This is the book that put symbolic AI (so-called "good old fashioned AI") on the map. It's casual, lighthearted, and non-technical, but don't be fooled: there are deep issues here to ponder and chew on. It's also old, and so in no way gives an accurate portrayal of modern-day AI, but there's nothing particularly perishable here. Haugeland ponders the nature of intelligence and what a "thinking machine" might be capable of; he surveys a variety of formal computer architectures that might be called on to realize such a thinking machine (while implementations of these architectures have changed dramatically since the 80's, the architectures themselves remain much the same); and he enumerates the major stumbling blocks with contemporary (as of the 80's) AI. Indeed, some of these hurdles are technical and so surmountable with the advent of better algorithms, more computing power, and so on. But most of them are *conceptual*, requiring what seem to be much more significant leaps in the development of computational cognitive science.
Much of the emphasis is on creating machines that can act, decide, and reason like humans; conspicuously, there is virtually no discussion of creating machines that can *learn*. In contrast, much of modern AI is taken up with machine learning--teaching machines to complete some task, whether like a human or not. These tasks are typically not about reasoning like humans, but about perception (image recognition) or even imitation (conversational text generators) and so the recent promise of "connectionist" AI might appear to be a rather parallel effort. But, it occurs while reading this book that perhaps the execution of reasoned action (what symbolic AI as presented by Haugeland concerns itself with) and the learning of what's being reasoned about are not so separate, and that treating them as isolated "modules" is the wrong way to do things.
As a researcher in applied machine learning, I picked this book up hoping for a quaint introduction to the "old" ways of thinking about AI, but instead have been greatly inspired by how many of the "old" problems remain unsolved and by just how formidable these problems are.