Reinforcement Learning

A process that uses rewards and punishments to teach machines how to do new tasks, improving with practice and feedback.

Supervised Learning

Machine-learning algorithms are taught to solve a specific task using training data which contains labels for the “correct answer” for each example

Transfer Learning

This method tries to take training data used for one thing and reused it for a new set of tasks, without having to retrain the system from scratch.

Unsupervised Learning

AI algorithms are given unlabeled data and must make sense of it without any instruction. Such machines “teach themselves” what result to produce. The algorithm looks for structure in the training data, like finding which examples are similar to each other, and groups them in clusters.

Variable Importance

Variable importance is a metric that indicates how much an independent variable contributes to predictions in a model. The higher the value shown for a variable in its ranking, the more important it is to the model generated.

Understanding the significance of predictors provides insights for interpreting results, and also may be useful for improving model quality. For instance, editing data sets to rationalize incorrect or incomplete columns — or removing irrelevant ones — can make models faster and more accurate.

Weak AI

The current state of AI, which does single tasks like playing games recognize images, or predicting outcomes. This is as opposed to Strong AI, also known as Artificial General Intelligence (AGI), which could do anything that humans do.