I'm going to guess the main take-away point is that the weights can be trained reliably if your transfer functions are sufficiently "stiff"? Not like you need t

I'm going to guess the main take-away point is that the weights can be trained reliably if your transfer functions are sufficiently "stiff"? Not like you need the training for the operations presented, anyone could choose the weights manually, but it could maybe extend to more complex mathematical operations? To be honest, it does feel a bit like Claude output (which the author states they used), reads convincingly "academic", but it seems like a drawn out tautology. For example, it's no surprise its precision is the same as floating point, as it's essentially carrying out the exact same operations on the CPU. Please do correct me if I'm wrong! I've not read the cited paper on "Neural Arithmetic Logic Units", which may clear some stuff up.