Machine learning is being used in a wide range of applications today. One of the most well-known examples is Facebook’s News Feed. The News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling to read or like a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly?

Machine learning is also entering an array of enterprise applications. Customer relationship management (CRM) systems use learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses. Business intelligence (BI) and analytics vendors use machine learning in their software to help users automatically identify potentially important data points. Human resource (HR) systems use learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions.

Machine learning also plays an important role in self-driving cars. Deep learning neural networks are used to identify objects and determine optimal actions for safely steering a vehicle down the road.