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~OT: I predict John Koza's Genetic Programming [1] approach will enjoy a revival in the near future. We've discussed the impact of the recent crop of "AI" syste

~OT: I predict John Koza's Genetic Programming [1] approach will enjoy a revival in the near future. We've discussed the impact of the recent crop of "AI" systems on various work fields, but my bet is on IT programming being one of the earlier successes of having machines doing grunt work. [1]: http://www.genetic-programming.com/#_John_Koza%E2%80%99s_Publications http://www.genetic-programming.com/#_John_Koza%E2%80%99s_Pub...

mrfusion 11y ago
Sounds interesting. Can you expand on why?
- justifier 11y ago
  because these genetic algorithms are best suited for consistent behaviour i noted in another comment that this ga cracker repo will only work on passes that resemble the ones it is trained on, but like all genetic algos is aggressively ineffective on inputs resembling anything else as for programming the practice, it is embarrassingly redundant and consistent due to the structure of programming being based on grammars and logic so imagine a program that has a data set you need ten bits of information about that data set, like max, min, mean, etc.. so you write 10 individual functions to loop over the data and interpret it that is all very consistent behavior and code now imagine you write a simple genetic algorithm that can interpret your interests and write those simple loops for you on the fly ten functions of 10 lines each is 100 lines of code one function of 30 lines that can generate then discard those ten previous functions gives your program a 70 line and immeasurable man hour advantage think if a search engine needed an engineer to explicitly write the discovery function of every possible query, it would be impractical, so instead you imagine generalisations that permit simple functions to take many varying inputs and produce many varying outputs what i think the parent comment is suggesting is that these generalisation functions themselves could be built by computers and one possible method being genetic algorithms furthering the abstraction all puns intended
- eternalban 11y ago
  There are interesting developments in formally defined and verified programs (including implementations). For certain domains that are non-intuitive, exploring the solution-space is now much nearer in reach. For example (as I noted in private communication to John Koza) exploring the protocol-space of distributed consensus algorithms on top of something like Microsoft Research's IronFleet (with the GP generating Gafny code) appears to be both possible and imo a fruitful avenue for researchers in the field. [p.s. Note that Genetic Programming is distinct from Genetic Algorithms.]

mrfusion 11y ago

Sounds interesting. Can you expand on why?

justifier 11y ago
because these genetic algorithms are best suited for consistent behaviour i noted in another comment that this ga cracker repo will only work on passes that resemble the ones it is trained on, but like all genetic algos is aggressively ineffective on inputs resembling anything else as for programming the practice, it is embarrassingly redundant and consistent due to the structure of programming being based on grammars and logic so imagine a program that has a data set you need ten bits of information about that data set, like max, min, mean, etc.. so you write 10 individual functions to loop over the data and interpret it that is all very consistent behavior and code now imagine you write a simple genetic algorithm that can interpret your interests and write those simple loops for you on the fly ten functions of 10 lines each is 100 lines of code one function of 30 lines that can generate then discard those ten previous functions gives your program a 70 line and immeasurable man hour advantage think if a search engine needed an engineer to explicitly write the discovery function of every possible query, it would be impractical, so instead you imagine generalisations that permit simple functions to take many varying inputs and produce many varying outputs what i think the parent comment is suggesting is that these generalisation functions themselves could be built by computers and one possible method being genetic algorithms furthering the abstraction all puns intended
eternalban 11y ago
There are interesting developments in formally defined and verified programs (including implementations). For certain domains that are non-intuitive, exploring the solution-space is now much nearer in reach. For example (as I noted in private communication to John Koza) exploring the protocol-space of distributed consensus algorithms on top of something like Microsoft Research's IronFleet (with the GP generating Gafny code) appears to be both possible and imo a fruitful avenue for researchers in the field. [p.s. Note that Genetic Programming is distinct from Genetic Algorithms.]