AI Inference is achieved through an “inference engine” that applies logical rules to the knowledge base to evaluate and analyze new information. In the process of machine learning, there are two phases. First, is the training phase where intelligence is developed by recording, storing, and labeling information. If, for example, you're training a machine to identify cars, the machine-learning algorithm is fed with many images of different cars the machine can later refer to. Second, is the inference phase where the machine uses the intelligence gathered and stored in phase one to understand new data. In this phase, the machine can use inference to identify and categorize new images as “cars" despite having never seen them before. In more complex scenarios, this inference learning can be used to augment human decision making.