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Cake day: September 27th, 2023

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  • As someone who’s learned Japanese a bunch: once you’re very familiar with the symbols, you don’t look at every little line to determine what character it is, just the general shape. The characters are built by combining a discrete and smaller set of “drawings” (called radicals). So the space of possible characters is limited to those combinations. On top of that, not every legal combination actually exists. You won’t suddenly run into 鬱, but with a different radical in the bottom left, unless you’re playing a trivia game of “spot the mistake” (which can even be difficult for native speakers, just in the same way it can be difficult for native English speakers to spell some words they’d have no trouble reading.)

    I would wager some misplaced lines wouldn’t hurt readabiliity much in the same way we, in English aren’t usually struggling to read a sentence even if some of the letters are swapped/missing or a “the” is duplicated, etc. I’m sure you’ve seen examples of that before in English (or your own native language if it isn’t English).

    Of course in some instances, even a tiny difference can change the meaning of a sentence entirely. This is also true both in English and logographic languages. Luckily our brains do a lot of subconscious work here too and figure out where special attention is and isn’t needed by using context and knowledge about the writing system.

    (Small caveat: of course, especially in languages, there are always exceptions to every rule. And also the brain can be tricked, intentionally or not, in a variety of ways.)



  • What we were taught and what I’ve seen a lot in the German speaking world was “punkt for strich”, “dot before line” since the addition and subtraction symbols are written with lines and the mult/div with dots (⋅ and :).

    The fact that parentheses/brackets are always top priority was taught separately (even before multiplication iirc) and once we got to powers/roots it was just quickly mentioned that they have higher prio than mult/div/add/sub.



  • The “5 seconds after they started moving” is relevant. If we assume this takes place on Earth (i.e. on the surface of a sphere with a set pair of north/south poles), the angle between the two vectors changes depending on their current position.

    If it’s not on the equator, it’s also slightly up to interpretation if “Due East” means they’ll turn to stay on the same latitude, always adjusting to stay moving east forever or if they’ll do a great circle. In the former case, the north moving one will eventually get stuck at the north-pole too instead of continuing their circle around the globe. Most likely not within 5 seconds though, unless the place they started was within 25 feet of the north-pole.

    To actually do the math we’ll need to know (or somehow deduce) where “the place where everything about them began” is though.








  • Mirodir@discuss.tchncs.detoMicroblog Memes@lemmy.worldMycology
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    5 months ago

    Part of the problem here is that AI is mostly done by companies with billions of investments and in turn they NEEEEEDDDDD engagement, so they all made their AI as agreeable as possible just so people would like it and stay, with results like these becoming much more “normal” than it should or could be

    I wonder how much of that is intentional vs a byproduct of their training pipeline. I didn’t keep up with everything (and those companies became more and more secretive as time went on), but iirc for GPT 3.5 and 4 they used human judges to judge responses. Then they trained a judge model that learns to sort a list of possible answers to a question the same way the human judges would.

    If that model learned that agreeing answers were on average more highly rated by the human judges, then that would be reflected in its orderings. This then makes the LLM more and more likely to go along with whatever the user throws at it as this training/fine-tuning goes on. Instead of the judges liking agreeing answers more on average, it could even be a training set balance issue, where there simply were more agreeing than disagreeing possible answers. A dataset imbalanced that way has a good chance of introducing a bias towards agreeing answers into the judge model. The judge model would then pass that bias onto the GPT model it is used for to train.

    Pure speculation time: since ChatGPT often produces two answers and asks the user which one the user prefers, I can only assume that the user in that case is taking the mantle of those human judges. It’s unsurprising that the average GenAI user prefers to be agreed with. So that’s also a very plausible source for that bias.


  • I accompanied my friend to a random LGS in Germany sometime last year. In there, the owner told me it was the LGS Kai Budde frequents and usually played in all the prereleases at. He also showed me his signature on a poster on the wall (“If you’re actually as into MTG as your friend claims, you should recognize this signature.” is how the conversation started. I did.) That’s also where I learned of his battle with cancer and that the prognosis was dire. Despite not really being very enfranchised in MTG over the last few years, I’ve thought back to that interaction and to Kai Budde quite a few times since then.

    I’m not entirely sure where I was going with that. Just sharing what popped into my mind.