A few years ago, people assumed that these AIs will continue to get better every year. Seems that we are already hitting some limits, and improving the models keeps getting harder and harder. It’s like the linewidth limits we have with CPU design.
I think that hypothesis still holds as it has always assumed training data of sufficient quality. This study is more saying that the places where we’ve traditionally harvested training data from are beginning to be polluted by low-quality training data
File it under “too good to happen”. Most writing jobs are proofreading AI-generated shit these days. We’ll need to wait until there’s real money in writing scripts to de-pollute content.
no, not really. the improvement gets less noticeable as it approaches the limit, but I’d say the speed at which it improves is still the same. especially smaller models and context window size. there’s now models comparable to chatgpt or maybe even gpt 4.0 (I don’t remember, one or the other) with context window size of 128k tokens, that you can run on a GPU with 16gb of vram. 128k tokens is around 90k words I think. that’s more than 4 bee movie scripts. it can “comprehend” all of that at once.
A few years ago, people assumed that these AIs will continue to get better every year. Seems that we are already hitting some limits, and improving the models keeps getting harder and harder. It’s like the linewidth limits we have with CPU design.
I think that hypothesis still holds as it has always assumed training data of sufficient quality. This study is more saying that the places where we’ve traditionally harvested training data from are beginning to be polluted by low-quality training data
It’s almost like we need some kind of flag on AI-generated content to prevent it from ruining things.
If that gets implemented, it would help AI devs and common people hanging online.
File it under “too good to happen”. Most writing jobs are proofreading AI-generated shit these days. We’ll need to wait until there’s real money in writing scripts to de-pollute content.
no, not really. the improvement gets less noticeable as it approaches the limit, but I’d say the speed at which it improves is still the same. especially smaller models and context window size. there’s now models comparable to chatgpt or maybe even gpt 4.0 (I don’t remember, one or the other) with context window size of 128k tokens, that you can run on a GPU with 16gb of vram. 128k tokens is around 90k words I think. that’s more than 4 bee movie scripts. it can “comprehend” all of that at once.
No they are increasingly getting better, mostly they fit in a bigger context of other discoveries