Throwing around terms like AI, GenAI, and ML without understanding what they mean is like ordering from a menu in a foreign language—you might end up with something you really don’t want! To avoid this undesirable outcome, the following post reviews AI fundamentals and clarifies the terms we use in roadmaps, announcements, and other product-related documentation. We hope that you’ll find it informative, and that it will facilitate clearer, more productive conversations and communications.
AI is an inclusive term that refers to the use of data (information) and algorithms (rules) to allow computers to learn, act, and perform functions that are normally associated with human intelligence. Here are few examples of AI capabilities:
GenAI and ML (discussed below) are two of the more well-known branches of AI. There are important differences between them, and while it is correct to say that GenAI and ML are AI, it is not correct to say that all AI is GenAI or ML.
GenAI refers to a specific subset of AI that uses programs to process large data sets, detect patterns, and then create new works of text, imagery, video, and even computer code based on the instructions it’s given (known as “prompts”). GenAI relies on artificial neural networks, which are methods for processing information that mimic biological neural networks (check out this post for more on neural networks). It’s limited by the data it's fed to train its models, so everything it produces is derivative of the data it learns from. (Related, the push to train models with bigger and bigger training sets is one of the factors driving demand for AI compute power.)
Here are few examples of GenAI capabilities:
ML systems learn and improve based on the data they consume. There are two major types of learning algorithms—supervised learning and unsupervised learning—which refer to the way the model uses data to improve its performance. With supervised learning, the ML algorithm is presented with inputs plus the desired outputs to help it discover a general rule that relates them; with unsupervised learning, the algorithm is presented unstructured data and left to discover relationships and patterns on its own. (Since precision matters in the scope of this post, it’s worth noting that GenAI often uses ML techniques, in addition to natural language processing.)
Here are examples of ML capabilities:
There are other common terms that can be helpful:
We may be tempted to use terms interchangeably, but we shouldn’t, because AI is not the same as ML, and GenAI diverges from traditional AI. These differences are important. And understanding them will help you navigate Fusion Apps enabling technologies, product enhancements, and roadmaps.
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