Machine learning is everywhere: across industries and affecting many aspects of daily living.
You may not realize it, but you probably come across machine learning just about everywhere—in retail, health care, public transportation and more—and it’s only going to increase in ways that we can’t even imagine.
While you probably had conversations with chatbots on websites or calls with customer service—there are many other types of machine learning applications that are silently working behind the scenes, including those that help organizations predict patterns and behaviors.
These apps are able to sift through huge amounts of data and make sense of it—once they have been “taught” what to look for. This has created demand for data scientists to build algorithms that can find patterns to predict outcomes and behaviors.
Now, with automated platforms, such as Microsoft Azure or Amazon Web Services (AWS) and machine learning libraries, it is easier and faster than ever for data scientists to develop these algorithmic models. And that not only means that you’ll get quicker customer service, but that you’ll also benefit from better, higher quality goods and services.
I’m seeing new applications for machine learning springing up in a variety of industries, and the possibilities are nearly endless. Here are a few examples of apps that are currently in use as well as some that will be available in the near future:
Defense: Working with satellite imagery, machine learning apps can sift through historical data on improvised explosive device (IED) explosions to help the military predict where these bombs might be located.
Public transportation: Using satellite imagery, imaging processing technology and historical data, data scientists can help machine learning software identify buses on a satellite map, as well as create an algorithm enabling it to predict where buses may be at any given time of day. To enable the software to “see” the buses with image processing, the data scientists use deep learning and neural networks, which are algorithms that mimic the way the brain works. By predicting these transportation patterns, the software enables transportation authorities to save money, while helping commuters save time.
Manufacturing: Using historical data, data scientists can build algorithms to predict potential product defects. Since these defects could be based on a number or combination of variables, such as room temperature when and where the parts were manufactured, etc., the more data the software has the better, to help identify the cause of the defects and prevent future problems.
Health care: Health care providers can predict different types of outcomes using machine learning, such as the likelihood of patients being admitted or readmitted following a hospital stay. These apps are taught to sift through the data and look for patterns based on several variables, such as patients’ medical conditions, when and how often they have refilled their medicine, and the frequency of follow-up doctors’ visits.
Security: Machine learning apps can help eliminate false alarms at airports, stadiums and other venues, and spot things human screeners might miss. This can not only speed up the security process significantly, but also improve safety.
Financial services: New algorithms are enabling financial institutions to predict when outstanding loans are going to become delinquent. Similarly, AI-enriched chatbots are providing personalized customer advice to increase loyalty and satisfaction.
Smart cars: In the future, machine learning will enable cars to learn about their drivers’ needs and their environment. The software could automatically adjust things, like internal temperature, music, and seat position, based on an understanding of specific drivers, and offer real time advice about traffic and road conditions—all while in self-driving mode.
Whether it’s being used today to recognize images or identify patterns from massive volumes of data, the ability of machine learning to predict behaviors and outcomes is having a major impact on many industries and touching many areas of daily life—impacting your safety, health, buying patterns, and the way you work. But it still is such a young field, offering greenfield possibilities that are only now being imagined. Stay tuned. The best is likely yet to come.
This article was written by Carlos Melendez from InfoWorld and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to firstname.lastname@example.org.
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