Machine learning (ML) and big data have become buzzwords lately. The concept that an algorithm can take in massive amounts of data, understand it, put it into groups, and detect patterns is a game-changer for many industries because predictive algorithms will be able to anticipate behavior. The speed and accuracy that it can complete procedures is unlike the capabilities of any human. This creates a whole new world of opportunity form healthcare to retail. When machine learning is combined with computer vision and a way to have human-machine interaction, the only limit is our imagination. Most of us don’t even think about how much machine learning is already impacting our daily lives.
There are many ways that algorithms take the heavy lifting off us humans when it comes to electronic mail. In addition to filters that can sort through incoming messages, ML seeks out other signals hidden such as within metadata and patterns of speech in the text body. Gmail is an example of ML that now boasts a 99.9% spam filter. In addition, Gmail’s algorithm knows to systematically sort emails into folders, such as promotional, social, primary. In recent years, there are now optional features such as smart replies that save time by letting the sender choose from a selection of appropriate phrases. In addition, the AI learns and is able to adapt and customize suggestions based on what the user chooses over time.
AI has made real-time navigation the only way to travel. While it is true that existing driving apps use data and the Internet of Things to communicate travel disruptions and alternate route recommendations, that is relatively minor compared to ML and the way it will be able to anticipate and predict upcoming occurrences at any point along the journey. Other drivers who share their own real-time experiences work in harmony with machines as the information is shared across the network.
The amount of daily transactions in the financial world is so massive that it would be impossible for a human to monitor it, especially in the case of fraud protection. To assist, AI-based systems are taught to recognize which types of actions are fraudulent by looking at the size and frequency of transactions, as well as the type of merchant.