At the core of today's state-of-the-art artificial intelligence (AI) algorithms is the ability to learn complex patterns from a sample of data. In the manufacturing context, an example of a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together. When considering AI, it’s important to understand what the data requirements are at the outset.
The algorithm learns the patterns by being shown many examples of the parameter values in question — typically between a few thousand and several million. This data sample is a representation of the history of the factory process. Now, if a trend exists in the sample to the effect that, for example, every increase in the process temperature by 1°C tends to be accompanied by a decrease in the process's time by 10 seconds, the AI will learn this apparent relationship between the temperature and time parameters. In this way, the AI effectively learns a model of the process. It does so automatically, assuming that it is properly designed and fed enough examples of the right data.