A "temporal" version of this function is often used as a test case for sequence prediction in recurrent networks. This function was shown to be a connectedness problem that couldn't be solved due to 3 the constraints of local processing in a book by Minsky and Papert, which created a notable, though critically misunderstood, controversy surrounding said book. Due to a lack of linear separability, this function is notoriously uncomputable by ADALINE or a single (*) perceptron, though it's easily solved 6 using a simple multilayer feedforward network with a hidden layer of two units: one of which computes NAND and the other of which computes OR. For 10 points, name thi
A "temporal" version of this function is often used as a test case for sequence prediction in recurrent networks. This function was shown to be a connectedness problem that couldn't be solved due to 3 the constraints of local processing in a book by Minsky and Papert, which created a notable, though critically misunderstood, controversy surrounding said book. Due to a lack of linear separability, this function is notoriously uncomputable by ADALINE or a single (*) perceptron, though it's easily solved 6 using a simple multilayer feedforward network with a hidden layer of two units: one of which computes NAND and the other of which computes OR. For 10 points, name this logical function whose output is 1 whenits two inputs are different.
A "temporal" version of this function is often used as a test case for sequence prediction in recurrent networks.
XOR function [or: exclusive-OR function] 