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

My biggest problem with AI is how it was pushed and marketed to us in ways that don’t make sense / are unethical. Even the environmental concerns would be ameliorated if AI weren’t being pushed into everything. (Using “AI” here to refer to things like LM, image, and art generators,etc.)

I honestly am skeptical about the medical stuff. Machine learning can’t even do the stuff it should be good at reliably, specifically identifying mushrooms/mycology in general.

LMs give the appearance of understanding, but as soon as you try to use them for anything that you actually are knowledgable in, the facade crumbles.

Even for repetitive tasks, you have to do a lot of manual checking to ensure they did not start hallucinating half way through.

Mr crabs would use unethical llms, very accurate

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 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

Ultimately, the issue is our current societies being fucked. If AI were refined, sensibly monitored and generally used by people who can recognize mistakes (where it matters), and keep their fossil fuel usage in check, AI could be a big step towards gay space communism. Like, who wants to do menial labor? Let AI do it where sensible and pay the former workers the money that’s saved by doing that. But as it is, it’s mostly going to be used to further the agendas of authoritarians and capitalists.

What does “AI for disabled people” entail? A lot of ‘good AI’ things I see are things I wouldn’t consider AI, e.g. VLC’s local subtitle generation.

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.

What I think is missing from your viewpoint (and from most people’s, this is [IMO] a problem at scale) is the distinction between “simple” and broad machine learning, and the very specific things that are Large Language Models.

For example, there are no small Large Language Models, and I think that the oxymoron speaks for itself. Machine learning is a very good thing, and automated classification is definitely its best use case, but they are not a small version of ChatGPT, the same way that the average Joe is not a smaller version of a billionaire.

For more details, these small models are trained on a small set of data, how small depending on how specific the task is; as an example, I worked with models that detect manufacturing defects on production lines, and theses need a few hundreds images in order to produce good results, this make it very easy to produce the data ourselves, and it is relatively cheap to train energy-wise.

Compared to that, Large Language Models, and their audiovisual counterparts, operate on billions of data, and work on a task so general that they provide incredibly bad results. As a little statistical reminder, anything below 95% confidence is a bust, LLMs are way below that.

It’s very important to distinguish the two, because all of the positives you list for AI are not about LLMs, but about simple machine learning. And this confusion is by design, techbros are trying to profit of the successes of other form of artificial intelligence by pretending that AI is this one single thing, instead of an entire class of things.

Otherwise, I generally agree with the rest of your points.

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

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.

Honest question, how does AI help disabled people, or which kinds of disabilities?

One of the few good uses I see for audio AI is translation using the voice of the original person (though that’d deal a significant blow to dubbing studios)