cross-posted from: https://nom.mom/post/121481
OpenAI could be fined up to $150,000 for each piece of infringing content.https://arstechnica.com/tech-policy/2023/08/report-potential-nyt-lawsuit-could-force-openai-to-wipe-chatgpt-and-start-over/#comments
What’s the basis for this? Why can a human read a thing and base their knowledge on it, but not a machine?
Because a human understands and transforms the work. The machine runs statistical analysis and regurgitates a mix of what it was given. There’s no understanding or transformation, it’s just what is statistically the 3rd most correct word that comes next. Humans add to the work, LLMs don’t.
Machines do not learn. LLMs do not “know” anything. They make guesses based on their inputs. The reason they appear to be so right is the scale of data they’re trained on.
This is going to become a crazy copyright battle that will likely lead to the entirety of copyright law being rewritten.
I don’t know if I agree with everything you wrote but I think the argument about llms basically transforming the text is important.
Converting written text into numbers doesn’t fundamentally change the text. It’s still the authors original work, just translated into a vector format. Reproduction of that vector format is still reproduction without citation.
But it’s not just converting them into a different format. It’s not even storing that information at all. It can’t actually reproduce anything from the dataset unless it is really small or completely overfitted, neither of which apply to GPT with how massive it is.
Each neuron, which represents a word or a phrase, is a set of weights. One source makes a neuron go up by 0.000001% and then another source makes it go down by 0.000001%. And then you repeat that millions and millions of times. The model has absolutely zero knowledge of any specific source in its training data, it only knows how often different words and phrases occur next to each other. Or for images it only knows that certain pixels are weighted to be certain colors. Etc.
This is a misunderstanding on your part. While some neurons are trained this way, word2vec and doc2vec are not these mechanisms. The llms are extensions of these models and while there are certainly some aspects of what you are describing, there is a transcription into vector formats.
This is the power of vectorization of language (among other things). The one to one mapping between vectors and words / sentences to documents and so forth allows models to describe the distance between words or phrases using euclidian geometry.
I was trying to make it as simple as possible. The format is irrelevant. The model is still storing nothing but weights at the end of the day. Storing the relationships between words and sentences is not the same thing as storing works in a different format which is what your original comment implied.
I’m sorry you failed to grasp how it works in this context.
You made me really interested in this concept so I asked GPT-4 what the furthest word away from the word “vectorization” would be.
Interesting game! If we’re aiming for a word that’s conceptually, contextually, and semantically distant from “vectorization,” I’d pick “marshmallow.” While “vectorization” pertains to complex computational processes and mathematics, “marshmallow” is a soft, sweet confectionery. They’re quite far apart in terms of their typical contexts and meanings.
It honestly never ceases to surprise me. I’m gonna play around with some more. I do really like the idea that it’s essentially a word calculator.
Try asking it how the vectorization of king and queen are related.
I think this is very unlikely. All of law is precedent.
Google uses copyrighted works for many things that are “algorithmic” but not AI and people aren’t shitting themselves over it.
Why would AI be different? So long as copyright isn’t infringed at least.
It’s also the scale of their context, not just the data. More (good) data and lots of (good) varied data is obviously better, but the perceived cleverness isn’t owed to data alone.
I do hope copyright law gets rewritten. It is dated and hasn’t kept up with society or technology at all.
That machine is a commercial product. Quite unlike a human being, in essence, purpose and function. So I do not think the comparison is valid here unless it were perhaps a sentient artificial being, free to act of its own accord. But that is not what we’re talking about here. We must not be carried away by our imaginations, these language models are (often proprietary and for profit) products.
I don’t see how that’s relevant. A company can pay someone to read copyrighted work, learn from it, and then perform a task for the benefit of the company related to the learning.
But how did that person acquire the copyrighted work? Was the copyrighted material paid for?
That’s the crux of the issue, Open AI isn’t paying for the copyrighted work they are “reading”, are they?
What does paying for anything have to do with what we’re talking about here. They’re ingesting freely available content, that anyone with a web browser could read