this post was submitted on 19 May 2025
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We weren't verifying things with our own eyes before AI came along either, we were reading Wikipedia, text books, journals, attending lectures, etc, and accepting what we were told as facts (through the lens of critical thinking and applying what we're told as best we can against other hopefully true facts, etc etc).
I'm a Relaxed Empiricist, I suppose :P Bill Bailey knew what he was talking about.
All of those have (more or less) strict rules imposed on them to ensure the end recipient is getting reliable information, including being able to follow information back to the actual methodology and the data that came out of it in the case of journals.
Generative AI has the express intention of jumbling its training data to create something "new" that only has to sound right. A better comparison to AI would be typing a set of words into a search engine and picking the first few links that you see, not scientific journals.
You never took a lab science course? Or wrote a proof in math?
In my experience, "writing a proof in math" was an exercise in rote memorization. They didn't try to teach us how any of it worked, just "Write this down. You will have to write it down just like this on the test." Might as well have been a recipe for custard.
That sounds like a problem in the actual course.
One of my course exams in first year Physics involved mathematically deriving a well known theorem (forgot which, it was decades ago) from other theorems and they definitelly hadn't taught us that derivation - the only real help you got was that they told you where you could start from.
Mind you, in different courses I've had that experience of one being expected to do rote memorization of mathematical proofs in order to be able to regurgitate them on the exam.
Anyways, the point I'm making is that your experience was just being unlucky with the quality of the professors you got and the style of teaching they favored.