- cross-posted to:
- technology@beehaw.org
- cross-posted to:
- technology@beehaw.org
A new paper suggests diminishing returns from larger and larger generative AI models. Dr Mike Pound discusses.
The Paper (No “Zero-Shot” Without Exponential Data): https://arxiv.org/abs/2404.04125
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My personal take is that the current generation of generative models peaked, for the reasons stated in the video (diminishing returns). This current gen will be useful, but progress-wise it’ll be a dead end.
In the future however I believe that models with a different architecture will cause a breakthrough, being able to perform better with less training. And probably less energy requirements, too.
Sam Altman gives a pretty good indication that your point is correct when he began asking for $7 trillion for new AI chip development.
I’ve already thought that in terms of major progression AI has peaked as early as in 2022 when chatgpt and various diffusers were all hyped up. It was kinda obvious, since our silicon tech is already basically maxed out. There are lots of potential optimizations, but they are minor advancements compared to the raw compute power growth we’ve had till the near past. And in order to make the next revolution in the AI field, those moneybags will have to spend the colossal amount of money to basically reinvent either computers themselves or the ML architechture.
