Broadly speaking, there are three types of artificial intelligence models: reinforcement learning, unsupervised learning and supervised learning.

In a reinforcement learning model, trainers set a series of algorithms against a given problem, watch to see which perform the best and then continue to refine the programs as they get closer and closer to the target. In an unsupervised learning model, a large set of data — typically unlabeled — is fed into an algorithm, and the model begins sorting things into different categories on its own.

Neural networks are an example of a supervised AI model. Trainers feed labeled data into an algorithm, teaching it to recognize the subtle changes that distinguish, for instance, a dog from a cat in a photograph. Neural networks are designed to continually learn over time, gradually reducing their “loss” (or miss rate).

Neural networks are already creating value across numerous industries.