Those papers are based on dense model architecture. The MoE architecture that I mentioned a few comments ago, does not follow the same laws. Architectural changes could push us past the wall.
Not to mention if we reach 90% accuracy (which was defined in those papers to be AGI level), there’s no reason we will need to keep making new models and training them. AGI is good enough. After that we improve inference performance and bring inference cost down.
Hallucinating 1 in 10 fractions of a statement is not AGI by how anybody with half a brain defines it.
A statistical model hallucinating 1 in 1000 isn’t even AGI.
AGI is the capability to solve a riddle without being trained on infinite copies of the same riddle, which these machines guessing the next word in a seque have never shown any capacity for.
Those papers are based on dense model architecture. The MoE architecture that I mentioned a few comments ago, does not follow the same laws. Architectural changes could push us past the wall.
Not to mention if we reach 90% accuracy (which was defined in those papers to be AGI level), there’s no reason we will need to keep making new models and training them. AGI is good enough. After that we improve inference performance and bring inference cost down.
Hallucinating 1 in 10 fractions of a statement is not AGI by how anybody with half a brain defines it.
A statistical model hallucinating 1 in 1000 isn’t even AGI.
AGI is the capability to solve a riddle without being trained on infinite copies of the same riddle, which these machines guessing the next word in a seque have never shown any capacity for.
I know humans that hallucinate more