Labor is only one input to algorithmic progress (compute for experiments is another), and algorithmic progress itself is only one component (though probably the majority, perhaps around 60% or 80%) of overall AI progress (scaling up training compute and spending more on data also contribute).
I notice I am confused about this algorithmic progress. Is this compatible with what Steven Byrnes wrote on LLM algorithmic progress? Does anyone here know? Or is there already something written up on how this conflicts with Steven’s picture. I was quite confused why there was so little discussion from people I would expect to know about that topic, to figure out what the consensus on this is. I find this somewhat cruxy for how confident to be that we are going to get to AGI soon, which is relevant for me, since currently I work on enabling genetic human enhancement. I guess if people believe discussing this in too much detail is exfohazardry, that would be good to know, so I can just look into it on my own.
I notice I am confused about this algorithmic progress. Is this compatible with what Steven Byrnes wrote on LLM algorithmic progress? Does anyone here know? Or is there already something written up on how this conflicts with Steven’s picture. I was quite confused why there was so little discussion from people I would expect to know about that topic, to figure out what the consensus on this is. I find this somewhat cruxy for how confident to be that we are going to get to AGI soon, which is relevant for me, since currently I work on enabling genetic human enhancement. I guess if people believe discussing this in too much detail is exfohazardry, that would be good to know, so I can just look into it on my own.