Simply put, machine learning uses computer algorithms to detect patterns in large data sets and predict outcomes.
In some machine learning applications, computers are initially programmed to learn how to solve problems, but can change and improve algorithms on their own—making faster, more accurate predictions. It essentially helps to find the needle in a haystack of data, taking in large quantities of complex data and identifying patterns to provide reliable, effective and repeatable results.
For example, according to the Google Research Blog, the company introduced machine learning to Google Maps, improving the usability of the service. The algorithms help the app extract street names and house numbers from photos taken by Google’s “Street View” cars and increase the accuracy of search results. With over 80 billion high-resolution photos collected by Street View cars, analyzing these images manually would have been impossible; instead, Google’s finely-tuned machine learning algorithms automatically extract information from geo-located images.
While machine learning and artificial intelligence (AI) are sometimes used interchangeably, they are different.
Machine learning is the technique that has most successfully made its way out of labs into the real world, while AI is a broad field covering areas such as robotics and natural language processing. Machine learning is also commonly mistaken as simply using averages or statistics. Instead, it entails a complex process of understanding and preparing the data that is analyzed, developing algorithms that produce valuable predictions and outcomes, and testing and refining the algorithms to ensure accuracy.
