Key insights:
Remember when we thought AI was just about robots doing repetitive tasks? Well, buckle up because we're about to dive into something way cooler. Scientists have been trying to recreate the magic of the human brain in digital form, and what they've come up with is pretty mind-blowing.
The journey from basic neural networks to today's sophisticated language models is like watching a baby learn to walk, then suddenly start doing parkour. Let's break down how these AI brains actually work.
Traditional AI was like a calculator on steroids, good at specific tasks but pretty useless at everything else. GPT models, on the other hand, are more like that friend who seems to know a little bit about everything. The secret sauce? Something called parameters, billions (or even trillions) of them.
Let's look at the evolution:
Imagine teaching a child to complete sentences. That's basically what happens during training, but at a massive scale. The model reads through mountains of text from the internet, books, and other sources, constantly trying to predict what word comes next.
When it makes a mistake, it adjusts its internal connections (parameters) to do better next time. It's like having a super-dedicated student who never gets tired of practicing.
Before we dive deeper into transformers, we need to understand how these models read text. They don't actually understand words like we do, they work with tokens.
A token can be a word, part of a word, or even a single character. Think of it like breaking down a sentence into bite-sized pieces that the AI can digest. For example, "ChatGPT" might be broken down into "Chat" and "GPT" as separate tokens.
Each token gets converted into a number (token ID) and then into a vector, which is basically its position in a high-dimensional space. If this sounds confusing, imagine organizing words in a giant 3D space where similar words cluster together.
Now we're getting to the really cool part. Self-attention is what makes these models actually understand context, and it's pretty clever.
When you read a sentence, you automatically understand which words are related to each other. Self-attention lets the model do the same thing by weighing the relationships between all words in a sentence. For example, in "The cat sat on the mat," it understands that "sat" is more strongly connected to "cat" than to "mat."
Multi-head attention is like having multiple people read the same text, each focusing on different aspects. One might focus on grammar, another on subject-verb relationships, and another on context. The model combines all these perspectives to understand the text better.
Want to learn more about these fascinating AI models? Check out the ChatGPT Course - Become a Generative AI Prompt Engineer where you'll dive deep into how these models work and how to use them effectively.
For a more detailed look at the technical aspects covered in this article, you can visit Futurise's website or follow them on Twitter.
To see these concepts in action and get an even better understanding, head over to the Leon Petrou YouTube channel where you'll find detailed visual explanations and examples of how transformers work.
