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…
Copilot then listed a string of crimes Bernklau had supposedly committed — saying that he was an abusive undertaker exploiting widows, a child abuser, an escaped criminal mental patient. [SWR, in German]
These were stories Bernklau had written about. Copilot produced text as if he was the subject. Then Copilot returned Bernklau’s phone number and address!
and there’s fucking nothing in place to prevent this utterly obvious failure case, other than if you complain Microsoft will just lazily regex for your name in the result and refuse to return anything if it appears
god, so this is actually the best the AI researchers can do with the tools they’ve shit out into the world without giving any thought to failure cases or legal liability (beyond their manager on slackTeams claiming it’s been taken care of)
so fuck it, let’s make the defamation machine a non-optional component of windows. we’ll just make it a P0 when someone who could actually get us in legal trouble complains! everyone else is a P2 that never gets assigned.
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?
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.
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
I’m having a sneaking suspicion that this is what they do for all the viral ‘here the LLM famously says something wrong’ problems, as I don’t think they can actually reliably train the model it made an error.
That’s the most straightforward fix. You can’t actually fix the output of an LLM, so you have to run something on the output. You can have it scanned by another AI but that costs money and is also fallible. Regex/delete is the most reliable way to censor.
Yes, and then the problem is that this doesn’t really scale well. Esp as it is always hard to regexp all the variants correctly without false positives and negatives. Time to regexp html ;).
Yeah, and you can really see this in image generation. There’s often blocks on using the names of celebrities in the prompts, but if you misspell the names enough it can bypass the censor, and the image generator still understands it.
and there’s fucking nothing in place to prevent this utterly obvious failure case, other than if you complain Microsoft will just lazily regex for your name in the result and refuse to return anything if it appears
it helps they did it to someone with contacts and it was on prime time news telly
god, so this is actually the best the AI researchers can do with the tools they’ve shit out into the world without giving any thought to failure cases or legal liability (beyond their manager on
slackTeams claiming it’s been taken care of)so fuck it, let’s make the defamation machine a non-optional component of windows. we’ll just make it a P0 when someone who could actually get us in legal trouble complains! everyone else is a P2 that never gets assigned.
llms are (approximately) advanced versions of predictive text, any censorship will make them worse.
worse at what, exactly?
Predicting words.
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?
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.
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.
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?
I’m having a sneaking suspicion that this is what they do for all the viral ‘here the LLM famously says something wrong’ problems, as I don’t think they can actually reliably train the model it made an error.
That’s the most straightforward fix. You can’t actually fix the output of an LLM, so you have to run something on the output. You can have it scanned by another AI but that costs money and is also fallible. Regex/delete is the most reliable way to censor.
Yes, and then the problem is that this doesn’t really scale well. Esp as it is always hard to regexp all the variants correctly without false positives and negatives. Time to regexp html ;).
Yeah, and you can really see this in image generation. There’s often blocks on using the names of celebrities in the prompts, but if you misspell the names enough it can bypass the censor, and the image generator still understands it.