Thread

1. qnleigh · 1791094321 · focus · HN ↗

   > After scratching the itch of knowing that something's been written completely rigorously, it's easy to call a work done when maybe we shouldn't call it done yet. And there's contexts where this is maybe OK, research comes to mind...

   It's funny, because all of the advice he gives in the rest of the video would be amazing to apply to research papers as well. I've had people discourage me from writing research papers pedagogically because it is "unusual," which really meant that it would be looked down upon. It's kind of tragic that the norm is to write research papers that strip away the human details of how a definition is motivated or how something was discovered. This kind of information is usually only shared through in-person discussions or occasionally research presentations, but it is often the core knowledge that enables one to do research in a given field.

   It's a great video though. He gives a little checklist of ways to check if your work is clear:

   - Do definitions have motivating examples?

   - Do proofs [or other results] feel rediscoverable?

   - Is there personality?

   - Are core ideas illustrated with diagrams?

   - Is relative importance highlighted?

   ...

   He also gives a beautiful example of how powerful it is to give a motivating example for an abstraction before introducing it. I really got the sense from watching this of what makes him such a great math educator. Highly recommend his YouTube channel 3Blue1Brown for anyone who hasn't heard of him

   1. hodgehog11 · 1791114527 · focus · HN ↗

      I think most supervisors try to discourage students from writing in this way because there is a delicate art to it that you are unlikely to be able to meet at that career stage. When I read some of the texts from Martin Hairer and Cedric Villani in my field (or even Riemann's famous paper on the zeta function), you can see that they are motivational and informative but still concise at the same time. Full of personality too, especially Villani. That's a hard balance and it requires exceptional understanding of the topic and the reader.

      At the same time, we really should be encouraging it more. I have found that in newer machine learning theory papers (strictly theory, not empirical work), there is something closer to a good balance that is expected even of students.

      Including a motivating example is key to this, and should be considered mandatory.

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