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Abstraction, non-uniform relevance of components, admitting it. #2

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sanjarcode opened this issue Jun 20, 2022 · 7 comments
Open

Abstraction, non-uniform relevance of components, admitting it. #2

sanjarcode opened this issue Jun 20, 2022 · 7 comments
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@sanjarcode sanjarcode self-assigned this Jun 20, 2022
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sanjarcode commented Jun 20, 2022 •
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This has really changed my world view
Prioritising is not a compromise just because resources are low. Prioritising is important because structure of many systems is inherently non-uniform in terms of relevance.

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Add to PVS

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Prioritisation and abstraction play well together, so they'll boost efficiency, if applicable. Nice.

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As a side note, this is why machine learning works. And why it is possible to come up with models, that learn (?).

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sanjarcode commented Jun 20, 2022 •
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What a coincidence... Knowledge trees are skewed in terms of their facets.

And a BFS scan is should be done before going deep in the tree, i.e. specialising (which I hate generally/innately, so I'm good 😃). Yes, going deep is also important, but not as much as understanding the fundamentals broadly.

Elon Musk's learning tree

https://youtu.be/NPCvFO7mxSA&t=4m25s

Understanding the important ideas - "it might be slower initially, but it's key in the long run".

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After effects of having a tree of knowledge.

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