It Took Many Years And Billions Of Dollars, But Microsoft Finally Invented A Calculator That Is Wrong Sometimes | Defector
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It’s not AI winter just yet, though there is a distinct chill in the air. Meta is shaking up and downsizing its artificial intelligence division. A new report out of MIT finds that 95 percent of companies’ generative AI programs have failed to earn any profit whatsoever. Tech stocks tanked Tuesday, regarding broader fears that […]

Money quote:

Excel requires some skill to use (to the point where high-level Excel is a competitive sport), and AI is mostly an exercise in deskilling its users and humanity at large.

  • theparadox@lemmy.worldEnglish
    19·
    1 day ago

    I think the concern is that you can come up with a number of formulas that will get correct answers for some combinations of values and not others.

    If you do not understand the logic of the formula, and what each function does, how do you verify they are correct and will always give you the results you think they will? Double check every result in its entirety?

    • Alaknár@sopuli.xyzEnglish
      1·
      6 hours ago

      I think you’re completely missing the point here.

      I’m not great at Excel. That doesn’t mean I can’t do basic math, it means I struggle designing an xlookup or hlookup.

      If AI does that for me, I’ll be a happy bunny. And then run a dozen different iterations of data to verify that the results I’m getting are correct.

      This is what this integration is for - it’s not a replacement for a human brain, it’s an assistant. As are all LLMs.

    • Lightfire228@pawb.socialEnglish
      7·
      2 days ago

      That’s my thinking

      If you know what you’re doing, it’s significantly easier to do it yourself

      You at least have some reassurance it’s correct (or at least thought through)

      • FishFace@lemmy.worldEnglish
        2·
        1 day ago

        Verification is important, but I think you’re omitting from your imagination a real and large category of people who have a basic familiarity with spreadsheets and computers, so are able to understand a potential solution and see whether it makes sense, but who do not have the ability to quickly come up with it themselves.

        In language it’s the difference between receptive and productive vocabulary: there are words which you understand but which you would never say or write because they’re part of your receptive, but not productive knowledge.

        There are times when this will go wrong, because the LLM will can produce something plausible but incorrect and such a person will fail to spot it. And of course if you blindly trust it with something you’re not actually capable of (or willing to) check then you will also get bad results.