Two authors sued OpenAI, accusing the company of violating copyright law. They say OpenAI used their work to train ChatGPT without their consent.
Two authors sued OpenAI, accusing the company of violating copyright law. They say OpenAI used their work to train ChatGPT without their consent.
Totally in agreement with you here. They did something wrong and should have to deal with that.
But my question is more about…
Is comprehension necessary for breaking copyright infringement? Is it really about a creator being able to be logical or to extend concepts?
I think we have a definition problem with exactly what the issue is. This may be a little too philosophical but what part of you isn’t processing your historical experiences and generating derivative works? When I saw “dog” the thing that pops into your head is an amalgamation of your past experiences and visuals of dogs. Is the only difference between you and a computer the fact that you had experiences with non created works while the AI is explicitly fed created content?
AI could be created with a bit of randomness added in to make what it generates “creative” instead of derivative but I’m wondering what level of pure noise needs to be added to be considered created by AI? Can any of us truly create something that isn’t in some part derivative?
Agreed. I think at this point we are in a strange place because most people think ChatGPT is a far bigger leap in technology than it truly is. It’s biggest achievement was being able to process synthesized data fast enough to make it feel conversational.
What worries me is that we will set laws and legal precedent based on a fundamental misunderstanding of what the technology does. I fear that had all the sample data been acquired legally people would still have the same argument think their creations exist inside the AI in some full context when it’s really just synthesized down to what is necessary to answer the question posed “what’s the statically most likely next word of this sentence?”
That’s part of it, yes, but nowhere near the whole issue.
I think someone else summarized my issue with AI elsewhere in this thread–AI as it currently stands is fundamentally plagiaristic, because it cannot be anything more than the average of its inputs, and cannot be greater than the sum of its inputs. If you ask ChatGPT to summarize the plot of The Matrix and write a brief analysis of the themes and its opinions, ChatGPT doesn’t watch the movie, do its own analysis, and give you its own summary; instead, it will pull up the part of the database it was fed into by its learning model that relates to “The Matrix,” “movie summaries,” “movie analysis,” find what parts of its training dataset matches up to the prompt–likely an article written by Roger Ebert, maybe some scholarly articles, maybe some metacritic reviews–and spit out a response that combines those parts together into something that sounds relatively coherent.
Another issue, in my opinion, is that ChatGPT can’t take general concepts and extend them further. To go back to the movie summary example, if you asked a regular layperson human to analyze the themes in The Matrix, they would likely focus on the cool gun battles and neat special effects. If you had that same layperson attend a four-year college and receive a bachelor’s in media studies, then asked them to do the exact same analysis of The Matrix, their answer would be drastically different, even if their entire degree did not discuss The Matrix even once. This is because that layperson is (or at least should be) capable of taking generalized concepts and applying them to specific scenarios–in other words, a layperson can take the media analysis concepts they learned while earning that four-year degree, and apply them to a specific thing, even if those concepts weren’t explicitly applied to that thing. AI, as it currently stands, is incapable of this. As another example, let’s say a brand-new computing language came out tomorrow that was entirely unrelated to any currently existing computing languages. AI would be nigh-useless at analyzing and helping produce new code for that language–even if it were dead simple to use and understand–until enough humans published code samples that could be fed into the AI’s training model.