What is causing my neural network to fail at learning the XOR problem?

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The XOR problem is not linearly separable, meaning that a line or curve cannot be drawn to completely separate the two classes of inputs (in this case, 0 and 1). This makes it challenging for a neural network that only uses linear operations (such as the perceptron algorithm) to learn the problem. It requires a more complex architecture, such as a multilayer perceptron or a recurrent neural network, which can handle non-linear relationships between the input and output. Overfitting can also be a problem, and it is important to properly train and validate the network to avoid this.