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Cake day: June 11th, 2023

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  • Y’know that physics principle called the lever principle, or principle of moment…?

    Thing is, if you grab a bottle by the neck and try to tilt it, you have to deal with the whole momentum / mass of the bottle, which is a significant amount of torque on your wrist, especially if you’re awkwardly trying to hold a cap that’s clearly not designed to be held this way at the same time.

    If you instead violently rip the cap out in an entirely justified fit of righteous rage and grab the bottle by it’s center of mass, as normal people do and have done since bottles have existed (well, except for the cap bit; that shit is rather new), you can effortlessly spin it to whatever angle you want, with perfect control all the way.

    Of course you can always hold it with two hands, which might be what you meant, but that’s a rather stupid waste of a free hand when most bottles are designed to be holdable with one single hand.


  • Luckily I’m not American, but I’ve never seen one of these contraptions that didn’t spin freely (and most of the ones I’ve seen spin freely and dangle all over the place, since the cap is tethered to the ring with a flexible strip of plastic).

    It’s a weight attached to a ring placed around a cylinder, after all. It’s bound to spin freely, it’s inherent to the design.


  • leftzero@lemmynsfw.comtoMildly Infuriating@lemmy.worldTethered Bottle Caps
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    3 days ago

    You can rotate the bottle before taking a sip to position it such that the cap doesn’t hit your face.

    And gravity will make the cap spin around, hit your face, get in the way of the liquid, and make it splash everywhere but your mouth.

    You can also pour liquid out of the bottle without having it run into the cap using the same rotation technique before pouring.

    Same issue. As soon as you tip the bottle the cap will spin (apparently whatever genius designed this useless annoyance didn’t realise that bottle necks are cylindrical), get in the way of the liquid, and make it spill everywhere but the container you’re trying to pour it into.

    They’re like a Pythagorean cup without the temperance lesson and well thought out design.

    The only way to use these without wasting 99% of the liquid and making a mess is to either awkwardly try to hold them up as you pour, or to violently rip them out before pouring in an entirely justified fit of righteous rage.

    What an utterly infuriating waste of plastic, time, and money.


  • The problem is not them being random.

    They are not random, that’s the point. They’re entirely deterministic and very precise, and they aren’t hiding anything; they will give you the most likely (not blacklisted) sequence of characters to follow your input according to their model. What they won’t give you is information, except by accident.

    If they were random (hidden or not) they’d be harmless, no one would trust them any more than one of those eight ball toys, or your average horoscope.

    The issue is that they’re very not random, so much that there’s no way to know if what they are saying bears any accidental semblance to the truth without fact checking… and that very soon they’ll have replaced any feasible way to fact check them, since all the supposed “facts” we’ll have access to will have been generated by LLMs train on LLM generated garbage.


  • If the models are random then we shouldn’t be trusting them to do anything, let alone serious applications.

    That’s not the reason we shouldn’t be using them for anything other than generating lorem ipsum style text or dialogue for non quest critical NPCs in games.

    The reason is that, paraphrasing Neil Gaiman, LLMs don’t generate information, they generate information shaped sentences.

    Specifically, an LLM takes a sequence of characters (not a word or text; LLMs have no concept of words, or text, or anything else for that matter; they’re just an application of statistics on large volumes of sequences of characters; no meaning or intelligence involved, artificial or not)… as I was saying, an LLM takes a sequence of characters, pushes it through its model, and outputs the sequence of characters most likely to follow it in the texts its model has been trained on (or rather, the most likely after discarding the ones its creators have labelled as politically incorrect).

    That’s all they do, and they’ll excellent at it (or would be if it weren’t for the aforementioned filters), but that’ll never give you a cure for cancer unless there already was one in their training data.

    They take texts written by humans, shred them, and give you their badly put back together dessicated corpses, drained of any and all meaning or information, but looking very convincingly (until you fact check them) like actually meaningful or informative texts.

    That is what makes them dangerous. That and the fact that the bastards selling them are marketing them for the jobs they’re least capable of doing, that is, providing reliable information.

    (And that’s while they can still be trained on meaningful and informative texts written by humans — inasmuch as anything found on reddit, facebook, or xitter can be considered to be meaningful or informative —, but given that a higher and higher percentage of the text on the internet is being generated by LLMs soon enough it’ll be impossible to train new models on anything but 99% LLM generated garbage, at which point the whole bubble will implode, as anyone who’s wasted time, paper, and toner playing with a photocopier or anyone familiar with the phrase “garbage in, garbage out” will already have realised… which is probably why the LLM peddlers are ignoring robots.txt and copyright laws in a desperate effort to scrape whatever’s left of the bottom of the barrel.)











  • Some of them are inventing completely new ways of doing things

    No, they’re not. All the money is now on the LLM autocomplete chatbots.

    Real progress on AI won’t resume until after the LLM bubble has burst. (And even then investors will probably be wary of putting money in AI for probably a few decades, because LLMs are being marked as AI despite having little to do with it.)

    It’s quite depressing, really.



  • I’m not talking about “machines” or any other generic term.

    I’m talking specifically about LLMs. And their limitations are evident. For instance, maths is one of the many things they can’t do (and will never be able to do in any efficient way).

    We have indeed, developed programs that play chess better than people (though sadly, until the LLM bubble pops we probably won’t get any further). But they’re not LLMs, or anything resembling an LLM. Because one of the other many things an LLM can’t do is play games of skill. Or reason. Or solve puzzles. Or even have a concept of strategy.

    LLMs, again, can only do one single thing. And that’s to pick up the one card from their deck that’s been picked up most often after the sequence of cards on the table according to their training model.

    That’s all they do. That’s all they’ll ever be able to do. Because that’s how they work. And, sure, with that you can make it look like they’re holding a conversation (until you ask them something that isn’t in their model), but that’s it.

    They’ll put words after another according to statistics (not, keep that in mind, according to meaning, or strategy, or anything like that; they don’t, and can’t know or care what the words mean, or whether the sentence they’ve put together makes any sense, or whether what it’s stating is true or false), and that’s that.

    They won’t play chess, they won’t write good innovative code, they won’t write original stories, and they won’t drive your car.

    We don’t need to know how what we call consciousness works to know that. We just need to know how LLMs work. And that we most definitely do.


  • Because there are many aspects of what we understand as “actual thinking” (understanding concepts, learning, or solving puzzles, for instance) that LLMs are fundamentally incapable of achieving no matter how larger or more complex we make them or how much we optimise them.

    They do one single thing (which, granted, they do relatively well): they take an input, they apply it to every token in their training data, generating a score for each of them, and they output the one with the highest score. And that’s all they do.

    And that’s why, for instance, you’ll never be able to make a LLM that’s any good at playing chess, because there simply wouldn’t be enough atoms in the universe for it to store all possible states of the game, which it would need to have in its training model in order to auto complete its next move (and that’s not even accounting for the actual score computation, both in space and time).

    They’re a cool fancy gimmick, possibly useful in certain cases as long as you can account for their hallucinations, but they’re not any closer to actual intelligence than Eliza ever was.


  • leftzero@lemmynsfw.comtoTechnology@lemmy.worldOpenAI Just Gave Away the Entire Game
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    1 month ago

    LLMs are incapable of “recognising” any patterns they haven’t been trained on.

    And they don’t really even recognise those, they’re just fancy auto complete engines, simply outputting the highest scored token from their training base based on their input.

    They’re pattern matching machines; there’s no recognition, inner modelling of new knowledge, self referencing, or understanding of any kind, merely blind statistics.

    They’re just bigger and fancier Eliza’s, and just as distant as Eliza was from any practical form of intelligence, artificial or natural.

    While I personally do believe that achieving AGI¹, on a Turing machine is possible, LLMs and how they work are an excellent example in support of John Searle’s arguments against it with his Chinese room though experiment.

    1— Or at least something equivalent to human intelligence, or better, in the measures by which we consider ourselves to be intelligent, though it’s arguable whether we can really be considered intelligent at all, or we’re just better, more complex, Chinese rooms.