Artificial Intelligence: Current Issues and Applications

Artificial Intelligence: Current Issues and Applications

Kijpokin Kasemsap (Suan Sunandha Rajabhat University, Thailand)
DOI: 10.4018/978-1-5225-2440-3.ch022

Abstract

This chapter explains the Artificial Intelligence (AI) techniques in terms of Artificial Neural Networks (ANNs), fuzzy logic, expert systems, machine learning, Genetic Programming (GP), Evolutionary Polynomial Regression (EPR), and Support Vector Machine (SVM); the AI applications in modern education; the AI applications in software engineering development; the AI applications in Content-Based Image Retrieval (CBIR); and the multifaceted applications of AI in the digital age. AI is a branch of science which deals with helping machines find the suitable solutions to complex problems in a more human-like manner. AI technologies bring more complex data-analysis features to the existing applications in various industries and greatly contribute to management's organization, planning, and controlling operations, and will continue to do so with more frequency as programs are refined.
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Background

The beginning of modern AI can be traced to the classical philosophers' attempts to describe human thinking as a symbolic system. But the field of AI was not formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term “artificial intelligence” was coined. The organizers included John McCarthy, Marvin Minsky, Claude Shannon, Nathaniel Rochester, all of whom went on to greatly contribute to the field. In the years following the Dartmouth Conference, impressive advances were made in AI. Machines were built that could solve school mathematics problems, and a program called Eliza became the world's first chatbot, occasionally fooling users into thinking that it was conscious.

The first “AI winter” lasted from 1974 until around 1980. It was followed in the 1980s by another boom, thanks to the advent of expert systems, and the Japanese fifth generation computer initiative, which adopted massively parallel programming. Expert systems limit themselves to solving narrowly defined problems from single domains of expertise (for instance, litigation) using vast databases. They avoid the messy complications of everyday life, and do not tackle the perennial problem of trying to inculcate common sense. The funding dried up again in the late 1980s because the difficulties of the tasks being addressed was once again underestimated, and also because desktop computers overtook mainframes in speed and power, rendering very expensive legacy machines redundant.

AI has crossed the threshold for the simple reason that it works. AI has provided effective services that make a huge difference in people's lives, toward enabling companies to make a lot of money. A central goal of AI is the design of automated systems that can accomplish a task despite uncertainty (Poupart, 2012). Such systems can be viewed as taking inputs from the environment and producing outputs toward the realization of some goals (Poupart, 2012). Modern intelligent agents approaches should combine methodologies, techniques, and architectures from many areas of computer science, cognitive science, operation research, and cybernetics (Marinagi, Panayiotopoulos, & Spyropoulos, 2005). AI planning is an essential function of intelligence that is necessary in intelligent agents applications (Marinagi et al., 2005).

Key Terms in this Chapter

Machine Learning: The artificial intelligence discipline geared toward the technological development of human knowledge.

Artificial Intelligence: The area of computer science that emphasizes the creation of intelligent machines that work and react like humans.

Expert System: The computer program that is designed to imitate human intelligence, skills, and behavior.

Optimization: The act of making something as good as possible.

Artificial Neural Network: The computational model based on the structure and function of biological neural networks.

Evolutionary Polynomial Regression: The evolutionary data-driven modeling technique for the identification and construction of models.

Support Vector Machine: The supervised machine learning algorithm, which can be used for the classification and regression methods.

Fuzzy Logic: The system of theories utilized in mathematics, computing, and philosophy to deal with the statements that are neither true nor false.

Data Mining: The process of analyzing the hidden patterns of data according to the different perspectives for the categorization into the useful information.

Genetic Programming: The model of programming, which utilizes the ideas of biological evolution to handle the complex problem.

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