Phenomenology is often thought to be irrelevant to artificial intelligence and cognitive science research because first-person descriptions do not reach to the level of genuine causal explanations. Though phenomenology taken in this weak sense may not be useful, the method of phenomenology taken more formally may well produce fruitful results. Husserl's phenomenological reduction, or epoché, sets the right frame of reference for a science of cognition because it makes explicit the difference between what belongs to cognition and what belongs to the natural world. Isolating this critical difference helps us assign the correct procedures to cognition and describe their functions. A formalized phenomenology of cognition can therefore aid initiatives in cognitive computing.

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Defining the FieldBecause the label "computing and philosophy" can seem like an ad hoc attempt to tie computing to philosophy, it is important to explain why it is not, what it studies (or does) and how it differs from research in, say, "computing and history," or "computing and biology". The American Association for History and Computing is "dedicated to the reasonable and productive marriage of history and computer technology for teaching, researching and representing history through scholarship and public history" (http://theaahc.org). More pervasive, work in computing and biology enjoys the convenient name of "bioinformatics...the science of using information to understand biology..., a subset of the larger field of computational biology, the application of quantitative analytical techniques in modeling biological systems" (http://oreilly.com/catalog/bioskills/chapter/ ch01.html). The recent venture of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers to publish the Transactions on Computational Biology and Bioinformatics (TCBB) bears witness to the reach of computing and biology and underscores its objective. TCBB intends to report "archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development and optimization of biological databases; and important biological results that are obtained from the use of these methods, programs, and databases" (http://tcbb.acm.org).In the case of "computing and history" and "bioinformatics," each discipline stands in a particular relationship to computers that raises questions unique to itself. But both are devoted to the development of computational tools to aid discovery. We might, therefore, expect the study of "computing and philosophy" to be an integration of the two in the same way, that it should ask computing technologies to help with philosophical discovery, and sometimes it does. However, the unlimited scope of philosophy and its self-reflective nature make

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