The Critical Differences between Machine Learning & Generative AI Today
The most popular classification models have generative AI being a subset of machine learning, and machine learning being a subset of AI. While this may technically and architecturally be the correct way to think about these tools, it has led to significant confusion.
First, because of our natural tendency to drop words to simplify and economize our language, this has led to many people referring to generative AI simply as AI. Muddling things further, because generative AI and machine learning are both lumped under AI as a big umbrella, all of these terms are being used somewhat interchangeably.
All of this makes it difficult for brands to evaluate these tools and understand how to use them to accomplish their goals. After all, most people aren’t building these tools. They’re using them.
So, let’s consider a more user-centric framework, one that focuses on what brands—and in particular, marketers—care about. Let’s also focus in on just machine learning and generative AI, which comprise most of the AI tools that marketers use. From a user standpoint, the biggest differences between machine learning and generative AI are twofold:
- Where the data comes from that fuels the tool’s output
- The primary benefit of the tool
Let’s first examine how machine learning looks when viewed through these two lenses…