The Internet of Things (IoT) has been one of the most popular technology buzzwords this past year. At first the discussion dug into what it is, to how to drive value from it, and then the focus was on how to make it work.
No doubt about it, IoT will produce a boatload of data that can help cities predict accidents and crimes, give doctors access to real-time insight into information from pacemakers or biochips, enable optimized productivity across industries through predictive maintenance on equipment and machinery, create smart homes with connected appliances, and provide communication between self-driving vehicles.
Image via kpcb.com.
Although this sounds fantastic, the issue will be figuring out a way to analyze the deluge of performance data and information these connected devices create. In order for IoT to live up to its promise, we must improve the speed and accuracy of big time data analysis. Otherwise things can take a turn for the worst, such as home appliances not working together as they should, pacemakers malfunctioning, or self-driving car pileups.
To keep up with IoT-generated data, we must look to machine learning.
According to Wikipedia, machine learning is defined as “a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions.”
This sounds like some sort of complicated science fiction, but it’s already present in everyday life. For example, it’s already used by Pandora to determine your taste in music, or by Amazon.com to suggest books and movies that might interest you. Both are based on what has already been learned about the user and this information is refined over time as the system learns more about the user’s behaviors.
When it comes to IoT, machine learning can help companies take the billions of data points and trim them down to what’s really important. An example related to this would be wearable devices that track your health. They’re already part of a booming industry, but will soon evolve to become interconnected and connected to the Internet, tracking your health and providing real-time updates to a health service.
In order to analyze data immediately as it’s collected to identify previously known and new patterns, machines that are capable of generating and aggregating this big data must also be used to learn normal behaviors for each patient and track, uncover and flag anything outside the norm that could indicate a life-threatening health issue.
The realization of IoT depends on being able to gain the insights hidden in the growing seas of available data. Because current approaches don’t scale to IoT volumes, the future of what IoT promises to bring is dependent on machine learning to find the patterns, correlations, and anomalies that have the potential of enabling improvements in almost every area of our daily lives.
Story via Wired.
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