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Artificial intelligence (AI) is loosely defined as the capacity of machines that can accomplish tasks that humans would accomplish through thinking. This definition does not say anything about AI achieving this performance in ways similar to how we humans do it, however. The term artificial intelligence is used with two meanings. On the one hand, it refers to (artificially) intelligent machines and the ways of making them; in this sense, AI is primarily computer science and engineering. On the other hand, AI is also a transdisciplinary field of studying these machines. AI gurus such as Herbert Simon often emphasized that studying AI involves studying the human mind, and if we get it right, we will understand both AI and the human mind better in the end. Therefore, the field of AI involves various branches of hard sciences and engineering but beyond these also biology, psychology, and philosophy. This entry introduces the types of AI and describes their origins and the ways in which the types differ from each other. Next, the entry pursues the question of what defines intelligence and whether AI machines can be said to think. The entry continues with a discussion of the two paradigms of AI research, strong AI and weak AI. This is followed by an examination of the nature of knowledge and learning and the differences between AI and the human mind—a distinction reinforced in concluding remarks on the importance of this essential consideration in shaping the future of humankind.

Types of AI

The term artificial intelligence was coined by the computer scientist and cognitive scientist John McCarthy during a 2-month workshop organized at Dartmouth College in summer 1956. At the time, only one program (discussed below) qualified for the name, so it was the result of philosophizing about what computers should be capable of. To unpack the concept of AI, it is useful to distinguish between the different types of AI and to delineate facts from beliefs in the process. In this section, the three main types of AI, namely symbolic reasoning systems, symbolic expert systems, and artificial neural networks (ANNs), are examined.

Symbolic Reasoning Systems

The first thinking machine—that is, the first AI implementation that worked—was called the Logic Theorist, or Logic Theory Machine. It was created in 1956 by the Nobel laureate Herbert Simon, his former PhD student Allen Newell, and Cliff Shaw from the RAND Corporation. The purpose of the Logic Theorist was “[ … ] to learn how it is possible to solve difficult problems such as proving mathematical theorems, discovering scientific laws from data, playing chess, or understanding the meaning of English prose” (Newell et al., 1963, p. 109). The Logic Theory Machine gradually evolved into the even more ambitious General Problem Solver, which was supposed to extend the original scope to all areas of human problem-solving. This was the first tentative attempt at what is today called artificial general intelligence (AGI).

The departure point was the philosopher Thomas Hobbes’s idea from the 17th century, popularized by AI pioneer Oliver Selfridge (1926–2008), suggesting that all human problem-solving could be represented as a manipulation of symbols. If this was true, Newell and Simon speculated that computers should be able to solve real-world problems, not only arithmetic problems. They tried to achieve this by recording the steps of human problem-solving, using the thinking aloud technique: They asked problem solvers to note all the steps of their thinking. Newell and Simon figured that if they repeated the same in many areas of human problem-solving, they should be able to extract the general principles of reasoning, and by applying these principles and generic steps, AI should be able to work across multiple domains. The idea was a radical departure from the optimization and operational research methods of the time, which started with a detailed model of the complete problem. The performance delivered by the Logic Theorist was astonishing; eventually, it proved 38 of the first 52 theorems in the Principia Mathematica, a foundational work in modern symbolic logic by Alfred North Whitehead and Bertrand Russell.

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