196

I used to think image generation was cool back when it was still in the "generating 64x64 pictures of cats" stage. I still think it's really cool, but I do struggle to see it being a net positive for society. So far it has seemed to replace the use of royalty free stock images from google more than it has replaced actual artists, but this could definitely change in the future.

There are some nicer applications of image generation too, like dlss upscaling or frame generation, but I can't think of all that much else honestly.

I agree 👍

I think we should avoid simplifying it to VLMs, LMs, Medical AI and AI for disabled people.

For instance, most automatic text capture ais (optical Character Recognition, or OCR) are powered by the same machine learning algorithms. Many of the finer-capability robot systems also utilize machine learning (Boston Dynamics utilizes machine learning for instance). There's also the ability to ID objects within footage, as well as spot faces and referencing it with a large database in order to find the person with said face.

All these are Machine Learning AI systems.

I think it would also be prudent to cease using the term 'AI' when what we actually are discussing is machine learning, which is a much finer subset. Simply saying 'AI' diminishes the term's actual broader meaning and removes the deeper nuance the conversation deserves.

Here are some terms to use instead

• Machine Learning = AI systems which increase their capability through automated iterative refinement.
• Evolutionary Learning = a type of machine learning where many instances of randomly changed AI models (called a 'generation') are run simultaneously, and the most effective is/are used as a baseline for the next 'generation'
• Neural Network = a type of machine learning system which utilizes very simple nodes called 'neurons' for processing. These are often used for image processing, LMs, and OCR.
• Convolution Neural Network (CNN) = a Neural network which has an architecture of neuron 'fliters' layered over each other for powerful data processing capabilities.

This is not exhaustive but hopefully will help in talking about this topic in a more definite and nuanced fashion. Here is also a document related the different types of neural networks

Mr crabs would use unethical llms, very accurate

There is an over arching issue with most of the extant models being highly unethical in where they got their data, effectively having made plagiarism machines.

It is not ok to steal the content of millions of small independent creators to create slop that drowns them out. Most of them were already offering their work for free. And I am talking about LMs here, writing is a skill.

Say what ever you want about big companies being bad for abusing IP laws, but this is not about the laws, not even paying people for their work, this is about crediting people when they do work, acknowledging that the work they did had value, and letting people know where they can find more.

Also, I don’t really buy the “it’s good for disabled people” that feels like using disabled people as a shield against criticism, and I’ve yet to see it brought up in good faith.

There are so many different things that are called AI, the term AI doesn't have any meaning whatsoever. Generally it seems to mean anything that includes machine learning somewhere in the process, but it's largely a marketing term.

Stealing art is wrong. Using ridiculous amounts of power to generate text is ridiculous. Building a text model that will very confidently produce misinformation is pretty dumb.

There are things that are called AI that are fine, but most aren't.

I'll just repeat what I've said before, since this seems like a good spot for this conversation.

I'm an idiot with no marketable skills. I want to write, I want to draw, I want to do a lot of things, but I'm bad at all of them. gpt like ai sounds like a good way for someone like me to get my vision out of my brain and into the real world.

My current project is a wiki of lore for a fictional setting, for a series of books that I will never actually write. My ideal workflow involves me explaining a subject as best I can to the ai (an alien technology or a kingdom's political landscape, or drama between gods, or whatever), telling the ai to ask me questions about the subject at hand to make me write more stuff, repeat a few times, then have the ai summarize the conversation back to me. I can then refer to that summary as I write an article on the subject. Or, me being lazy, I can just copy-pasta the summary and that's the article.

As an aside, I really like chatgpt 4o for lore exploration, but I'd prefer to run an ai on my own hardware. Sadly, I do not understand github and my brain glazes over every time I look at that damn site.

It is way too easy for me to just let the ai do the work for me. I've noticed that when I try to write something without ai help, it's worse now than it was a few years ago. generative ai is a useful tool, but it should be part of a larger workflow, it should not be the entire workflow.

If I was wealthy, I could just hire or commission some artists and writers to do the things. From my point of view, it's the same as having the ai do the things, except it's slower and real humans benefit from it. I'm not wealthy though, hell, I struggle to pay rent.

The technology is great, the business surrounding it is horrible. I'm not sure what my point is.

Genuinely, the only problem I see with the development of LLMs and AI in general is that said development has a massive tumor on its back called Corporate Interest. That's pretty much the one and only cause for absolutely every destructive, shady, or downright immoral aspect tied to these things nowadays...

As tools in and of themselves, yes! LLMs have an immense potential not of replacing people, but of helping people get stuff done faster, which in turn would give us a lot of extra time to polish the everloving spit out of the stuff we make!

LLM/AI research should be 100% non-profit and democratised, with well-established guidelines and full transparency, as I see it. This is a huge step in our development as a species, and Altman-likes are not the people who should be in charge of it.

Edit: as for VLMs, I kinda' see them as a fad, to be honest. It still irks me when anyone adds "art" to anything artificially generated at the moment, but I get the feeling people will tire of the novelty once the need for genuine art will cease being satisfied by the above-mentioned.

Smorty!!!!
Thank you for this conversation

This list is missing: AI generated images are not art.

A lot of those points boil down to the same thing: "what if the AI is wrong?"

If it's something that you'll need to check manually anyway, or where a mistake is not a big deal, that's probably fine. But if it's something where a mistake can affect someone's well-being, that is bad.

Reusing an example from the pic:

• Predicting 3D structures of proteins, as in the example? OK! Worst hypothesis the researchers will notice that the predicted structure does not match the real one.
• Predicting if you have some medical problem? Not OK. A false negative can cost a life.

That's of course for the usage. The creation of those systems is another can of worms, and it involves other ethical concerns.

I wish people stopped treating these fucking things as a knowledge source, let alone a reliable one. By definition they cannot distinguish facts, only spit out statistically correct-sounding text.

Are they of help to your particular task? Cool, hope the model you're using hasn't been trained on stolen art, or doesn't rely on traumatizing workers on the global south (who are paid pennies btw) to function.

Also, y'know, don't throw gasoline to an already burning planet if possible. You might think you need to use a GPT for a particular task or funny meme, but chances are you actually don't.

That's about it for me I think.

edit: when i say "you" in this post i don't mean actually you OP, i mean in general. sorry if this seems rambly im sleep deprived as fuckj woooooo

I think generative AI is mainly a tool of deception and tyranny. The use cases for fraud, dehumanization and oppression are plentiful. I think Iris Meredith does a good job of highlighting the threat at hand. I don’t really care about the tech in theory: what matters right now is who builds it and how it is being deployed onto the world.

i'm personally not too fond of llms, because they are being pushed everywhere, even when they don't make sense and they need to be absolutely massive to be of any use, meaning you need a data center.

i'm also hesitant to use the term "ai" at all since it says nothing and encompasses way too much.

i like using image generators for my own amusement and to "fix" the stuff i make in image editors. i never run any online models for this, i bought extra hardware specifically to experiment. and i live in a city powered basically entirely by hydro power so i'm pretty sure i'm personally carbon neutral. otherwise i wouldn't do it.

the main things that bother me is partially the scale of operations, partially the philosophy of the people driving this. i've said it before but open ai seem to want to become e/acc tech priests. they release nothing about their models, they hide them away and insinuate that we normal hoomans are unworthy of the information and that we wouldn't understand it anyway. which is why deepseek caused such a market shake, it cracked the pedestal underneath open ai.

as for the training process, i'm torn. on the one hand it's shitty to scrape people's work without consent, and i hope open ai gets their shit smacked out of them by copyright law. on the other hand i did the math on the final models, specifically on stable diffusion 1.0: it used the LAION 5B scientific dataset of tagged images, which has five billion ish data points as the name suggests. stable diffusion 1.0 is something like 4GB. this means there's on average less than eight bits in the model per image and description combination. given that the images it trained on were 512x512 on average, that gives a shocking 0.00003 bits per pixel. and stable diffusion 1.5 has more than double the training data but is the same size. at that scale there is nothing of the original image in there.

the environmental effect is obviously bad, but the copying argument? i'm less certain. that doesn't invalidate the people who are worried it will take jobs, because it will. mostly through managers not understanding how their businesses work and firing talented artists to replace with what is basically noise machines.

I don't see how AI is inherently bad for the environment. I know they use a lot of energy, but if the energy comes from renewable sources, like solar or hydroelectric, then it shouldn't be a problem, right?