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I'm not sure how to explain this any clearer. I am talking about neural net compression algorithms. As in, it is literally just a neural net encoding some copyr
by tga_d 3y ago
I'm not sure how to explain this any clearer. I am talking about neural net compression algorithms. As in, it is literally just a neural net encoding some copyrighted work, and nothing else. It is ultimately no more intelligent than a zip file, other than the file and program are the same. You can't seriously believe that these programs allow you to avoid copyright claims, can you? Movie studios, music producers, and book publishers should just pack it in, pirates just need to switch to compressing by training a NN, and seeding those instead, and there's no legal precedence to stop them? If you do think that, do you at least understand why nobody is going to take your position seriously?
- csallen 3y agoA neural net designed to do nothing other than compress and decompress a copyrighted work is completely different than GPT-4, unless I'm uninformed. To me that sounds like comparing a VCR to a brain. GPT-4's technology is clearly something that "learns" in order to be able to produce novel thoughts and ideas, rather than merely compressing. A judge or jury would easily understand that it wasn't designed just to reproduce copyrighted works. > It is clearly not the same as your mind, because your mind can and, assuming you want to stay in good standing, will provide credit for influence I forgot to respond to this, but it's not true. Your mind is incapable of providing credit for 99.9% of its influence and inspiration, even when you want it to. You simply don't remember where you've learned most of the things you've learned. And when you have a seemingly novel idea, you can't always be aware of every single influential example of another person's work/art that combined to generate that new idea.
- mlyle 3y ago> A neural net designed to do nothing other than compress and decompress a copyrighted work is completely different than GPT-4, unless I'm uninformed. Compression and the output from LLMs are cousins. The model tries to predict what continuations are likely, given context. Indeed, it takes a lot of effort to make LLMs less willing to just output training data verbatim. And conversely, you can get compression algorithms to do things similar to what LLMs do (poorly). Whether this also describes most of human cognitive process, is subject to debate.