“General absolute” was probably a poor choice of words, but I meant to express a system capable of recognizing all types of patterns in all contexts. There is an absolute, non arbitrary pattern here, do you recognize it?
I see your meaning—and no practical system is capable of recognizing all types of patterns in all contexts. A universal/general learn algorithm is simply one that can learn to recognize any pattern, given enough time/space/training. That doesn’t mean it will recognize any random pattern it hasn’t already learned.
I see hints of structure in your example but it doesn’t ring any bells.
Kolmogorov complexity is a fundamental character, but it’s not at all clear that we should want a Kolmogorov complexity optimizer acting on our universe
No, and that’s not my primary interest. Complexity seems to be the closest fit for something-important-which-has-been-changing over time on earth. If we had a good way to measure it, we could then make a quantitative model of that change and use that to predict the rate of change in the future, perhaps even ultimately reducing it to physical theory.
For example, one of the interesting new recent physics papers (entropic gravity) proposes that gravity is actually not a fundamental force or even spacetime curvature, but actually an entropic statistical pseudo-force. The paper is interesting because as a side effect it appears to correctly derive the mysterious cosmological constant for acceleration. As an unrelated side note I have an issue with it because it uses the holographic principle/berkenstein bound for information density which still appears to lead to lost-information paradoxes in my mind.
But anyway, if you look at a random patch of space-time, it is always slowly evolving to a higher-entropy state (2nd law), and this may be the main driver of most macroscopic tendencies (even gravity). It’s also quite apparent that a closely related measure—complexity—increases non-linearly in a fashion perhaps loosely like gravitational collapse. The non-linear dynamics are somewhat related—complexity tends to increase in proportion to the existing local complexity as a fraction of available entropy. In some regions this appears to go super-critical, like on earth, where in most places the growth is minuscule or non-existent.
It’s not apparent that complexity is increasing over time. In some respects, things seem to be getting more interesting over time, although I think that a lot of this is due to selective observation, but we don’t have any good reason to believe we’re dealing with a natural category here. If we were dealing with something like Kolmogorov complexity, at least we could know if we were dealing with a real phenomenon, but instead we’re dealing with some ill defined category for which we cannot establish a clear connection to any real physical quality.
For all that you claim that it’s obvious that some fundamental measure of complexity is increasing nonlinearly over time, not a lot of other people are making the same claim, having observed the same data, so it’s clearly not as obvious as all that.
I see your meaning—and no practical system is capable of recognizing all types of patterns in all contexts. A universal/general learn algorithm is simply one that can learn to recognize any pattern, given enough time/space/training. That doesn’t mean it will recognize any random pattern it hasn’t already learned.
I see hints of structure in your example but it doesn’t ring any bells.
No, and that’s not my primary interest. Complexity seems to be the closest fit for something-important-which-has-been-changing over time on earth. If we had a good way to measure it, we could then make a quantitative model of that change and use that to predict the rate of change in the future, perhaps even ultimately reducing it to physical theory.
For example, one of the interesting new recent physics papers (entropic gravity) proposes that gravity is actually not a fundamental force or even spacetime curvature, but actually an entropic statistical pseudo-force. The paper is interesting because as a side effect it appears to correctly derive the mysterious cosmological constant for acceleration. As an unrelated side note I have an issue with it because it uses the holographic principle/berkenstein bound for information density which still appears to lead to lost-information paradoxes in my mind.
But anyway, if you look at a random patch of space-time, it is always slowly evolving to a higher-entropy state (2nd law), and this may be the main driver of most macroscopic tendencies (even gravity). It’s also quite apparent that a closely related measure—complexity—increases non-linearly in a fashion perhaps loosely like gravitational collapse. The non-linear dynamics are somewhat related—complexity tends to increase in proportion to the existing local complexity as a fraction of available entropy. In some regions this appears to go super-critical, like on earth, where in most places the growth is minuscule or non-existent.
It’s not apparent that complexity is increasing over time. In some respects, things seem to be getting more interesting over time, although I think that a lot of this is due to selective observation, but we don’t have any good reason to believe we’re dealing with a natural category here. If we were dealing with something like Kolmogorov complexity, at least we could know if we were dealing with a real phenomenon, but instead we’re dealing with some ill defined category for which we cannot establish a clear connection to any real physical quality.
For all that you claim that it’s obvious that some fundamental measure of complexity is increasing nonlinearly over time, not a lot of other people are making the same claim, having observed the same data, so it’s clearly not as obvious as all that.