One of the most widely adopted of the seven patterns of AI is the Patterns and Anomalies pattern. Machine learning is particularly good at digesting large amounts of data very quickly and identifying patterns or finding anomalies or outliers in that data. The “pattern-matching pattern” is one of those applications of AI that itself seems to repeat often, and for good reason as it has broad applicability.
The goal of the Patterns and Anomalies pattern of AI is to use machine learning and other cognitive approaches to learn patterns in the data and discover higher order connections between that data. The objective is to determine whether a given data point fits an existing pattern or if it is an outlier or anomaly, and as a result find what fits with existing data and what doesn’t. As one of the more widely used patterns, there are many ways in which this pattern is applied.
Digging deeper into your data
Data is at the heart of AI so it’s no surprise that computers excel at recognizing patterns in data. Whether it’s patterns of behavior, actions, input, or other patterns, AI systems are able to quickly spot it. Using artificial intelligence to spot patterns is ideal because humans, by nature, are unpredictable. AI is able to detect patterns that humans may not have even thought to look for. Also, artificial intelligence is able to pay attention to a lot more information at one time as opposed to the limited amount of data that humans can process and analyse.
Machine learning is all about using data and learning from it. Most of this learning comes from determining patterns inherent in the data. Rather than creating a program to tell a computer what to do with specific rules, machine learning allows a system to learn over time through examples and data. With programming, a human needs to set these rules. Therefore the system is limited by the number of possibilities programmed in. Machine learning on the other hand is not limited by such things.
There are many applications of AI in which you may want to use machines to spot patterns, or find anomalies and outliers in data. One widely implemented example of pattern or anomaly identification using AI is fraud detection. Fraud is simply defined as someone doing something they shouldn’t be doing. To find fraud an AI can look for actions that are not following the pattern of what they should be doing. If these actions look out of the ordinary the system can flag it for human review.