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Comment by fractallyte | Hacker News Reader

It's very comprehensive, taking the reader from the simplest concepts right up to the state-of-the-art (in 2012). Very friendly, confident and (oddly enough for the subject), exciting! There's a collection of detailed examples, with code. However, unusually for Springer, it clearly wasn't proofread. Lots of repetition and grammatical errors in the text (the code seems solid). It deserves a second edition!

That said, I haven't come across a better book. I'd buy it again without hesitation, regardless of expense. Erlang is way ahead of other popular platforms for neural architectures.

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2012 was the year when the modern ML era really began and the explosion of techniques, research, and applications has been tremendous since that year -- sounds like this book ends right when things started getting interesting?

The modern ML era is mired in hype and statistics. Most of the time, all there is to show for it is yet another expert system with baubles.

Neuroevolution is one of those overlooked niches where I think we'll see real progress in real AI.

How much of the book requires Erlang or focuses on that language's features, and how much can be generalized to other languages?

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.

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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