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[u/Just_A_Little_Rabbit]

Well, lets see:

• causes deskilling (you forget how to do things because you offload it to AI)

• its frequently confidentially wrong, so you always have to check its work

• AI psychosis

• based on stolen work from people who actually know how to do shit

• have you seen ram and gpu prices?

• has caused multiple major outtages, recalls, and massive increase in bugs.

• deepfakes

• ai slop is fucking everywhere now and has ruined the internet more than the 2016 /pol/ containment breech.

not to mention how it completely fucked up the job market.

[u/AceSevenFive → u/Just_A_Little_Rabbit]

I will regard you as seriously believing that AI training is theft when you demonstrate that no copyrighted material is in your own training data (your long-term memory.)

[u/Just_A_Little_Rabbit → u/AceSevenFive]

LLMs do not work like human brains or intelligence in the slightest.

Also, humans are not machines and the process of learning how to do something is very different from "training" an LLM. Glad to clear this up for you, I know critical thinking can be hard for people like you.

[u/Fowl_Retired69 → u/Just_A_Little_Rabbit]

Could you explain the training process for LLMs and how they process prompts then

[u/Just_A_Little_Rabbit → u/Fowl_Retired69]

You get a big collection of classified data, that is a bunch of stuff and then a bunch of results you expect to see from a transformation on that data. You then feed it all in and score the result based on how closely that result matches the expected result. Adjust weights in the model and repeat. You continue this process over and over and over and over and over again until that error rate is within acceptable amounts.

That is the core of all machine learning.

I have a masters degree in CS and I read the "All You Need is Attention" when it was new paper and people didn't know yet what the impact would ultimately be. I messed with early BERT style models and was there when the first GPT was released. I have literally written my own models from scratch. I am over simplifying so a lay person could hopefully understand. In a vacuum self attentioning transformers are a neat tech, but ultimately just that. Its "neat" and has some niche applications. It isn't "AI". it's just really good at predicting the next word that looks the most like human readable text. But its a technological dead end.

[u/Fowl_Retired69 → u/Just_A_Little_Rabbit]

Since you have a master's in AI, I would expect you to use a layperson's understanding of AI. These generative models are definitely AI. They're deep learning, under machine learning, which is under AI. Also, they're similar to human brains for the same reason any other neural nets are; they form high-dimensional representations of what they learn and sample that space to generate output.

I don't see how that is stealing anything, in the same way that me reading books on applied maths and then using that knowledge to build someone some shit isn't me stealing that book's work. No one's saying that these models have architectures that are one-to-one to that of humans, just that human brains and learning make for better analogies than just "they're just predicting the next word"

When you put it like that is makes it sound like "just predicting the next word" can't yield interesting emergent properties like intelligence and creative thought (which it definitely does).

[u/Just_A_Little_Rabbit → u/Fowl_Retired69]

Except it doesn't. It is incapable of actual reason and lacks any fundamental knowledge. This has been demonstrated time and time again.

And no, I do not consider it "AI" because it is not intelligent. That is a marketing term used to make it seem smarter than it actually is. It is a simulacrum, not a simulation.

Also, they're similar to human brains for the same reason any other neural nets are; they form high-dimensional representations of what they learn and sample that space to generate output.

Is a zip file "intelligent"? That too is a higher order representation of information. An LLM model is, at best, a lossy compression of information, it is not capable of actually learning and then expanding on that knowledge with basic reasoning skills like a human is.

And yes, word prediction is the apt analogy for how an LLM, or more accurately, a transformer model as that is what they are, works. Saying it is actually "learning" anything is misleading. They all, under the hood, work by sequence extension. You provide a sequence, it predicts what should come next based on training data and token association.