- lean proofs are hard, and a lot of the time there is so much mathematical machinery that folks are working on that you would need to not only prove your result, but also all of the machinery that your subfield it is built on. it would be infeasible for many authors to do all of this work (this might be a major part of multiple careers, and when there are 5 folks in your entire subfield, the payoff is not really worth it)

- human proofs are readable, and can illustrate concepts better than lean proofs. human proofs give insights into how to think about a type of problem, and this is often the most valuable part of a proof/result.

- lean proofs are often very difficult to read; while they give you a "verified" check mark, they do not necessarily improve the bounds of human understanding if that makes sense.

but for something like reducing disease infection, i find it a lot harder to understand why we don't create this naturally unless there is some equal-but-opposite cost that we'd incur, or if there is not actually as big of a benefit as we believe (or if this is all just colinear with the weight loss stuff).

note: i have no background in any of this and have no idea what i'm talking about

i'm sure you could come up with another breakdown, but these also have the benefit of tracking roughly with history. numbers and geometry (euclid), then eventually algebra. probability was a fundamentally new way of looking at the world. analysis comes via newton/leibniz and then dynamics tries to tackle complex systems (kinda where newton left off, e.g., 3-body problem type stuff).

also: dimension reduction is not a bad thing. this is like a decomposition: we can decompose so much of what research mathematics is doing into 6 different basis elements. that's pretty darn neat.

(edit: liebnitz -> leibniz)

for example, intuitively, i and j are _basically_ the same "shape" as one another, and f and c and s and v are _basically_ the same "shape" as each other, but the two sets of shapes are definitely _not_ the same as one another...to quantify this and actually capture it in math you gotta do topology, and once you get past the point set stuff it gets real abstract real fast. but they really just wanna say "hey, my donut kinda looks like my coffee mug".

also, probability theory vs statistics is an important distinction: prob theory is a nice clean mathematical subject, while statistics is almost the philosophy of applying probability theory to the world.

>>> And now you've learned that almost all interesting problems are undecidable and of the remaining ones, almost all are NP-hard. For the project of computer science, that puts the final nail in the coffin.

> Sheesh. Not sure if everyone got such a dire framing but that would explain.

Honestly, this is what makes computer science fun.

as evidence, what it _means_ to be a man, woman, etc, differs from society to society. if you ask me to quantify this precisely, i will struggle. but it's plain for all to see.

the second post is actually about (a) me having understanding for a position of someone i disagree with harshly, and (b) the logical structure of an argument, not the underlying topic itself. it was in reference to the content of the link that was posted.

anyway, mods can take it down, i get why the rules are there. you're also right to ask folks to keep it clean. but i stand by it; dude just seems to have a trifecta of awful traits and i'm so so so tired of super rich tech dudes ruining the world.

but more to the point, it is not currently used in a racist manner by the vast majority of the US, and certainly does not carry the same connotations as "yellow", so not really comparable imo