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this post was submitted on 19 Nov 2023
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Using the end product and having any idea how it works are two VERY different things.
I agree, my argument is that both aren't challenging for even the average person if they really want/need to understand how these models produce refined noise informed by human patterns.
There are electricians everywhere you know.
This isn't a random person thoughtlessly yelling one-sentence nonsense pablum on the Internet like you.
You think this person can't understand something as straightforward as programming, coming from law?
https://en.wikipedia.org/wiki/Barack_Obama
Please link your Wikipedia below 🫠
It's a bit more complicated than you're making it out to be lmfao, there's a reason it's only really been viable for the past few years.
The principles are really easy though. At its core, neural nets are just a bunch of big matrix multiplication operations. Training is still fundamentally gradient descent, which while it is a fairly new concept in the grand scheme of things, isn't super hard to understand.
The progress in recent years is primarily due to better hardware and optimizations at the low levels that don't directly have anything to do with machine learning.
We've also gotten a lot better at combining those fundamentals in creative ways to do stuff like GANs.