dual_sport_dork ,
@dual_sport_dork@lemmy.world avatar

Say it with me again now:

For fact-based applications, the amount of work required to develop and subsequently babysit the LLM to ensure it is always producing accurate output is exactly the same as doing the work yourself in the first place.

Always, always, always. This is a mathematical law. It doesn't matter how much you whine or argue, or cite anecdotes about how you totally got ChatGPT or Copilot to generate you some working code that one time. The LLM does not actually have comprehension of its input or output. It doesn't have comprehension, period. It cannot know when it is wrong. It can't actually know anything.

Sure, very sophisticated LLM's might get it right some of the time, or even a lot of the time in the cases of very specific topics with very good training data. But its accuracy cannot be guaranteed unless you fact-check 100% of its output.

Underpaid employees were asked to feed published articles from other news services into generative AI tools and spit out paraphrased versions. The team was soon using AI to churn out thousands of articles a day, most of which were never fact-checked by a person. Eventually, per the NYT, the website's AI tools randomly started assigning employees' names to AI-generated articles they never touched.

Yep, that right there. I could have called that before they even started. The shit really hits the fan when the computer is inevitably capable of spouting bullshit far faster than humans are able to review and debunk its output, and that's only if anyone is actually watching and has their hand on the off switch. Of course, the end goal of these schemes is to be able to fire as much of the human staff as possible, so it ultimately winds up that there is nobody left to actually do the review. And whatever emaciated remains of management are left don't actually understand how the machine works nor how its output is generated.

Yeah, I see no flaws in this plan... Carry the fuck on, idiots.

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