My biggest problem with the concept of LLMs, even if they weren’t a giant plagiarism laundering machine and disaster for the environment, is that they introduce so much unpredictability into computing.
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@anyia@lgbtqia.space This is largely a work in progress for myself, as well, so there are some edges that I'm not too sure about myself, and it's definitely a subjective thing - this certainly works like that for me, but for yo or anyone else, I don't have the faintest idea.
Type A is mostly structured, and basically there to share something with you that I find extremely interesting. To do this justice, and to give you the full picture like you deserve, I have to give you the exhaistive rundown. I looked hard into this, and I'm just so excited to share this with you! This is mostly motivated by some need to share, meant to convey a complex bit of information, and I'll probably get upset if you're not excited, as well.
Type B is more exploratory, where I mostly verbalize the train of thought going on in my head. And believe you me, I can think and speak like an extremely pedantic text book. What I say draws on other things I know, but I am not quite sure where this one goes. This is mostly motivated by sharing my thoughts on a topic as they happen, meant to collaboratively work on a topic, but unfortunately, I'll get very upset if you cut into this, because that's cutting right into my thought process, and who likes to be interrupted just as you have an idea at the tip of your tongue.
Anyway, I don't know which belongs where, or even if they belong to specific neurotypes, but it is a hypothesis. From the outside, both probably feel quite like getting this text read at you at pretty high speed.
@twipped@twipped.social @JoscelynTransient@chaosfem.tw @faithisleaping@anarres.family@thatfrisiangirlish neat, thank you! I'd say I'm more likely to venture down path B. Path A feels like a lot of prep work. Or maybe it's a mix of the two? Often as I'm explaining something I realise I need to detour to provide necessary foundational knowledge, before returning to the first train of thought. Sometimes I get lost in the nesting.
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@thatfrisiangirlish neat, thank you! I'd say I'm more likely to venture down path B. Path A feels like a lot of prep work. Or maybe it's a mix of the two? Often as I'm explaining something I realise I need to detour to provide necessary foundational knowledge, before returning to the first train of thought. Sometimes I get lost in the nesting.
@anyia@lgbtqia.space There's not really much specific preparation involved in Type A infodumps. Preparation in immersing myself enough to do it, yes, but I did that, anyway, because I wanted to for my own reasons?
For example, the genetics of horse coats. I could spontaneously give a little speech and presentation about the Leopard complex that feels like I only lack a presentation running in the background to give it on a stage somewhere, but that's just from the whole topic well understood and filed away. It was definitely a useful trait to have in university, when no one in the group actually prepared their part in the presentation, but since I understood what we were writing about... @twipped@twipped.social @JoscelynTransient@chaosfem.tw @faithisleaping@anarres.family -
My biggest problem with the concept of LLMs, even if they weren’t a giant plagiarism laundering machine and disaster for the environment, is that they introduce so much unpredictability into computing. I became a professional computer toucher because they do exactly what you tell them to. Not always what you wanted, but exactly what you asked for.
LLMs turn that upside down. They turn a very autistic do-what-you-say, say-what-you-mean commmunication style with the machine into a neurotypical conversation talking around the issue, but never directly addressing the substance of problem.
In any conversation I have with a person, I’m modeling their understanding of the topic at hand, trying to tailor my communication style to their needs. The same applies to programming languages and frameworks. If you work with a language the way its author intended it goes a lot easier.
But LLMs don’t have an understanding of the conversation. There is no intent. It’s just a mostly-likely-next-word generator on steroids. You’re trying to give directions to a lossily compressed copy of the entire works of human writing. There is no mind to model, and no predictability to the output.
If I wanted to spend my time communicating in a superficial, neurotypical style my autistic ass certainly wouldn’t have gone into computering. LLMs are the final act of the finance bros and capitalists wrestling modern technology away from the technically literate proletariat who built it.
"There is zero artificial intelligence today. There could have been, but 50 years ago the decision was made by most scientists and companies to go with machine learning, which was quick and easy, instead of the difficult task of actually reverse engineering and then replicating the human brain.
So instead what we have today is machine learning combined with mass plagiarism which we call ‘generative AI’, essentially performing what is akin to a magic trick so that it appears, at times, to be intelligent.
While the topic of machine learning is complex in detail, it is simple in concept, which is all we have room for here. Essentially machine learning is simply presenting many thousands or millions of samples to a computer until the associative components ‘learn’ what it is, for example pictures of a daisy from all angles and incarnations.
Then companies scoured the internet in the greatest crime of mass plagiarism in history, and used the basic ability of machine learning to recognize nouns, verbs, etc. to chop up and recombine actual human writings and thoughts into ‘generative AI’.
So by recognizing basic grammar and hopefully deducing the basic ideas of a query, and then recombining human writings which appear to match that query, we get a very faulty appearance of intelligence - generative AI.
But the problem is, as I said in the beginning, there is no actual intelligence involved at all. These programs have no idea what a daisy, or love, or hate, or compassion, or a truck, or horse, or wagon, or anything else, actually is. They just have the ability to do a very faulty combinatorial trick to appear as if they do.
And while the human brain consumes around 20 watts, these massive pattern matching computers consume ever increasing billions.
However there is hope that actual general intelligence can be created because, thankfully, a handful of scientists rejected machine learning and instead have been working on recreating the connectome of the human brain for 50 years, and they are within a few decades of achieving that goal and truly replicating the human brain, creating true general intelligence.
In the meantime it's important for our species to recognize the danger of relying on generative AI for anything, as it's akin to relying on a magician to conjure up a real, physical, living, bunny rabbit.
So relying on it to drive cars, or control any critical systems, will always result in massive errors, often leading to real destruction and death."
SearingTruth -
@EmilyEnough Another thing is that it seems to hijack the thinking autonomy of a lot of people. People defer to an LLM instead of putting the struggle and effort into researching and learning. I'm not anti-convenience, but when we don't need to think about things anymore, the brain's thinking facilities just atrophy.
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As a system architect, this is also what I do. The thing is, I absolutely depend on the people who do the implementation having good judgement. They need to fill in the gaps (if there were no gaps, I would have an implementation already) but also tell me if there are real problems with some of the ideas. This is why the first thing I do with a design is have it reviewed by people who will implement it. If they tell me ‘actually, this thing you forgot to consider is where our critical path is’ then that often leads to a complete redesign, or at least to significant change. The LLM will just produce something. With an ‘agentic’ loop and some automated testing, it will produce something that passes my tests. But it won’t tell me I’m solving the wrong problem.
I don’t have a problem with constrained nondeterminism in general. There are loads of places where this is fine. The place I used machine learning in my PhD was in prefetching. Get it right and everything is faster. Get it wrong and you haven’t lost much. This kind of asymmetry is great for ML-based probabilistic approaches: the benefit of a correct answer massively outweighs the cost of an incorrect one. The other place it works well is if you have a way of immediately validating the output. I supervised a student using some machine-learning techniques to find better orderings of passes for LLVM. They were tuning for code size (in a student project, this was easier than performance, which requires more testing). You run the old and new versions, one is smaller. That gives you an immediate signal and so using non-deterministic state-space exploration is great. You (probably) won’t get the optimal solution but you will get a good one, for far less effort than trying to reason about the behaviour of the interactions between dozens of transforms.
It’s not clear to me that LLMs for programming have either of these properties.
@david_chisnall @rupert @EmilyEnough
"This kind of asymmetry is great for ML-based probabilistic approaches: the benefit of a correct answer massively outweighs the cost of an incorrect one."
@david_chisnallGood god. Not if the incorrect answer leads to the mass death of the innocent. Which it most always does.
ST"Evil knows no ideology or boundary, only an eloquent stance behind them."
SearingTruth -
@david_chisnall @rupert @EmilyEnough
"This kind of asymmetry is great for ML-based probabilistic approaches: the benefit of a correct answer massively outweighs the cost of an incorrect one."
@david_chisnallGood god. Not if the incorrect answer leads to the mass death of the innocent. Which it most always does.
ST"Evil knows no ideology or boundary, only an eloquent stance behind them."
SearingTruth@SearingTruth @david_chisnall @EmilyEnough
I don't think anyone's claiming that there's any benefit of a correct answer that "massively outweighs the cost" of mass death. -
@SearingTruth @david_chisnall @EmilyEnough
I don't think anyone's claiming that there's any benefit of a correct answer that "massively outweighs the cost" of mass death.@rupert @david_chisnall @EmilyEnough
"This kind of asymmetry is great for ML-based probabilistic approaches: the benefit of a correct answer massively outweighs the cost of an incorrect one."
@david_chisnall -
@rupert @david_chisnall @EmilyEnough
"This kind of asymmetry is great for ML-based probabilistic approaches: the benefit of a correct answer massively outweighs the cost of an incorrect one."
@david_chisnall@SearingTruth @david_chisnall @EmilyEnough Right, and if that asymmetry doesn't apply, as in your example, then it's not a good candidate for ML.
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@SearingTruth @david_chisnall @EmilyEnough Right, and if that asymmetry doesn't apply, as in your example, then it's not a good candidate for ML.
@rupert @david_chisnall @EmilyEnough
It's a perfect example.
As machine learning comprehends nothing.
ST -
@rupert @david_chisnall @EmilyEnough
It's a perfect example.
As machine learning comprehends nothing.
ST@SearingTruth @david_chisnall @EmilyEnough Which is why the decision to apply it is made by people. And people can decide how to weight the mass death of innocents and we should not allow those decisions to be made by people who will get it wrong.
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@gourd @mikemccaffrey @EmilyEnough I completely agree, and what is "natural language" anyway?! Sounds like an ableist agenda, right?
@ennenine @gourd @mikemccaffrey @EmilyEnough I guess I'm the wrong kind of disabled because this is how search engines do work now
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My biggest problem with the concept of LLMs, even if they weren’t a giant plagiarism laundering machine and disaster for the environment, is that they introduce so much unpredictability into computing. I became a professional computer toucher because they do exactly what you tell them to. Not always what you wanted, but exactly what you asked for.
LLMs turn that upside down. They turn a very autistic do-what-you-say, say-what-you-mean commmunication style with the machine into a neurotypical conversation talking around the issue, but never directly addressing the substance of problem.
In any conversation I have with a person, I’m modeling their understanding of the topic at hand, trying to tailor my communication style to their needs. The same applies to programming languages and frameworks. If you work with a language the way its author intended it goes a lot easier.
But LLMs don’t have an understanding of the conversation. There is no intent. It’s just a mostly-likely-next-word generator on steroids. You’re trying to give directions to a lossily compressed copy of the entire works of human writing. There is no mind to model, and no predictability to the output.
If I wanted to spend my time communicating in a superficial, neurotypical style my autistic ass certainly wouldn’t have gone into computering. LLMs are the final act of the finance bros and capitalists wrestling modern technology away from the technically literate proletariat who built it.
"I found a computer. Wait a second, this is
cool. It does what I want it to do. If it makes a mistake, it's because I screwed up."Horrible that this amazing core trait of computers is getting eroded.
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"I found a computer. Wait a second, this is
cool. It does what I want it to do. If it makes a mistake, it's because I screwed up."Horrible that this amazing core trait of computers is getting eroded.
@EmilyEnough
Not to be gatekeeping, but normies should have never gotten control of the Internet. -
@wallabra @EmilyEnough This isn't unique to LLMs. I've seen people defer to an Excel spreadsheet that plainly had been built with faulty assumptions.
@DocBohn @EmilyEnough That is true! People defer to things they shouldn't all the time. I just think LLMs are the next level of this, one that's about to be way worse, and way more societally impactful, than any before. I mean, look at what it's doing to primary education, like smartphones - the shiny silicon tablets designed to a tee to trap your attention - didn't do enough damage to it already.
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"There is zero artificial intelligence today. There could have been, but 50 years ago the decision was made by most scientists and companies to go with machine learning, which was quick and easy, instead of the difficult task of actually reverse engineering and then replicating the human brain.
So instead what we have today is machine learning combined with mass plagiarism which we call ‘generative AI’, essentially performing what is akin to a magic trick so that it appears, at times, to be intelligent.
While the topic of machine learning is complex in detail, it is simple in concept, which is all we have room for here. Essentially machine learning is simply presenting many thousands or millions of samples to a computer until the associative components ‘learn’ what it is, for example pictures of a daisy from all angles and incarnations.
Then companies scoured the internet in the greatest crime of mass plagiarism in history, and used the basic ability of machine learning to recognize nouns, verbs, etc. to chop up and recombine actual human writings and thoughts into ‘generative AI’.
So by recognizing basic grammar and hopefully deducing the basic ideas of a query, and then recombining human writings which appear to match that query, we get a very faulty appearance of intelligence - generative AI.
But the problem is, as I said in the beginning, there is no actual intelligence involved at all. These programs have no idea what a daisy, or love, or hate, or compassion, or a truck, or horse, or wagon, or anything else, actually is. They just have the ability to do a very faulty combinatorial trick to appear as if they do.
And while the human brain consumes around 20 watts, these massive pattern matching computers consume ever increasing billions.
However there is hope that actual general intelligence can be created because, thankfully, a handful of scientists rejected machine learning and instead have been working on recreating the connectome of the human brain for 50 years, and they are within a few decades of achieving that goal and truly replicating the human brain, creating true general intelligence.
In the meantime it's important for our species to recognize the danger of relying on generative AI for anything, as it's akin to relying on a magician to conjure up a real, physical, living, bunny rabbit.
So relying on it to drive cars, or control any critical systems, will always result in massive errors, often leading to real destruction and death."
SearingTruth@SearingTruth @EmilyEnough It's cute to read humans say they are the height of invention
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@EmilyEnough I think you're absolutely correct on this. Yet another reason why we need to find a way to irrevocably destroy this abomination.
But also it's not just the style of "communication" that these algorithms are pretending to do, it's that you cannot trust that their output is even correct because they have no understanding of what they are "saying". They could be "hallucinating" complete nonsense but they'll output it in an authoritative way and may even make up references that don't exist. They're 100% bullshit generators (it's even been scientifically proven).
@evildrganymede This post is a hallucination. It's weird how concepts people came up with 2 years ago and have been since disproven are repeated as fact. You're not an LLM, but here you are, bullshitting because you need updated training. Not sure why you're better, I guess because you have authority as a human being, and have totally mislead us... that's better?
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My biggest problem with the concept of LLMs, even if they weren’t a giant plagiarism laundering machine and disaster for the environment, is that they introduce so much unpredictability into computing. I became a professional computer toucher because they do exactly what you tell them to. Not always what you wanted, but exactly what you asked for.
LLMs turn that upside down. They turn a very autistic do-what-you-say, say-what-you-mean commmunication style with the machine into a neurotypical conversation talking around the issue, but never directly addressing the substance of problem.
In any conversation I have with a person, I’m modeling their understanding of the topic at hand, trying to tailor my communication style to their needs. The same applies to programming languages and frameworks. If you work with a language the way its author intended it goes a lot easier.
But LLMs don’t have an understanding of the conversation. There is no intent. It’s just a mostly-likely-next-word generator on steroids. You’re trying to give directions to a lossily compressed copy of the entire works of human writing. There is no mind to model, and no predictability to the output.
If I wanted to spend my time communicating in a superficial, neurotypical style my autistic ass certainly wouldn’t have gone into computering. LLMs are the final act of the finance bros and capitalists wrestling modern technology away from the technically literate proletariat who built it.
@EmilyEnough THIS! So much this. I've said before that the worst thing about how we use LLMs is they destroy the basic computing concept of Garbage In=Garbage Out. They turn it into Anything In=Maybe Garbage Out.