Machine Learning Examples + Applications
The financial services industry is championing machine learning for its unique ability to speed up processes with a high rate of accuracy and success. The technology is being employed in virtually every aspect of our financial systems. What has taken humans hours, days or even weeks to accomplish can now be executed in minutes. American Express handles over $1 trillion in transactions from more than 110 million of their credit cards each year. The company relies on machine learning to manage their data, discover spending trends and offer customers individualized offers.
Additionally, machine learning is used by lending and credit card companies to manage and predict risk. These computer programs take into account a loan seeker’s past credit history, along with thousands of other data points like cell phone and rent payments, to deem the risk a lending company is taking on by offering the individual a loan. By taking other data points into account, lenders are able to offer loans to a much wider array of individuals who couldn’t get loans with traditional methods.
Trading firms are using machine learning to amass a huge lake of data and determine the optimal price points for which to execute trades. These complex high-frequency trading algorithms take thousands, if not millions, of financial data points into account to buy and sell shares at the right moment.
The healthcare industry is championing machine learning as a tool to manage medical information, discover new treatments and even detect and predict disease. Medical professionals, equipped with machine learning computer systems, have the ability to easily view patient medical records without having to dig through files or have chains-of-communication with other areas of the hospital. Updated medical systems can now pull up pertinent health information on each patient in the blink-of-an-eye.
AI and machine learning are predicted to save the healthcare industry around $150 billion annually because of the time and resources they save in drug development. Machine learning-enabled AI tools are working alongside drug developers to generate drug treatments at faster rates than ever before. Essentially, these machine learning tools are fed millions of data points, and they configure them in ways that help researchers view what compounds are successful and what aren’t. Instead of spending millions of human hours on each trial, machine learning technologies can produce successful drug compounds in weeks to months.
Machine learning has made disease detection and prediction much more accurate and swift. Right now, machine learning is being employed by radiology and pathology departments all over the world to analyze CT and X-RAY scans and find disease. After being fed thousands of images of disease through a mixture of supervised, unsupervised or semi-supervised algorithms, some machine learning systems are so advanced that they can catch and diagnose diseases (like cancer or viruses) at higher rates than humans. Machine learning has also been used to predict deadly viruses, like Ebola and Malaria, and is used by the CDC to track instances of the flu virus every year.
Machine learning is being employed by social media companies for two main reasons: to create a sense of community and to weed out bad actors and malicious information. Machine learning fosters the former by looking at pages, tweets, topics, etc. that an individual likes and suggesting other topics or community pages based on those likes. It’s essentially using your preferences as a way to power a social media recommendation engine.
The massive spread of “fake news” in the 2016 election prompted social media companies, like Facebook and Twitter, to put machine learning at the forefront of their systems. Machines are simply faster than humans at identifying false news and deleting it before it ever becomes a problem. Both Twitter and Facebook are using upgraded computer systems to quickly identify harmful patterns of false information, flag malicious bots, view reported content and delete when necessary in order to build online communities based on truth.
Retail and E-commerce
The retail industry is quickly relying on machine learning for its ability to optimize sales and gather data on individualized shopping preferences. Machine learning offers retailers and online stores the ability to make purchase suggestions based on a user’s clicks, likes, past purchases, etc. Once customers feel like retailers understand their needs, they are less likely to stray away from that company and will purchase more items. This use of machine learning boosts customer satisfaction, while maximizing profits for retailers.
Visual search is becoming a huge part of the shopping experience. Instead of typing in queries, customers can now upload an image to show the computer exactly what they’re looking for. Machine learning will analyze the image (using layering) and will produce search results based on its findings. For instance, you may upload a photo of a red sweater you found on Instagram. From there, the machine learning-based system will pull up that exact sweater and then other suggestions based on the same look or maybe varying colors of the exact sweater- all within milliseconds.
Machine learning has also been an asset in predicting customer trends and behaviors. These machines will look holistically and at individual purchases to determine what types of items are selling (and what items will be selling in the future). For example, maybe a new food has been deemed a “super food”. A grocery store’s systems might identify increased purchases or that product and could send customers coupons for all variations of that item. Additionally, a system could look at individual purchases to send you future coupons. Maybe, for example, you’ve been browsing newborn baby clothes. A retailer’s machine learning systems will identify that you are pregnant or a new parent and offer you items that it thinks would be helpful to your new child.