The workshop at Dartmouth College in Hanover, New Hampshire in 1956 is usually taken as the start of the field and study of ‘artificial intelligence’. The organizers described their goal as follows:
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. (McCarthy et al. 1955)
Since then, definitions of AI have been many and vague. AI experts show no consensus (Faggella 2018c). Compounding this definitional problem is the ‘AI Effect’, whereby as soon as AI can do something, it is no longer considered to require intelligence. Pamela McCorduck noted that in the history of AI ‘every time somebody figured out how to make a computer do something – play good checkers, solve simple but relatively informal problems – there was chorus of critics to say, “that’s not thinking”’ (2004: 204). One recent AI textbook quotes Elaine Rich’s pithy definition of AI from 1983: ‘the study of how to make computers do things at which, at the moment, people are better’ (quoted in Ertel 2018: 2). A more formal definition of AI we find useful is:
The essence of AI – indeed, the essence of intelligence – is the ability to make appropriate generalizations in a timely fashion based on limited data. The broader the domain of application, the quicker conclusions are drawn with minimal information, the more intelligent the behaviour. (Kaplan 2016: 5–6)
This definition distinguishes AI from mere computation and allow us to differentiate between different types of existing and hypothetical AIs by considering their speed, quantity of information required, and generality of application. Kaplan’s definition, however, says nothing about what AI looks like out in the world. AI does not mean robot, a confusion that can be blamed on pop culture. The roboticist Alan Winfield offers three complementary definitions of a robot:
introduction: ai-capital
9