Machine learning will be an indispensable future for the development of the Internet of Things

In recent years, there is nothing like machine learning that has driven the massive growth of the Internet of Things. Whether it is inspiring human creativity, surpassing human efficiency, or paving the way for newer technological breakthroughs and reshaping the Internet of Things, machine learning is undoubtedly the super fuel that drives the Internet of Things into the 21st century. So what are the three major ways of machine learning, and how will it drive the development of the Internet of Things?

Make data useful

One of the characteristics of the Internet of Things is that it generates a huge amount of data. Even so, if businesses and individuals cannot use this data, it is completely useless. So how can the market make use of this valuable data? Through machine learning.

Today’s machine learning algorithms comb through data sets in ways that humans can’t. The continued growth of the Internet of Things, which is estimated to be worth $1.6 trillion by 2021, means more algorithms will be needed to keep up with the corresponding increase in data.

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As ABI Research points out, recent advances in machine learning have enabled it to perform predictive analytics, meaning that businesses that adopt these algorithms can better anticipate future market trends and more successfully target future customers.

Making IoT more secure

Machine learning is not just used by businesses or innovators, it is also used for security purposes and machine learning algorithms are currently being used to combat cyber threats.

Like data analytics, the use of machine learning algorithms can greatly aid cybersecurity analysis. Whether it’s helping to solve workforce issues in the industry, working to attract top human capital that caters to affluent clients, or finding and closing IoT vulnerabilities, machine learning is a huge boon to the security industry.

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It is also worth mentioning the range of operations that these algorithms can handle. Machine learning can be used to more effectively monitor data exchanges, such as Bitcoin mining, but it can also be used to analyze historical data to predict threats and criminal behavior even before they occur.

Expanding the scope of the Internet of Things

One of the biggest advantages of machine learning and its algorithms delivered to IoT is how easily it can be integrated into IoT platforms. For example, the rapid growth of mobile devices around the world is one of the key drivers of IoT, and machine learning is often adapted to the development, programming, and maintenance of mobile devices.

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There are already many examples of how machine learning is associated with specific gadgets that have attracted the most attention to the Internet of Things. Not only mobile devices, but also self-driving cars and smart cities and factories can benefit from machine learning. As IoT products and services are made cheaper to produce, easier to market and consume by adopting machine learning strategies, more consumers will flock to them and further expand their coverage across the globe.

The era of artificial intelligence is controversial, but it has not reached the doomsday predictions of Hollywood, and it has indeed changed the world. In the next decade or two, as billions of devices become popular, these algorithms and cost-cutting advances for businesses and consumers will make machine learning even more indispensable. As more and more people register and log in on social media platforms, buy smart devices and commute in self-driving cars, the impact and even control of the Internet of Things on society will become more powerful in the wonderful world of machine learning.