I think the response to this has two prongs:

- Some families of ML techniques (SVMs, random forests, gaussian processes) got their inspiration elsewhere and never claimed to be really related to how brains do stuff.

- Among NNs, even if an idea takes loose inspiration from neuroscience (e.g. the visual system does have a bunch of layers, and the first ones really are pulling out 'simple' features like an edge near an area), I think it's relatively uncommon to go back and compare specifically what's happening in the brain with a given ML architecture. And a lot of the inspiration isn't about human-specific cognitive abilities (like language), but is really a generic description of neurons which is equally true of much less intelligent animals.