Much of the discussion around the Internet of Things over the past few years has focused on the smart connected devices themselves—what they are, how many there are and how to secure them.
While all those little endpoints are important, what matters more in IoT is the massive amounts of data these devices generate—and the business insights that can be derived from them through analytics. When it comes to charting a course toward those kinds of critical insights, artificial intelligence (AI) and machine learning (ML) are the technologies that light the way.
For decades, data analytics mostly involved computers compiling and storing information and presenting it to humans for analysis, a process
that was slow, error-prone and incapable of deciphering trends buried deep in the data. Those shortcomings are exacerbated in an IoT
environment, where the volume of data produced by a legion of sensors and mobile devices is exponentially greater.
Networking giant Cisco predicts
there will be more than 12 million connected IoT and mobile devices by 2022, with mobile internet traffic hitting almost a zettabyte. When you add high-speed 5G connectivity to the mix, the challenges become even more daunting.
AI and its subsets, ML and deep learning, become critical tools as all of this
data piles up.
ML uses algorithms to sort data, learn from it and find patterns and trends that can be used to inform business decisions, make predictions, provide alerts and solve problems. By applying well-tuned algorithms to huge amounts of data, ML systems can be trained to understand how a job should be done and adapt to change along the way.
Deep learning uses neural networks that function in ways similar to the human brain. Rather than using 100 billion connected neurons in the brain, a neural network leverages a set of algorithms that pass data through a series of computational layers. These layers recognize and extract elements such as images, sounds or text before finally arriving at a desired output.
AI and related disciplines are hardly new. Scientists have been working with AI for decades, and on artificial neural networks since the 1990s. What has changed in recent years, however, is the
development of computers powerful enough to process huge amounts of data, aided largely by high-performance GPU accelerators with
ramped up parallel-computing capabilities.
At the same time, data storage capacity has exploded and the amount of data being generated that can be used to train the machines has skyrocketed. The more data fed to the algorithms, the faster they learn and the better they perform. That's a huge boon to IoT, which increasingly depends on unearthing and mastering patterns in behavioral data.
It's also significant that the cloud has turned out to be especially well suited to the tasks of ML training and inference.
All of this has made advanced computational and analytical capabilities an engine for IoT systems and processes. Organizations of all sizes can now leverage AI and ML to tame the flood of data from IoT networks and make better business decisions, get real-time insights and enjoy greater operational efficiencies and reduced costs.
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or contact us at firstname.lastname@example.org