> But above all, "pseudoscience" is something different and is certainly not the right word to describe this issue of terminology.
The definition of pseudoscience is "a collection of beliefs or practices mistakenly regarded as being based on scientific method."
Arguably the idea that neural networks reproduce how brains work is pseudoscience.
In the very least, it's a deeply misguided marketing blurb. It's now considered a metaphor, just like the ones driving some corners of the artificial intelligence field.
Pseudoscience is an issue of method, not of wrongly held beliefs. Before Einstein, Newton's law of gravitation was believed to be correct. It was not, but it wasn't "pseudoscience", because it was falsifiable and the theory was correct up to a certain error value. "Wrong" and "pseudoscience" are not synonymous.
Moreover, you're dismissing an entire extremely active research field, which tries to understand up to which point artificial and biological NNs are or aren't similar. It's the field, for example, of Turing laureate Yoshua Bengio.
Neural networks CAN reproduce how the brain work (if you're doing computational neuroscience, in which case what you call NNs is something different, and it's an issue of terminology). Even simple binary NNs were originally born to understand the cognitive functions of the brain (McCulloch and Pitts, 1943). The field later diverged (with the advent of backprop), but still today they have a lot in common even inadvertently, for example the representations they learn in navigational tasks (Banino et al. Nature 2018) or in the visual cortex vs. CNNs for computer vision.
The definition of pseudoscience is "a collection of beliefs or practices mistakenly regarded as being based on scientific method."
Arguably the idea that neural networks reproduce how brains work is pseudoscience.
In the very least, it's a deeply misguided marketing blurb. It's now considered a metaphor, just like the ones driving some corners of the artificial intelligence field.