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Joined 4 months ago
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Cake day: July 10th, 2024

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  • Fair point. Although one may say this is fine here for comic purposes.

    The same argument could be made about the statement “Gods perfect creation”.
    But I’d argue that the suggestion of a creationist god expands the distance to scientific contexts even more while simple speech bubbles are fine due to less ideological conflict potential.

    Admittedly, I am also rather allergic to religions, which is why I am having a difficult time with that part of the meme.







  • The level of your argumentation:
    Are you a firefighter or a medical doctor? If not, you’re obviously in favour of fires, death and disease.
    Why aren’t you donating all of your stuff to homeless people? Or are you happy all those people don’t have a home?
    Why aren’t you saving the world already???

    You know, demanding change and maybe showing some sort of protest does not mean you need to do those things exactly as you would like to see them, especially if those efforts wouldn’t change anything on the larger scale and rather lead to a bunch of problems in your life.


  • I feel this. Fell into a similar rabbit hole when I tried to get realtime feedback on the program’s own memory usage, discerning stuff like reserved and actually used virtual memory. Felt like black magic and was ultimately not doable within the expected time constraints without touching the kernel I suppose. Spent too much time on that and had to move on with no other solution than to measure/compute the allocated memory of the largest payload data types.








  • If we’re speaking of transformer models like ChatGPT, BERT or whatever: They don’t have memory at all.

    The closest thing that resembles memory is the accepted length of the input sequence combined with the attention mechanism. (If left unmodified though, this will lead to a quadratic increase in computation time the longer that sequence becomes.) And since the attention weights are a learned property, it is in practise probable that earlier tokens of the input sequence get basically ignored the further they lie “in the past”, as they usually do not contribute much to the current context.

    “In the past”: Transformers technically “see” the whole input sequence at once. But they are equipped with positional encoding which incorporates spatial and/or temporal ordering into the input sequence (e.g., position of words in a sentence). That way they can model sequential relationships as those found in natural language (sentences), videos, movement trajectories and other kinds of contextually coherent sequences.