On 10/21/07, Edward W. Porter <[EMAIL PROTECTED]> wrote: > > Ben, > > > > Good Post > > > > I my mind the ability to map each of N things into a model of a space is a > very valuable thing. It lets us represent all of the N^2 spatial > relationships between those N things based on just N mappings. This is > something we all know, but it is one of the many wonderful efficiencies of > mathematics we often don't stop to appreciate. >
Yes, a spatial index/embedding lets you efficiently get answers to a variety of queries that are inefficient to answer based on many other indices/representations.. For instance: Given X, find all entities within radius r of X ... or, find the N items most similar to X ... Thus, even for non-spatial data, it may benefit an AGI system to project data into some N-space, in such a way that Euclidean distance mimics "conceptual similarity" between data items, so as to make this kind of query efficient to answer... We have prototyped this trick in Novamente for a couple purposes... and eventually it will be integrated into the core system as a default service to be utilized by all MindAgents as appropriate... -- Ben G ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=56056845-c8fa33
