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How New Technology Gathers Information to Predict Real-Life Outcomes

Modern Technology

Among the many benefits of artificial intelligence solutions is the ability to use a combination of historical data and current data to make predictions about future outcomes. Today, the vast majority of AI advances are made thanks to machine learning (ML), which is the application of computer algorithms that can improve themselves automatically by either studying data sets or simply through experience. Machine learning has even paved the way for deep learning, which seeks to mimic human thought processes via an artificial neural network.

The real-life applications of machine learning are too numerous to list, but some basic examples include how your streaming services can recommend new shows or movies based on your past viewing history, how machines can react to human play styles in games in order to mimic and overcome them, or how businesses can analyze past and real-time customer data to predict future customer needs. The use of statistics, modeling, and machine learning to predict future outcomes is referred to as predictive analytics. In order to understand how modern technology can use data to predict outcomes, it’s important to understand the basic types of machine learning and how they work.

Supervised Learning Models


Machine learning algorithms are essentially designed to either classify data or make predictions based on it. With supervised learning, a learning algorithm is trained using labeled data sets in order to learn how to make classifications or predictions accurately. The ML algorithm is shown a series of inputs along with the correct outputs, so it can understand how to perform its assigned task correctly. Among the many present-day applications for supervised learning is natural language processing. Computers can be taught to understand natural language when it’s separated into fragments, so the learning algorithm can study the meaning and context behind the words.

An early application of natural language was dictation. Of course, these days, there are digital assistants everywhere, whether they’re on your smartphone or embedded in smart home technology. Natural language processing allows these assistants to understand wake words and phrases (think “Hey, Google”), so they won’t react to every sound in the background.

Unsupervised Learning Models


With unsupervised learning, computer algorithms are instructed to analyze and cluster unlabeled data. Instead of being shown the correct inputs and outputs, the algorithm analyzes all the data to look for patterns and commonalities. This ability to uncover hidden patterns without human intervention makes unsupervised learning ideal for predictive analytics.

Another great application of unsupervised learning can be seen with intelligent security solutions, such as those offered by This technology is able to identify potential physical threats to your company by combing and analyzing content from open sources like social media networks, blogs, forums, and even the deep and dark web. Threatening posts can be identified based on commonalities, and you’ll be alerted each time a threat is uncovered.

Reinforcement Learning Models

Reinforcement is a behavioral ML model that functions similarly to supervised learning, except the algorithm doesn’t work with labeled data. Instead, the algorithm is fed inputs and attempts to find the correct outputs based on trial and error. Whenever a task is completed successfully, a “reward state” is triggered to reinforce that behavior. This style of training is meant to assist algorithms in making judgments when the outcomes aren’t guaranteed.

Reinforcement learning can be seen at work in autonomous vehicles. Driving requires a constant series of judgments in order to plan a safe route, react to objects on the road, maintain speed limits, and avoid collisions. In these cases, information is fed to the learning algorithms via cameras and sensors on the car.

Ultimately, new technology can gather data from a variety of sources, whether it’s fed directly to the algorithms, or they have to find it on their own. To make the best use of predictive analytics, it’s recommended that companies have all their data sources integrated, so real-time data can be constantly collected in a single source of truth.