151–160 of 250 posts
One thing I noticed in Gboard is it uses homeomorphic encryption to do federated learning of common words used amongst public to do encrypted suggestions. E.g. there are two common spelling of bizarre which are popular on Gboard : bizzare and bizarre. Can something similar help in model encryption?
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If you mean that you might be able to decrypt a copyrighted work because you used that same encryption method on a non-copyrighted work, then definitely not. The work under protection will be considered. (Otherwise, I am unsure what you meant.)
From what I recall, it was the actual protection method that was protected by DMCA - when DVD protection was cracked it was forbidden to distribute a particular section of code so they just printed it on a Tee-shirt to troll the powers that be.
> Outside the Internet and the mass media, the key has appeared in or on T-shirts, poetry, songs and music videos, illustrations and other graphic artworks, tattoos and body art, and comic strips.
Using the encryption key to decrypt the data on a DVD is illegal “circumvention” per DMCA 1201, if it’s done without authorization from the copyright owner of the data on the DVD. If it were really illegal to simply publish the key on a website, then printing it on clothing that they sold instead would not be a viable loophole.
I’m glad it is still referred to as a controversy that they were issuing cease and desist letters for publishing information when the actual crime they had in mind, which was not alleged in the letters, is using the information to decrypt a DVD.
This is cool, but only the first part in extracting a ML model for usage. The second part is reverse engineering the tokenizer and input transformations that are needed to before passing the data to the model, and outputting a human readable format.
One thing I noticed in Gboard is it uses homeomorphic encryption to do federated learning of common words used amongst public to do encrypted suggestions. E.g. there are two common spelling of bizarre which are popular on Gboard : bizzare and bizarre. Can something similar help in model encryption?
Well done you seem to have liberated an open model trained on open data for blind and visually impaired people. Paper: https://arxiv.org/pdf/2204.03738 Code: https://github.com/microsoft/banknote-net Training data: https://raw.githubusercontent.com/microsoft/banknote-net/ref... model: https://github.com/microsoft/banknote-net/blob/main/models/b... Kinda easier to download it straight from github. Its licenced under M…
The same method/strategy applies to closed tools and models too, although you should probably be careful if you've handed over a credit card for a decryption key to a service and try this ;)
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Please don't cross into personal attack or otherwise break the site guidelines when posting here. Your post would be fine with just the first sentence. https://news.ycombinator.com/newsguidelines.html
This is cool, but only the first part in extracting a ML model for usage. The second part is reverse engineering the tokenizer and input transformations that are needed to before passing the data to the model, and outputting a human readable format.
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I don't see how this is a double standard. Comparing a person interacting with their culture is not comparable in any way. IMHO, it's kind of a wacky argument to make.
Can you elaborate on how it's not comparable? It seems obvious to me that it is -- they both learn and then create -- so what's the difference? If I can hire an employee who draws on knowledge they learned from copyrighted textbooks, why can't I hire an AI which draws on knowledge it learned from copyrighted textbooks? What makes that argument "wacky" in your eyes?
"Can you elaborate on how it's not comparable?"
The process of individual people interacting with their culture is a vastly different process than that used to train large language models. In what ways to you think these processes have anything in common?
"It seems obvious to me that it is -- they both learn and then create -- so what's the difference?"
This doesn't seem obvious to me (obviously)! Maybe you can argue that an LLM "learns" during training, but that ceases once training is complete. For sure, there are work-arounds that meet certain goals (RAG, fine-tuning); maybe your already vague definition of "learning" could be stretched to include these? Still, comparing this to how people learn is pretty far-fetched. AFAICT, there's no literature supporting the view that there's any commonality here; if you have some I would be very interested to read it. :-)
Do they both create? I suspect not; an LLM is parroting back data from it's training set. We've seen many studies showing that tested LLMs perform poorly on novel problem sets. This article was posted just this week:
https://news.ycombinator.com/item?id=42565606
The court is still out on the copyright issue, for the perspective of US law we'll have to wait on this one. Still, it's clear that an LLM can't "create" in any meaningful way.
And so on and so forth. How is hiring an employee at all similar to subscribing to an OpenAI ChatGPT plan? Wacky indeed!