[sigmoid10]

It's a bit more complicated than that. Your argument is essentially the universal approximation theorem applied to perceptrons with one hidden layer. Yes, such a model can approximate any algorithm to arbitrary precision (which by extension includes the human mind), but it is not computationally efficient. That's why people came up with things like convolution or the transformer. For these architectures it is much harder to say where the limits are, because the mathematical analysis of their basic properties is infinitely more complex.