The "birth date" of AI is generally traced to the Dartmouth Conference which was held in the summer of 1956. Earlier attempts at using computers to design "intelligent" systems had been attempted within the framework of "neural-like" networks. AI followed a more abstract road in that it sought to understand "intelligence" and design "intelligent" systems within the framework of symbol systems. A discussion and explicit statement of this approach is recounted in the the Tenth Turing Award Lecture given in 1976 by Allen Newell and Herbert A. Simon.

In the formal work on computation that was previously covered we saw that symbols and the way in which symbols were allowed to be "remembered" and "accessed" yielded a hierarchy of machines. The machines at the top of the hierarchy, the Turing Machines, are, it is conjectured, capable of computing any function that is in fact computable. But should we call a machine intelligent because it can be programmed to compute some function?

The move to speaking about machines and intelligence in the same breath involves presuming that the ability to achieve goals in the face of variations, difficulties, and complexities posed by the task environment is an essential characteristic of intelligence. The Physical Symbol System Hypothesis together with the idea of Heuristic Search constitute Newell and Simon's proposal for how to computationally realize intelligence. Click on these topics to examine each of these ideas in turn.