Sometimes, I feel like a fraud, just because I happen to know a concept or the right word, but I really don't know much more than that. For example, I was talking with a graphic artist once and I used the phrase 'negative space'. The guy was impressed, and I didn't see fit to tell him that that was about all I know about the concept. But there's a history of this kind of thought on my part. When I worked for another company, several years ago, they had a laudable effort to establish a corps of techies who would be able to speak knowledgeably on a range of subjects -- the term that the program originators used was 'deep conceptual understanding'. You wouldn't know exactly how the magic was done, but you would know what the magic did and how it might be used in any given environment. For example, there's a scene in The Hunt For Red October where one character is theorizing how the Russian submarine might be using a 'caterpillar' as a stealthy propulsion method. When the hero doesn't understand, the character says 'Magneto hydrodynamic propulsion -- you follow? Its like a jet engine for water'. That tells you virtually nothing about how it actually works, but you can take it and start to think about how you might use such a concept. Another example -- an article in the Washington Post, several months ago, spoke about traffic flow on the I495 Beltway, and how 'phantom accidents' occur that cause drivers to slow down, even though there is no reason to do so; this slowing causes a ripple effect, and what starts as a tap on the brakes becomes a full-fledged stop of a semi three miles back. The article used the concept of airflow modeling as an analogy for how this 'stop' was transmitted back through the traffic flow, and how models do calculations for 'chunks of space', determining the effect of the change to one space on the spaces around it. The machines doing this modeling used to have to work with fairly large chunks of space, so their predictive ability was good but not great; as computers got more powerful, the chunks of space that the model handled got smaller, and so the model got more precise and its predictive abilities got better. The equivilent for traffic flow is cars -- used to be that you could only model very small sections at a granular level, or big sections at a coarse level. Now, you can do both -- for air flow and for traffic models. Well, that gave me a general understanding of what might cause traffic to ebb and flow, not to mention how airflow modeling generally works. It’s not actual understanding, but its DCU, and it's fun. For a geek, anyway.