Free software people: A major goal of free software is for individuals to be able to cause software to behave in the way they want it toLLMs: (enable that)Free software people: Oh no not like that
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Free software people: A major goal of free software is for individuals to be able to cause software to behave in the way they want it to
LLMs: (enable that)
Free software people: Oh no not like that@mjg59 I think the issue is more on the forcing of LLMs/AI in *everything* right now, not specifically F/OSS projects. It reeks of dot-com bubble era marketing and in many cases is completely unnecessary.
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@engideer @david_chisnall @mjg59 @ignaloidas I don’t think llms are “rando”. They have randomized elements during training and inference, but they’re not a random number generator. I also would trust a “rando” less than an expert in real life. I wouldn’t trust either blindly either.
@engideer @david_chisnall @mjg59 @ignaloidas also I didn’t say anything of what you quoted, and I don’t know where you got it from.
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@ignaloidas @mjg59 @david_chisnall @newhinton how did you gain your confidence? How can you call machine learning a bunch of dice? I try to study and build things everyday and yes I don’t trust my code at all, which I think is a healthy attitude to have? I am definitely not able to produce perfect code on the first try.
@mnl@hachyderm.io @mjg59@nondeterministic.computer @david_chisnall@infosec.exchange @newhinton@troet.cafe through repeated checks and knowledge that humans are consistent.
And like, really, you don't trust your code at all? I, for example, know that the code I wrote is not going to cheat by unit tests, not going to re-implement half of the things from scratch when I'm working on a small feature, nor will it randomly delete files. After working with people for a while, I can be fairly sure that the code they've written can be trusted to the same standards. LLMs can't be trusted with these things, and in fact have been documented to do all of these things.
It is not a blind, absolute trust, but trust within reason. The fact that I have to explain this to you is honestly embarrassing. -
@mjg59 but wait, there's more
What if you're not renowned security expert and open-source celebrity @mjg59 (that currently works at nvidia btw, profiting from the LLM boom, sorry) but just some guy trying to make ends meet doing some coding?...
Now you get an LLM mandate from your company that comes with the implication that 'either you boost your productivity with 80% or we fire you and contract a cheap prompter in your place'...
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@engideer @david_chisnall @mjg59 @ignaloidas I don’t think llms are “rando”. They have randomized elements during training and inference, but they’re not a random number generator. I also would trust a “rando” less than an expert in real life. I wouldn’t trust either blindly either.
@mnl@hachyderm.io @engideer@tech.lgbt @david_chisnall@infosec.exchange @mjg59@nondeterministic.computer LLMs are very much a random number generators. The distribution is far, far from uniform, but the whole breakthrough of LLMs was the introduction of "temperature", quite literally random choices, to break them out of monotonous tendencies.
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@mnl@hachyderm.io @mjg59@nondeterministic.computer @david_chisnall@infosec.exchange @newhinton@troet.cafe through repeated checks and knowledge that humans are consistent.
And like, really, you don't trust your code at all? I, for example, know that the code I wrote is not going to cheat by unit tests, not going to re-implement half of the things from scratch when I'm working on a small feature, nor will it randomly delete files. After working with people for a while, I can be fairly sure that the code they've written can be trusted to the same standards. LLMs can't be trusted with these things, and in fact have been documented to do all of these things.
It is not a blind, absolute trust, but trust within reason. The fact that I have to explain this to you is honestly embarrassing.@ignaloidas @mjg59 @david_chisnall @newhinton but “fairly sure” is not full trust. I can also be “fairly sure” that something works, but I’m not going to trust my judgment and instead will try to validate it and provide proper guardrails so that if it is misbehaving, it is at least contained. Some things will be just fine even if broken, some less and will make me invest me more of my time. I am not going to try to prove the kernel correct just because I am changing a css color. I don’t see how that is different with llms, and I use them everyday. If anything, they allow me to validate more.
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@ignaloidas @mjg59 @david_chisnall @newhinton but “fairly sure” is not full trust. I can also be “fairly sure” that something works, but I’m not going to trust my judgment and instead will try to validate it and provide proper guardrails so that if it is misbehaving, it is at least contained. Some things will be just fine even if broken, some less and will make me invest me more of my time. I am not going to try to prove the kernel correct just because I am changing a css color. I don’t see how that is different with llms, and I use them everyday. If anything, they allow me to validate more.
@mnl@hachyderm.io @mjg59@nondeterministic.computer @david_chisnall@infosec.exchange @newhinton@troet.cafe you are falling down the cryptocurrency fallacy, assuming that you cannot trust anyone and as such have to build stuff assuming everyone is looking to get one over you.
This is tiresome, and I do not care to discuss with you on this any longer, if you cannot understand that there are levels between "no trust" and "absolute trust", there is nothing more to discuss. -
@mnl@hachyderm.io @engideer@tech.lgbt @david_chisnall@infosec.exchange @mjg59@nondeterministic.computer LLMs are very much a random number generators. The distribution is far, far from uniform, but the whole breakthrough of LLMs was the introduction of "temperature", quite literally random choices, to break them out of monotonous tendencies.
@ignaloidas @mjg59 @david_chisnall @engideer temperature based sampling is just one of the many sampling modalities. Nucleus sampling, top-k, frequency penalties, all of these introduce controlled randomness to improve the performance of llms as measured by a wide variety of benchmarks.
A random sampling of tokens would actually be uniformly distributed… and obviously grammatically correct sentences is a clear sign that we are not randomly sampling tokens.
Are we talking about the same thing?
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@mnl@hachyderm.io @mjg59@nondeterministic.computer @david_chisnall@infosec.exchange @newhinton@troet.cafe you are falling down the cryptocurrency fallacy, assuming that you cannot trust anyone and as such have to build stuff assuming everyone is looking to get one over you.
This is tiresome, and I do not care to discuss with you on this any longer, if you cannot understand that there are levels between "no trust" and "absolute trust", there is nothing more to discuss.@ignaloidas @mjg59 @david_chisnall @newhinton I think you are misreading what I am saying. That is exactly what I am saying. I never fully trust my code, not a single line of it, partly because every line of my code usually requires billions of lines of code I haven’t written to run. I can apply methods and use my experience to trust it enough to run it.
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@ignaloidas @mjg59 @david_chisnall @engideer temperature based sampling is just one of the many sampling modalities. Nucleus sampling, top-k, frequency penalties, all of these introduce controlled randomness to improve the performance of llms as measured by a wide variety of benchmarks.
A random sampling of tokens would actually be uniformly distributed… and obviously grammatically correct sentences is a clear sign that we are not randomly sampling tokens.
Are we talking about the same thing?
@mnl@hachyderm.io @mjg59@nondeterministic.computer @david_chisnall@infosec.exchange @engideer@tech.lgbt the fact that something is random does not mean that it has a uniform distribution. "controlled randomness" is still randomness. Taking random points in a unit circle by taking two random numbers for distance and direction will not result in a uniform distribution, but it's still random.
like, do you even read what you're writing? I'm starting to understand why you don't trust the code you wrote -
@mnl@hachyderm.io @mjg59@nondeterministic.computer @david_chisnall@infosec.exchange @engideer@tech.lgbt the fact that something is random does not mean that it has a uniform distribution. "controlled randomness" is still randomness. Taking random points in a unit circle by taking two random numbers for distance and direction will not result in a uniform distribution, but it's still random.
like, do you even read what you're writing? I'm starting to understand why you don't trust the code you wrote@ignaloidas @mjg59 @david_chisnall @engideer now you are talking about absolute trust. I do think we are indeed talking about different things. Do you use LLMs? Do you assign the same level of trust to qwen-3.6 than to gpt-2? because I do not, partly based on benchmarks, partly on personal experience, partly on my (admittedly perfunctory) theoretical understanding of its training and inference setup.
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Look, coders, we are not writers. There's no way to turn "increment this variable" into life changing prose. The creativity exists outside the code. It always has done and it always will do. Let it go.
@mjg59 Indeed.
This is why code generation is not a solution to the problem.
Which problem? People will phrase it differently, but the basic idea is to outsource *the hard part*, which is analysis and phrasing requirements to guide the LLM.
LLMs suck at dealing with shitty specs. They even suck at dealing with good specs. They even suck at dealing with specs they themselves suggested.
Outsourcing Thought Is Going Great
On AI generated test code, and how mind-bogglingly stupid that is.
Mad Ramblings of a Cyber Arcanist (finkhaeuser.de)
So using LLMs isn't solving the problem, which is that thinking is hard.
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@mjg59 but wait, there's more
What if you're not renowned security expert and open-source celebrity @mjg59 (that currently works at nvidia btw, profiting from the LLM boom, sorry) but just some guy trying to make ends meet doing some coding?...
Now you get an LLM mandate from your company that comes with the implication that 'either you boost your productivity with 80% or we fire you and contract a cheap prompter in your place'...
If the cheap prompter can produce the same results, what are the arguments against this?
- copyright violation in the training material
- excessively high use of the world's resources for training and inferenceIf both of those were handled (that's a big if. Maybe someday, maybe not) what were the arguments be against choosing the cheap Proctor?
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@mjg59 you’re doing the thing where you’re romanticizing another profession by assuming the grass is greener. most writers are not novelists. most are writing pretty dry ad copy or instruction manuals or something, just like most programmers aren’t writing especially novel or beautiful algorithms (or, for that matter, video games where algorithmic processes evoke a feeling). you’re just confusing form and content here
@mjg59 and yeah, “not like that” is actually valid, it’s just “having standards”, when “like that” is plagiaristic and error-prone and unsustainable and ecologically damaging on a world-historic scale. you don’t have to cancel every ethical principle you have so you can make a button a color you like better, even if you don’t really know how to code. you can argue that this ethical calculus is *wrong* but it is very silly indeed to pretend it’s contradictory gibberish
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Look, coders, we are not writers. There's no way to turn "increment this variable" into life changing prose. The creativity exists outside the code. It always has done and it always will do. Let it go.
@mjg59 you’re doing the thing where you’re romanticizing another profession by assuming the grass is greener. most writers are not novelists. most are writing pretty dry ad copy or instruction manuals or something, just like most programmers aren’t writing especially novel or beautiful algorithms (or, for that matter, video games where algorithmic processes evoke a feeling). you’re just confusing form and content here
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If the cheap prompter can produce the same results, what are the arguments against this?
- copyright violation in the training material
- excessively high use of the world's resources for training and inferenceIf both of those were handled (that's a big if. Maybe someday, maybe not) what were the arguments be against choosing the cheap Proctor?
@seanfurey @mjg59 lmao. Assuming a total of 20 million software developers world-wide, what is the problem with firing 5-10 million people in the span of 1-2 years? You really can't think of any problem with this except the blatant copyright violations and disastrous environmental impact? Those are people my guy, they and their families need food, shelter, healthcare, and people can't just choose a new craft, let alone while competing with a couple of million in the same situation...
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Free software people: A major goal of free software is for individuals to be able to cause software to behave in the way they want it to
LLMs: (enable that)
Free software people: Oh no not like thatif i am honest the price of such, psychotic breaks, isn't worth the freedom of per request billing
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if i am honest the price of such, psychotic breaks, isn't worth the freedom of per request billing
@mjg59 it is a fair criticism of free software that they haven't managed to meaningfully increase people's agency over the computer
but it is a flight of fancy to suggest that extractive labor and outsourcing gives people that agency or control
even before we get to the "software that kills teenagers" part of the faustian pact
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R relay@relay.infosec.exchange shared this topic
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@mjg59 and yeah, “not like that” is actually valid, it’s just “having standards”, when “like that” is plagiaristic and error-prone and unsustainable and ecologically damaging on a world-historic scale. you don’t have to cancel every ethical principle you have so you can make a button a color you like better, even if you don’t really know how to code. you can argue that this ethical calculus is *wrong* but it is very silly indeed to pretend it’s contradictory gibberish
@glyph I think I've covered why the plagiarism bit feels less true to me for code than for other fields, and I don't think the error prone aspect of it matters for the cases I'm thinking of. The world burning and economic destruction and loss of human skills are certainly a consequence of how these things are currently deployed but it's not inherent (at least, not to anywhere near this scale), and having it be an immediate "no" rather than "Is there an ethical way to do this" feels rough
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@glyph I think I've covered why the plagiarism bit feels less true to me for code than for other fields, and I don't think the error prone aspect of it matters for the cases I'm thinking of. The world burning and economic destruction and loss of human skills are certainly a consequence of how these things are currently deployed but it's not inherent (at least, not to anywhere near this scale), and having it be an immediate "no" rather than "Is there an ethical way to do this" feels rough
@mjg59 it sounds unconvincing to me. the plagiarism thing has to do with sustainability, not just aesthetics. software errors tend to be chaotic and compounding and thus you’d need strong edges to the sandbox where the agents were allowed to play, which we don’t have. and the “inherent”-ness is a red herring. it doesn’t matter if there’s a *pretend* version of this tech that is ethical, the real-life version we have has the problems it has, and I haven’t heard any plausible way to separate them
