Where does meaning enter the picture in artificial intelligence? How can we say that a machine possesses understanding? Where, and how, does such understanding happen? These are among the deepest and hardest questions faced by the field, which, as many skeptics claim, has not yielded much about them so far. Consider, for instance, that most current research in AI can be roughly classified over two distinct classes:
(1) Low-level perception. The best example of this type of work comes obviously from computer vision systems. These systems, given a set of input images, usually extract some important information from this input, generating, well, other images (i.e. depth image, edge contours etc.). But this extracted information is usually on a still very low, meaningless, level, to be used by, for instance, a theorem-proving system. To make it clear to all readers what is meant by "meaning", consider the information-processing that must occur whenever an animal, given its massive sensorial information, perceives danger. Going from a set of images and sounds to a feeling of danger involves extracting meaning from the original input, and this is not what is done by current low-level perception projects. It is almost as if these perceptual processes "delegate" the extraction of meaning to another upcoming process. To get into the meaning of a situation, low-level perceptual processes are not enough; there is a clear need for further perceptual processing.
(2) GOFAI symbolic manipulation. This is the other side of the AI coin, dubbed by philosopher John Haugeland as GOFAI, for "good-old-fashioned artificial intelligence", where programs usually handle (syntactically) a representation that supposedly should have been formed by a perceptual process. These systems, such as theorem-proving systems, chess playing, and others, do perform some impressive feats, but they do not have a clue about the semantics of their symbol manipulation. As an example, consider the following predicate-calculus statement: (philosopher (Socrates)). We all fully understand what that means, but what about the machine that executes it? Does it have any meaning to the machine? It is obvious that the answer is no, for that is just a syntactic symbol, as meaningful to the computer as (XzE (GgGggGG)), which doesn't mean anything. But how can a system that only manipulates meaningless syntactic symbols posses any meaning on those symbols? This seems to be an intrinsic problem to GOFAI projects.
Both of these avenues of AI research seem to be based on an unspoken hypothesis of a "center of meaning" arising in the brain (maybe the mind's eye?). The low-level perceptual processes should operate on information that has yet to reach such place, and GOFAI systems in turn handle information that seems to have long reached it. The problem is, what happens at the point of crossing the line? Nobody really knows.
Maybe, then, there is no such line after all - as Hofstadter clearly considers as true, by presenting us with an original alternative. His main thesis is based on the idea that meaning comes from an emergent process that combines perception with analogy-making. He argues, following philosopher Immanuel Kant, that perceptual processes are inseparable from high-level cognitive processes, and, moreover, that (1) perception is guided by analogy-making, and (2) this analogy-making process is itself derived from perception. This thesis has profounds implications for AI.
In his systems, perceptual observations activate concepts, and these activated concepts in turn guide (probabilistically) further perceptual observations. Hofstadter and his group ressurect the HEARSAY II architecture and extend it to other pattern-analysis domains. There is a mixture of bottom-up and top-down processing that eventually leads to the understanding of a situation arising as a combination of "platonic" concepts. This iterative (perception/analogy mapping) process gradually develops a coherent view of the context of the problem it is working on, and that view constitutes, in a sense, on the extracted meaning of the problem. We can say that "understanding p" is, in a sense, "to know what p is like", and this "what p is like" information comes from such analogy-mapping.
Not surprisingly, his projects cannot be found on the symbolic versus connectionist menu. Hofstadter points out that GOFAI (symbolic) systems are too optimal, too rational to be psychologically realistic (he calls them "the Boolean dream"), and that, on the other hand, connectionist systems operate on a level "too low" to be relevant, at present, to a greater understanding of the cognitive issues. Obviously, all mental phenomena may be reducible to a connectionist-system level, but, then again, these same phenomena will be reducible to a quantum physics level. What we should strive for at the moment, he argues, is the right level on which to conduct research. And that level may just be the level of the HEARSAY II speech-understanding system.
Probably the most ambitious AI project under development today is the Letter Spirit project, described on the last chapter. Striving to develop a system that deserves credit for its own creations, with a sense for esthetics, with true creativity and true style - almost taboo issues in AI -, this project messes with many important topics that lack serious study. And, just in case a skeptical reader is wondering, "but, doesn't the project X mess with these exact issues?", then, well, I would recommend Hofstadter's own criticism of "related" projects, given on the epilogue "On Computers, Creativity, Credit, Brain Mechanisms, and the Turing Test".
In summary, this is not your average AI book. This is a full redefinition of artificial intelligence, on a class of its own, an excellent book that deals with deep issues largely ignored by the AI community. Like all the great AI books, this one shuffles between philosophy, methodology, and architecture. Some, maybe even most, highly established AI researchers will not comprehend it completely -- they'll never realize its full scope. However, it is highly recommended to Graduate Students on AI (though not as an introduction to the field). It also seems to be making its mark among philosophers, and I think that neural network researchers will appreciate it as well, for, by extending the HEARSAY II architecture to other domains, it presents an alternative (emergent) architecture that brings us much closer to understanding what understanding is all about.