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The book relies on Erlang's unique features. In Sher's words: "Erlang was created to develop distributed, process based, message passing paradigm oriented, robust, fault tolerant, concurrent systems. All of these features are exactly what a programming language created specifically for developing neural network based systems would have."

If another language can do that (Go, perhaps?), I suppose the code can be made to work adequately.

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Modeling each neuron in an NN as a process is incredibly computationally wasteful, and we often see this in Neuroevolution implementations where neurons are represented as objects. It's clear from advances in DL that neurons and weights should be represented by matrices.

I've never given much credence to Sher's book because of this. Switching to Erlang just isn't necessary given current techniques.

How about using Erlang because it provides fault tolerance, and any faults resulting during the neuroevolutionary process will be contained and won't bring down the whole system? Or evolving groups of neurons in parallel, and across a distributed cluster? These things are much easier in Erlang.

I have worked through it in Erlang, but others have tried to translate it to LFE (Lisp Flavoured Erlang) and Elixir. It's still relying on the Erlang ecosystem.

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