Next Steps

 

So fast has been manís transition into the Information Age that many men have not the ability to acclimate, and instead of deliberating on the meaning and ramifications of this change, are consumed with keeping up with the paradigm shifts, lest they be left behind. Luckily, the age of information is also the age of automation, so we can make the time to follow the trends by automating those tasks which we deem lower priority. Already we have spell and grammar checkers so well integrated in our applications that we could get by knowing only the most rudimentary phonetic spellings and structures, resting easy with the knowledge that our user agents will act as our proof reading surrogates. And if we donít want to bother learning the basics of typing at all, we can use speech recognition software to translate our spoken word into written with a few simple ďlearningĒ sessions. Heuristic learning algorithmic approaches are replacing linear flowchart style if-then programming, meaning the longer a software program works with a human partner as his/her agent, the better suited will the software agent become at interpreting and anticipating itís masterís meaning and needs.

 

Search engines are not immune to the application of personalization and the incorporation of user agent technology to act as human surrogate for the simpler tasks. And the simpler tasks become more complex every day. Available to those who know where to look are agent programs whose sole purpose it is to surf syndicated content and to assemble executive summaries and links that the user will be most interested in for personal or professional use. (Teevan, et al) In the next evolution of search, user agents will be constantly on the watch for important items to bring to their masters attention before the human is even aware that he wants to know. User agents will partner with personal information management (PIM) agents to determine relevant local information to bring to their masters attention using a combination of search and heuristic learning approaches designed to anticipate human needs and wants. The active user agent might notice that the wireless location access point through which the human is passing is close to a trusted jewelry shop with a searchable advertised special on diamond tennis bracelets. Remotely accessing the humanís PIM, the user agent further notes that the human has a wedding anniversary in a weekís time. Evaluating historical purchasing habits and current financial records, again accessed remotely, the user agent determines that the human should detour into the store that is mere yards away and alerts its master to this. The evolved search agent has made rapid human-like associations in a fashion approximating the form and function of human memory. Yet another evolutionary step may see this agent authorize an appropriate purchase for an anniversary gift online and arrange shipping, only alerting the human in passing that this has been dome on his/her behalf.