While LLMs have been used for… a lot, it seems like this use might be one where it’s not only reliable but it appears to outperform existing methods of image compression. Being able to cram more data into less space tends to lead to interesting developments, so I will be keeping my eye on this.
What do you guys think? Seem like it’s deserving of less hype than I’m giving it? What kind of security holes do you think this could open?
It’s neat from a research and proof-of-concept perspective but practically speaking I’d like to see the CPU cycles required for the LLM compression compared to PNG or FLAC compression. We’ve always known we can increase compression by throwing more computing power at the problem but we settle on a happy medium at the intersection of “good enough” for compression and performance.
Bad. The time performance is bad. Even if it was O(n) there’s billions of parameters involved in an LLM.
I think this model has billions of weights. So I believe that means the model itself is quite large. Since the receiver needs to already have this model, I’d suggest that rather than compressing the data, we have instead pre encoded it, embedded it in the model weights, and thus the “compression” is just basically passing a primary key that points to the data to be compressed in the model.
It’s like, if you already have a copy of a book, I can “compress” any text in that book into 2 numbers: a page offset, and a word offset on that page. But that’s cheating because, at some point, we had to transfer to book too!
Yeah, it’s like saying I can “compress” a png of the Mona Lisa to just the string “Mona Lisa” because I have a database of art.
With a neural network, you wouldn’t be able to mathematically prove that the signal is perfectly recovered 100% of the time for all possible inputs. That is the case with PNG and FLAC. If you’re just listening to music and need a good compression ratio, then sure, it won’t be a big deal if a couple of bits are wrong. But that’s also why we have lossy compression. If the goal is to make signal degradation imperceptible to a human, then you could get a much better compression ratio using neural networks. If it’s truly critical that the signal isn’t corrupted, it would probably be better to just use the original method.
Seems like another “hey, what if we used LLMs for this” scenarios. It might be more effective, but exactly how many more resources are being used to make it do the same work as current compression algorithms? Effective doesn’t mean efficient and I think for lossless applications efficient is truly more important.
Ok but what if we used LLM AND blockchain for this
Im sure we can squeeze an nft in there somewhere
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I want 200k and 30% of the company
Best I can do is $125k and $300k in company stock over 4 years
