Archiving Boundaries for Artificial Intelligence Learning and Observational Data Storage

The sensors being designed for artificial intelligent robotic components, robots and androids are getting more and more sophisticated all the time. They are getting smaller and more robust in their abilities, unfortunately this causes an issue in that the more powerful the sensors the more data is collected and the more processing power needed and the higher speed the system will be running.This means machines, androids and robots will run hotter and special materials will be needed, as well as consideration of fire and spontaneous combustion of a very expensive piece of hardware. Not to mention the added weight of a liquid cooled system and the additional power needed to run it. If the average digital camera takes a picture of let's say 235 Mb and an artificial intelligent robotic android will be taking full motion video constantly you can imagine that even with adequate compression, frame burst methods of common objects and occasional data dumping and defraging techniques you are taking an enormous amount of hard drive capacity and random access memory requirements.

Therefore there must be archiving boundaries set for artificial intelligent learning and those data sets, which are stored which are consider observational. If the boundaries are set too high, you will not have sufficient RAM to complete human assigned tasks in a adequate response time and thus not satisfy the customer. If the boundaries are set too low, then you fail to capture the needed data needed to help in the decision matrix process for learning and thus waste your chance of self-programming and learning strategies to allow you system to get better with use. So please consider all this in 2006.

By: Lance Winslow