2 ms·
> Progress is sigmoidal approaching a limit. Yep, but a lot of the current hype for GPT is that we're in the near-vertical segment of the sigmoid, and so peopl
by mcphage 4y ago
> Progress is sigmoidal approaching a limit.
Yep, but a lot of the current hype for GPT is that we're in the near-vertical segment of the sigmoid, and so people are like "woah, if that's where we are now, where will we be in 5-10 years?!". And the answer to that might be, pretty much where we are now. And maybe not, maybe it'll be better, but you can't assume linear improvements on a sigmoid curve.
- CuriouslyC 4y agoConsidering that AI is just "taking off" now, I'd say we're still pretty early in the growth curve (the alternate hypothesis, that people have been oblivious for a while, seems unlikely). Regardless, I think even if we were well into the linear portion of the curve, we could still do a lot with layers of tooling on top of marginal improvements on current models and technology.
- telotortium 4y agoWe're in the hype cycle now at the moment, but the capabilities have been taking off for a few years. GPT-3 came out in 2020, and in retrospect, could have been trained several years before that, given that the 175 billion parameters of GPT-3 turned out to be way too many for its level of performance (as discovered by Chinchilla). I agree that there are a lot of refinements that can be made even with the current LLMs, but it's unclear how far LLMs will continue to improve.