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693 points jsheard | 1 comments | | HN request time: 0s | source
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AnEro ◴[] No.45093447[source]
I really hope this stays up, despite the politics involvement to a degree. I think this is a situation that is a perfect example of how AI hallucinations/lack of accuracy could significantly impact our lives going forward. A very nuanced and serious topic with lots of back and forth being distilled down to headlines by any source, it is a terrifying reality. Especially if we aren't able to communicate how these tools work to the public. (if they even will care to learn it) At least when humans did this they knew at some level at least they skimmed the information on the person/topic.
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geerlingguy ◴[] No.45093831[source]
I've had multiple people copy and paste AI conversations and results in GitHub issues, emails, etc., and there are I think a growing number of people who blindly trust the results of any of these models... including the 'results summary' posted at the top of Google search results.

Almost every summary I have read through contains at least one glaring mistake, but if it's something I know nothing about, I could see how easy it would be to just trust it, since 95% of it seems true/accurate.

Trust, but verify is all the more relevant today. Except I would discount the trust, even.

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add-sub-mul-div ◴[] No.45093911[source]
We all think ourselves as understanding the tradeoffs of this tech and that we know how to use it responsibly. And we here may be right. But the typical person wants to do the least amount of effort and thinking possible. Our society will evolve to reflect this, it won't be great, and it will affect all of us no matter how personally responsible some of us remain.
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iotku ◴[] No.45094235[source]
I consider myself pretty technically literate, and not the worst at programming (though certainly far from the very best). Even so I can spend plenty of time arguing with LLMs which will give me plausible looking but extremely broken answers to some of the programming problems.

In the programming domain I can at least run something and see it doesn't compile or work as I expect, but you can't verify that a written statement about someone/something is the correct interpretation without knowing the correct answer ahead of time. To muddy the waters further, things work just well enough on common knowledge that it's easy to believe it could be right about uncommon knowledge which you don't know how to verify. (Or else you wouldn't be asking it in the first place)

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add-sub-mul-div ◴[] No.45094369[source]
Even with code, "seeing" a block of code working isn't a guarantee there's not a subtle bug that will expose itself in a week, in a month, in a year under the right conditions.
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1. giantrobot ◴[] No.45095051[source]
I've pointed this out a lot and I often get replies along the lines of "people make mistakes too". While this is true, LLMs lack institutional memory leading to decisions. Even good reasoning models can't reliably tell you why they wrote some code they did when asked to review it. They can't even reliably run tests since they'll hardcode passing values for tests.

The same code out of an intern or junior programmer you can at least walk through their reasoning on a code review. Even better if they tend to learn and not make that same mistake again. LLMs will happily screw up randomly on every repeated prompt.

The hardest code you encounter is code written by someone else. You don't have the same mental model or memories as the original author. So you need to build all that context and then reason through the code. If an LLM is writing a lot of your code you're missing out on all the context you'd normally build writing it.