(I’m sure you know more than I do about algorithms.)
What kind of example do you have in mind?
~10% of the log-space progress on problems people care about comes from big jumps vs 90% from relatively smooth improvements,
I’m thinking of the difference between insertion sort / bubble sort vs radix sort / merge sort.
(Knuth gives an interesting history here (Art of Programming Vol 3, section 5.5, p 383); apparently in 1890 the US census data was processed using the radix sorting algorithm running on a mechanical-electronic-human hybrid. There was an order-preserving card-stack merging machine in 1938. Then in 1945, von Neumann wrote down a merge sort, while independently Zuse wrote down an insertion sort.)
I guess we’re talking past each other because we’re answering different versions of “What is continuous in what?”. Performance on a task can be, and is, much more continuous in time than “ideas” are continuous in time, because translating ideas into performance on a task takes resources (money, work, more ideas). So I concede that what I said here:
I think often performance on a task gets jumps
was mostly incorrect, if we don’t count the part where
you get to a pretty OK algorithm quite quickly
So one question is, is TAI driven by ideas that will have a stage where they get to a pretty okay version quite quickly once the “idea” is there, or no, or what? Another question is, do you think “ideas” are discontinuous?
(I’m sure you know more than I do about algorithms.)
I’m thinking of the difference between insertion sort / bubble sort vs radix sort / merge sort.
(Knuth gives an interesting history here (Art of Programming Vol 3, section 5.5, p 383); apparently in 1890 the US census data was processed using the radix sorting algorithm running on a mechanical-electronic-human hybrid. There was an order-preserving card-stack merging machine in 1938. Then in 1945, von Neumann wrote down a merge sort, while independently Zuse wrote down an insertion sort.)
I guess we’re talking past each other because we’re answering different versions of “What is continuous in what?”. Performance on a task can be, and is, much more continuous in time than “ideas” are continuous in time, because translating ideas into performance on a task takes resources (money, work, more ideas). So I concede that what I said here:
was mostly incorrect, if we don’t count the part where
So one question is, is TAI driven by ideas that will have a stage where they get to a pretty okay version quite quickly once the “idea” is there, or no, or what? Another question is, do you think “ideas” are discontinuous?