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0 pointsjohnwatson112186y ago0 comments

I don't think a caching layer would work. One example would be an online mortgage estimator. You input the loan amount, interest rate, length of loan etc. all as http input parameters. I'm suggesting that the LSTM can eventually figure out that those variables are being used by the application code to go in to a formula. That application code and its formula would all be replaced by the LSTM.

I just don't know how you can achieve that with static cache ... only if somebody else requested that exact mortgage calculation before and it is still in the cache.

Also, my idea of the "given input" from the earlier comment would have to include results of sql queries that would form the entire input to the LSTM.

But honestly I think over trained auto encoders can be used as hash maps. That would be an application more in line with what I think you are saying.

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jodrellblank6y ago

Seems that either the NN memoizes all the inputs and outputs until the function is totally mapped - then functions as a memoized lookup table, or the NN has discerned what the mortgage calculation is, and is doing exactly the calculation your {Python} backend does, but migrated into an NN middleware layer instead, which sounds like it would be slower.

And then you're hoping that the NN would act like a JIT compiler/optimiser and run the same code faster. But if it was possible to process (compile? transpile? JIT compile?) the Python code to run faster, then writing a tool to do that sounds easier than writing an AI which contains such a tool within it.

So there's a handwave step where the AI develops its own innate Python-subset optimiser, without anyone having to know how to write such a thing, which would be awesome indeed .. is that possible?

johnwatson11218OP6y ago

If I ever get any actual code running I'll try and post it here.

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