This is a fancy way of saying that we can use computers to understand human language, which is pretty cool if you ask me.

So how does it work? Well, let’s start with an example. Let’s say I have this sentence: “The quick brown fox jumps over the lazy dog.” Now, we want our computer to understand what that means and maybe even translate it into another language or answer a question about it.

To do this, we use something called a Transformer model. This is basically a fancy algorithm that can learn how to transform one piece of text (like “The quick brown fox jumps over the lazy dog”) into another piece of text (like “Le rapide renard brun saute sur le chien paresseux”).

But wait, you might be thinking…how does a computer know what words mean? Well, that’s where something called embeddings comes in. Embeddings are basically just fancy numbers that represent each word in our sentence. For example, the word “quick” might have an embedding of [0.23456789, 0.12345678, -0.98765432].

Now, when we feed this sentence into our Transformer model, it takes all these embeddings and uses them to figure out what the sentence means. It does this by using something called attention (which is where the name “Transformers” comes from). Attention basically allows the computer to focus on certain parts of the sentence that are more important than others.

For example, in our sentence “The quick brown fox jumps over the lazy dog,” the word “jumps” might be given a lot of attention because it’s an action verb and helps us understand what’s happening in the sentence. On the other hand, the word “the” might not get as much attention because it’s just setting up the scene and doesn’t really add anything to our understanding of what’s going on.

That’s a simplified explanation of how Transformers work in NLP and AI. It’s pretty cool, right?