On this week's episode of the AI series, we'll be discussing something totally different... EMBEDDINGS. Yhhhh, another word that sounds complicated until you actually understand what it means.😂 We've established that AI doesn't read words the exact same way humans do. It processes tokens. But here's the next question: How does a computer actually work with those tokens? The answer is by turning them into numbers. This is where embeddings come in. An embedding is basically a numerical representation of a word, token, sentence, image or even a document that allows an AI model to work with relationships between them. Think of it like placing things on a mathematical map. Words or concepts that are related can have representations that are closer together, while unrelated ones can be further apart. For example, "dog" and "cat" might have representations that are more closely related than "dog" and "airplane." And this isn't just about words. Embeddings are also used for things like semantic search, recommendations, document retrieval and even systems that allow AI to search through your own files. So the interesting part is... AI doesn't need to understand a word the way we do. It needs to turn that information into something it can calculate with. And that something is numbers. See you in the next>>>