Neural Network

A neural network is an artificial intelligence computing system modeled after the way a human brain works. It uses layers of interconnected nodes as artificial neurons to process data and solve a given problem. The system learns over time by adjusting the connections between neurons and the functions performed by each one.

AI developers use artificial neural networks to train artificial intelligence algorithms for different tasks. Computer vision neural networks learn to analyze images to recognize objects, allowing them to quickly identify faces, label images, and help moderate content. They learn to perform speech recognition to transcribe conversations and add captions to videos. Neural networks can also learn natural language processing to analyze written language to organize documents, generate article summaries, and serve as chatbots.

How Neural Networks Work

Each node in a neural network contains a mathematical function. Nodes are arranged in several layers — an input layer, several intermediate layers, and an output layer. Each node is connected to any number of nodes on the layers above and below it. A node takes input from lower-level nodes, assigns a weight value to each input, and performs a calculation on the combined values. If the result meets a pre-determined threshold, the node passes it onto the next level. This process repeats until the neural network passes the final value to the output layer.

During the initial "training" phase, developers give neural networks various sets of data. For example, if the goal is to recognize faces, the data may be photos of different people. The first few times the system analyzes the data, the output may not be accurate. However, on subsequent analyses, it "learns" by changing the weight and threshold values for each node. The neural network is successfully trained once it consistently produces accurate results for the training data.