No—at least not for these reasons—see my longer reply lower down but what probably matters most is total search volume (model size * training time), which is basically FLOPS/memops. A smaller model can train longer to get up to the same capabilities for roughly the same total compute budget, but for AGI the faster learning model is more intelligent in any useful sense. And of course the human brain is probably pretty close to practical limits for equivalent FLOPs learning efficiency.
To first order approximation total flops predicts ANN/BNN capabilities quite well. GPT3 training was 3e23 flops, a 30 year old human brain is roughly 1e23 flops equivalent (1e9 seconds * 1e14 flops/s). GPT3 is only really equivalent to say 10% of the human brain at best (linguistic related cortices), but naturally the brain is still at least an OOM more flops efficient.
No—at least not for these reasons—see my longer reply lower down but what probably matters most is total search volume (model size * training time), which is basically FLOPS/memops. A smaller model can train longer to get up to the same capabilities for roughly the same total compute budget, but for AGI the faster learning model is more intelligent in any useful sense. And of course the human brain is probably pretty close to practical limits for equivalent FLOPs learning efficiency.
To first order approximation total flops predicts ANN/BNN capabilities quite well. GPT3 training was 3e23 flops, a 30 year old human brain is roughly 1e23 flops equivalent (1e9 seconds * 1e14 flops/s). GPT3 is only really equivalent to say 10% of the human brain at best (linguistic related cortices), but naturally the brain is still at least an OOM more flops efficient.