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.
PS: I find it hilarious. If anything it elevates the AI system to art, since it now provides an emotionally provoking mirror about white identity.
I asked a generator to make me a “queen monkey in a purple gown sitting on a throne” and I got maybe two pictures of actual monkeys. I even tried rewording it several times to be a real monkey, described the hair and everything.
Stable diffusion online version, several weeks ago. Might not be the same situation anymore, idk how often that stuff gets updated, and I’m not able to test it at the moment.
It’s also possible that some sort of “sticky idea” got into its head and made it start generating it that way after it did one like that. I’ve heard that sort of thing isn’t uncommon.
To be clear, stable diffusion isn’t one model, it’s the generation platform. From there, you have models that sit on top of it. Online generators can use any model, depending on how they’re set up. Each model includes different training data, meaning different results from the same prompts, sometimes vastly.
It’s a bit like driving somewhere, having someone ask how you found the place, and saying your phone. Technically a correct answer, but they’re probably looking for more specific answers, like GPS, or a map. Not trying to nit-pick, just giving a bit of information.
To the machine, the query is “draw the founding fathers but diversely” it’s not the data that is corrupt, the usage is, clearly the system prompt in this case
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 prompt “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).
So all the stuff “conflicting with each other and making a giant spider web of issues to juggle” might be what you can train an AI to pull apart into “appeal emotion” and “materialistic view” or “belief in inequality” or “preemptive bias counteractor”. Maybe it actually could extract and help us communicate better.
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.
Yeah, I imagine generative AI as like one small part of a human mind, so we’d need to create a whole lot more for AGI. But it’s shocking (at least for me) that it works at all just through more data and compute power. That you can make qualitative leaps with just increasing the quantity. Maybe we’ll see more progress now.
I don’t see any reason these kinds of relationships can’t be integrated into generative AI, they just HAVEN’T yet
No, it’s just fucking pointless. You’re talking about adding sand to a beach. These things are way more complicated and trying to shovel these things in just makes a mess. See literally the OP.
each time you increase how the relationships interact, you’re also drastically increasing the size and complexity of the algorithm and model.
No youre not. Not even fucking close. You clearly don’t understand this at all.
The ALGORITHM will always be the same. Except for new generations of these bots. Claiming adding things like racial bias is going to alter the algorithm is just nonsensical.
The MODEL is the huge fucking corpus of internet data. Anything you tack onto it is a drop in an ocean. It’s not steering anything.
Whats changing is they’re editing inputs because that’s all you can really do to shift where these things go. Other changes would turn this into a very different beast, and can’t be done at the fine grained level like “race”.
Claiming this has any significant impact on the size or complexity of any of this is just total hog wash and you must not understand how these work or how big they are.
In what world does changing the algorithm used in order to generate anything, something that would be NECESSARY to make the model incorporate a new dimension of data, not change the algorithm used to generate?
I’m not just talking adding more prompts, keying more specific terms to specific patterns of pixels, I’m talking building in entirely new ways for the AI to understand.
You seem to think I’m just talking about linearly expanding the vocabulary of the model, I’m talking about giving it an entirely new paradigm through which to work.
Anyway, this is why no one likes pedants. If you want to actually engage in conversation, sure. If you want to just keep being a vitriolic ass, go back to your cave, yeah?
You’re just rephrasing the same approach, over, and over, and over. It’s like you’re not even reading what I’m saying.
The answer is no. This is not a feasible approach. LLMs are just parrots and they don’t understand anything. They were essentially a “shortcut” that gets something that acts intelligent without actually having to build something intelligent. You’re not going to convince it to be intelligent. You’re not going to solve all it’s short comings by shoe horning something in. It’s just more work than building actual intelligence.
It’s like if a costal town got overrun by flooding from a hurricane. And some guy shows up and is like “hey, I’ve got a bucket, I’ll just pull all the water to the sea”. And I’m like “that’s infeasible, we need a different solution, your bucket even has fucking holes in it”. And you’re over here saying “well, what if we got some duct tape? And then we can patch the holes. And then we can call our friends, and we can all bucket the water”.
It’s just not happening.
Eh I really need to learn more about AI to understand the limits
Yeah. This. You just keep repeating the same approach over and over without understanding or listening to the basic failings of these chat bots. It’s just not happening. You’re just perpetuating nonsense.
These things are basically slightly more complicated versions of the auto complete in your phone keyboard. Except that they’re fed hug amounts of the internet. They get really good at parroting sentences, but they have no sense of “intelligence” or what they’re actually doing. You’re better off trying to convince your auto correct to sound like Shakespeare than you are to remove the failings like racial bias from things like Gemini and ChatGPT. You can chip at small corners here and there but this is just not the path forward.
You’re just rephrasing the same approach, over, and over, and over. It’s like you’re not even reading what I’m saying.
No I read what you are saying. I just think that you are something that “acts intelligent without actually being intelligent”. Here is why: All that you’ve written is based on very simple primitive brain cells and synapses and synaptic connections. It’s self evident that this is not really something that is designed to be intelligent. You’re just “really good at parroting sentences”. And you clearly agree that I’m doing the same 😄
Clearly LLMs are not intelligent and don’t understand, and it would need many other systems to make them so. But what they do show is that the “creative spark” even though they are very mediocre in their quality, can be created by using a critical mass of quantity. It’s like it’s just one small part of our mind, the “creative writing center” without intelligence. But it’s there, just because we added more data and processing.
Quality through quantity, that is what we seem to be and what is so shocking. And it’s obvious that there is a kind of disgust or bias against such a notion. A kind of embarrassment of the brain to just be thinking meat.
Now you might be absolutely right that my specific suggestion for an approach is bullshit, I don’t know enough about it. But I am pretty sure we’ll get there without understanding exactly how it works.
None of this has been pushed, by any researcher, by any company, by any open source group even, as “intelligence”
In fact, it was unanimously disliked as a term by everyone working with the models and transformers, but media circus combined with techbros laymen hard on hype have won. Since then everyone has given up trying to be semantically correct on this front.
I didn’t say any researcher or anything had named it intelligence. Nor am I trying to be semantically correct.
Read the guys comments. He’s trying to push the idea that we can “change” it’s “understanding” about the things it’s discussing. He is one of the people who has fallen for the tech bros etc convincing people it is intelligent. I’m not fighting semantics, I’m trying to explain to him that it’s not intelligent. Because he himself clearly doesn’t understand that.
Why do you seem to think it’s impossible to change how AI understands things? It’s just an algorithm. It’s just a fancy set of math functions that gets you from noise to something that looks like something. Of COURSE we can change how this process works, have it weigh other things, and get something that generates based on a different paradigm than we currently have. All you seem to try to do is be semantically correct.
That’s just silly, as if there is no nuance whatsoever. You can ofc change its understanding. Depending on your definition, different types of models could be interpreted as intelligent in certain areas. You can be rational, you know, not everything needs to be black and white. It’s also possible that since even the experts in the field don’t fully grasp it, maybe you don’t either.
I don’t know who “them” is here. I thought from the context it was obvious that I meant whoever is managing these AIs. I guess I could’ve been clearer.
But what, do you think they’re behind the scenes to insert the word woke in every search by default or something?
I mean they literally are inserting stuff in the prompts to make the results more diverse? It’s not some hidden thing but rather a solution to issues with the undiverse training data. But obviously here they’ve “overcorrected” to beyond all sense.
No idea. I don’t fully understand why any of these dog whistles are pulled out I just know what they are. Another big one is triple () meaning the same thing.
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.
PS: I find it hilarious. If anything it elevates the AI system to art, since it now provides an emotionally provoking mirror about white identity.
Significant racist bias is an understatement.
I asked a generator to make me a “queen monkey in a purple gown sitting on a throne” and I got maybe two pictures of actual monkeys. I even tried rewording it several times to be a real monkey, described the hair and everything.
The rest were all women of color.
Very disturbing. Pretty ladies, but very racist.
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Stable diffusion online version, several weeks ago. Might not be the same situation anymore, idk how often that stuff gets updated, and I’m not able to test it at the moment.
It’s also possible that some sort of “sticky idea” got into its head and made it start generating it that way after it did one like that. I’ve heard that sort of thing isn’t uncommon.
To be clear, stable diffusion isn’t one model, it’s the generation platform. From there, you have models that sit on top of it. Online generators can use any model, depending on how they’re set up. Each model includes different training data, meaning different results from the same prompts, sometimes vastly.
It’s a bit like driving somewhere, having someone ask how you found the place, and saying your phone. Technically a correct answer, but they’re probably looking for more specific answers, like GPS, or a map. Not trying to nit-pick, just giving a bit of information.
Stable diffusion is a generative technology, not a model
“A bit” overboard yeah
To the machine, the query is “draw the founding fathers but diversely” it’s not the data that is corrupt, the usage is, clearly the system prompt in this case
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.
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 prompt “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).
So all the stuff “conflicting with each other and making a giant spider web of issues to juggle” might be what you can train an AI to pull apart into “appeal emotion” and “materialistic view” or “belief in inequality” or “preemptive bias counteractor”. Maybe it actually could extract and help us communicate better.
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.
Yeah, I imagine generative AI as like one small part of a human mind, so we’d need to create a whole lot more for AGI. But it’s shocking (at least for me) that it works at all just through more data and compute power. That you can make qualitative leaps with just increasing the quantity. Maybe we’ll see more progress now.
No, it’s just fucking pointless. You’re talking about adding sand to a beach. These things are way more complicated and trying to shovel these things in just makes a mess. See literally the OP.
No youre not. Not even fucking close. You clearly don’t understand this at all.
The ALGORITHM will always be the same. Except for new generations of these bots. Claiming adding things like racial bias is going to alter the algorithm is just nonsensical.
The MODEL is the huge fucking corpus of internet data. Anything you tack onto it is a drop in an ocean. It’s not steering anything.
Whats changing is they’re editing inputs because that’s all you can really do to shift where these things go. Other changes would turn this into a very different beast, and can’t be done at the fine grained level like “race”.
Claiming this has any significant impact on the size or complexity of any of this is just total hog wash and you must not understand how these work or how big they are.
In what world does changing the algorithm used in order to generate anything, something that would be NECESSARY to make the model incorporate a new dimension of data, not change the algorithm used to generate?
I’m not just talking adding more prompts, keying more specific terms to specific patterns of pixels, I’m talking building in entirely new ways for the AI to understand.
You seem to think I’m just talking about linearly expanding the vocabulary of the model, I’m talking about giving it an entirely new paradigm through which to work.
Anyway, this is why no one likes pedants. If you want to actually engage in conversation, sure. If you want to just keep being a vitriolic ass, go back to your cave, yeah?
You’re just rephrasing the same approach, over, and over, and over. It’s like you’re not even reading what I’m saying.
The answer is no. This is not a feasible approach. LLMs are just parrots and they don’t understand anything. They were essentially a “shortcut” that gets something that acts intelligent without actually having to build something intelligent. You’re not going to convince it to be intelligent. You’re not going to solve all it’s short comings by shoe horning something in. It’s just more work than building actual intelligence.
It’s like if a costal town got overrun by flooding from a hurricane. And some guy shows up and is like “hey, I’ve got a bucket, I’ll just pull all the water to the sea”. And I’m like “that’s infeasible, we need a different solution, your bucket even has fucking holes in it”. And you’re over here saying “well, what if we got some duct tape? And then we can patch the holes. And then we can call our friends, and we can all bucket the water”.
It’s just not happening.
Yeah. This. You just keep repeating the same approach over and over without understanding or listening to the basic failings of these chat bots. It’s just not happening. You’re just perpetuating nonsense.
These things are basically slightly more complicated versions of the auto complete in your phone keyboard. Except that they’re fed hug amounts of the internet. They get really good at parroting sentences, but they have no sense of “intelligence” or what they’re actually doing. You’re better off trying to convince your auto correct to sound like Shakespeare than you are to remove the failings like racial bias from things like Gemini and ChatGPT. You can chip at small corners here and there but this is just not the path forward.
No I read what you are saying. I just think that you are something that “acts intelligent without actually being intelligent”. Here is why: All that you’ve written is based on very simple primitive brain cells and synapses and synaptic connections. It’s self evident that this is not really something that is designed to be intelligent. You’re just “really good at parroting sentences”. And you clearly agree that I’m doing the same 😄
Clearly LLMs are not intelligent and don’t understand, and it would need many other systems to make them so. But what they do show is that the “creative spark” even though they are very mediocre in their quality, can be created by using a critical mass of quantity. It’s like it’s just one small part of our mind, the “creative writing center” without intelligence. But it’s there, just because we added more data and processing.
Quality through quantity, that is what we seem to be and what is so shocking. And it’s obvious that there is a kind of disgust or bias against such a notion. A kind of embarrassment of the brain to just be thinking meat.
Now you might be absolutely right that my specific suggestion for an approach is bullshit, I don’t know enough about it. But I am pretty sure we’ll get there without understanding exactly how it works.
None of this has been pushed, by any researcher, by any company, by any open source group even, as “intelligence” In fact, it was unanimously disliked as a term by everyone working with the models and transformers, but media circus combined with techbros laymen hard on hype have won. Since then everyone has given up trying to be semantically correct on this front.
I didn’t say any researcher or anything had named it intelligence. Nor am I trying to be semantically correct.
Read the guys comments. He’s trying to push the idea that we can “change” it’s “understanding” about the things it’s discussing. He is one of the people who has fallen for the tech bros etc convincing people it is intelligent. I’m not fighting semantics, I’m trying to explain to him that it’s not intelligent. Because he himself clearly doesn’t understand that.
Why do you seem to think it’s impossible to change how AI understands things? It’s just an algorithm. It’s just a fancy set of math functions that gets you from noise to something that looks like something. Of COURSE we can change how this process works, have it weigh other things, and get something that generates based on a different paradigm than we currently have. All you seem to try to do is be semantically correct.
That’s just silly, as if there is no nuance whatsoever. You can ofc change its understanding. Depending on your definition, different types of models could be interpreted as intelligent in certain areas. You can be rational, you know, not everything needs to be black and white. It’s also possible that since even the experts in the field don’t fully grasp it, maybe you don’t either.
We all expected the AIs to launch nukes, and they simply held up a mirror.
This means war!
That guy sucks, and ruined my life!
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I mean the companies behind these AI things
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I don’t know who “them” is here. I thought from the context it was obvious that I meant whoever is managing these AIs. I guess I could’ve been clearer.
I mean they literally are inserting stuff in the prompts to make the results more diverse? It’s not some hidden thing but rather a solution to issues with the undiverse training data. But obviously here they’ve “overcorrected” to beyond all sense.
https://www.bbc.com/news/business-68364690
Generally on the internet when someone says “they” in quotes then they’re referring to “them” as Jewish people.
It’s a dog whistle.
So they were saying I’m Jewish? Why?
No idea. I don’t fully understand why any of these dog whistles are pulled out I just know what they are. Another big one is triple () meaning the same thing.