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Joined 1 year ago
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Cake day: June 10th, 2023

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  • Sounds like you’re anxious, which will lead to a stressful experience no matter where you’re seated. Airports tend to be large, crowded, confusing, and loud, with people constantly rushing around.

    The best way to improve your travel experience is to find techniques that help relax you as much as possible.

    If it’s a short haul flight, save yourself some hassle and put the seat selection out of your mind. You can use the time you would be worrying about and changing your seat to improve on ways you self-calm in stressful environments.

    If the flight is more than a couple of hours, I’d recommend switching to a window or aisle. The benefit of the aisle seat is you can occasionally stretch your legs in the aisle, and more importantly, you can leave your seat unimpeded. The window gives you something to lean on, as well as cool views, particularly during takeoff and landing. If you’re a nervous flyer that might be a negative.

    I find it helpful to remember that just because everyone else is in a rush, you don’t have to be. You don’t have to run to your terminal, you don’t have to rush to the front of the boarding line. You don’t need to be the first on or off the plane. You can get to the airport a tad early, to give yourself time to walk slowly and rest as you need it.

    There’s ample staff at just about every airport, if you don’t know where to go or what to do, ask them. Same is true on the plane itself, the flight crew is available to assist you.

    Enjoy your trip!








  • Let me expand a little bit.

    Ultimately the models come down to predicting the next token in a sequence. Tokens for a language model can be words, characters, or more frequently, character combinations. For example, the word “Lemmy” would be “lem” + “my”.

    So let’s give our model the prompt “my favorite website is”

    It will then predict the most likely token and add it into the input to build together a cohesive answer. This is where the T in GPT comes in, it will output a vector of probabilities.

    “My favorite website is”

    "My favorite website is "

    “My favorite website is lem”

    “My favorite website is lemmy”

    “My favorite website is lemmy.”

    “My favorite website is lemmy.org

    Woah what happened there? That’s not (currently) a real website. Finding out exactly why the last token was org, which resulted in hallucinating a fictitious website is basically impossible. The model might not have been trained long enough, the model might have been trained too long, there might be insufficient data in the particular token space, there might be polluted training data, etc. These models are massive and so determine why it’s incorrect in this case is tough.

    But fundamentally, it made up the first half too, we just like the output. Tomorrow some one might register lemmy.org, and now it’s not a hallucination anymore.


  • Very difficult, it’s one of those “it’s a feature not a bug” things.

    By design, our current LLMs hallucinate everything. The secret sauce these big companies add is getting them to hallucinate correct information.

    When the models get it right, it’s intelligence, when they get it wrong, it’s a hallucination.

    In order to fix the problem, someone needs to discover an entirely new architecture, which is entirely conceivable, but the timing is unpredictable, as it requires a fundamentally different approach.