this post was submitted on 21 May 2025
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But what good is that if AI can do it anyway?
That is the crux of the issue.
Years ago the same thing was said about calculators, then graphing calculators. I had to drop a stat class and take it again later because the dinosaur didn't want me to use a graphing calculator. I have ADD (undiagnosed at the time) and the calculator was a big win for me.
Naturally they were all full of shit.
But this? This is different. AI is currently as good as a graphing calculator for some engineering tasks, horrible for some others, excellent at still others. It will get better over time. And what happens when it's awesome at everything?
What is the use of being the smartest human when you're easily outclassed by a machine?
If we get fully automated yadda yadda, do many of us turn into mush-brained idiots who sit around posting all day? Everyone retires and builds Adirondack chairs and sips mint juleps and whatever? (That would be pretty sweet. But how to get there without mass starvation and unrest?)
Alternately, do we have to do a Butlerian Jihad to get rid of it, and threaten execution to anyone who tries to bring it back... only to ensure we have capitalism and poverty forever?
These are the questions. You have to zoom out to see them.
Because if you don't know how to tell when the AI succeeded, you can't use it.
To know when it succeeded, you must know the topic.
The calculator is predictable and verifiable. LLM is not
I'm not sure what you're implying. I've used it to solve problems that would've taken days to figure out on my own, and my solutions might not have been as good.
I can tell whether it succeeded because its solutions either work, or they don't. The problems I'm using it on have that property.
That says more about you.
There are a lot of cases where you can not know if it worked unless you have expertise.
This still seems too simplistic. You say you can't know whether it's right unless you know the topic, but that's not a binary condition. I don't think anyone "knows" a complex topic to its absolute limits. That would mean they had learned everything about it that could be learned, and there would be no possibility of there being anything else in the universe for them to learn about it.
An LLM can help fill in gaps, and you can use what you already know as well as credible resources (e g., textbooks) to vet its answer, just as you would use the same knowledge to vet your own theories. You can verify its work the same way you'd verify your own. The value is that it may add information or some part of a solution that you wouldn't have. The risk is that it misunderstands something, but that risk exists for your own theories as well.
This approach requires skepticism. The risk would be that the person using it isn't sufficiently skeptical, which is the same problem as relying too much on their own opinions or those of another person.
For example, someone studying statistics for the first time would want to vet any non-trivial answer against the textbook or the professor rather than assuming the answer is correct. Answer comes from themself, the student in the next row, or an LLM, doesn't matter.