I said longer term—using hypothetical brain-parity neuromorphic computing (uploads or neuromorphic AGI). We need enormous hardware progress to reach that.
Current tech on GPUs requires large supercomputers to train 1e25+ flops models like GPT4 that are approaching, but not quite, human level AGI. If the rurmour of 1T params is true, then it takes a small cluster and ~10KW just to run some smallish number of instances of the model.
Getting something much much smarter than us would require enormous amounts of computation and energy without large advances in software and hardware.
I said longer term—using hypothetical brain-parity neuromorphic computing (uploads or neuromorphic AGI). We need enormous hardware progress to reach that.
Sure. We will probably get enormous hardware progress over the next few decades, so that’s not really an obstacle.
It seems to me your argument is “smarter than human intelligence cannot make enormous hardware or software progress in a relatively short amount of time”, but this has nothing to do with “efficiency arguments”. The bottleneck is not energy, the bottleneck is algorithmic improvements and improvements to GPU production, neither of which is remotely bottlenecked on energy consumption.
Getting something much much smarter than us would require enormous amounts [...] energy without large advances in software and hardware.
No, as you said, it would require like, a power plant worth of energy. Maybe even like 10 power plants or so if you are really stretching it, but as you said, the really central bottleneck here is GPU production, not energy in any relevant way.
Sure. We will probably get enormous hardware progress over the next few decades, so that’s not really an obstacle.
As we get more hardware and slow mostly-aligned AGI/AI progress this further raises the bar for foom.
It seems to me your argument is “smarter than human intelligence cannot make enormous hardware or software progress in a relatively short amount of time”, but this has nothing to do with “efficiency arguments”.
That is actually an efficiency argument, and in my brain efficiency post I discuss multiple sub components of net efficiency that translate into intelligence/$.
The bottleneck is not energy, the bottleneck is algorithmic improvements and improvements to GPU production, neither of which is remotely bottlenecked on energy consumption.
Ahh I see—energy efficiency is tightly coupled to other circuit efficiency metrics as they are all primarily driven by shrinkage. As you increasingly bottom out hardware improvements energy then becomes an increasingly more direct constraint. This is already happening with GPUs where power consumption is roughly doubling with each generation, and could soon dominate operating costs.
See here where I line the roodman model up to future energy usage predictions.
All that being said I do agree that yes the primary bottlneck or crux for the EY fast takeoff/takeover seems to be the amount of slack in software and scaling laws. But only after we agree that there isn’t obvious easy routes for the AGI to bootstrap nanotech assemblers with many OOM greater compute per J than brains or current computers.
I said longer term—using hypothetical brain-parity neuromorphic computing (uploads or neuromorphic AGI). We need enormous hardware progress to reach that.
Current tech on GPUs requires large supercomputers to train 1e25+ flops models like GPT4 that are approaching, but not quite, human level AGI. If the rurmour of 1T params is true, then it takes a small cluster and ~10KW just to run some smallish number of instances of the model.
Getting something much much smarter than us would require enormous amounts of computation and energy without large advances in software and hardware.
Sure. We will probably get enormous hardware progress over the next few decades, so that’s not really an obstacle.
It seems to me your argument is “smarter than human intelligence cannot make enormous hardware or software progress in a relatively short amount of time”, but this has nothing to do with “efficiency arguments”. The bottleneck is not energy, the bottleneck is algorithmic improvements and improvements to GPU production, neither of which is remotely bottlenecked on energy consumption.
No, as you said, it would require like, a power plant worth of energy. Maybe even like 10 power plants or so if you are really stretching it, but as you said, the really central bottleneck here is GPU production, not energy in any relevant way.
As we get more hardware and slow mostly-aligned AGI/AI progress this further raises the bar for foom.
That is actually an efficiency argument, and in my brain efficiency post I discuss multiple sub components of net efficiency that translate into intelligence/$.
Ahh I see—energy efficiency is tightly coupled to other circuit efficiency metrics as they are all primarily driven by shrinkage. As you increasingly bottom out hardware improvements energy then becomes an increasingly more direct constraint. This is already happening with GPUs where power consumption is roughly doubling with each generation, and could soon dominate operating costs.
See here where I line the roodman model up to future energy usage predictions.
All that being said I do agree that yes the primary bottlneck or crux for the EY fast takeoff/takeover seems to be the amount of slack in software and scaling laws. But only after we agree that there isn’t obvious easy routes for the AGI to bootstrap nanotech assemblers with many OOM greater compute per J than brains or current computers.
How much room is there in algorithmic improvements?