I think AI is neat.

  • @Feathercrown
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    310 months ago

    They can summarize articles fairly well

    • @[email protected]
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      210 months ago

      No, they can summarise articles very convincingly! Big difference.

      They have no model of what’s important, or truth. Most of the time they probably do ok but unless you go read the article you’ll never know if they left out something critical, hallucinated details, or inverted the truth or falsity of something.

      That’s the problem, they’re not an intern they don’t have a human mind. They recognise patterns in articles and patterns in summaries, they non deterministically adjust the patterns in the article towards the patterns in summaries of articles. Do you see the problem? They produce stuff that looks very much like an article summary but do not summarise, there is no intent, no guarantee of truth, in fact no concern for truth at all except what incidentally falls out of the statistical probability wells.

      • @Feathercrown
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        110 months ago

        That’s a good way of explaining it. I suppose you’re using a stricter definition of summary than I was.

        • @[email protected]
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          10 months ago

          I think it’s really important to keep in mind the separation between doing a task and producing something which looks like the output of a task when talking about these things. The reason being that their output is tremendously convincing regardless of its accuracy, and given that writing text is something we only see human minds do it’s so easy to ascribe intent behind the emission of the model that we have no reason to believe is there.

          Amazingly it turns out that often merely producing something which looks like the output of a task apparently accidentally accomplishes the task on the way. I have no idea why merely predicting the next plausible word can mean that the model emits something similar to what I would write down if I tried to summarise an article! That’s fascinating! but because it isn’t actually setting out to do that there’s no guarantee it did that and if I don’t check the output will be indistinguishable to me because that’s what the models are built to do above all else.

          So I think that’s why we to keep them in closed loops with person -> model -> person, and explaining why and intuiting if a particularly application is potentially dangerous or not is hard if we don’t maintain a clear separation between the different processes driving human vs llm text output.

          • @Feathercrown
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            110 months ago

            Yeah this is basically how I think of them too. It’s fascinating that it works.

          • @[email protected]
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            10 months ago

            You are so extremely outdated in your understanding, For one that attacks others for not implementing their own llm

            They are so far beyond the point you are discussing atm. Look at autogen and memgpt approaches, the way agent networks can solve and develop way beyond that point we were years ago.

            It really does not matter if you implement your own llm

            Then stay out of the loop for half a year

            It turned out that it’s quite useless to debate the parrot catchphrase, because all intelligence is parroting

            It’s just not useful to pretend they only “guess” what a summary of an article is

            They don’t. It’s not how they work and you should know that if you made one

              • @[email protected]
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                09 months ago

                No they don’t, and your idiotic personal attacks won’t change how the tech works. You are just wrong. I don’t love them, I don’t care about Altman. I was just trying to tell you you are spreading misinformation. But nah defensive slurs it is