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.
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.
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.
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.
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.
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.