3 ways machine learning is revolutionizing IoT

Few things have propelled the IoT’s dizzying growth in recent years as much as machine learning and the innovators who are pushing it. Independent, intelligent machines that can comb through data to make their own decisions are, to some, the only reason such phenomenon as the IoT can exist in the first place. So what are the top three ways in which machine learning has and will shape the IoT?

Whether it’s inspiring human creativity, surpassing human efficiency, or paving the way for even newer technologies to themselves break through and reshape the IoT, machine learning is the fuel that’s driving the IoT forward into the 21 century. Here’s how:

The gargantuan mountains of data generated by the IoT is perhaps it’s defining characteristic. Nonetheless, all of the data in the world is completely useless if companies and individuals can’t make sense or use of it. So how exactly has the market exploited this valuable data? Through machine learning.

Today’s machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime’s worth of work. As the IoT continues to grow, with some estimating it could reach the dizzying heights of $1.6 b in value by 2021, more algorithms will be needed to keep up with the rising sums of data that accompany said growth.

Machine learning doesn’t just sort through preexisting data to the benefits of companies, either. As ABI Research points out,  recent advancements in machine learning have enabled it to do predictive analysis, meaning companies which employ these algorithms can better predict future market trends and more successfully target future customers.

Companies who want to succeed in today’s marketplace understand the valuable potential hidden in machine learning, and are starting to justifiably treat their algorithms as valued parts of their workforce. But is machine learning only useful for those trying to make it in the commercial marketplace?  

Machine learning isn’t just used by companies or innovators hoping to make a quick buck off trading and using data. It’s also used for security purposes; already, machine learning algorithms are scouring the Darknet for cyberthreats.