IoT Has Machine Learning to Thank for its Success
The Internet of Things (IoT) probably wouldn’t be where it is today without machine learning. As machine learning continues to evolve, its influence on IoT will become more noticeable. Here’s a look at how machine learning is propelling IoT into the future.
Machine learning algorithms are proving quite useful for uncovering cyber threats. Indeed, the problems that they highlight are essential for helping IT developers create patches for their hardware and software. Machine learning is also helping the security industry when it comes to cybersecurity analytics. These algorithms can handle an incredible variety of tasks, whether it’s monitoring data changes like Bitcoin mining or analyzing historical data in order to predict crimes and other threats before they occur. The decades of refinement that machine learning has undergone have made it tremendously useful in this regard.
Broadening IoT’s Scope
The ubiquity of mobile devices has given the Internet of Things a tremendous boost, and machine learning plays a big role in the development and maintenance of these devices. It helps to bring down production costs and place them in reach of more consumers, facilitating the spread of its reach across the planet. Billions more of these devices are expected to be in use in the next few decades, and machine learning algorithms are a big part of that. It’s also useful for smart cities, factories, and autonomous vehicles.
Giving Data Utility
The IoT is known for its huge amount of data. This might sound good on the surface, but it’s only of value if firms can make sense of it, and that’s where machine learning algorithms come into play. They can go through sets of data at rates that humans could never hope to achieve, and the technology is also making great strides in its predictive analysis abilities, allowing firms to have a better idea of what future market trends to expect and how to target future customers successfully.
This blog post was based off of an article from Network World. View the original here.