Martin Bernklau is a German journalist who reported for decades on criminal trials. He looked himself up on Bing, which suggests you use its Copilot AI. Copilot then listed a string of crimes Bernk…
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.
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?
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?