You’re right, but there’s still a lot of different kinds of AI. The hype is all about LLMs. Of course there are similarities between them. Most AIs use artificial neural networks for instance. The training process of these networks, and their architecture is usually what separates them.

Reinforcement learning is oftenly used to play video games, or control robots. They learn by taking actions in an environment, and getting feedback for their actions.

Supervised learning can be used for object detections in images for instance. You manually create a dataset where you label each pixel of an image containing a dog for instance, and you end up with a dataset of labeled images with dogs. Then you train the neural network to spot the similarities. One researcher trained such a network to separate between dogs and wolves a few years back. But when they performed explainability analysis on the output they could see that the AI had instead noticed that snow was present on most images with wolves, so it would classify every image they provided containing snow was a wolf lol.

Then you have unsupervised learning which is basically to just let the neural network learn on its own the structure and relationships in the data.

Of course there are many different variations of training, oftentimes multiple stages consisting of some of, or even all of the above. It’s a mess

They use machine learning heavily and thats the main “magic part”. But thats still only a part of the llm structure. Machine learning on its own basically only wants to maximize a value.

Every Bourbon is a Whiskey, but if you label every Whiskey a Bourbon then you’re breaking the law.