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[–] 27 points 1 year ago (10 children)

The example I saw them use was turning one line text reviews into a simple positive or negative so you can count them.

So it could be useful for things like that, even if we ignore the "then why not just ask for the star rating" that probably went along with that review...

MS is now an AI company that sells to excited bosses who would love to fire somebody somewhere to save a few bucks.

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  • [–] 10 points 1 year ago (5 children)

    "... to save a few bucks."

    In the short term, people who rushed into AI are finding out that a 1 in 100 error rate is absurdly high when literally every action is done through an LLM.

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  • [–] 10 points 1 year ago (4 children)

    No third-party research ever arrived at such absurdly low number as 1% error rate.

    Internal OpenAi research from May 2025 said 50% error rate and growing.

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  • [–] 3 points 1 year ago (3 children)
  • [–] 2 points 1 year ago (2 children)

    I'm not sure what you're trying to say here?

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  • [–] 9 points 1 year ago* (last edited 1 year ago) (2 children)

    At the same time, that sounds like something you'd just use old-fashioned sentiment analysis for.

    It's less accurate, but also far less demanding, and doesn't risk hallucinating.

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  • [–] 1 point 1 year ago* (1 child)

    It’s less accurate

    and doesn’t risk hallucinating

    I might be mistaken, but don't these two lines mean the exact opposite in this context?

    Is AI more often right, or more often wrong?

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  • [–] 1 point 1 year ago

    Both, because the way it's right and wrong are different.

    Sentiment analysis might misclassify some of the data, but it doesn't risk making things up wholescale like an LLM would.

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  • I used to work for Comcast as a mobile app developer. We used to get uncountable numbers of reviews along the lines of "I gave this app one star because you can't give an app zero stars". Honestly depressing even though I wasn't personally responsible for the apps or the company.

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