Machine learning is a subset of artificial intelligence that can be found almost anywhere. Believe it or not, even coffee roasters are beginning to employ machine learning algorithms to better understand when coffee lovers will crave their next cup of joe. The ability to innovate is one of the most highly prized qualities in today’s tech-driven world, which has led to a stunning range of machine learning applications across multiple industries. Here are the top five real-world examples.
Considered one of the commonly used machine learning applications, image recognition has made massive strides over the years. It’s all about an algorithm being able to make sense of the objects in front of them based on what it “sees,” and then categorizing the images. To put it simply, you can present a machine learning model with an assortment of images that have already been correctly identified—images of dogs, for example—and then have the model try to identify new images by itself. Over time, after being exposed to numerous images of dogs, the model will learn to extract the necessary information to categorize new images—and hopefully not confuse you with poor Fido.
Social media is perhaps the easiest example to understand how machine learning is used to recognize images in the real world. If you upload an image of yourself, your family, and some friends cooking out at the neighborhood pool to Facebook, the social media giant is able to distinguish and recognize objects—such as food, grill, people, and pool—before you even enter a description. The ability of an algorithm to distinguish between objects from this one photo, after training on billions of images in a dataset, isn’t just an impressive toy. Let’s say you have a family member in this photo at the pool who’s visually impaired. By combining machine learning, image recognition, and screen-reader technology, Facebook enables the visually impaired to hear who and what is in the photo.
Accurate Medical Diagnoses
Artificial intelligence and machine learning play a critical role in healthcare. For everything from handling scores of patient data to developing new medical procedures, you can bet that the healthcare field is making use of these technologies to revolutionize modern medicine. In particular, machine learning algorithms have proved useful in aiding radiologists to reduce false alarms when analyzing mammograms to detect breast cancer.
In early 2019, Stanford researchers developed and tested a machine learning model that could help radiologists reduce false positives without increasing the number of missed cancers. The model trained on over 112,000 mammogram cases collected from two teaching hospitals. The researchers then compared the radiologists’ performance to that of the machine learning model and found significant variability. Specifically, the machine learning model reported 3,612 fewer false positives than the radiologists. This showed that machine learning models can significantly reduce the report of false positives from mammograms.
Consumer Demand Forecasting
Employing machine learning algorithms behind the scenes gives retailers a competitive advantage from reduced costs and improved relationships with customers. Retailers must also be ready to handle unexpected crises, such as a hurricane or a pandemic, and keep plentiful inventory when customers need to stock up. Machine learning algorithms that specialize in demand forecasting can be used to predict consumer demand in a time of crisis like the COVID-19 outbreak.
Crisp, a food supply chain company, recently announced a new data modeling tool that “Leverages real-time European consumer food-buying data to help U.S. grocers and manufacturers better predict American consumer behaviors during the pandemic.” Since European countries got hit by coronavirus at a larger scale a few weeks before the U.S., there was ample data to use in training the machine learning model. With these forecasts on hand, grocers and food suppliers can predict consumer demand for particular products so they know what to keep an extra stock of throughout the pandemic.
You knew this one was coming. Optimistically, we can expect autonomous vehicles to be safe and reliable within the next 10 years, as car manufacturers and tech giants race to make them commercially available. Companies have explored various machine learning algorithms, which need to be retrained and recalibrated often as vehicles collect new data on the roads, especially when traversing new areas. And, of course, drivers need to be able to trust that their self-driving cars will make the right decisions when they encounter even the craziest situations.
Waymo, a subsidiary of Alphabet that develops autonomous vehicles, outfits its vehicles with high-resolution perimeter cameras, sensors, and software that can detect all kinds of objects within 300 meters. Equipped with Light Detection and Ranging (LiDAR), RADAR-based object detection, and sophisticated machine learning algorithms, Waymo’s technology can get a 360-degree view of the vehicle’s surroundings. The perimeter cameras feed Waymo’s machine learning algorithms data in order to reliably identify objects and assess the traffic scene. In combination with other advanced technologies, machine learning ensures a safer driving experience for everyone.
Fraud is considered both an art and a crime. For our purposes, we want to be able to detect and prevent fraud from happening. A study by LexisNexis Risk Solutions in 2019 found that for every dollar of fraud, financial services companies incur $3.13 in costs, and that number keeps rising. It’s no surprise that fraud can destroy a business, but thankfully, machine learning models can help them detect fraudulent transactions. Best of all, financial service companies don’t need a data science team to do this.
In one case, a fintech startup in Mexico was struggling with large amounts of fraud. Of the 24,000 transactions it processes per year, approximately 20% were fraudulent. The amount of loss due to fraud was so great that the company estimated it had only two years to fix the problem or risk going out of business. The startup used SparkCognition’s DarwinTM platform to harness the power of automated machine learning models and tackle this problem. Able to process 500 transactions in less than a second, the model trained on historical data from 20,000 normal transactions and 3,500 fraudulent transactions. In the end, it was able to detect fraudulent transactions with 90% accuracy. The model flags about 4,320 fraudulent transactions each year, meaning it saves the startup $457,214 per year, an ROI of 4.5 times in the first year and 10 times in the second year.
Machine learning is everywhere, and for good reason. Companies across all industries around the globe are finding a need to implement machine learning into their day-to-day operations—both for competitive advantage, and because its limitless potential has the ability to transform the way we live, work, and tackle complex problems.