Neural network models, also known as artificial neural networks (ANNs or just NNs), are a type of machine learning model that mimics the neurons and synapses of organic brains. By training an NN on historic patterns and outcomes (supervised learning), NNs can learn to match new data to trained patterns and classify them or predict future events.  Several classes of industrial AI software use neural network programming including predictive quality, predictive equipment reliability, and machine vision applications.  NNs can also be trained via unsupervised learning in some cases where the application automatically compares predicted outcomes to observed outcomes and self-corrects.  Many industrial NNs are only three layers deep and a dozen or so neurons in size, far simpler than applications like ChatGPT or even an insect brain.  Despite appearances, most industrial NNs do not understand the process they are examining.  They are only looking at statistical correlations between input patterns and output patterns.