I’m really curious as to where you’re getting the $500B number from. I felt like I didn’t understand this argument very well at all, and I’m wondering what sorts of results you’re imagining as a result of such a program.
It’s worth noting that 1E30-1E40 is only the cost of simulating the neurons, and an estimate for the computational cost of simulating the fitness function is not given, although it is stated that the fitness function “is typically the most computationally expensive component”. So the evaluation of the fitness function (which presumably has to be complicated enough to accurately assess intelligence), isn’t even included in that estimate.
It’s also not clear to me at least that simulating neurons is capable of recapitulating the evolution of general intelligence. I don’t believe it is a property of individual neurons that causes the brain to be divided into two hemispheres. I don’t know anything about brains, but I’ve never heard of left neurons or right neurons. So is it the neurons that are supposed to be mutating or some unstated variable that describes the organization of the various neurons. If the latter, then what is the computational cost associated with that super structure?
I feel like “recapitulating evolution” is a poor term for this. It’s not clear that there’s a lot of overlap between this sort of massive genetic search and actual evolution. It’s not clear that computational cost is the limiting factor. Can we design a series of fitness functions capable of guiding a randomly evolving algorithm to some sort of general intelligence? For humans, it seems that the mixture of cooperation and competition with other equally intelligent humans resulted in some sort of intelligence arms race, but the evolutionary fitness function that led to humans, or to the human ancestors isn’t really known. How do you select for an intelligent/human like niche in your fitness function? What series of problems can you create that will allow general intelligence to triumph over specialized algorithms?
Will the simulated creatures be given time to learn before their fitness is evaluated? Will learning produce changes in neural structure? Is the genotype/phenotype distinction being preserved? I feel like it’s almost misleading to include numerical estimates for the computational cost of what is arguably the easiest part of this problem without addressing the far more difficult theoretical problem of devising a fitness landscape that has a reasonable chance to produce intelligence. I’m even more blown away by the idea that it would be possible to estimate a cash value to any degree of precision for such a program. I have literally no idea what the probability distribution of possible outcomes for such a program would be. I don’t even have a good estimate of the cost or the theory behind the inputs.
I’m really curious as to where you’re getting the $500B number from. I felt like I didn’t understand this argument very well at all, and I’m wondering what sorts of results you’re imagining as a result of such a program.
It’s worth noting that 1E30-1E40 is only the cost of simulating the neurons, and an estimate for the computational cost of simulating the fitness function is not given, although it is stated that the fitness function “is typically the most computationally expensive component”. So the evaluation of the fitness function (which presumably has to be complicated enough to accurately assess intelligence), isn’t even included in that estimate.
It’s also not clear to me at least that simulating neurons is capable of recapitulating the evolution of general intelligence. I don’t believe it is a property of individual neurons that causes the brain to be divided into two hemispheres. I don’t know anything about brains, but I’ve never heard of left neurons or right neurons. So is it the neurons that are supposed to be mutating or some unstated variable that describes the organization of the various neurons. If the latter, then what is the computational cost associated with that super structure?
I feel like “recapitulating evolution” is a poor term for this. It’s not clear that there’s a lot of overlap between this sort of massive genetic search and actual evolution. It’s not clear that computational cost is the limiting factor. Can we design a series of fitness functions capable of guiding a randomly evolving algorithm to some sort of general intelligence? For humans, it seems that the mixture of cooperation and competition with other equally intelligent humans resulted in some sort of intelligence arms race, but the evolutionary fitness function that led to humans, or to the human ancestors isn’t really known. How do you select for an intelligent/human like niche in your fitness function? What series of problems can you create that will allow general intelligence to triumph over specialized algorithms?
Will the simulated creatures be given time to learn before their fitness is evaluated? Will learning produce changes in neural structure? Is the genotype/phenotype distinction being preserved? I feel like it’s almost misleading to include numerical estimates for the computational cost of what is arguably the easiest part of this problem without addressing the far more difficult theoretical problem of devising a fitness landscape that has a reasonable chance to produce intelligence. I’m even more blown away by the idea that it would be possible to estimate a cash value to any degree of precision for such a program. I have literally no idea what the probability distribution of possible outcomes for such a program would be. I don’t even have a good estimate of the cost or the theory behind the inputs.