I think you are focusing heavily on one particular model of an 'intelligence algorithm'. The model you are assuming sounds a lot like the type used for chess program. I played along with the 'culling algorithm' bit for the sake of convenience. But I'm not sure there is good enough evidence to assume that as the underlying model.
The definition of speed I am using is analogous to the clock speed or memory retrieval speed in a computer and it does seem at one level to be the way you are thinking of it as well. I think this fits in with what the researchers are measuring.
> The reason I didn't mention the possible variation in working memory capacity was a single word that I left out, leaving it, like I said, to the Computer Scientist: "exponential."
The memory requirements of a particular algorithm don't necessarily grow exponentially with the solution space or the input data. As with time complexity common space complexity relationships to the input size are n (you need to store at least the input), nlog(n), n^k and k^n. Most tractable problems would be nlog(n) or at worst n^k where k was small. The implication of this is that often small differences in memory can make a huge difference. Possibly you are forgetting that every extra bit of memory 'exponentially' increases the number of options it that can represent.
One of the first things you learn in CS algorithms is that the an efficient (let's say nlog(n)) algorithm running on a very slow computer will beat an inefficient one (say n^2) on a much faster computer. Sometimes resources such as memory will make the difference between being able to use an nlog(n) rather then a n^2 algorithm. To me this is a good analogue to apply to intelligence. For particular classes of problems (the harder ones) the better algorithm will trump a faster processor. Having a store of 'right algorithms' probably has a lot more to do with intelligence than raw thought speed.
Your view seems to be that the fundamental thinking algorithm is a fixed tree-pruning-like one. If it was then yes speed would be the main differentiating factor. I think this is the crux of our disagreement.
The definition of speed I am using is analogous to the clock speed or memory retrieval speed in a computer and it does seem at one level to be the way you are thinking of it as well. I think this fits in with what the researchers are measuring.
> The reason I didn't mention the possible variation in working memory capacity was a single word that I left out, leaving it, like I said, to the Computer Scientist: "exponential."
The memory requirements of a particular algorithm don't necessarily grow exponentially with the solution space or the input data. As with time complexity common space complexity relationships to the input size are n (you need to store at least the input), nlog(n), n^k and k^n. Most tractable problems would be nlog(n) or at worst n^k where k was small. The implication of this is that often small differences in memory can make a huge difference. Possibly you are forgetting that every extra bit of memory 'exponentially' increases the number of options it that can represent.
One of the first things you learn in CS algorithms is that the an efficient (let's say nlog(n)) algorithm running on a very slow computer will beat an inefficient one (say n^2) on a much faster computer. Sometimes resources such as memory will make the difference between being able to use an nlog(n) rather then a n^2 algorithm. To me this is a good analogue to apply to intelligence. For particular classes of problems (the harder ones) the better algorithm will trump a faster processor. Having a store of 'right algorithms' probably has a lot more to do with intelligence than raw thought speed.
Your view seems to be that the fundamental thinking algorithm is a fixed tree-pruning-like one. If it was then yes speed would be the main differentiating factor. I think this is the crux of our disagreement.