Machine-Learning Series, Part 1

Blog Machine-Learning Series, Part 1

Machine-Learning and Anomaly Detection: Stop Problems Before They Start 

Part One: What does machine-learning have to do with the future of IoT?


Machine-Learning Leads to Anomaly Detection

Anomaly detection, also known as outlier detection, occurs through the process of data mining. It often includes identifying events or items that do not mold to meet expected patterns in current datasets. These events can result in serious technical errors such as structural problems or even bank fraud.


Machine-learning is a form of computer science involving pattern recognition and computational learning. Algorithms are thoroughly examined, particularly their construction, and how they’re able to make educated guesses regarding data processing.


To an extent, the Internet of Things is a form of machine-learning. It has arrived in full force, with 2020 slated to be the big year when everything changes. Approximately 50-80 billion items and products will be connected; when that occurs, companies and enterprises will change how they do business practically overnight, and executives are working hard to prepare.


Advertisements will fit the shopping histories and personal needs of individual customers. Transportation will decipher a person’s best route via public transit systems, and manufacturers can alter their factories’ practices to produce the most materials in the shortest amounts of time. One could say the ability to learn and understand is needed to make all this imminently possible – thus, the IoT lies deeply within the “learning category.”


Gathering and interpreting data



But this is merely scratching the surface. Yes, the IoT is capable of many feats and wonders, but it would be useless without the data it produces. This data is exactly what allows factories to become more efficient and stores to work directly for their customers. It’s what will make our hospitals safer and our buses faster. The data is produced quickly – too quickly, in fact, for human processing. Machines will be at the heart and soul of the future, and without machine-learning, the data would serve little to no purpose. If we had hundreds of people working to process IoT data in 24-hour shifts, we’d still take too darn long.


What you may not know is that machine-learning has already begun integrating itself into our daily lives. As the world’s largest search engine, Google can scour a wide index of search results that match the keywords visitors place in the search bar. This is all done through the power of machine-learning. The same is done on the websites of companies like Home Depot. Search results emerge that are respective of the words entered by a visitor. If they look up bathroom sinks of certain shapes, Home Depot will take that visitor’s keywords to develop or find appropriate results. The IoT is just taking things further.


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The problem is, about 99 percent of data produced by current IoT applications goes completely ignored and unanalyzed, partly because we’re still too reliant on humans for technical know-how. Machines have proven to be much faster than people, and can use everything we’re missing to make smarter decisions, thereby helping businesses become more efficient down the line. This is where machine-learning steps in. While numbers are still low, some enterprises are already embracing machine-learning to find the value and potential their “human counterparts” seem to be missing.


Information technology research company Gartner describes machine-learning as one of the most “versatile technologies of the past decade,” and says it will “gain even more traction in a digital business. The research, aimed at the architects of digital business, discusses the technology basics, benefits and pitfalls, and the abundance of use cases.” Allegedly, six out of every ten companies regard machine-learning as a “must have” for operations to flourish.



Machine-learning can also benefit the IoT through a process known as “embodied cognition,” which brings cognitive power to objects and areas (i.e. manufacturing plants and factories) so they can better understand their atmospheres and individual users. These objects learn about where they are and what they’ll encounter, allowing them to make appropriate decisions and interact better with humans. Understanding verbal commands, hand gestures and even written communication is suddenly within the realm of reality.



Machine-learning adds true value to IoT implementations in various industries. While the IoT can exist without it, business outcomes will be stronger granted machine-learning has its place. If businesses and industries are to operate at their maximum potentials, machines must be “trained” to take over where human minds may falter. In so doing, the future will arrive in a positive and timely manner.

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Jaffe, Mark. “IoT Won’t Work Without Artificial intelligence.” Wired. Conde Nast, 06 Aug. 2015. Web. 16 Apr. 2017.

Hupfer, Susanne. “AI is the Future of the IoT.” IBM Internet of Things Blog. 15 Dec. 2016. Web. 16 Apr. 2017.

Biewald, Lukas. “How Real Businesses are Using Machine-Learning.” TechCrunch. AOL Inc., 19 Mar. 2016. 22 Apr. 2017.

“Machine-Learning Drives Digital Business.” Gartner, Inc. Gartner, Inc. 11 Aug. 2014. Web. 22 Apr. 2017.



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