What is AI?

AI may mean several different things and it is defined in many different ways. When Alan Turing introduced the so-called Turing test (which he called an ‘imitation game’) in his famous 1950 essay about whether machines can think, the term ‘artificial intelligence’ had not yet been introduced. Turing considered whether machines can think, and suggested that it would be clearer to replace that question with the question of whether it might be possible to build machines that could imitate humans so convincingly that people would find it difficult to tell whether, for example, a written message comes from a computer or from a human (Turing 1950).

The term ‘AI’ was coined in 1955 by a group of researchers—John McCarthy, Marvin L. Minsky, Nathaniel Rochester and Claude E. Shannon—who organised a famous two-month summer workshop at Dartmouth College on the ‘Study of Artificial Intelligence’ in 1956. This event is widely recognised as the very beginning of the study of AI. The organisers described the workshop as follows:

We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. (Proposal 1955: 2)

Another, later scholarly definition describes AI as:

the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. (Copeland 2020)

In the early twenty-first century, the ultimate goal of many computer specialists and engineers has been to build a robust AI system which would not differ from human intelligence in any aspect other than its machine origin. Whether this is at all possible has been a matter of lively debate for several decades. The prominent American philosopher John Searle (1980) introduced the so-called Chinese room argument to contend that strong or general AI (AGI)—that is, building AI systems which could deal with many different and complex tasks that require human-like intelligence—is in principle impossible. In doing so, he sparked a long-standing general debate on the possibility of AGI. Current AI systems are narrowly focused (that is, weak AI) and can only solve one particular task, such as playing chess or the Chinese game of Go. Searle’s general thesis was that no matter how complex and sophisticated a machine is, it will nonetheless have no ‘consciousness’ or ‘mind’, which is a prerequisite for the ability to understand, in contrast to the capability to compute.

Searle’s argument has been critically evaluated against the counterclaims of functionalism and computationalism. It is generally argued that intelligence does not require a particular substratum, such as carbon-based beings, but that it will also evolve in silicon-based environments, if the system is complex enough (for example, Chalmers 1996, chapter 9).

In the early years of the twenty-first century, many researchers working on AI development associated AI primarily with different forms of the so-called machine learning—that is, technologies that identify patterns in data. Simpler forms of such systems are said to engage in ‘supervised learning’—which nonetheless still requires considerable human input and supervision but the aim of many researchers, perhaps most prominently Yann LeCun, had been set to develop the so-called self-supervised learning systems. These days, some researchers began to discuss AI in a way that seems to equate the concept with machine learning. This article, however, uses the term ‘AI’ in a wider sense that includes—but is not limited to—machine learning technologies. - Sven Nyholm PhD Professor (Full)?at?Ludwig-Maximilians-University of Munich. Germany

Source: Ethics of Artificial Intelligence | Internet Encyclopedia of Philosophy https://iep.utm.edu/ethic-ai/ 2 of 30 2/22/21, 11:55 AM

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