this post was submitted on 05 Feb 2024
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That's the thing, how do you determine whether or not the "AI solution functions enough" without having a human review it?
The economics aren't there because LLM outputs aren't trustworthy, and the kind of expertise you'd need to validate them is functionally equivalent to that which could be employed to write the code in the first place.
"Generative AI" is an inefficient solution to a problem that's already been solved by the existence of coding support forums like StackOverflow. Sure, it can be neat to ask it for example code or a bedtime story, but once the novelty wears off all you're left with is an expensive plagirism machine that won't even notice when it confidently lies to you.
I have a strong opinion that the problem is more one of people attempting to solve every problem with their shiny new hammer. AI, in the current incarnations, is very good at many things. When implemented properly, LLMs are great at filtering huge amounts of text data or performing semantic analysis. SD does produce images and can be directed.
LLMs are not a replacement for thought. SD is not a replacement for an artist. They are all tools for helping people do things.
I am designing a hypothetical LLM architecture for analyzing the relational structure of a story and mapping it out. I am hoping that it will be capable of generating a meaningful relationship network at the end. It is a very specific goal and a very specific structure. It won't write a story; it won't produce dialog; it won't build a plot. What it will do is build a network of places and characters that can be used to make decisions for all of those things. I want something that helps with internal consistency of models doing other things. So if a GPT model were to write something, it could be fact-checked against the world network to see if what it is saying is reasonable.