent things: sensors and devices increasingly exploit AI and advanced machine learning technologies. Cearley and Gartner cite drones, autonomous vehicles, and smart appliances as examples. Garner sees AI and ML as a kind of force multiplier for intelligent things.

Device Collaboration and Modeling Reality

Right now, we have standalone "intelligent" devices that work more or less individually, e.g., sensors transmitting data to be collected (and possibly analyzed) by some downstream source.

In the future, Gartner predicts a new collaborative model, in which intelligent things will communicate with and respond to one another. This approach has its risks. The distributed denial-of-service attacks that struck the U.S. in late October highlight at least one way in which collaborative intelligent devices -- or more precisely, the network connectivity that makes them possible -- can pose threats, too.

Finally, Gartner predicted that organizations will invest heavily in so-called digital twins that use sensor data to model the state of a thing or a system. In effect, they are dynamic software models of physical reality.

"Within three to five years," Gartner predicted, "hundreds of millions of things will be represented by digital twins. Organizations will use digital twins to proactively repair and plan for equipment service, to plan manufacturing processes, to operate factories, to predict equipment failure or increase operational efficiency, and to perform enhanced product development."

About the Author

Stephen Swoyer is a technology writer with 20 years of experience. His writing has focused on business intelligence, data warehousing, and analytics for almost 15 years. Swoyer has an abiding interest in tech, but he’s particularly intrigued by the thorny people and process problems technology vendors never, ever want to talk about. You can contact him at

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