Fundamentally no, linear progress requires exponential resources. The below article is about AGI but transformer based models will not benefit from just more grunt. We’re at the software stage of the problem now. But that doesn’t sign fat checks, so the big companies are incentivized to print money by developing more hardware.
https://timdettmers.com/2025/12/10/why-agi-will-not-happen/
Also the industry is running out of training data
https://arxiv.org/html/2602.21462v1
What we need are more efficient models, and better harnessing. Or a different approach, reinforced learning applied to RNNs that use transformers has been showing promise.
TropicalDingdong@lemmy.world 1 day ago
Yeah I’ve read that before. I don’t necessarily agree with their framework. And even working within their framework, this article is about a challenge to their third bullet.
I’m just not quite ready to rule out the idea that if you can scale single models above a certain boundary, you’ll get a fundamentally different/ novel behavior. This is consistent with other networked systems, and somewhat consistent with the original performance leaps we saw (the ones I think really matter are ones from 2019-2023, its really plateaued since and is mostly engineering tittering at the edges). It genuinely could be that 8 in a MoE configuration with single models maxing out each one could actually show a very different level of performance. We just don’t know because we just can’t test that with the current generation of hardware.
Its possible there really is something “just around the corner”; possible and unlikely.
in_my_honest_opinion@piefed.social 16 hours ago
I mean what you’re proposing was the initial push of gpt3. All the experts said, these GPTs will only hallucinate more with more resources and they’ll never do anything more than repeat their training data as a word salad posing as novelty.
https://gwern.net/scaling-hypothesis