Читать книгу Artificial intelligence elements application in applied problems solving. Textbook - Vadim Shmal - Страница 4

The path from a specific problem to an AI solution is called the «machine learning» process. Machine learning methods are a combination of an algorithm with a set of parameters and data, as well as a set of parameters and a set of data Examples of machine learning algorithms include machine learning in the form of neural networks that can identify patterns in the real world and classification systems that can identify different objects in a given set of images.

One of the important features of AI is that the quality of predictions can be improved by changing the parameters (called «features») and the data set (in the case of classification algorithms). For example, in the case of classification algorithms, if the dataset is based on the identification of different colors, then when the dataset changes, the predictions will change and can better predict the colors. This feature of machine learning plays a key role in understanding the accuracy of AI algorithms.

AI is a dynamic and rapidly evolving area of research with a wide range of different applications. There are several interpretations of AI. AI is not a single technology, but a whole range of technologies, in particular, machine learning, artificial neural networks, large-scale distributed systems, and so on. In particular, machine learning and deep learning are two different terms used in different disciplines. Machine learning is a method of applying machine learning algorithms to a machine that requires any kind of input, such as a car that will drive itself.