Artificial intelligence elements application in applied problems solving. Textbook

Artificial intelligence elements application in applied problems solving. Textbook
Sergey Pavlov

Pavel Minakov

Vadim Shmal

Sergey Pavlov, master Plekhanov Russian University of Economics.Vadim Shmal, Ph. D., associate professor Russian University of Transport (MIIT).Pavel Minakov, Ph. D., associate professor Russian University of Transport (MIIT).

Artificial intelligence elements application in applied problems solving

Textbook

Sergey Pavlov

Vadim Shmal

Pavel Minakov

© Sergey Pavlov, 2022

© Vadim Shmal, 2022

© Pavel Minakov, 2022

ISBN 978-5-0059-3991-3

Created with Ridero smart publishing system

Introduction

Various subfields of AI research are centered around specific goals and the use of specific tools. Traditional AI research goals include reasoning, knowledge representation, planning, learning, natural language processing, perception. General intelligence (the ability to solve arbitrary tasks) is one of the long-term goals in this area. To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics. AI also draws on computer science, psychology, linguistics, philosophy, and many other fields. There is no single AI system that solves all problems or solves them effectively.

A key advantage of AI is its ability to solve problems in the real world. But, there are also many potential problems. An important task in the field of AI is to determine which of the possible problems are most likely to be solved with the help of AI, and which require different methods. Some of the main areas that contribute to solving complex AI problems are theory, engineering, and mathematics. While most AI researchers believe that AI will play an important role in future economic and technological development, there are many skeptics. Their skepticism includes concerns about the possible misuse of AI, concerns about its negative impact, and uncertainty about AI’s ability to solve real problems. However, this dispute is not the only one in this area. Many AI researchers believe that it is impossible to predict which tasks will be solved by AI in the future. The reasons for this are that while there are many important problems to be solved in the real world, there is no single mechanism or technology that solves them all.

What is AI?

At a high level, AI is the concept of computing systems that work with greater and greater complexity to understand, predict, and solve problems in the real world. This definition of AI is a definition of intelligence and is not limited to computer systems.

AI is a field of research that focuses on creating intelligent machines, devices, systems, algorithms, and so on. Computers are at the heart of AI, and an intelligent machine is designed to be able to efficiently solve problems in the real world.

To solve such problems, you can use many different algorithms and intelligent systems. A machine can be intelligent if it can perform intelligent tasks – this concept is different from an AI system, which has a certain set of rules, including the ability to learn, learn to perform intelligent tasks, and also have a long-term memory. All kinds of algorithms can be used to solve intellectual problems – learn how to behave, detect patterns and distinguish the real world from its simulations.

AI researchers believe that all intelligent systems can be improved by improving their ability to perform intelligent tasks – this is called algorithmic intelligence, or the ability of a machine to learn. However, there is some controversy in this area over the definition of intelligent machines, and the robustness and reliability of existing methods for designing and improving intelligent systems.

AI evolution

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.

AI concept

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.

AI Applications and Capabilities

Artificial intelligence can be used to analyze information and make decisions based on data. Through these solutions, businesses can gain insights to help them make better decisions. This means that AI can provide feedback in a variety of ways, from simple ideas such as optimizing a marketing approach to complex systems such as a decision within the context of a decision. This will help the business optimize the solution and make it better, but also simpler.

As AI technology advances, new applications emerge. For example, artificial intelligence technologies can help improve healthcare – for example, to detect cancer in patients. On the other hand, AI can also help us solve business and technical problems by developing more efficient processes.

Machine learning algorithms, as they are more commonly known, can take data in the form of text, images, audio, video, or measurements, process it, and determine a set of rules. Based on the set of rules that the machine learns, it can make decisions and perform actions based on that decision. This allows AI technologies to improve systems, products, processes and information. AI applications are more commonly referred to as a class of applications, but they can be used for different purposes.

Simulation intelligence

The general problem of modeling (or creating) intelligence is divided into subtasks. They consist of certain traits or capabilities that researchers expect from an intelligent system. The traits described below have attracted the most attention in the past, although this list is far from exhaustive.

Design (construction) of intelligence. Imitation of intelligence. Show intelligence.

The first concerns the availability of intelligent systems capable of simulating the behavior observed in a wide range of situations and conditions. It is often assumed that artificial intelligence systems will be built to replicate many of the features displayed by real intelligence, with the intention of eventually showing that real intelligence is possible.

The demo part is dedicated to demonstrating real intelligence. This suggests that true intelligent systems exist.

We have specific examples of real intelligent systems with large datasets. Such systems run useful algorithms in real situations. Algorithms do not necessarily mimic the behavior observed in the real world; however, they were designed to achieve specific goals.

Applications of intelligence include the recognition of events and actions that are not explicitly defined by current human programming. This is a characteristic of artificial intelligence systems, which today is called predictive intelligence.