This is fucking ridiculous. This AI is the worst of them all. I don’t mind it when they subtly try to insert some diversity where it makes sense but this is just nonsense.

They are experimenting and tuning. Apparently without any correction there is significant racist bias. Basically the AI reflects the long term racial bias in the training data. According to this BBC article it was an attempt to correct this bias but went a bit overboard.

Apparently without any correction there is significant racist bias.

This doesn’t make it any less ridiculous. This is a central pillar of this kind of AI tech, and they’re trying to shove a band aid over the most obvious example of it. Clearly, that doesn’t work. It’s also only even attempting to fix one of the “problems” - they’re never going to be able to “band aid” every single place where the AI exhibits this problem, so it’s going to leave thousands of others un-fixed. Even if their band aid works, it only continues to mask the shortcomings of this tech and makes it less obvious to people that it’s horrendously inacurrate with the other things it does.

Basically the AI reflects the long term racial bias in the training data. According to this BBC article it was an attempt to correct this bias but went a bit overboard.

Exactly. This is a core failing of LLM tech. It’s just going to repeat all the shit it was fed to it. You’re never going to fix that. You can attempt to steer it in different directions, but the reason this tech was used was because it is otherwise impossible for us to trudge through all the info that was fed to it. This was the only way to get it to “understand” everything. But all of it’s understandings are going to have these biases, and it’s going to be just as impossible to run through and fix all of these. It’s like you didn’t have enough metal to build the titanic so you just built it out of Swiss cheese and are trying to duct tape one hole closed so it doesn’t sink. It’s just never going to work.

This being pushed as some artificial INTELLIGENCE is the problem here. This shit doesn’t understand what it’s doing, it’s just regurgitating the things it’s consumed. It’s going to be exactly as flawed as whatever was put into it, and you can’t change that. The internet media it was trained on is racist, biased, full of undeniably false information, and massively swayed by propaganda on all sides of the fence. You can’t expect LLMs to do anything different when trained on that data. They’re going to have all the same problems. Asking these things to give you any information is like asking the average internet user what the answer is. And the average internet user is not very intelligent.

These are just amped up chat bots with data being sourced from random bits of the internet. Calling them artificial INTELLIGENCE misleads people into thinking these bots are smart of have some sort of understanding of what they’re doing. They don’t. They’re just fucking internet parrots, and they don’t have the architecture to be “fixed” from having these problems. Trying to patch these problems out is a fools errand and only masks their underlying failings.

Would it be possible to create a kind of “formula” to express the abstract relationship of ethical makeup, location, year and field? Like convert a table of population, country, ethnicity mix per year and then train the model on that. It’s clear that it doesn’t understand the meaning or abstract concept, but it can associate and extrapolate things. So it could “interpret” what the image description says while training and then use the prompt better. So if you’d say “english queen 1700” it would output white queen, if you input year 2087 it would be ever so slightly less pasty.

I don’t know, maybe that would work, for this one particular problem. My point is it’s more than that. Even if you go through the trouble of fixing this one particular issue with LLMs, there are literally thousands of other problems to solve before it’s all “fixed”. At some point, when you’ve built and maintained thousands of workarounds, they start conflicting with each other and making a giant spider web of issues to juggle.

And so you’re right back at the problem that you were trying to solve by building the LLM in the first place. This approach is just futile and nonsensical.

Yeah. But maybe this is how you teach an AI a broader understanding of the real world. Or really a slightly less narrow view. Human brains also have to learn and reconcile all these conflicting data points and then create a kind of understanding from it. For any machine learning it would only be an intuitive instinct.

Like you would have a bunch of these “tables” that show relationships between various tokens and embody concepts. Maybe you need to combine different kind of models that are organized and trained differently to resolve such things. I only have a very surface level understanding of how machine learning works so I know this is very speculative. Maybe you’re right and it can only ever reflect the training data. Then maybe you’d need to edit the training data, but you could also maybe use other AIs to “reinterpret” training data based on other models.

Like all the data on reddit, could you train a model to detect sarcasm or lies or to differentiate between liberal, leftist and fascist type of arguments? Not just recognizing the tokens or talking points, but the semantic of an argument? Like detecting a non sequitur. You probably need need “general knowledge” understanding for that. But any kind of AI like that would be incredibly interesting for social media so you client can tag certain posts, or root out bot / shill networks that work for special interests (fossil fuel, usa, china, russia).

Eh I really need to learn more about AI to understand the limits.

The broad answer is, I’m pretty sure everything you’ve mentioned is possible, and you’re right in that this is similar to how humans integrate new data. Everything we learn competes with and bolsters every bit of knowledge we already have, so our web of understanding is this ever shifting net of relationships between concepts.

I don’t see any reason these kinds of relationships can’t be integrated into generative AI, they just HAVEN’T yet, and each time you increase how the relationships interact, you’re also drastically increasing the size and complexity of the algorithm and model. I think we’re just realizing that what we have now is OK, but needs to be significantly better before it’s really mind blowing.