Why are several hidden layers beneficial in an artificial neural network?

Several hidden layers are beneficial in an artificial neural network primarily because they enable the network to identify and learn very complex problems. Each layer in a neural network can learn different features at various levels of abstraction. The initial layers might detect simple patterns or features, while deeper layers can capture more intricate and abstract representations. This hierarchical learning process allows the network to model complex functions and relationships in the data, making it highly effective for tasks like image and speech recognition, where patterns can be very nuanced and layered.

The other options, while they touch on aspects related to neural networks, do not accurately capture the main advantage of having multiple hidden layers. For example, making the network faster is not directly related to the number of layers; in fact, adding layers can increase computation time. Similarly, having several layers does not inherently simplify the learning process; rather, it can add complexity to training. Lastly, while reducing the chances of overfitting can be a concern with too many layers, techniques like dropout or regularization are typically employed to address this issue, rather than simply adjusting the number of hidden layers.