To reiterate the model of EY that I am critiquing is one where an AGI quickly rapidly fooms through many OOM efficiency improvements. All key required improvements are efficiency improvements—it needs to improve it’s world modelling/planning per unit compute, and or improve compute per dollar and or compute per joule, etc.
In EY’s model there are some perhaps many OOM software improvements over the initial NN arch/aglorithms, perhaps then continued with more OOM hardware improvements. I don’t believe “buying more GPUs” is a key part of his model—it is far far too slow to provide even one OOM upgrade. Renting/hacking your way to even one OOM more GPUs is also largely unrealistic (I run one of the larger GPU compute markets and talk to many suppliers, I have inside knowledge here).
Both scenarios (going both big, in that you just use whole power-plant levels of energy, or going down in that you improve efficiency of chips) require changing semiconductor manufacturing, which is unlikely to be one of the first things a nascent AI does, unless it does successfully develop and deploy drexlerian nanotech
Right, so I have arguments against drexlerian nanotech (Moore room at the bottom, but also the thermodynamic constraints indicating you just can’t get many from nanotech alone), and separate arguments against many OOM from software (mind software efficiency).
I don’t understand the relevance of thermodynamic efficiency to a foom scenario “on current hardware”.
It is mostly relevant to the drexlerian nanotech, as it shows there likely isn’t much improvement over GPUs for all the enormous effort. If nanotech were feasible and could easily allow computers 6 OOM more efficient than the brain using about the same energy/space/materials, then I would more agree with his argument.
To reiterate the model of EY that I am critiquing is one where an AGI quickly rapidly fooms through many OOM efficiency improvements. All key required improvements are efficiency improvements—it needs to improve it’s world modelling/planning per unit compute, and or improve compute per dollar and or compute per joule, etc.
In EY’s model there are some perhaps many OOM software improvements over the initial NN arch/aglorithms, perhaps then continued with more OOM hardware improvements. I don’t believe “buying more GPUs” is a key part of his model—it is far far too slow to provide even one OOM upgrade. Renting/hacking your way to even one OOM more GPUs is also largely unrealistic (I run one of the larger GPU compute markets and talk to many suppliers, I have inside knowledge here).
Right, so I have arguments against drexlerian nanotech (Moore room at the bottom, but also the thermodynamic constraints indicating you just can’t get many from nanotech alone), and separate arguments against many OOM from software (mind software efficiency).
It is mostly relevant to the drexlerian nanotech, as it shows there likely isn’t much improvement over GPUs for all the enormous effort. If nanotech were feasible and could easily allow computers 6 OOM more efficient than the brain using about the same energy/space/materials, then I would more agree with his argument.