Shulou(Shulou.com)05/31 Report--

Among the seven modes of artificial intelligence, one of the most widely used modes is "patterns and exceptions". Machine learning is particularly good at quickly digesting large amounts of data, identifying patterns in data, or finding anomalies. "pattern matching patterns" is one of the applications that AI itself seems to repeat frequently, for good reason, because of its wide applicability.

The goal of AI's patterns and abnormal patterns is to use cognitive methods such as machine learning to learn patterns in data and discover high-level connections between data. The goal is to determine whether a given data point is suitable for the existing schema, or whether it is an outlier or an outlier, so as to find out what is suitable for existing data and what is not suitable for existing data. As one of the more widely used patterns, there are many ways to apply this pattern.

Delve into your data.

Data is at the heart of AI, so it's not surprising that computers are good at recognizing data patterns. Artificial intelligence system can quickly find out whether it is behavior mode, action mode, input mode or other mode. Discovering patterns using artificial intelligence is ideal because humans are inherently unpredictable. Artificial intelligence can detect patterns that humans are not looking for at all. In addition, compared with the limited amount of data that human beings can process and analyze, artificial intelligence can pay attention to more information at one time.

Machine learning means using data and learning from it. Most of this learning comes from identifying patterns inherent in the data. Instead of creating programs to tell computers how to deal with specific rules, machine learning is about learning the system step by step through examples and data. Through programming, humans need to set these rules. Therefore, the system is limited by the possibility of programming. On the other hand, machine learning is not restricted by these things.

In many applications of artificial intelligence, you may want to use machines to discover patterns or to discover anomalies and outliers in data. An example of a widespread implementation of pattern or anomaly recognition using artificial intelligence is fraud detection. The simple definition of fraud is that someone has done something he should not have done. To detect fraud, artificial intelligence can look for behaviors that do not follow the actions that should be taken. If these operations are different, the system can mark them for manual inspection.

Another example of this pattern is the pattern that many people use every day, but they may not even know they are using AI. When we use predictive typing on computers or smartphones, it will be supported by artificial intelligence mode. Look at the writing mode of the computer and predict the words that may appear next. Over time, typing patterns may become very personalized so that the model can quite accurately understand what you are going to type next.

Personnel departments also use artificial intelligence to identify job seekers' patterns. The artificial intelligence system can check the application and background of potential employees to identify potential excellent candidates and reject candidates who do not meet the job requirements. By using artificial intelligence to help the selection process, it is hoped that it will help screen candidates, get them to the next round, and reduce bias in the recruitment process.

There are many other ways to see the patterns and exception patterns of AI in action. Intelligent monitoring, finding errors or errors and adjusting them as needed, cyber security applications and stock market analysis are all examples of ways to use the AI monitoring mode.

When the system searches for patterns on its own, it can find things that humans may miss. An example of this is Wal-Mart's buying behavior before and after the hurricane. Wal-Mart is used to test the sales model. One of the many trends they found was the link between hurricanes and the popularity of strawberries. In fact, people went to Wal-Mart before the hurricane and accumulated strawberry pie in addition to all the regular items such as water and batteries. This insight enabled Wal-Mart to deliver more Pop-Tart trucks to stores on the hurricane route. This abnormal trend is sometimes hard to spot, but computers are good at it.

But, like anything you learn from the data, you need to pay attention to the training content of AI. Amazon was censored a few years ago after it found that its artificial intelligence recruitment tool was biased towards technicians. Artificial intelligence patterns and abnormal patterns, such as recognition and hyperpersonalization patterns, are particularly vulnerable to biased data sets. If you use deviation data to train pattern recognition systems, it is not surprising that these systems will show the same deviations as the training data.

By thinking about AI projects in accordance with various AI patterns, you can better handle, plan, and execute AI projects. For example, once you know that you are implementing patterns and exception patterns, you can gain a deep understanding of the various solutions that have been applied to the problem and provide insights into the data patterns, use cases and examples, algorithm and model development skills required by the pattern, as well as other insights that can help accelerate the delivery of high-quality AI projects. These models help to guide the organization to implement AI correctly, and the project is more likely to succeed.

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Tag: AI.

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