Artificial neural networks are probably the most popular approach to AI nowadays and are based on the structure of the human brain. These computational models consist of interconnected nodes, or "neurons," organized into layers: normally include an input layer, one or more hidden layers and an output layer.

These are layers in a neural network where data is fed in, and is processed. Every neuron multiplies the input data with weights and add biases and finally apply activation function which introduces non-linearity. This process assists the network also in its ability identify patterns and interactions in the data.

Usually neural networks are trained as with backpropagation algorithms that modify the weights according to the error made by the neural network. Hence through repetitive minimization of this error the network proves better at the prediction or classification requirement.

There are several types of neural networks tailored to different tasks: There are several types of neural networks tailored to different tasks:

Feedforward Neural Networks (FNNs) are employed for simple cases of grouping and course of action.

Convolutional Neural Networks (CNNs) best solve image and video problems by identifying edges, and texture.

Recurrent Neural Networks (RNNs) that are used in the processing of sequential data: language modeling and time series prediction.

Neural networks are at the heart of many AI systems – from voice-enabled assistants to recommendation engines, from self-drive cars to diagnostic aids. It is for these reasons that they form part of the fundamentals that have led to the advancement of AI and the revelation of better and more efficient means across every discipline.