- cross-posted to:
- technology@lemmy.ml
- technology@beehaw.org
- cross-posted to:
- technology@lemmy.ml
- 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
What you mentioned is assumed video and paper in question.
The main argument being that no matter our computational techniques, the diminishing returns in predictive precision is reached far sooner than we achieve general intelligence.
That’s very bold presumption. How can they be so sure of this, that any future models can’t tackle the issue? have they got proof or something.
No, they just calculate with increased size of the training roster… it’s not that complicated. Which is a fair presumption as that is how we’ve increased the predictive precision so far.
It seems far more bold to presume that general intelligence will be created any time soon when current machine learning is nowhere close.
No the argument is current techniques give logarithmic returns in data size, which is bad. But it said nothing about other potential techniques or made any suggestion that this was a general result.
Well obviously they cannot rule out techniques no one has though of but likewise they obviously accounted for what they deemed to be within the realm of possibility