I recently came across this description of economists attributed by Paul Krugman to Robert Solow. "There are two kinds of economists: those who look for general results and those who look for illuminating examples." This dichotomy struck me as rather interesting and upon reflection, it was pretty clear to me that my own AI-research methodology is overwhelmingly towards the general results side. I am drawn to fairly basic and general questions and issues. Very rarely am I motivated by a specific example. Note that despite the superficial similarity between the dichotomy under discussion and the "theory versus empirical" dichotomy, they are quite unrelated. One can take a specific example, e.g., a specific human capability or indeed a specific human failing and then either build a theory of intelligence or do empirical work from that. Linguists do that. Cognitive scientists do that. Psychologists do that. Is AI research by its nature restricted to the general results side of the divide? Maybe this is the divide that separates AI from say cognitive science. The former seeks general results while the latter explains illuminating examples.
To all of the 3 people that might read this: Are there examples of AI research that stemmed from illuminating examples? What role do illuminating examples play in AI?