Real-World Examples of Machine Learning Applications

In a previous post "What Is Machine Learning?" I discuss how machine learning developed as a way to overcome certain limitations in the early days of artificial intelligence. Without machine learning, machines would be able to do only what they were told or were programmed to do. Machine learning expands their capabilities beyond what they were merely programmed to do.

As shown below, machine learning has real-world applications across a wide variety of fields ranging from data security and software development to investing and healthcare.

Practical Applications of ML

One of the best ways to understand machine learning is to look at the various applications of machine learning in the real world:

Data security: Malware (viruses, worms, etc.) is constantly evolving to avoid detection, but changes to malware code typically constitute only about two to ten percent of code; the rest of the code remains unchanged. With machine learning, security software can identify patterns in the code and distinguish what has changed from what hasn't. This enables the software to identify new versions of malware. Machine learning is also useful for detecting early warning signs of infection from unknown malware, such as an unexplained drop in available system resources.

Investing: Machine learning algorithms drive about 70 percent of all trading volume on the U.S. stock exchanges. With machine learning, computers can process vast amounts of financial data and quickly analyze stocks, bonds, trading trends, and other information to identify which investments have the greatest potential for positive returns. Computers are also capable of executing trades faster than humanly possible, which may provide investors with another advantage.

Online software development: Software developers can use machine learning to create software that automatically adapts to user behaviors. For example, as someone who plays an online game becomes more skilled, the game can make itself more challenging. Developers can also use machine learning to identify ideas for new features and new ways to monetize the software.

Healthcare: It is highly unlikely that machines will replace doctors anytime soon, but machine learning has become a valuable tool in the healthcare field. Machine learning can identify patterns in medical images or symptoms to improve the accuracy of diagnoses and treatments. Machines may also be better at reviewing the medications a patient is taking and alerting the patient or pharmacist of possible drug interactions.

Personalized marketing: Companies have been using machine learning for some time to market their products and services to consumers. For example, Google and Amazon keep track of your search and purchase history in order to make targeted product recommendations. Netflix and Spotify use machine learning to recommend movies and music based on your viewing or listening history.

Fraud detection and prevention: Credit card companies keep track of where cardholders use their cards, what they buy, the average transaction amount, and more. These companies then use machine learning algorithms to identify any transactions that break the cardholder's usage patterns. Any suspicious activity triggers a fraud alert and possibly an automatic suspension of the account. The cardholder may then be required to call the credit card company to have the suspension lifted.

Online searches: Google, Bing, Yahoo!, and other search engines use machine learning to rank items in their search results, which is why search results typically differ based on several factors, including your browser's search history, your current geographical location, and the relevance of various websites to the search word or phrase. If you use your smartphone to search for "grocery store," for example, you're likely to be presented a list of grocery stores in your general vicinity.

Smart devices: Smart devices collect data regarding their usage, then personalize their operation based on those patterns. For example, a smart home may learn that whenever you unlock the front door at a certain time in the evening, it means you have returned home from work. The smart lock then signals the smart thermostat to adjust the temperature accordingly. Smart devices may even use facial recognition technology and security cameras to identify a home's residents and then warn the homeowner (or notify police) if someone other than a resident approaches or enters the home at certain times.

Self-driving cars: Self-driving cars have made the transition from science fiction to the real world. By combining machine learning, video, GPS, robotics, and a host of other technologies, cars can now drive themselves, although some mishaps have occurred.

These are only a few of the vast number of machine learning applications that are possible. As machine learning matures, you are likely to see many more real-world applications and consumer products and services driven by machine learning.

Democratizing data involves making it available to personnel throughout an organization and providing them with the tools and training needed to query and analyze that data. In this post, I discuss the potential benefits and drawbacks of data democratization and provide some general guidance for democratizing data.

Benefits of Data Democratization

Distributing data and business intelligence throughout an organization delivers the following benefits:

Potential Drawbacks of Data Democratization

Nearly every organization that democratizes its data properly reports that the benefits of doing so far outweigh any potential drawbacks. However, organizations do need to address the following concerns:

Drivers of Democratization

Traditionally, the IT department has owned the data and was in charge of extracting meaning from it and presenting the information to executives and managers. The development of various technologies, tools, and techniques is driving the movement toward greater democratization of data:

Democratizing Data in Your Organization

Democratizing data is not a simple matter of providing everyone in the organization unfettered access to all of the organization’s data, especially if the organization stores sensitive data. To democratize data safely and effectively, consider the following guidelines:

If your organization currently places the power of its data in the hands of a few, I hope this article encourages you to strongly consider the possibility of democratizing your organization’s data. By placing the power of data and analytics in the hands of the many, you’re likely to be surprised by the resulting increase in innovation and agility. Your organization will be much better equipped to adapt in a competitive landscape that’s constantly changing.