Dr. Dobb's Journal - October 2007 - (Page 17)

Again! already growing rapidly, like our ubiquitous search trees…” (Barr, Cohen, and Feigenbaum, The Handbook of Artificial Intelligence, Volume 4, 1989.) And largely that meant it was the heyday of the symbolist approach. Analysis and Synthesis In the same year that Feigenbaum et al. were publishing The Handbook of Artificial Intelligence, G.E. Hinton and J.A. Anderson came out with their Parallel Models of Associative Memory and David Rumelhart and James McClelland, joined by Hinton, started work on a project that resulted in the two-volume Parallel Distributed Processing. If The Handbook was the handbook of GOFAI (“good old-fashioned artificial intelligence,” the attempt to model human intelligence at the symbolic level), then Parallel Distributed Processing was the handbook of connectionism. Symbolism and connectionism have been competing themes in AI work throughout its history. The Handbook of Artificial Intelligence, though, shone its light on AI successes, which then were in the symbolist tradition, mostly expert systems, models of specific subject-matter domains embodying domain-specific rules and an inference engine through which the system can draw conclusions based on the rules. MYCIN, Eward Shortliffe’s medical-advice program, is a good example. Implemented in the mid-1970s, MYCIN engaged in a dialog with a doctor about a patient to assemble information on the basis of which to suggest a diagnosis and recommended treatment. Its advice compared favorably with that of domain experts in several disease domains. Expert systems represent only an early example of the symbolist approach. The logic of that approach is that, “since the phenomenon of interest is human symbolic reasoning, we should be modeling at that level, both in order to succeed and in order to understand our success—to understand how human brains work once we have a working AI system,” according to Larry Yaeger of Indiana University. Marvin Minsky, Douglas Hofstadter, and Douglas Lenat are among those promulgating the symbolist view today. (Although Hofstadter, whose work on fluid concepts seems squarely in the symbolist tradition, says he hasn’t read any AI journals in the past 10 to 15 years. “I just pursue my own goals and ignore just about everyone and everything else,” he says. And that is in itself a comment on the state of AI today.) Today, “the symbolic paradigm…has turned out to be a dead end,” Terry Winograd says. That seems harsh, given that many presentations at AAAI were arguably in the symbolist tradition. There was a whole track on AI and the Web, much of which dealt with Web 3.0 issues like ontologies and semantic descriptions. Some of those seem pretty intelligent. “It’s amazing how intelligent a computer program can seem to be when all it’s doing is following a few simple rules…within a limited universe of discourse,” says Don Woods, who, as the creator of the classic game Adventure, showed the world how to do just that. But the limited universe of discourse is the problem. We tend to regard brittleness at the edge of domains to be evidence of lack of intelligence. “[E]xplain your symptoms in terms of drops rather than drips,” says Yaeger, and “the best medical diagnosis software…won’t have a clue.” Maybe a bigger universe of discourse is the answer? With more intelligence built into the universe itself? MIT’s Rodney Brooks thinks that’s important: “We have reached a new threshold in AI brought about by the massive amount of mineable data on the web and the immense amount of computer power in our PCs.” James Hendler points to “an early wave of Web 3.0 applications now starting to hit the Web,” and sees big opportunities in nontext search. “Wouldn’t it be nice if you could ask a future Google to recommend some potential friends for your MySpace links?” Hendler, Tim BernersLee, and Ora Lassila wrote the defining article on the Semantic Web (www.w3.org/2001/sw), and while Berners-Lee says the Semantic Web is not AI, it is tempting to see it as the ultimate AI knowledge base. Or maybe that would be Doug Lenat’s Cyc project (www.cyc.com). “It started with the goal of entering an entire October 2007 l www.ddj.com l Dr. Dobb’s Journal 17 http://www.w3.org/2001/sw http://www.cyc.com http://www.ddj.com

Table of Contents Feed for the Digital Edition of Dr. Dobb's Journal - October 2007

Cover Contents Hmmmm Alia Vox Developer Diaries Developer’s Notebook AI: It’s OK Again! Conversations Visual Cryptography and Bit-Plane Complexity Segmentation Inside the Windows Vista Disk Encryption Algorithm Memory-Aware Components Software and the Core Description Process Logging In C++ Effective Concurrency The Agile Edge Swaine’s Flames

Dr. Dobb's Journal - October 2007

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