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Also, just because an acronym isn't used doesn't mean you'll understand the jargon. For example, also on the front page is a paper titled "Neurons in Large Language Models: Dead, N-Gram, Positional". No acronyms, but I would certainly need to Google (or actually read the paper, not just the abstract) to know what the 3 terms at the end means (well, I do know what a dead neuron is, but not the other two).
> you’ve missed the most important hyperlink: what the fuck is a MNIST?
Anyway my main beef isn’t with this GitHub repo or the author, it’s that nobody remembers how to write a goddamned overview anymore. The point of tree structured data is making it cheap to backtrack when doing a semi random search. The overview is an important part of making hypertext work, particularly when it feeds through catch-all lists like a news site or a landing page for a wiki.
When a hyperlink is organically embedded into a paragraph, you can usually guess what it’s for, from the sentence and the context. When it’s just a title that is lost.
Psst psst psst. NO. Bad kitty.
Such as what?
I recall a guy with good transcription skills starting a long thread on… must have been slashdot? About how it was faster for him to transcribe the numbers again than to verify the OCR was correct, but he couldn’t tell his boss that and what should he do?
There’s also a famous case where the compression algorithm in a copy/fax machine had a bug in a predefined dictionary that resulted in changing digits to other digits when making copies or faxes. Because they weren’t just compressing, they were trying to use a specialized OCR to do aggressive compression. I believe it was spitting out perfectly formed zeroes where another digit was in the original. Yikes.
JBIG2 lossy compression. Covered in another hn story: https://news.ycombinator.com/item?id=29223815
From the story and the comments:
> This is not an OCR problem (as we switched off OCR on purpose), it is a lot worse – patches of the pixel data are randomly replaced in a very subtle and dangerous way: The scanned images look correct at first glance, even though numbers may actually be incorrect.
It's lossy compression turned to 11 that looks convincingly non-lossy.
It’s like Jim Gaffigan’s joke about working in a TexMex restaurant in the northern Midwest. It’s a tortilla, cheese, meat and beans… I tell you what I’ll just bring you something.
I wonder if any executive assistants got fired over that. Surely.
Optical Character Recognition. Very old, and very infamous attempts at replacing humans with software. Secretaries and executives know what OCR means.
Person A complains about MNIST not being defined. Person B was sending them up for complaining about not defining the meaning of acroynms and then using one they don't define.