Terrible Advice From a Great Scientist

In your analogy, you're talking about a very high-level split that can be done cleanly. One person does the creative work of coming up with a game design (storyline, play control, etc.) without worrying about the underlying implementation details. Then another person can certainly do the engineering and coding work to implement that.

But it should be obvious that for some other problems this won't work. For example, it doesn't make sense to try and split the coding into a "creative coder" (who knows nothing about programming) and an "implementation coder" who turns the creative's ideas into actual code. The creative would toss out nonsensical ideas (like "instead of using vectors, why not use genetic algorithms?"), and then the implementer would have to explain why all those ideas are silly... or else they would just have to ignore the creative type and simply code something that makes sense.

In other words, generating good source code requires someone who knows enough about programming that they can see creative solutions. Their intuition is not separate from their programming talent: their intuition is based upon years of training and experience with source code, math, engineering, and so forth. That's where the good ideas come from.

Coming up with good scientific ideas is similar. Analysing scientific data even moreso. It's only once you have a deep, subliminal understanding of the important concepts that you're going to make substantive progress. Whether a deep understanding of math counts as an "important concept" depends on the field, of course... but I would argue that for science generally, the more mathematical know-how you have, the more informed and powerful your ideas will be.

Source: http://rss.slashdot.org/~r/Slashdot/slashdotScience/~3/9M2q6uv0OX0/story01.htm

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