Читать книгу Artificial intelligence elements application in applied problems solving. Textbook - Vadim Shmal - Страница 5

AI is commonly used to describe technology that uses information processing and information management principles such as computing, storing, routing, and processing input signals or information to make intelligent predictions or decisions – this is called artificial intelligence. AI has different definitions based on different fields of study and different applications.

AI systems can be intelligent in three different ways:

1. Learning: AI systems can learn to recognize patterns in the real world and classify them. For example, artificial intelligence systems can recognize patterns in images and classify them according to their features.

2. Intelligence: AI systems can be intelligent if they understand the processes involved in decision making or in the interaction between a human and an intelligent system.

3. Reasoning: AI systems can also reason using various inputs – for example, AI systems can understand rules that make inferences. For example, AI systems can understand how a person learns based on certain logic and analyze that logic to predict the best learning strategy.

Advanced machine learning techniques will be used to improve AI systems and make better decisions. For example, AI systems can learn logical structure through concepts like perception, decision, action, etc. They can then start learning to act on logic. In fact, AI systems can learn both from a set of real data and from rules that have been established by reinforcing previous decisions – this is called machine learning.

This process takes place on a large scale in computers. For example, it is possible to predict a person’s behavior based on their observed behavior and their predicted behavior. In another sense, machine learning is often referred to as the process of combining past events with data from the current scenario and predicting the future of the current situation. From this point of view, machine learning is a task that is performed in the current situation.

On the other hand, in terms of vision, AI systems can make decisions. AI systems can determine the correct answers based on various inputs and understand the reasons for a decision made by the system. In this context, AI systems basically learn to behave based on their experiences.

The term AI is widely known, but many people do not understand the concept and various applications of AI. The reason people get confused about AI is because AI is defined based on different areas of research and AI is used in different applications – and they are also called different technologies.

Some AI applications are as simple as using a machine learning algorithm to classify images. In another sense, it may also be the process of discovering new patterns in data and making decisions based on those patterns. For example, a computer may make decisions based on images that are classified into such categories.

There are two approaches that can be used to determine the quality of an AI system. One approach is a general approach and does not necessarily make an AI system a great solution. The second approach is called the concrete approach and aims to make the AI system a great solution. In a general approach, the goal is to have AI systems that can handle limited tasks. A specific approach is designed to solve one specific problem.

Each approach has its strengths and weaknesses. For example, a specific approach is better suited for making decisions based on specific requirements. For example, it is better to perform a specific task. The general approach is usually very effective for decision making, but not always effective for solving a specific problem. For example, a general approach can be effective for improving an existing model.