- cross-posted to:
- technology@lemmy.world
- cross-posted to:
- technology@lemmy.world
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Putting “AI functionality”, i.e. the denoising models I developed that are included in Darktable in the same realm as LLM slop is an interesting choice.
I collected all of the training data myself and did all of the training on a university GPU (which is consumer grade, I plan on continuing the work on my own machine). Everything is open (research, data, code) and reproducible.
I’m curious what’s the ethical or other issue there.
Edit: I posted a discussion on their issue tracker: https://codeberg.org/ethical-foss/open-slopware/issues/1628 , of course it was closed right away and I was blocked from the repository and my response was deleted and they limited responses to contributors-only.
Looks like ethical AI is too sensitive of a subject for “ethical-foss-admin”.
Most of these arguments are conflating AI with the people using them.
Labor exploitation, scraping without consent, military contracts, surveillance policing, grid strain and hardware prices driven up by speculation are real harms. But none of them are properties of a statistical language model.
They come from concentrated capital and state power, and whatever technology arrives next will get used the same way.
Just like we’ve seen everything in your list, done by the same people, using different technology.
We have had hardware speculation during the crypto craze.
These same companies (Google) have been taking your data without consent to power their surveillance advertising.
Datacenters have always been power and water hungry, the ones powering LLMs are exactly the same ones that exactly the same companies have been building since the Internet began.
Essentially every major advance in technology has been used for state surveillance and in the military. AI is simply the latest capability.
Models reproducing licensed code, poor code quality and floods of junk contributions. These are real problems with the technology itself. And the solution to most of these things is to iterate, build better models trained on consentually obtained data.
Yes, you should still be mad at what the AI companies are doing. I am too. Just keep in mind that Google was evil before Gemini and will certainly be on the forefront of fuckery with whatever technology comes next as long as people keep getting tricked into misdirecting their anger onto the technology.
This is mostly true, but you’re glossing over the fact that AI will always be tied to concentrated capital due to the cost of training state of the art models. I understand why you argue that these flaws are not inherent to the technology, but in a practical sense, they might as well be considered inherent. It’s unlikely that the market will open up any time soon so that you can get your LLM from someone who isn’t a fascist billionaire tech bro.
This is mostly true, but you’re glossing over the fact that AI will always be tied to concentrated capital due to the cost of training state of the art models.
I think this is a false assumption.
We don’t know what the ultimate state of AI will be, we only know that we’re at the beginning of the journey.
Look at other technologies in similar positions. At one point computers were like this. Nobody but the military and the largest companies could afford them and now you can buy children’s toys with more storage and processing power than was available to the entire US military at the dawn of computers.
We had no idea what computers would become, even the experts making guesses that sounded wild at the time were off by several orders of magnitude. Bill Gates once said “640K [of RAM] ought to be enough for anybody”, modern PCs have around 100,000 times that.
The reason that AI seems like it’s only affordable to concentrated capital is because models using a large amount of parameter require a large amount of RAM. The 5TB of RAM estimated for some of the largest frontier models is a lot, about 100x a workstation PC. That’s a few orders of magnitude less than 100,000x. Given equal scaling speed, we’re closer to running frontier models on our desktop than we are to the dawn of computers.
In addition, the research is leaning towards there being a ceiling on how useful it is to add more parameters (use more RAM). Doubling parameters doesn’t result in doubling of capability so there is effectively a soft cap on the amount of parameters.
Hardware is growing near exponentially while the RAM demands of models have a ceiling.
Keep in mind that this only relates to the state of the largest LLMs. You can train generative models on consumer graphics cards. Object recognition, classification, etc are all tiny compared to LLMs. These are also AI and, I’d argue, that they actually produce more value than LLMs (which are failing to deliver on their promise of replacing white collar labor).
It’s unlikely that the market will open up any time soon so that you can get your LLM from someone who isn’t a fascist billionaire tech bro.
You can find open weight models (of all kinds, not just LLMs) freely available here: https://huggingface.co/
Many can be run on locally on gaming hardware. Some of the classification and object recognition models can be run on microcontrollers (a popular use it to use them to make aimbots in FPS games).
There are larger models that can be run on more expensive dedicated hardware (like NVIDIA DGX Spark, ~$5,000) if you want to keep your projects completely internal. Or you can run those open weight models on hardware hosted in datacenters ran by companies you research to be ethical.
You can use AI without using one of the AI companies.
Given equal scaling speed
This is already not the case for computers. And you’re trying to argue that we might one day be able to run the bigger models on consumer hardware, but I said it’s too expensive to train them. And you know that the cost isn’t just the RAM right?
Sure, my saying they will “always” be tied to concentrated capital is slightly hyperbolic. You never know. But it takes a whole lot of optimism to hope that LLMs will be decoupled from the billionaire class, hence why most analyses of the problems LLMs cause discuss them in this context.
This is already not the case for computers. And you’re trying to argue that we might one day be able to run the bigger models on consumer hardware, but I said it’s too expensive to train them. And you know that the cost isn’t just the RAM right?
If you replace everything I said about RAM with compute and replace the Bill Gates quote with Thomas Watson saying that the “world market [has room for] for maybe five computers” then it’s the same argument. They’re all ultimately built on technical advances in lithography and so growth in RAM and compute largely stem from similar sources.
Sure, my saying they will “always” be tied to concentrated capital is slightly hyperbolic. You never know. But it takes a whole lot of optimism to hope that LLMs will be decoupled from the billionaire class, hence why most analyses of the problems LLMs cause discuss them in this context.
I understand why it is done as shorthand.
I know this likely doesn’t apply to you, as you seem to be capable of understanding the nuance, it’s that a lot of people on social media (by people I’m including the bots) don’t make that distinction.
The same vitriol that is, rightfully, directed at OpenAI/Anthropic/Google/NVIDIA is also directed at things completely outside of the corporate LLM hellscape.
I’m mostly frustrated at seeing the sciences, researchers and open source AI projects catching strays and being painted with the same brush.
It’s useful shorthand among people who understand the nuance, but using that shorthand on social media invites misunderstanding and that is causing collateral damage.
Keep in mind that this only relates to the state of the largest LLMs. You can train generative models on consumer graphics cards. Object recognition, classification, etc are all tiny compared to LLMs. These are also AI […]
The linked Codeberg page refers to LLMs in their current, corporate-controlled shapIe. It is about using LLMs for coding.
Your argument is rather leading away from that.
Unfortunately “AI,” and LLMs in general are, at present, wholly inseparable from the people who use them, and especially, (and more pressingly), the
peoplecorporations that make them. This is the reason I maintain my opposition to “AI” (and LLMs in general) despite machine learning as a field and LLMs as technology being truly fascinating, and having immense potential.In my ideal world the corporations pushing “AI” wouldn’t exist, the datasets and recipes would be open, and model training would be a community led effort with Folding@Home style distributed compute used for training, pretty much removing the viability of “AI” companies. Without “AI” companies pushing for adoption, strong social pushback can be used to reign in the slopification of the commons. With those two together (and likely many other smaller changes elsewhere) LLMs can be seen for what they really are and their real pros and cons evaluated.
But that world isn’t currently feasible, and probably won’t ever be. Neither is toppling even a single corrupt corporation on the level of Alphabet, or Microsoft. So all we can do is evaluate whether we consider certain software’s use of “AI”/LLMs is acceptable, and advocate for not using them. Which is why I think efforts like openslopware, and pages like this are a positive thing, even if I don’t necessarily agree with some of the specifics.
You do realize LLMs are made outside the US as well. I have zero problem with Chinese companies making LLMs that are released in the open and you can even run on your own hardware.
Shitty corporations are a nigh universal constant, not something exclusive to America. That’s American exceptionalism talking.
The difference is that China actually deals with corporations when they act shitty as happened with Alibaba and Jack Ma because China has a dictatorship of the proletariat which creates different material conditions from the dictatorship of capital you have in burgerland. No exceptionalism needed to explain this, just minimal capacity for critical thought that some people lack.
True, but I am afraid that the definition of “they act shitty” in China is very different from the one in the US.
Jack Ma paid because he criticized the regime, not because Alibaba was failing.Jack Ma paid because he tried to expand Alibaba into a finance and do what western tech bros do. I love how you call Chinese government ‘the regime’ really highlights the quality of your ‘intellect’.
" to expand Alibaba into a finance" is not intrinsically a crime in a free country, so if China is really a free country, why Jack Ma was paid for something he don’t yet did ?
Jack Ma was punished because he had the courage (or stupidity given the situation) to criticize Chinese estabilishment for stifling innovation and economics, then Xi Jinping retaliate and Jack Ma went off the radar (only to be remerges after Xi Jinping “rehabilitated” him)
So yes, to me China is a regime. And if you think my intellect is not up to your idea of quality, you are free to move to China and to live in what I suppose you consider a better country.
It is lacking the “money problem”: the high cost is adsorbed by big capitalist organizations (at the cost of the hardware price inflating) that hope to get everyone addicted/dependent on the tool to then surge the prices.
This is not open tech, this is not for everyone, this is not to make a better world. This is business.
This is business.
So was the Opium trade.
you know, license washing can work both ways
Exhibit A: All the retro game recomps.
Can the blind anti LLM jerk stop already it’s the same 2024 2025 arguments. Agentic coding has massively improved this year even local open source LLM’s are getting good
Stop trying to take performative outrage away from liberals, it’s literally their whole identity.
