Submitted by abhitopia t3_ytbky9 in MachineLearning
abhitopia OP t1_ixdkoos wrote
Reply to comment by miguelstar98 in [Project] Erlang based framework to replace backprop using predictive coding by abhitopia
Hey u/miguelstar98
> but you haven't really answered my questions, or explained the source of your confidence or perhaps I haven't fully grasped enough of the nuances of the problem to even have useful responses for you.
I am not sure which questions? Did you mean what you mentioned in your deleted post (which wasn't accessible to me)?
Anyways, I can see your original post now. Thanks for undeleting it.
>Software Designer's perspective:
I think actor model just makes a lot of sense to do asynchronous concurrent computations. Having said that, since Erlang is slow, I am actually considering using Actix library in Rust (The first step is for me to just write a pseudo code of the algorithm based on message passing)
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>From a hardware design perspective:
I am not sure what you want to say. The difference here is not hardware but change in algorithm (BP vs PC). Afaik, BP requires synchronised forward and backward passes.
>From the Biologist's perspective:
I am not sure again. The intention isn't to say biological plausible is superior or we MUST imitate nature. It is rather something than current ML libraries don't do but seems doable in light of new PC research.
>From my personal perspective: I hope you can help clear up my understanding but what is the difference between predictive coding and model ensembles? I know that probably sounds like a dumb question, but can’t we just take a bunch of models that are really good at particular tasks and have a software layer that controls when to use which model and then combine their outputs to solve any general problem? Also if I need fault tolerance or I need to run inference, can’t I just use a cluster computer, why not 2? Isn’t this a solved problem when training large language models?
Hmm. Model ensembles and learning algorithms to train those models are two different topics. The focus here is not on the "inference" (FP) part which current libraries are really good at but the "learning" (BP) part. Not sure what else to say.
I highly recommend reading this tutorial on PC (and contrast against BP)
miguelstar98 t1_ixdthaw wrote
Yeah sorry it slipped my mind that it was deleted (I guess I'm more used to discord) and thanks I'll read up on that paper first then.
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