Jim Rutt defines a 'neural network' as a computational model inspired by the way biological neural systems process information. These networks consist of interconnected nodes, or "neurons," which transmit signals through synaptic-like connections. A neural network can learn to perform a variety of tasks by adjusting the weights of these connections based on data input, often using algorithms such as backpropagation. The collective behavior of these simple, interconnected units results in the ability to recognize patterns, make decisions, and even predict future events. Jim emphasizes that, while inspired by the brain, artificial neural networks operate on mathematical principles and are a cornerstone of modern artificial intelligence, enabling significant advances across numerous domains including speech recognition, image processing, and autonomous systems.

See also: artificial intelligence, deep learning, self-organization, cognitive science