Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

Sewit T. Yohannes, Simar Mansi, Sanaa Kaddoura
DOI: 10.4018/978-1-6684-6937-8.ch001
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Abstract

Artificial intelligence (AI) has been a topic of high interest in this day and age. AI has emerged through the early nineties and continues to grow at an unprecedented rate. The idea of having machines that are able to process certain cognition to come to a decision without the intervention of humans is the ultimate idea that is being pursued. Though the stage in which AI is able to completely outperform humans in its cognitive skills is yet to be achieved, there has been remarkable progress towards that area. This chapter aims to provide a brief introduction about AI and the area covered under the topic. Various algorithms are used in programming AI on machines such as evolutionary algorithms, genetic algorithms, and swarm intelligence. AI encompasses machine learning, which will be further discussed in this chapter. Furthermore, the impact of AI on society and futuristic predictions the chapter reviews.
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Background

Artificial intelligence can be defined as the way machines are programmed to mimic the intelligence of humans to perform tasks that require humans by being independent or partially dependent. Though there were many studies and research on the topic in the previous years, intelligent machines started to be taken seriously during the 1950s starting from the Bomb, a machine, that was able to break the enigma code. While the idea of machines having the ability to think seemed ideal, the Turing test introduced by Alan Turing started proving a point. The Turing test is conducted by two individuals and a machine where one of the individuals tries to guess if he is conversing with a machine or a human. The Turing test is held every year and will be continuing until the intelligence of such a machine becomes a replica of human intelligence. Though there are no machines that completely pass this test, this was one of the tests that convinced people of the possibility of AI and how close it is to human intelligence.

Afterward, in 1955 Professor John McCarthy named the technology “the science and engineering of making intelligent machines”. In the following year, 1956, he organized a research team at Dartmouth College and came up with the term Artificial Intelligence (Buchanan, 2005). In 1961 the Unimate robot became the first mechanical robot with a granted patent; it performed repetitive tasks that were performed by employees (Wallén, 2008). Then there was a major research and contribution in the area of algorithms the machines used which birthed the first chatbot named Eliza which was developed between 1964 and 1966 at MIT.

In 1966 the first electronic person was launched named Shakey stirred up predictions of super-powerful AI in the following 8 years (Wu, Liu & Wu, 2018). The robot was a general-purpose mobile robot with multiple sensors and cameras, and it was designed to make decisions after considering multiple input factors to perform a reasonable task (Wu, Liu & Wu, 2018). There was a dynamic improvement in computer vision and language processing.

While there was an increase in the attention and funding AI was receiving from various scientists, the WABOT project began in 1967 in Japan and was completed in 1972 (Liu et al., 2019). The WABOT-1 became the first full-scale intelligent humanoid robot (Liu et al., 2019). The robot had arms and legs and it could perform some of the simplest tasks like picking up a cup or just dancing to music.

Key Terms in this Chapter

Evolutionary Algorithm: Is an algorithm that follows the natural order of evolution, such as the survival of the fittest, to come up with the desired results.

Machine Learning: Is a section of AI that deals with the ability of a machine to learn for the set of experiences it has been exposed to.

Artificial Intelligence: Is the ability of machines to perform task by simulating human behaviors.

Genetic Programming: Genetic programming is a programming that is patterned according to the style of genetic reproduction of cells.

Deep Learning: Is a section of machine learning that uses numerous receptors of different network levels to learn new scenarios.

Unsupervised Learning: Is part of machine learning that realistically resembles the human learning process by having the ability to learn from set of activities without having a specified outcome.

Supervised Learning: Is part of machine learning that uses the input data to predict the output patter with the help of conditions set by the programmer.

Algorithm: A set of rules or steps that make a machine perform a certain task.

Genetic Algorithm: Is an algorithm that is created after the structure of genetics and the inter-breeding of DNA.

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