The use of machine learning, both supervised and unsupervised, to facilitate pattern recognition in artificial intelligence programs can be extremely useful for many tasks. Statistical data mining, for example, involves highly complex calculations. AI programs accomplish these calculations more easily, but in order to perform the necessary functions, the program has to learn to process the data through the use of machine learning techniques.
Machine Learning
Machine learning is a process used to “teach” an artificially intelligent computer program how to perform a task. Programmers accomplish this process by using supervised learning, unsupervised learning, or a combination of the two.
- Supervised machine learning allows the computer program to learn how to accomplish a task by following examples provided by a learning algorithm. The learning algorithms provided in supervised learning may show examples of the process, the final product or both, as well as providing feedback during the training process, in order to show the AI program the approved method of completing a task.
- A computer program that learns by unsupervised learning may not have access to any guidance. The AI program that uses unsupervised learning will have no feedback during the training process, or directives to guide the program during completion. This forces the program to create a procedure for data classification and task completion that relies solely on efficiency.
Combining supervised learning algorithms with unsupervised learning allows a computer programmer to take advantages of the best parts of both types of learning. The programmer can use feedback and example data sets to guide the AI program, but also allow the program to teach itself the fastest way to process the information in many cases.
Statistical Data Mining and Pattern Recognition
Data mining uses pattern recognition, and often some variety of natural language processing, to find meaning within a sample of data. AI programs will find different meanings in a data set, depending on the needs of the user, and the training received.
Once the programmer prepares the AI program with supervised or unsupervised learning techniques, the program is able to sort and analyze raw data. After evaluating the data, the program can present the information to a business owner in more easily understood forms, such as charts and graphs.
Data Mining and Business Intelligence
Businesses use data mining to learn about sales trends, customer behavior, and other information. Studying the meaning behind numbers can give valuable information to business owners, but analyzing the numbers and statistics can be made easier through the use of pattern recognition in AI programs.
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