What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

This paper critiques the standard presentation of arguments from analogy in logic textbooks and offers an alternative way of understanding them which renders them both more plausible and more easily evaluated for their strength. The typical presentation presents analogies as inductive arguments in which a set of properties, known to be shared by two logical domains, supports an inference about a further property, known to belong to one domain and inferred to belong to the target domain. But framed in these (...) terms, the strength of the argument depends entirely on the relevance of the known shared properties to the inferred shared property, meaning the argument rests on an unstated assumption. Against this view, the author maintains that arguments from analogy are figurative ways of addressing properties of the target logical domain. By comparing or contrasting two logical domains, analogies articulate a general principle which illustrates something difficult to imagine or to describe literally about the target domain. Analogies can be reformulated and evaluated as deductive arguments on this view, with their chief logical import being the inquiry they incite in students as to whether the general principle is true and sufficiently general to apply to the target domain. (shrink)