Neural networks function as computational models inspired by the human brain. They consist of interconnected layers of nodes (neurons), each performing a simple computation. Here’s a concise overview of how they work:

1. Structure: Neural networks typically have an input layer, one or more hidden layers, and an output layer. Each neuron in a layer is connected to neurons in the next layer, with each connection assigned a weight.
2. Activation and Forward Propagation: Neurons receive input values, which are multiplied by their respective weights and summed. This sum passes through an activation function (like ReLU or sigmoid) to introduce non-linearity, enabling the network to learn complex patterns. The resulting values are forwarded to the next layer.
3. Training and Backpropagation: Neural networks learn through training, using a dataset to adjust weights. During training, the network’s predictions are compared to actual outcomes using a loss function. Backpropagation adjusts weights by calculating gradients of the loss function with respect to each weight, minimizing the error through iterative optimization (e.g., gradient descent).

Applications in Artificial Intelligence:

1. Computer Vision: Neural networks power image recognition, object detection, and facial recognition systems.
2. Natural Language Processing (NLP): They enable language translation, sentiment analysis, and chatbots.
3. Speech Recognition: Neural networks convert spoken language into text, as used in virtual assistants like Siri and Alexa.
4. Healthcare: Applications include medical image analysis, predictive diagnostics, and personalized treatment plans.
5. Finance: Neural networks are used for algorithmic trading, fraud detection, and risk management.

In essence, neural networks drive advancements in AI by enabling machines to learn from data, recognize patterns, and make intelligent decisions across diverse applications.