How Does Artificial Intelligence Learn?

Artificial neurons are mathematical models of biological neurons that receive real numbers through multiple input channels, which are multiplied by weights and converted into an output by an activation function, with the simplest artificial neuron, the perceptron, performing a binary classification through a threshold function. Artificial neural networks consist of interconnected artificial neurons that process input signals and forward output signals, with the neurons being arranged in layers. The weights of the connections are summarized in the form of a weight matrix. Simple networks consisting of only one input and one output layer can only learn linear classifications and are therefore unsuitable for more complex problems such as the XOR problem, which is why multilayer neural networks with hidden layers are used to learn nonlinear transformations of the input data and capture more complex relationships and decision boundaries. In deep learning, very deep networks are used, i.e., networks with many intermediate layers. Four fundamentally different methods of learning in artificial neural networks can be distinguished: supervised, unsupervised, and self-supervised learning as well as reinforcement learningreinforcement learning.