Oh ok, you want to claim this is compressing the entirety of the internet in a model that isn’t even 1 terabyte of data and be unimpressed that is something.
But it isn’t compression. It is a mathematical fact that neural networks are universal function approximators, this is undisputed, and analytic functions are continuous so to be an analytical function approximator it must be able to fill in the gaps between discrete data points by itself, which necessarily means spiting out data outside of the input distribution, data it has not seen.
It is a partial analogy, it takes into consideration the outputs which are related to some specific training data and disconsiders the outputs which cannot be directly related to any specific training data.
For example, make up a new meme template and a new joke on the spot, it couldn’t have seen it before if you make sure your joke and template are new. If the AI can explain it then compression is a horrendous analogy.
Lossy compression explains outputs being similar but not identical when trying to recover the original data, it doesn’t explain brand new content that makes sense standalone. Imagine a lossy audio compression resulting in a brand new song midway through playback, or a lossy image compression resulting in a brand new coherent image being overlayed onto some pixels of the original image. That is not what happens, lossy audio compression results in noise, lossy image compression results in noise, not in coherent unheard songs and unseen images.
Not sure why you feel the need to put words in my mouth. It wasn’t trained on “the entirety of the Internet,” but rather less than a terabyte of it. So yeah, that would probably take up less than a terabyte.
Arguing over this is just dumb, you can yourself take any picture you want at this very moment or come up with a brand new meme template on the spot and upload it to ChatGPT to see you are wrong, it is free btw.
Oh ok, you want to claim this is compressing the entirety of the internet in a model that isn’t even 1 terabyte of data and be unimpressed that is something.
But it isn’t compression. It is a mathematical fact that neural networks are universal function approximators, this is undisputed, and analytic functions are continuous so to be an analytical function approximator it must be able to fill in the gaps between discrete data points by itself, which necessarily means spiting out data outside of the input distribution, data it has not seen.
TBF, compression is related to ML. Hence, the Hutter Prize. Thinking of LLMs as lossy compression algorithms is a decent analogy.
It is a partial analogy, it takes into consideration the outputs which are related to some specific training data and disconsiders the outputs which cannot be directly related to any specific training data.
For example, make up a new meme template and a new joke on the spot, it couldn’t have seen it before if you make sure your joke and template are new. If the AI can explain it then compression is a horrendous analogy.
Lossy compression explains outputs being similar but not identical when trying to recover the original data, it doesn’t explain brand new content that makes sense standalone. Imagine a lossy audio compression resulting in a brand new song midway through playback, or a lossy image compression resulting in a brand new coherent image being overlayed onto some pixels of the original image. That is not what happens, lossy audio compression results in noise, lossy image compression results in noise, not in coherent unheard songs and unseen images.
Not sure why you feel the need to put words in my mouth. It wasn’t trained on “the entirety of the Internet,” but rather less than a terabyte of it. So yeah, that would probably take up less than a terabyte.
Then why did I just make this meme up right now and chat gpt can explain it?
https://i.ibb.co/NYHRnTY/Screenshot-20240531-072008-Chat-GPT.jpg
Arguing over this is just dumb, you can yourself take any picture you want at this very moment or come up with a brand new meme template on the spot and upload it to ChatGPT to see you are wrong, it is free btw.