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  • How do you measure good/bad at predicting words? What's the metric? Cause it doesn't seem to be "the words make factual sense" if you're defending this.

    • like fuck, all you or I want out of these wandering AI jackasses is something vaguely resembling a technical problem statement or the faintest outline of an algorithm. normal engineering shit.

      but nah, every time they just bullshit and say shit that doesn’t mean a damn thing as if we can’t tell, and when they get called out, every time it’s the “well you ¡haters! just don’t understand LLMs” line, as if we weren’t expecting a technical answer that just never came (cause all of them are only just cosplaying as technically skilled people and it fucking shows)

      • It's weird how these people want everyone to believe that they're a new class of tech-priest but they also give off the vibe that they'd throw away their laptop if they accidentally deleted the Microsoft Edge icon.

      • I was thinking about this after reading the P(Dumb) post.

        All normal ML applications have a notion of evalutaion, e.g. the 2x2 table of {false,true}x{positive,negative}, or for clustering algorithms some metric of "goodness of fit". If you have that you can make an experiment that has quantifiable results, and then you can do actual science.

        I don't even know what the equivalent for LLMs is. I don't really have time to spare to dig through the papers, but like, how do they do this? What's their experimental evaluation? I don't seen an easy way to classify LLM outputs into anything really.

        The only way to do science is hypothesis->experiment->analysis. So how the fuck do the LLM people do this?

        • Right? "AI" is great if you want to sort a few million images of galaxies into their various morphological classifications and have it done before the end of the decade. A++, good job, no notes.

          You can't grift off of that very easily, though.

        • I’d really like to know too, especially given how many times we’ve already seen LLMs misused in scientific settings. it’s starting to feel like the LLM people don’t have that notion — but that’s crazy, right?

    • No. Predicting words is barely related to facts. I'll defend AI as an occasionally useful tool, but nothing it ever says should be taken as fact without confirmation. Sometimes that confirmation can be experimental — does this recipe taste good? Sometimes you need expert supervision to say this part was translated wrong or this code won't work because of xyz. Sometimes you have to go out and look it up.

      I like AI but there is a real problem treating it like the output means anything. It might give you a direction to look closer at, but it can never be the endpoint. We'd be better off not trying to censor it, but understanding it will bullshit you without blinking.

      I summarize all of that by saying AI is a useful tool, but a terrible product.

      • We’d be better off not trying to censor it

        this claim keeps getting brought up and every time it doesn’t seem to mean a damn thing, particularly since no, censoring the output of an LLM doesn’t do anything to its ability to predict text. censoring its training set would, but seeing as the topic of this thread is a fact an LLM fabricated by being just a dumb text predictor — there’s no real way to censor the training set to prevent this, LLMs are just shitty.

        I summarize all of that by saying AI is a useful tool

        trying to find a use case for this horseshit has broken your brain into thinking these worthless tools would have value if only they weren’t “being censored” or whatever cope you gleaned from the twitter e/accs

      • You're dodging the question. How do you evaluate if it's good at predicting words? How do you evaluate if a change made it better or worse?

52 comments