In Preface 4, Hofstadter discusses the Eliza effect and why it had worried him and his team back in the 1980's. The Eliza effect is basically defined, as Hoftstadter says, as: "the susceptibility of people to read far more understanding than is warranted into strings of symbols--especially words-- strung together by computers." In other words, it also refers to how people mistakingly believe that computers understand things, or can empathize with things, like humans far more than they actually do.
It is because of this that Hoftstadter and his team were losing morale. Other AI research teams were being credited with achieving goals in AI development which Hoftstadter believes was unwarranted, at least as much as they got. I can understand his thinking, and although this preface does seem to be a bit on the complaining side, his complaints seem valid.
From reading about his work with CopyCat and Jumbo, it is very obvious that he is aiming to truly model human cognitive processes with his work in artificial intelligence. This is in contrast to many other AI research groups who develop AI with the goal of achieving the image of intelligence with their projects. For some people, it simply is not important whether the computer truly understands the domains it is meant to be dealing with. For example, AI in video games is meant to mimic human behavior, but with as little overhead as possible. For these purposes, truly modeling human cognitive processes is not entirely important for most people, I guess. However, for people claiming to have a computer program that truly understands something like creating analogies, it has no purpose to claim the goal has been achieved if it has not truly been achieved in a way that mirrors a human approach.
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