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I think the problem here is that you are still the one that needs to develop the equations of motion that will be tested for compression. What this describes i

by indiv0 9y ago

I think the problem here is that you are still the one that needs to develop the equations of motion that will be tested for compression. What this describes is a method for testing theories, not generating them. You still need to study the data to try and properly analyze it so you can understand the rules yourself, or at least be able to see the edges of them. Now if you could find a way to generate moderately accurate equations automatically by analyzing the dataset, I think that would be a real leap forward. Is there a related mechanism to the one presented by the parent which could be used to generate theories?

d_burfoot 9y ago
Yes, it is a method for testing theories; it does not give you a procedure for developing the theories. It is agnostic about whether the theories are developed by humans or algorithms. There is not going to be any universal and practical algorithm for automatically generating the theories, at least in the short term. But you can think about algorithms that apply to particular domains. For example in my field of text analysis, you can imagine techniques for automatically inferring grammar rules from a large quantity of text. Some such methods already exist, but have not been pursued extensively. We could also develop algorithms for inferring equations of motion from astronomical and celestial observations, as the parent mentioned. The compression principle wouldn't give you the real Newtonian equations, but it would correctly identify them as being superior to other candidates. But this would be a bit of an academic exercise, since we already have the Newtonian equations and relativity. What's more interesting to me is an attempt to build modern astronomical theories (eg about black holes, the Big Bang, cosmic inflation, dark energy, etc) into a compressor that will apply to an astronomical data set like the Sloan Digital Sky Survey. My guess is that such an attempt would reveal important shortcomings in the theories, or perhaps highlight regions of space that confound the theories.

by indiv0 9y ago

d_burfoot 9y ago

Yes, it is a method for testing theories; it does not give you a procedure for developing the theories. It is agnostic about whether the theories are developed by humans or algorithms. There is not going to be any universal and practical algorithm for automatically generating the theories, at least in the short term. But you can think about algorithms that apply to particular domains. For example in my field of text analysis, you can imagine techniques for automatically inferring grammar rules from a large quantity of text. Some such methods already exist, but have not been pursued extensively. We could also develop algorithms for inferring equations of motion from astronomical and celestial observations, as the parent mentioned. The compression principle wouldn't give you the real Newtonian equations, but it would correctly identify them as being superior to other candidates. But this would be a bit of an academic exercise, since we already have the Newtonian equations and relativity. What's more interesting to me is an attempt to build modern astronomical theories (eg about black holes, the Big Bang, cosmic inflation, dark energy, etc) into a compressor that will apply to an astronomical data set like the Sloan Digital Sky Survey. My guess is that such an attempt would reveal important shortcomings in the theories, or perhaps highlight regions of space that confound the theories.