Viewpoints on Artificial Neural Networks

Several scientific communities contribute to the theory of artificial neural networks, and most of these have their own viewpoints on them. These communities include electrical engineering, signal processing, mathematical statistics, computational science, complexity theory, artificial intelligence and even some quantitative neurobiologists.

This has the effect that if you speak with an artificial intelligence researcher, s/he might tell you that networks can be seen as one building block that can be used in forging novel intelligence, whereas the guys down at electrical engineering will argue that this is just a case of non-parametric function-approximation with adaptive basis-functions. A frequentist statistician might mumble about Bayesian non-stringency, and a quantitative neurobiologist could speak for hours and hours on how unfathomably complex the real wetware networks actually are. Take your pick; most of these people can tell really cool stories!