mjm@hplb.CSNET (Martin Merry) (10/30/87)
Ken Laws argues that critical reviews and reconstructions of existing AI software are at the moment only peripheral to AI. > An advisor who advocates duplicating prior work is cutting his > students' chances of fame and fortune from the discovery of the > one true path. It is always true that the published works can > be improved upon, but the original developer has already gotten > 80% of the benefit with 20% of the work. Why should the student > butt his head against the same problems that stopped the original > work (be they theoretical or practical problems) when he could > attach his name to an entirely new approach? I had hoped that Drew McDermott's "AI meets Natural Stupidity" had exploded this view, but apparently not. Substantial, lasting progress in any field of AI is *never* achievable within the scope of a single Ph.D thesis. Progress follows from new work building upon existing work - standing on other researcher's shoulders (instead of, as too often happens, their toes). This is not an argument for us all to become theorists, working on obscure extensions to non-standard logics. However, a nifty program which is hacked together and then only described functionally (i.e. publications only tell you what it does, with little detail of how it does it, and certainly no information on the very specialised kluges which make it work in this particular case) does not advance our knowledge of AI. Too often in AI, early results from a particular approach may appear promising and may yield great credit to the discoverer ("80% of the benefit") but don't actually go beyond solving toy problems. There is a lot of work to do in going beyond these first sketches ("80% of the work") but if we don't encourage people to do this we will remain in the sandbox. Martin Merry Standard disclaimer on personal HP Labs Bristol Research Centre opinions apply P.S. For those who haven't seen it, the Drew McDermott paper appears in SIGART Newsletter 57 (Aug 1976) and is reprinted in "Mind Design" (ed Haugeland), Bradford Books 1981. It should be required reading for anyone working in AI....