I know Lemmy hates AI with a fiery passion (and I too hate it for various reasons), but the ability to make this sort of prediction in a way far more stable than whatever else came before with natural language processing (fancy term of the day for those who havem’t heard of it), and however inefficiently built and ran it is, is useful if you can nudge it enough in a certain direction. It can’t do functional things reliably, but if you contain it to only parse human language and extract very specific information, show it in a machine-parsable way, and then use that as input for something you can program, you’ve essentially built something that feels like it can understand you in human language for a handful of tasks and carry out those tasks (even if the carrying out part isn’t actually done by an LLM). So pedantically, it’s not AI, but most people not in tech don’t know or care about the difference. It’s all magic all the way down like how computers should just magically do what they’re thinking of. That’s not changed.
My point though, and this isn’t targeting you specifically dear OC, is that we can circlejerk all we want here, but echoing this oversimplification of what LLMs can do is pretty irrelevant to the bigger discourse. Call these companies out on their practices! Their hypocrisy! Their indifference to the collapse of our biosphere, human suffering, letting the most vulnerable to hang high and dry!
Tech is a tool, and if our best argument is calling a tool useless when it’s demonstrably useful in specific ways, we’re only making a fool of ourselves, turning people away from us and discouraging others from listening to us.
But if your goal is to feel good by letting one out, please be my guest.
The only way to know if LLM output is accurate is to know what an accurate output should look like, and if you know that, you don’t need an LLM. If you don’t know what an accurate output should look like, an LLM is equally likely to confidently lie to you as it is to help you, making you dumber the more you use it. The only other situation is if you know what an accurate output should look like, but you want an inaccurate one, which is a bad thing to encourage.
“Demonstrably useful” is a lie. It’s a blatant and obvious lie. LLMs are so actively detrimental to their users, and society as a whole, that calling them useless is being generous. And even if they were the most beneficial thing on the planet, there is still no reason to use the billionaire’s toxic Nazi plagiarism machine.
The only way to know if LLM output is accurate is to know what an accurate output should look like, and if you know that, you don’t need an LLM
I empathize with your overall standpoint, but that’s just plain wrong. There are a lot of problems where verifying an answer is much easier for a human (or non-LLM computer program) than coming up with a correct answer.
Anything that involves language manipulation, for example. I’ll have a much easier time checking a translation from English to German for accuracy than doing the full translation myself, assuming the model gets most of it correct and I don’t have to rewrite anything major (which is generally the case with current models). Or letting an LLM proof-read a text I wrote - I can’t be sure it got everything, but the things it does find are trivial for me to verify, and will often include things that slipped past me and three other people who proof-read the same text. Less useful, but still applicable to the premise: Producing a set of words that rhyme with a given one. Coming up with new ones after the first couple that pop into your head gets pretty hard, but checking if new candidates actually do rhyme is trivially easy.
Moving on from language-stuff, finding security issues in software is a huge one - finding those is often extremely hard, but verifying them is mostly pretty straightforward if the report is well prepared. Models are just now getting good enough to reliably produce good security reports for actual issues.
Answering questions about a big codebase, where the actual value doesn’t lie in the specific response the model gives, but pointing me to the correct places in the code where I can check for myself.
Producing code or entire programs is a bit more debatable and it depends heavily on the goal and the skill level of the operator whether complete verification is actually easier than doing it yourself.
Just a couple of examples. As I said I get where you’re coming from, but completely denying any kind of utility does not help your cause at all, it just make you look like an absolutist who doesn’t know what they’re talking about.
If you know enough to verify a translation as accurate, or you have the tools to figure out an accurate translation through dictionaries or some such, then you know enough to do the translation yourself. If you don’t, then I cannot trust your translation.
And if you can’t trust the output to be comprehensive or correct, then why would you trust something like system security to an LLM? Any security analyst who deserves their job would never take that risk. You don’t cut those corners.
Quick reminder: rhyming dictionaries exist. LLMs solved a solved problem, but worse.
Once again, even if the billionaire’s toxic Nazi plagiarism machine was useful, it is so morally repugnant that it should never be used, which makes it functionally useless. This is an absolute statement, but trying to “um actually” that makes you look like either a boot-licker, a pollutant, a Nazi, a plagiarist, an idiot, or some combination of those.
I would rather look like an absolutist. How about you?
If you know enough to verify a translation as accurate, or you have the tools to figure out an accurate translation through dictionaries or some such, then you know enough to do the translation yourself.
Correct. But it’s going to take me a lot more work and time, possibly to the point of not being feasible and probably even matching the energy cost of using the LLM over the entirety of the task.
why would you trust something like system security to an LLM?
I wouldn’t. I don’t know where you got that. Adding LLM-based analysis to your toolkit to spot important issues that otherwise might not have been found is just that: an addition. Not replacing anything. And it is demonstrablyusefulforthat at this point, there’s just no denying that.
Once again, even if the billionaire’s toxic Nazi plagiarism machine was useful, it is so morally repugnant that it should never be used, which makes it functionally useless.
My point is that if you are this confidently wrong about the capabilities of LLM-based tools, then why should I believe you to be any less wrong about the moral and ethical issues you’re raising? It looks like you’re either completely misinformed or deliberately fighting a strawman for a part of your argument, so it gives anyone on the other side an easy excuse to just not engage with the rest of it and just dismiss it entirely. That’s what I’m trying to get across here.
Are you saying that you need to have perfect technical knowledge of AI to know if a person that promotes it is immoral? It looks like a non sequitur to me.
No, that’s not what I’m saying. I’m saying that if someone wants their argument to be taken seriously, they should be willing to reevaluate parts of it that they’re very obviously wrong about, especially if, by their own admission, those parts don’t even matter in the face of the rest of the argument.
I’m just fed up with people feeling the need to have strong opinions on everything, even if they don’t actually know much about it. It’s fine if you don’t know anything about how capable current LLMs actually are. Especially as an opponent of LLMs for moral reasons, it makes total sense that you’d just be avoiding them and thus not really be that informed. It does not in any way weaken your argument. As long as you seem to have a good grip on what you know and what you don’t know, it’s all good. But being confidently wrong about things and refusing to reevaluate when getting pushback on that just signals that you neither know nor care about the limits of your own knowledge, and makes the entirety of your argument untrustworthy.
Surely, the energy cost to verify the translation would be the same as translating it? If you’re struggling that much, why are you translating it at all? I cannot trust your translation.
If you tell an LLM to generate reports, it will, regardless of the actual quality of the environment. It doesn’t know what’s secure and what isn’t. All you’ve shown it to do is convince the kinds of security analysts with a system so insecure as to have a LOT of good reports that their system is more secure than it is. Which is useless at best, detrimental at worst.
It’s useless for translation. It’s useless for security analysis. It’s useless for rhyming (I notice you didn’t mention that one). You’re trying so hard to prove how useful it is, and your failure demonstrates how useless it is.
You can’t condemn confident wrongness and defend LLMs. And you can’t defend the billionaire’s toxic Nazi plagiarism machine while questioning someone else’s morals. You can’t cherry-pick my argument and claim I’m the one fighting a strawman. …Well, not if you’re arguing in good faith.
Look, I’m not trying to argue against your moral stance. I’m neither saying it’s wrong nor that it’s outweighed by any usefulness, real or not. What I’m trying is get you to see that your claims about uselessness are undermining your moral argument, which would be a hell of a lot stronger if you were not hell-bent on denying any kind of utility! Because in the eyes of people that do perceive LLMs as useful (which is exactly the kind of people that need to hear about the moral issues), that just makes you seem out of touch and not worth listening to.
It’s useless for security analysis.
Have you looked at any of the four links I provided? You might be working on old data here because it’s a very recent development, but a lot of high profile open source maintainers are saying that AI-generated security reports are now generally pretty good and not slop anymore. They’re fixing actual bugs because of it, and more than ever. How can you call that useless?
Surely, the energy cost to verify the translation would be the same as translating it?
Uh, no? Have you ever translated something? Verifying a translation happens mostly at attentive reading speed, double it for probably reading it twice overall to focus on content and grammar separately, plus some overhead for correcting the occasional flaw and checking one or two things that I’m unsure about from the top of my head, so for the sake of argument let’s say three times slower than just reading normally. I don’t know about you, but three times slower than reading is still a lot faster than I would be able to produce a translation from scratch, weighing different word options against each other, how to get some flow into the reading experience, etc. If I’m translating into a language that I’m fluent but not native in that takes even longer, because the ratio between my passive and active vocabulary is worse. I can read (and thus verify) English at a much more sophisticated level than I’m able to talk or write, because the words and native idioms just don’t come to me as naturally, or sometimes even at all without a lot of mental effort and a Thesaurus. LLMs are just plain better at writing English than I have any hope of achieving in my lifetime, and I can still fully understand and verify the factual, orthographic and grammatical correctness of what they’re outputting easily. Those two things are not mutually exclusive.
It’s useless for rhyming (I notice you didn’t mention that one)
Yeah, because I’m focusing on the more relevant things. I disagree that it’s completely useless for rhyming, but it is a much weaker and more contrived point than the others, and going into that discussion would just derail things more for no added value.
Also, funny that you call me out for that, when you just fully ignored two use cases I mentioned in my initial comment (LLM proofreading texts, and answering questions about unfamiliar code bases). Those have a lot of legitimate utility for someone who’s not aware of or doesn’t care about the moral issues. And once again, that’s my point here - those people will not listen if they perceive you as talking about a fictional world where LLMs are completely useless, which fails to match up with their experience.
I know Lemmy hates AI with a fiery passion (and I too hate it for various reasons), but the ability to make this sort of prediction in a way far more stable than whatever else came before with natural language processing (fancy term of the day for those who havem’t heard of it), and however inefficiently built and ran it is, is useful if you can nudge it enough in a certain direction. It can’t do functional things reliably, but if you contain it to only parse human language and extract very specific information, show it in a machine-parsable way, and then use that as input for something you can program, you’ve essentially built something that feels like it can understand you in human language for a handful of tasks and carry out those tasks (even if the carrying out part isn’t actually done by an LLM). So pedantically, it’s not AI, but most people not in tech don’t know or care about the difference. It’s all magic all the way down like how computers should just magically do what they’re thinking of. That’s not changed.
My point though, and this isn’t targeting you specifically dear OC, is that we can circlejerk all we want here, but echoing this oversimplification of what LLMs can do is pretty irrelevant to the bigger discourse. Call these companies out on their practices! Their hypocrisy! Their indifference to the collapse of our biosphere, human suffering, letting the most vulnerable to hang high and dry!
Tech is a tool, and if our best argument is calling a tool useless when it’s demonstrably useful in specific ways, we’re only making a fool of ourselves, turning people away from us and discouraging others from listening to us.
But if your goal is to feel good by letting one out, please be my guest.
Peace
The only way to know if LLM output is accurate is to know what an accurate output should look like, and if you know that, you don’t need an LLM. If you don’t know what an accurate output should look like, an LLM is equally likely to confidently lie to you as it is to help you, making you dumber the more you use it. The only other situation is if you know what an accurate output should look like, but you want an inaccurate one, which is a bad thing to encourage.
“Demonstrably useful” is a lie. It’s a blatant and obvious lie. LLMs are so actively detrimental to their users, and society as a whole, that calling them useless is being generous. And even if they were the most beneficial thing on the planet, there is still no reason to use the billionaire’s toxic Nazi plagiarism machine.
I empathize with your overall standpoint, but that’s just plain wrong. There are a lot of problems where verifying an answer is much easier for a human (or non-LLM computer program) than coming up with a correct answer.
Anything that involves language manipulation, for example. I’ll have a much easier time checking a translation from English to German for accuracy than doing the full translation myself, assuming the model gets most of it correct and I don’t have to rewrite anything major (which is generally the case with current models). Or letting an LLM proof-read a text I wrote - I can’t be sure it got everything, but the things it does find are trivial for me to verify, and will often include things that slipped past me and three other people who proof-read the same text. Less useful, but still applicable to the premise: Producing a set of words that rhyme with a given one. Coming up with new ones after the first couple that pop into your head gets pretty hard, but checking if new candidates actually do rhyme is trivially easy.
Moving on from language-stuff, finding security issues in software is a huge one - finding those is often extremely hard, but verifying them is mostly pretty straightforward if the report is well prepared. Models are just now getting good enough to reliably produce good security reports for actual issues.
Answering questions about a big codebase, where the actual value doesn’t lie in the specific response the model gives, but pointing me to the correct places in the code where I can check for myself.
Producing code or entire programs is a bit more debatable and it depends heavily on the goal and the skill level of the operator whether complete verification is actually easier than doing it yourself.
Just a couple of examples. As I said I get where you’re coming from, but completely denying any kind of utility does not help your cause at all, it just make you look like an absolutist who doesn’t know what they’re talking about.
If you know enough to verify a translation as accurate, or you have the tools to figure out an accurate translation through dictionaries or some such, then you know enough to do the translation yourself. If you don’t, then I cannot trust your translation.
And if you can’t trust the output to be comprehensive or correct, then why would you trust something like system security to an LLM? Any security analyst who deserves their job would never take that risk. You don’t cut those corners.
Quick reminder: rhyming dictionaries exist. LLMs solved a solved problem, but worse.
Once again, even if the billionaire’s toxic Nazi plagiarism machine was useful, it is so morally repugnant that it should never be used, which makes it functionally useless. This is an absolute statement, but trying to “um actually” that makes you look like either a boot-licker, a pollutant, a Nazi, a plagiarist, an idiot, or some combination of those.
I would rather look like an absolutist. How about you?
Correct. But it’s going to take me a lot more work and time, possibly to the point of not being feasible and probably even matching the energy cost of using the LLM over the entirety of the task.
I wouldn’t. I don’t know where you got that. Adding LLM-based analysis to your toolkit to spot important issues that otherwise might not have been found is just that: an addition. Not replacing anything. And it is demonstrably useful for that at this point, there’s just no denying that.
My point is that if you are this confidently wrong about the capabilities of LLM-based tools, then why should I believe you to be any less wrong about the moral and ethical issues you’re raising? It looks like you’re either completely misinformed or deliberately fighting a strawman for a part of your argument, so it gives anyone on the other side an easy excuse to just not engage with the rest of it and just dismiss it entirely. That’s what I’m trying to get across here.
Are you saying that you need to have perfect technical knowledge of AI to know if a person that promotes it is immoral? It looks like a non sequitur to me.
No, that’s not what I’m saying. I’m saying that if someone wants their argument to be taken seriously, they should be willing to reevaluate parts of it that they’re very obviously wrong about, especially if, by their own admission, those parts don’t even matter in the face of the rest of the argument.
I’m just fed up with people feeling the need to have strong opinions on everything, even if they don’t actually know much about it. It’s fine if you don’t know anything about how capable current LLMs actually are. Especially as an opponent of LLMs for moral reasons, it makes total sense that you’d just be avoiding them and thus not really be that informed. It does not in any way weaken your argument. As long as you seem to have a good grip on what you know and what you don’t know, it’s all good. But being confidently wrong about things and refusing to reevaluate when getting pushback on that just signals that you neither know nor care about the limits of your own knowledge, and makes the entirety of your argument untrustworthy.
Surely, the energy cost to verify the translation would be the same as translating it? If you’re struggling that much, why are you translating it at all? I cannot trust your translation.
If you tell an LLM to generate reports, it will, regardless of the actual quality of the environment. It doesn’t know what’s secure and what isn’t. All you’ve shown it to do is convince the kinds of security analysts with a system so insecure as to have a LOT of good reports that their system is more secure than it is. Which is useless at best, detrimental at worst.
It’s useless for translation. It’s useless for security analysis. It’s useless for rhyming (I notice you didn’t mention that one). You’re trying so hard to prove how useful it is, and your failure demonstrates how useless it is.
You can’t condemn confident wrongness and defend LLMs. And you can’t defend the billionaire’s toxic Nazi plagiarism machine while questioning someone else’s morals. You can’t cherry-pick my argument and claim I’m the one fighting a strawman. …Well, not if you’re arguing in good faith.
Look, I’m not trying to argue against your moral stance. I’m neither saying it’s wrong nor that it’s outweighed by any usefulness, real or not. What I’m trying is get you to see that your claims about uselessness are undermining your moral argument, which would be a hell of a lot stronger if you were not hell-bent on denying any kind of utility! Because in the eyes of people that do perceive LLMs as useful (which is exactly the kind of people that need to hear about the moral issues), that just makes you seem out of touch and not worth listening to.
Have you looked at any of the four links I provided? You might be working on old data here because it’s a very recent development, but a lot of high profile open source maintainers are saying that AI-generated security reports are now generally pretty good and not slop anymore. They’re fixing actual bugs because of it, and more than ever. How can you call that useless?
Uh, no? Have you ever translated something? Verifying a translation happens mostly at attentive reading speed, double it for probably reading it twice overall to focus on content and grammar separately, plus some overhead for correcting the occasional flaw and checking one or two things that I’m unsure about from the top of my head, so for the sake of argument let’s say three times slower than just reading normally. I don’t know about you, but three times slower than reading is still a lot faster than I would be able to produce a translation from scratch, weighing different word options against each other, how to get some flow into the reading experience, etc. If I’m translating into a language that I’m fluent but not native in that takes even longer, because the ratio between my passive and active vocabulary is worse. I can read (and thus verify) English at a much more sophisticated level than I’m able to talk or write, because the words and native idioms just don’t come to me as naturally, or sometimes even at all without a lot of mental effort and a Thesaurus. LLMs are just plain better at writing English than I have any hope of achieving in my lifetime, and I can still fully understand and verify the factual, orthographic and grammatical correctness of what they’re outputting easily. Those two things are not mutually exclusive.
Yeah, because I’m focusing on the more relevant things. I disagree that it’s completely useless for rhyming, but it is a much weaker and more contrived point than the others, and going into that discussion would just derail things more for no added value. Also, funny that you call me out for that, when you just fully ignored two use cases I mentioned in my initial comment (LLM proofreading texts, and answering questions about unfamiliar code bases). Those have a lot of legitimate utility for someone who’s not aware of or doesn’t care about the moral issues. And once again, that’s my point here - those people will not listen if they perceive you as talking about a fictional world where LLMs are completely useless, which fails to match up with their experience.
Sometimes i use AI even if i know the answer because i am a lazy person, and holy shit, i can confirm that it lies a lot and tells wrong shit
We already have tools that can give us incorrect answers in natural human language.
And they post their videos to youtube for free.