On Defining Artificial Intelligence

Abstract

This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.

The impact of artificial intelligence along the insurance value chain and on the insurability of risks

“…21 The first definitions of the term 'artificial intelligence' date from this time. However, as a result of the various conceptions and the rather vague nature of (human) intelligence, there is no widely accepted definition of artificial intelligence but rather a multitude of coexisting definitions (Wang 2019; see also Bhatnagar et al 2018;Monett and Lewis 2018). 22 McCarthy 2007, who played a leading role in coining the term artificial intelligence in 1955, describes it as the science and engineering of manufacturing intelligent machines.…”

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

“…The most suitable technique capable of administering a volume of complex and extensive information may be Machine Learning (ML). This scientific discipline stems from Artificial Intelligence (AI), i.e., a computer science field performing tasks capable of emulating human performance, generally learning to understand complex data, an endeavor that requires human intelligence (Bawack, 2019;Wang, 2019;Graham et al, 2020). ML algorithms have progressively gained popularity for several reasons, including their ability to automatically learn the inherent structure of a dataset (Kononenko, 2001;Abu-Mostafa et al, 2012;Facal et al, 2019) without requiring a priori hypotheses about relationships between variables (Miotto et al, 2017;Vieira et al, 2017;Graham et al, 2019Graham et al, , 2020.…”

Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

“…Big Data are extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations, and can be used to inform AI and machine learning. mHealth can be distinguished from AI in that it utilizes mobile phones, tablets, and other interpersonal communication technologies to address a social problem by communicating or delivering a service to end-users (Harrington, 2018), rather than AI, which works with knowledge and resources, using finite processing capacity, to "learn" and provide solutions to a question [adapted from Wang (2019)]. mHealth interventions, although they may draw on AI, are a different type of product and-in many cases-are simpler in construction and application and do not necessarily rely on large datasets or complex learning algorithms (although they may).…”