It’s not always easy to distinguish between existentialism and a bad mood.

  • 7 Posts
  • 395 Comments
Joined 3 years ago
cake
Cake day: July 2nd, 2023

help-circle


  • In other Scott of Siskind news, he just posted an entirely unnecessary amount of words to aggressively push back against the adage that “all exponentials sooner or later turn into sigmoids” as if it was by itself a load bearing claim of the side arguing against the direct imminence of the machine god.

    It’s just a bunch of arguing by analogy ( “helping you build intuition” ) and you-can’t-really-knows while implying AI 2027 was very science much rigorous, but it also feels kind of desperate, like why are you bothering with this overperformative setting-the-record-straight thing, have you been feeling inadequate as an AI-curious stats fondler of note lately?






  • Well, you could maybe sort of train it not to generate “all men are cats”, but then that might also prevent it from making the more correct generalization “all cats are mortal” or even completely valid generalizations like combing “all men are mortal” and “Socrates is man” to get “Socrates is mortal”.

    Just wanted to say that that ‘tal’ comes after ‘mor’ when ‘soc-rate-s’ is in the near context and in agreement with the attention mechanism is a very different type of logic than what this phrasing implies. This is also in combination with the peculiarities of word embeddings (the technique by which the tokens are translated to numeric vectors) like how it has a hard time making something useful out of numbers, it uh gets uh complicated.

    The monofacts thing seems very post hoc and way too abstracted in comparison, and also the amount of text that can be categorized as strictly true or false isn’t that big all things considered.

    Still if the point was to formalize the very no-duh observation that a neural net isn’t supposed to output it’s dataset verbatim at all times hence hallucinations, then fine, I guess. Their proposed sort of solution (controlled miscalibration) even amounts to forcing the model to generalize less by memorizing more, which used to be the opposite of why you would choose to use this type of topography.