While 2019 has come and gone without artificial humans walking the streets, the first two decades of the 21st century have witnessed dramatic advancements in artificial intelligence (AI) research. Machine learning has proven to be one of the most successful and widespread applications of this technology, affecting a wide range of industries and impacting billions of users every day.
What is Machine Learning?
An application of AI research, machine learning uses algorithms and statistical models to teach a computer system how to perform a variety of tasks without any human interaction. Unlike traditional computer programming, which requires the programmer to strictly define decision logic, machine learning makes it possible for the system to adapt its behavior based on available data.
How Does Machine Learning Work?
At its core, machine learning operates by gathering and analyzing data about specific situations to make better predictions about which responses will produce a desired outcome. The program evaluates what has worked in the past and adjusts its behavior accordingly, allowing it to make better decisions in the future. This ability to adapt gives the impression that a program is “learning” in the same way a human might learn to complete a task through repetition.
AI vs Machine Learning Examples
While machine learning algorithms are an application of AI, not all artificial intelligence systems are considered examples of machine learning. In general, AI refers to a broad range of technology applications that allow computer systems to behave in “smart” ways, allowing them to adapt and respond dynamically to situations that approximate human behavior. Most machine learning examples use algorithms and rapid data analysis to improve performance, but this represents just one application (albeit an effective one) of the principles behind artificial intelligence concepts.
6 Machine Learning Examples and Use Cases for 2020
1. Customer Service Automation
Managing the growing number of online customer interactions has pushed many organizations to the breaking point. They simply don’t have the customer support staff on-hand to deal with the number of inquiries they’re receiving and the old solution of outsourcing issues to a call center is simply unacceptable for many of today’s customers. Advances in machine learning algorithms have made it possible for chatbots and other automated systems to fill these needs. By automating routine and low priority tasks, companies can free up employees to handle more high-level customer service. When implemented properly, machine learning in business can streamline issue resolution and ensure that customers can get the kind of helpful assistance that turns them into loyal brand advocates.
As networks become increasingly complex, cybersecurity experts have worked hard to respond to the ever-expanding scope of security threats. Rapidly changing malware and hacking techniques are difficult enough to counter, but the proliferation of Internet of Things (IoT) devices have fundamentally altered the cybersecurity landscape. Attacks can come from anywhere, at any time, and in any form. Fortunately, machine learning algorithms have allowed cybersecurity efforts to keep pace with these rapid changes. Predictive analytics make it possible to identify and mitigate threats faster than ever, and machine learning can track user behavior within a network to spot irregularities and gaps in existing security measures.
3. Object Recognition
While the technology to gather and read data has existed for quite some time, teaching computer systems to actually understand what they’re looking at has proved to be a deceptively complicated problem. Thanks to machine learning applications, more and more devices now feature object recognition capabilities. An autonomous vehicle, for instance, knows another car when it sees one, even if programmers didn’t provide it with an exact example of that car to use as a reference. Retail stores are even using this technology to help speed up the checkout process. Cameras detect the items customers place in their cart and can automatically charge their accounts when they leave the store.
4. Fraud Detection
The increasing number of financial transactions taking place online has raised consumer awareness about various forms of fraud. While they enjoy the convenience of being able to make purchases and payments online, they want to know that their financial data is being protected in the process. Credit card companies and banks have responded by turning to machine learning algorithms that can review vast amounts of transactional data to identify suspicious activity. While these sorts of checks are nothing new, machine learning in business has drastically expanded and accelerated the scope of these reviews. According to industry research, machine learning solutions can detect up to 95 percent of fraud and minimize investigation time by 70 percent.
Avoiding mistakes and misunderstandings is important in any kind of communication, but especially so for today’s businesses. Whether it’s email correspondence, customer reviews, video conferencing, or text-based documents in all their varied forms, simple grammatical errors, inappropriate tone, or inaccurate translations can cause a variety of problems. Machine learning programs have taken communication far beyond the heady days of Microsoft’s Clippy. Thanks to natural language processing, real-time language translation, and speech recognition, these machine learning examples have been able to help people communicate clearly and accurately. While many people like to complain about autocorrect features, they also appreciate being saved from embarrassing mistakes and inappropriate tone.
6. Digital Marketing
Much of today’s marketing initiatives are carried out online through a variety of digital platforms and software applications. As companies gather data about customers and their purchasing habits, marketing teams can use that information to create a complex picture of their target audience and identify which people are more likely to seek out their products and services. Machine learning algorithms help marketers to make sense of all that data, identifying key trends and features that allow them to segment opportunities more narrowly. The same technology enables digital marketing automation on a massive scale. Ad platforms can be set up to dynamically identify new potential customers and direct the appropriate marketing material to them in the right place at the right time.
As machine learning continues to advance, the range of applications and use cases will certainly expand in the 2020s. With the new decade just getting underway, it’s worth keeping an eye on how machine learning use cases will be deployed to improve efficiency, reduce costs, and deliver better user experiences.
About Alan Seal
Alan Seal is the VP of Engineering at vXchnge. Alan is responsible for managing teams in IT support and infrastructure, app development, QA, and ERP business systems.
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