My point is that this description literally applies just as much to humans. Humans are also trained on vast quantities of things they’ve seen before and meanings associated with them.
it’s a collage of other art
This is genuinely a misunderstanding of how these programs work.
when AI is used for art it takes jobs from artists and prevents the craft from advancing.
Because the only art anyone has ever done is when someone else paid them for it? There are a lot of art forms that generally aren’t commercially viable, and it’s very odd to insist that commercial viability is what advances an art form.
I do actually get regularly paid for a kind of work that is threatened by these things (although in my case it’s LLMs, not images). For the time being I can out-perform ChatGPT and the like, but I don’t expect that that will last forever. Either I’ll end up incorporating it or I’ll need to find something else to do. But I’m not going to stop doing my hobby versions of it.
Technology kills jobs all the time. We don’t have many human calculators these days. If the work has value beyond the financial, people will keep doing it.
My point is that this description literally applies just as much to humans. Humans are also trained on vast quantities of things they’ve seen before and meanings associated with them.
In which case the machine would get the copyright (which legally they can’t now), not the prompter.
Copyright just isn’t compatible with AI, we need to abolish it.
If a picture gets generated, who is the owner? The one writing the prompt? The AI that generated it? The researchers that created the AI? The artist on which the picture is based?
How about none of them? It is a picture, a piece of information. It doesn’t need an owner.
Human brains don’t have perfect recollection. Every time we retell a story or remember a memory or picture an image in our head it is distorted with our own imperfections.
When I prompt an AI to create an image it samples the images it learned from with perfect recollection.
This is incorrect actually. The models these AIs run from by definition have imperfect recall otherwise they would be ENORMOUS. No, that’s actually exactly the opposite of how these work.
They train a statistically weighted model to predict outputs based on inputs. It has no actual image data stored internally, it can’t.
This is incorrect actually. The models these AIs run from by definition have perfect recall and that is why they require ENORMOUS resources to run and why ChatGPT became less effective when the resources it was allocated were reduced.
No, they take exponentially increasing resources as a consequence of having imperfect recall. Smaller models have “worse” recall. They’ve been trained with smaller datasets (or pruned more).
As you increase the size of the model (number of “neurons” that can be weighted) you increase the ability of that model to retain and use information. But that information isn’t retained in the same form as it was input. A model trained on the English language (an LLM, like ChatGPT) does not know every possible word, nor does it actually know ANY words.
All ChatGPT knows is what characters are statistically likely to go after another in a long sequence. With enough neurons and layers combined with large amounts of processing power and time for training, this results in a weighted model which is many orders of magnitude smaller than the dataset it was trained on.
Since the model weighting itself is smaller than the input dataset, it is literally impossible for the model to have perfect recall of the input dataset. So by definition, these models have imperfect recall.
In other words they require exponentially more input because the AI doesn’t know what it is looking at.
It uses its perfect recollection of that input to create a ‘model’ of what a face should look like and stores that model like a collage of all the samples and then uses that to reproduce a face.
Well, what you described is simply not a perfect recollection. It is many small tidbits of information that combined together can make a larger output.
If our brains worked exactly the same as AI programming then AI wouldn’t be needed because it would be no different than how we are doing things without AI.
I feel like you keep misrepresenting what I’m saying. Nowhere did I say that our brains work completely and exactly the same as AI. However, we do learn in much the same way. By amortizing small amounts of information and drawing connections between them
The reason they fuck up hands is because hands are usually moving during pictures and have many different configurations compared to any other body part.
So when these image AIs refer back to all the pictures of hands they’ve been fed and use them to create an ‘average approximation’ of what a hand looks like they include the motion blur from some of their samples, a middle finger sticking up from another sample or extra fingers from the sample pictures of people holding hands etc and mismatch them together even when it doesn’t fit in the picture being created.
The AI doesn’t know what a hand is. It is just mixing together samples from its perfect recollection.
How many pictures do you see online where the hands are in motion, or even blurred?
Hands are usually behind objects when they hold something and can indeed have tons of variations and configurations. Even human artists fuck up all the time or just not draw them at all.
AI don’t combine samples. If they did they wouldn’t be able to generate new pictures of whatever subject you want in a specific style you want and then have multiple variations of that picture.
It isn’t a copy and paste, it is interpreting the drawing and modifying it based upon the prompt.
My point is that this description literally applies just as much to humans. Humans are also trained on vast quantities of things they’ve seen before and meanings associated with them.
This is genuinely a misunderstanding of how these programs work.
Because the only art anyone has ever done is when someone else paid them for it? There are a lot of art forms that generally aren’t commercially viable, and it’s very odd to insist that commercial viability is what advances an art form.
I do actually get regularly paid for a kind of work that is threatened by these things (although in my case it’s LLMs, not images). For the time being I can out-perform ChatGPT and the like, but I don’t expect that that will last forever. Either I’ll end up incorporating it or I’ll need to find something else to do. But I’m not going to stop doing my hobby versions of it.
Technology kills jobs all the time. We don’t have many human calculators these days. If the work has value beyond the financial, people will keep doing it.
In which case the machine would get the copyright (which legally they can’t now), not the prompter.
Copyright just isn’t compatible with AI, we need to abolish it.
If a picture gets generated, who is the owner? The one writing the prompt? The AI that generated it? The researchers that created the AI? The artist on which the picture is based?
How about none of them? It is a picture, a piece of information. It doesn’t need an owner.
Can we get UBI before we start abolishing people’s income though?
Human brains don’t have perfect recollection. Every time we retell a story or remember a memory or picture an image in our head it is distorted with our own imperfections.
When I prompt an AI to create an image it samples the images it learned from with perfect recollection.
AI does not learn the same way humans do.
This is incorrect actually. The models these AIs run from by definition have imperfect recall otherwise they would be ENORMOUS. No, that’s actually exactly the opposite of how these work.
They train a statistically weighted model to predict outputs based on inputs. It has no actual image data stored internally, it can’t.
This is incorrect actually. The models these AIs run from by definition have perfect recall and that is why they require ENORMOUS resources to run and why ChatGPT became less effective when the resources it was allocated were reduced.
-ChatGPT
No, they take exponentially increasing resources as a consequence of having imperfect recall. Smaller models have “worse” recall. They’ve been trained with smaller datasets (or pruned more).
As you increase the size of the model (number of “neurons” that can be weighted) you increase the ability of that model to retain and use information. But that information isn’t retained in the same form as it was input. A model trained on the English language (an LLM, like ChatGPT) does not know every possible word, nor does it actually know ANY words.
All ChatGPT knows is what characters are statistically likely to go after another in a long sequence. With enough neurons and layers combined with large amounts of processing power and time for training, this results in a weighted model which is many orders of magnitude smaller than the dataset it was trained on.
Since the model weighting itself is smaller than the input dataset, it is literally impossible for the model to have perfect recall of the input dataset. So by definition, these models have imperfect recall.
In other words they require exponentially more input because the AI doesn’t know what it is looking at.
It uses its perfect recollection of that input to create a ‘model’ of what a face should look like and stores that model like a collage of all the samples and then uses that to reproduce a face.
It’s perfect recollection with an extra step.
Well, what you described is simply not a perfect recollection. It is many small tidbits of information that combined together can make a larger output.
That’s exactly how our brains work too
If our brains worked exactly the same as AI programming then AI wouldn’t be needed because it would be no different than how we are doing things without AI.
I feel like you keep misrepresenting what I’m saying. Nowhere did I say that our brains work completely and exactly the same as AI. However, we do learn in much the same way. By amortizing small amounts of information and drawing connections between them
I’m pretty sure that the way they constantly fuck up hands is a solid demonstration that these AI tools do not have a perfect recollection
The reason they fuck up hands is because hands are usually moving during pictures and have many different configurations compared to any other body part.
So when these image AIs refer back to all the pictures of hands they’ve been fed and use them to create an ‘average approximation’ of what a hand looks like they include the motion blur from some of their samples, a middle finger sticking up from another sample or extra fingers from the sample pictures of people holding hands etc and mismatch them together even when it doesn’t fit in the picture being created.
The AI doesn’t know what a hand is. It is just mixing together samples from its perfect recollection.
What? No
How many pictures do you see online where the hands are in motion, or even blurred?
Hands are usually behind objects when they hold something and can indeed have tons of variations and configurations. Even human artists fuck up all the time or just not draw them at all.
AI don’t combine samples. If they did they wouldn’t be able to generate new pictures of whatever subject you want in a specific style you want and then have multiple variations of that picture.
It isn’t a copy and paste, it is interpreting the drawing and modifying it based upon the prompt.