6 ms·

it's not learning anything if you turn the temperature value down to zero it will produce the same thing every time given the same input (in the same way zlib

it's not learning anything if you turn the temperature value down to zero it will produce the same thing every time given the same input (in the same way zlib or jpeg will) it's a lossy compression function that is implemented using a neural net copyright isn't removed if I take someone's existing image and re-save it as a jpg

alxhill 4y ago
This is false and represents a poor understanding of how these models work - they do abstract concepts and no you can't trivially get training images out.
- blibble 4y ago
  it's exactly the argument that the court cases against training on copyrighted works without permission are using if the courts agree and this is ruled to be infringement then we'll left with products with level of quality we see here meaning the technology will be an economic dead end here's to hoping
  - Robotbeat 4y ago
    Copyright is about copying. It is not about observing. Reading a copyrighted book isn't infringement. Writing out copies of it is, even if you don't use a computer or anything.
- DiscourseFan 4y ago
  >they do abstract concepts I don't think, given that we don't even have a particularly full understanding of how human conceptual logic functions, that we can claim that even AIs are using it as well. It's only "abstracting" in the sense that it has labeled one million objects in its training set with the word "tree," and fuses many of those images together to form a general picture dependent on specific parameters made to limit its set (oak tree, winter tree, etc.) But that is different from me or you using the word tree, which is just a signifier among signifiers, it stands for nothing but a negation of the very thing it points to in a certain set of symbolic relations. Humans communicate in the order of symbolic structures, our minds function much more like LLMs, creating multitudinous pattern relations. What you call "abstract concepts" are of a secondary order imposed to create rigorous exactitude overtop the riddled mess that we call the human psyche.
RC_ITR 4y ago
Ok, so let's assume it's using a Visual Transformer architecture, since AFAICT, we don't know. What the model does is turn the picture into numbers in a clever way (embedding). Then, the model compares the relationships between all the embedded tokens (self-attention). Then the model does the fancy linear algebra stuff we've come to know and love (feed forward neural network). What you're basically getting at with your temperature comment is "rather than having it semi-randomly pick one of the top n options that the feed-forward network recommends for which pixel goes where, we can always have the model simply choose the top answer each time." That is not "we can make it reproduce training images" The point of embedding is to see multiple examples of similar images and generalize from them. The process in and of itself breaks the ability to re-create a photo, unless the training set is very skewed. If that's the argument of legal cases, they are likely to fail or win on ignorance.
- blibble 4y ago
  > That is not "we can make it reproduce training images" didn't say that, all I said is it's a deterministic lossy compression function of the inputs (unless randomness is deliberately introduced) and as such it should be treated the same way as zlib or jpeg for copyright purposes
  - RC_ITR 4y ago
    I think you're really stretching the definition of deterministic here. For example, switching training order of images has a known impact on outputs, but I don't see the equivalent in traditional compression. I agree with you that it is computer code that runs on circuits (and is therefore deterministic in a very loose sense), but it is fundamentally incorrect to compare it to traditional image compression.
  - f_devd 4y ago
    > deterministic lossy compression function This would only be true if it didn't mix or 'forget' any of the training data for each prompt, and only then for the exact same inputs of the training data. That is a lot of qualifiers that aren't practically applicable. Slight side note: a deterministic lossy compression function should not always be treated the same as jpeg/zlib, as hash functions are technically also in that category.
Robotbeat 4y ago
The human brain is a lossy compression function implemented with a neural net. Making absurd arguments is fun! (The reason this is absurd is the number of parameters in the model is incredibly small compared to the size of the training set. You wouldn't even have enough bits per training image to keep a unique identifier for each training image, let alone a compressed representation. And to the degree there's redundancy in the training set, this is undesirable, actually counter-productive to the functioning of the model.) (In a way, the human brain is actually more capable of copyright infringement since the number of parameters in the human brain is about 4-5 times that of a typical image generative AI neural net, and we're trained on a smaller and less generalizable data set. Humans can and do make copies of things from memory, which generative AI massively struggles with and can only do for corner cases.)

alxhill 4y ago

This is false and represents a poor understanding of how these models work - they do abstract concepts and no you can't trivially get training images out.

blibble 4y ago
it's exactly the argument that the court cases against training on copyrighted works without permission are using if the courts agree and this is ruled to be infringement then we'll left with products with level of quality we see here meaning the technology will be an economic dead end here's to hoping
- Robotbeat 4y ago
  Copyright is about copying. It is not about observing. Reading a copyrighted book isn't infringement. Writing out copies of it is, even if you don't use a computer or anything.
DiscourseFan 4y ago
>they do abstract concepts I don't think, given that we don't even have a particularly full understanding of how human conceptual logic functions, that we can claim that even AIs are using it as well. It's only "abstracting" in the sense that it has labeled one million objects in its training set with the word "tree," and fuses many of those images together to form a general picture dependent on specific parameters made to limit its set (oak tree, winter tree, etc.) But that is different from me or you using the word tree, which is just a signifier among signifiers, it stands for nothing but a negation of the very thing it points to in a certain set of symbolic relations. Humans communicate in the order of symbolic structures, our minds function much more like LLMs, creating multitudinous pattern relations. What you call "abstract concepts" are of a secondary order imposed to create rigorous exactitude overtop the riddled mess that we call the human psyche.

RC_ITR 4y ago

Ok, so let's assume it's using a Visual Transformer architecture, since AFAICT, we don't know. What the model does is turn the picture into numbers in a clever way (embedding). Then, the model compares the relationships between all the embedded tokens (self-attention). Then the model does the fancy linear algebra stuff we've come to know and love (feed forward neural network). What you're basically getting at with your temperature comment is "rather than having it semi-randomly pick one of the top n options that the feed-forward network recommends for which pixel goes where, we can always have the model simply choose the top answer each time." That is not "we can make it reproduce training images" The point of embedding is to see multiple examples of similar images and generalize from them. The process in and of itself breaks the ability to re-create a photo, unless the training set is very skewed. If that's the argument of legal cases, they are likely to fail or win on ignorance.

blibble 4y ago
> That is not "we can make it reproduce training images" didn't say that, all I said is it's a deterministic lossy compression function of the inputs (unless randomness is deliberately introduced) and as such it should be treated the same way as zlib or jpeg for copyright purposes
- RC_ITR 4y ago
  I think you're really stretching the definition of deterministic here. For example, switching training order of images has a known impact on outputs, but I don't see the equivalent in traditional compression. I agree with you that it is computer code that runs on circuits (and is therefore deterministic in a very loose sense), but it is fundamentally incorrect to compare it to traditional image compression.
- f_devd 4y ago
  > deterministic lossy compression function This would only be true if it didn't mix or 'forget' any of the training data for each prompt, and only then for the exact same inputs of the training data. That is a lot of qualifiers that aren't practically applicable. Slight side note: a deterministic lossy compression function should not always be treated the same as jpeg/zlib, as hash functions are technically also in that category.

Robotbeat 4y ago

The human brain is a lossy compression function implemented with a neural net. Making absurd arguments is fun! (The reason this is absurd is the number of parameters in the model is incredibly small compared to the size of the training set. You wouldn't even have enough bits per training image to keep a unique identifier for each training image, let alone a compressed representation. And to the degree there's redundancy in the training set, this is undesirable, actually counter-productive to the functioning of the model.) (In a way, the human brain is actually more capable of copyright infringement since the number of parameters in the human brain is about 4-5 times that of a typical image generative AI neural net, and we're trained on a smaller and less generalizable data set. Humans can and do make copies of things from memory, which generative AI massively struggles with and can only do for corner cases.)