20. An apparatus for identifying, extracting, capturing, and leveraging expertise and knowledge, comprising: means for observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises any user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, a processor for detecting user think time for the asset; the processor employing automatic techniques to extract patterns from at least the user think time; and the processor learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of: automatically determining each user's peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the terms as the terms become associated with more assets.
20. An apparatus for identifying, extracting, capturing, and leveraging expertise and knowledge, comprising: means for observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises any user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, a processor for detecting user think time for the asset; the processor employing automatic techniques to extract patterns from at least the user think time; and the processor learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of: automatically determining each user's peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the terms as the terms become associated with more assets. 23. The apparatus of claim 20 , further comprising: the processor analyzing all observations; and via the analysis, said processor generating a set of recommendations comprising distilled experiences from a community of users; wherein the recommendations age over time and are discarded if they have relatively little value; and wherein recommendations which are most valuable based on repeated usage are stored into a long term memory.