User:Graeme E. Smith/Collections/Model Series/Datamining/Acceleration Techniques

Acceleration Techniques
An acceleration Technique, is a method by which you reduce the time it takes for an Intuition to pass through the explicit memory and into the Declarative Memory. There are two main factors that affect the time it takes for in intuition to come to fruition, The first is depth of analysis. The more crimps there are in the data, the deeper your analysis engine has to dig, to complete its analysis, even with oversampling techniques there is no substitute for a larger sample population. So the more different sources you draw your information from, the better your analysis will be. The other factor is completeness, if there is a missing piece your synthesis could be held up for years. Sometimes the knowledge just doesn't exist to complete the synthesis. In these cases the intuition could be held off for a life-time. It is possible in these cases to sometimes use an analogy from a different stream of thought, to fill in the gap, but which analogy from which stream of thought should you use? Inspiration often comes when you combine two threads of thought unintentionally and find a missing key piece for the synthesis, or when you are asleep and your mind is automatically updating memory and confuses two different streams of memory because they have a common look to them.

Cross Polination
There are techniques to synthetically create this cross pollination such as brain storming, and the expand to contract heuristic, but they are not guaranteed to work on any specific problem. Brain storming is a creative process where you try to come up with as many possible alternate solutions as possible without judging them, then when you have a list of at least 5 to 15, prioritize them to find out which are the most important insights, the top 5 can be used if they apply at all to the problem. Expand to contract, is very similar, in that the idea is to add a new element at random into the mix, and try again to synthesize using it. Since the synthesis mechanism stops at the first synthesis that meets most data, it is an optimizing problem, and such problems tend to get stuck on local optima. By introducing a new factor and starting synthesis again, we can sometimes cause the synthesis to optimize to a different optima, and thus explain more of the nature of the problem. These are useful, but what we need are some strategies keyed to the exact nature of intuition, that can be learned and applied strategically. So, let us look at the problem, logically, the problem is that often intuition is not completed because there is missing data. We want to speed up the intuition process, by supplying that data if possible, and by finding some alternate possibilities if the data is not immediately available. To do this we need to find an analogy, that will work to break the log jamb.

Cramming for fun and profit
One way that is especially effective when you are first starting out in a field, is cramming. Essentially you want information from as many sources as possible, as quickly as possible. The brain once it gets a certain load of data, will attempt to reduce the cost of storing it, by rearranging the storage. As a natural result it will automatically create analogies, and in doing so might break the log jamb. The main problem with Cramming is that if you are not especially organized you loose the ability to determine where the information came from, or to evaluate it for accuracy. This can be a problem later when you find yourself with insights out of the main stream of science, or try to write a paper with full academic rigor. Essentially you have to be able to go back over the information you crammed, later, and build your professional attribution at that time.

Scanning Related Topics for Relevance
Another technique that works almost as well, but does not rely on limiting itself to a specific topic is a scanning technique based on quickly reviewing related concepts. An example of what you might do, in academia, for instance is put a search on google scholar, and quickly scan all the abstracts that show up to see if any trigger insight. The main difference between this and cramming is that you never bother to look into the meat of most of the articles because you know they are off topic. This is especially valuable for its role in log-jamb smashing because it allows you to draw analogies from related topics that you would normally not study during cramming. The main reason for this is that search engines like Google Scholar have a larger hit rate and if you were cramming you would ignore most of the outliers because they have nothing to do with your topic,But by scanning them, you are tricked into thinking in terms of their own viewpoint which might trigger an analogy and thus break the log-jamb. In some cases there simply isn't enough work being done in a particular field to advance knowledge very fast, and scanning is the best way to come up with new ideas, because cramming saturates the market for articles too quickly.