If you Google the topic of artificial intelligence , you will most likely go down a deep and winding rabbit hole. If you venture just a little below the surface, you'll see such simple terms as “perceptron,” “sigmoid neuron,” and “nonlinearly separable classifications.” To avoid getting stuck in this hole, this article will give you a brief and clear explanation of AI vs. Generative AI. We'll also cover three other common types of AI, giving you enough information to understand the basics without feeling like you need a master's degree in AI development. 4 types of artificial intelligence (AI) Generative AI. What it does: Generative AI creates something new from what it has learned during training. Its result can be a code, a recipe, an image, etc. Example: You probably learned about the concept of generative AI through ChatGPT, unless you've been isolated from the world for the last six months (in which case, welcome back!). ChatGPT is a tool that can chat with someone as if they were a human and give an original answer to a question. However, one could argue that the results that ChatGPT generates are not really “original”, because it has obtained its knowledge from Internet sources such as Reddit and Wikipedia and is simply predicting which word is most likely to be correct based on what it has learned. (Maybe that's what we do too and don't realize it? Tell Shakespeare, see what he thinks.) Predictive AI. What it does: Predictive AI is the process by which a machine can make predictions based on a combination of previous inputs and an analysis of current trends and scenarios. Example: Predictive AI can be used, among other things, for programmatic ad buying. In this example, based on historical knowledge of ad prices and performance, an algorithm can predict when a company should purchase ad space to get the best rate. You can also find predictive AI in the stock market in the form of high frequency trading (HFT). These AI models use algorithms to perform high-volume trades based on predictive analytics. Anomaly-based AI. What it does: Anomaly-based AI detects anomalies in a pattern. This type of AI is trained to recognize regularities, so it can also detect any time an exception arises. Example: This type of AI is especially useful in cybersecurity. Its anomaly-based AI can learn what type of activity is common on your network. If you detect activity outside that pattern, you can trigger an alert to help your team respond in real time. Anomaly-based AI is also useful in supply chain management , as it learns normal demand patterns from purchasing data. If demand spikes above or below that range, AI can detect it and alert your team to adjust prices accordingly or contact suppliers. Decision-based AI. What it does: Decision-based AI helps make decisions similar to how a human would, such as classifying things based on their characteristics. Example: Let's say you have four different types of products and you want to classify your customer service emails based on the product category they belong to. By giving your decision-based AI model a few sample emails to learn from, you can train your AI model to identify which email is related to which product, and then direct each one to the appropriate department.