Chapter 2 - The Transformers: Not the Robots, But the Brains Behind ChatGPT

Welcome back to the "Not So Mysterious" series, where we demystify the tech marvels of our time, without requiring you to be a techy.

In the first installment, I gave you a broad overview of how ChatGPT works.

Today, we're going to pull back the curtain on one of the most crucial components of ChatGPT's magic: Transformers

And no, not the type that morphs from cars into giant robots to save the planet. Although I dare say, the transformers we're discussing might just be as revolutionary.

Once upon a time in AI

Imagine if humans only had the tendency to remember the last sentence of a conversation and nothing before that. And that too with no context at all. You'll be stuck in a constant loop of reintroductions, forever.

That's how AI models worked back in the day. They could handle one piece of text at a time but struggled to see the big picture.

Enter the transformer, the hero of our story, introduced by Google in 2017.

Its superpower? An incredible memory for detail and context, allowing it to weave nuances of the human language.

So, What Makes Transformers Special?

So, what's this big deal with transformers, you ask?

Let me put it simply: Transformers can pay attention. I mean, not in the way your dog does when you have a treat in your hand, but close. They can look at a piece of text and decide which words are important and which can be momentarily ignored.

Imagine you're at a party. Amidst the cacophony of voices, your brain automatically tunes into the conversations that interest you, maybe the mention of your favorite movie, and tunes out the less interesting bits, like the discussion on the economics of poultry farming (unless you're into that).

Transformers do something similar with text.

They use something called "attention mechanisms" to weigh the importance of words in a sentence or a paragraph. This lets them understand context, irony, and even the nuances of language, much like a human does.

Breaking It Down: The Transformer Model

It Starts with Tokens

As I mentioned in the previous article, think of tokens as pieces of a puzzle (or lego).

In the worlds of transformers, these puzzles are words or parts of words. Just like how you'd start a jigsaw puzzle by sorting out the corner and edge pieces, transformers begin by breaking down text into these tokens.

The Embedding

Next, each token gets turned into a vector through a process called embedding.

Now I've found a better way to explain this than I did in the previous article - Imagine you're in a huge library. Each book is stacked among books it is similar to. Right? And the position of each book is represented by the cabinet number, the row number, etc.

That's what embedding does. It places words in a high-dimensional space (the library) based on their meaning. So, words with similar meanings are closer together. And just as the position of a book in a library, these words are represented by a list of numbers, called vectors.

The Heart of the Transformer: Attention Mechanism

Here comes the star of the show - the Attention Mechanism. It's what allows the transformer to focus on different parts of the sentence as it tries to understand the overall meaning.

It's like having a spotlight that highlights the parts of the stage (or sentence) that are most important at any given moment.

The Power of Context

With the attention mechanism, transformers can look at a word and see it in the context of all the other words around it. This is a game-changer. It means that the word "bank" would be understood differently if the surrounding text talks about rivers than if it discusses money.

The Art of Conversation

After understanding the context and the nuances of language, the real magic of transformers comes into play: generating text.

But how does a model, based on numbers and matrices, talk? Let's break it down.

Predicting the Future, One Word at a Time

Text generation in transformers is like a game of predicting the future, but instead of a crystal ball, they use probabilities.

Starting with an initial input (or prompt), the transformer predicts what the next word will be, chooses it, and then repeats the process, using the newly generated word as part of the input.

It's like writing a story where each word is chosen based on how likely it is to follow the previous ones, creating a chain of words that forms coherent sentences and paragraphs.

This process is iterative and can continue as long as we want the transformer to generate text.

The Role of Probability

Remember the part about the transformer's attention mechanism and how it determines the importance of different words? Here's where it comes full circle.

By understanding which words are key in a given context, the transformer model calculates a probability distribution—a fancy term for predicting how likely each word in its vocabulary is to come next.

Using a function called softmax (yes, it's as cool as it sounds), the model transforms these probabilities into a format it can use to select the next word.

The result is a balance between predictability and creativity, allowing transformers to generate text that feels both coherent and surprisingly human.

Keeping It Interesting: The Role of Temperature

The concept of "temperature" comes into play, adjusting how conservative or adventurous the model is in generating text.

• A low temperature means playing it safe, choosing words that are very likely to follow.
• A high temperature encourages more creative risks, picking less likely words for a bit of unpredictability and flair.

It's akin to choosing between ordering your favorite dish at a restaurant every time (low temperature) or letting the chef surprise you with something new (high temperature).

Both can lead to satisfying results, but the latter adds an element of surprise and discovery.

Why Text Generation Is a Game-Changer

This capability to generate text has vast implications, from powering chatbots that can hold a conversation to creating stories, composing poetry, or even generating code. The potential is limited only by the data these models have been trained on and our creativity in applying them.

The Art and Science of Conversing with AI

Through the interplay of tokenization, embeddings, attention mechanisms, and probabilistic text generation, transformers like ChatGPT navigate the complexities of human language.

This blend of art and science enables them to not just understand but also participate in our world of words.

Looking Ahead: Training AI - The Science Behind Pretraining and Fine-Tuning

Now that we've unveiled how transformers generate text, turning a prompt into a paragraph, what's next?

In our upcoming article, we'll explore the nuts and bolts of training these AI models. How do they learn from data? What's the deal with pretraining and fine-tuning?

Stay tuned to the "Not So Mysterious" series as we unravel the science behind making AI models not just talk but communicate with understanding and relevance.

There you have it—the secret sauce behind how transformers generate text, making them not just smart but also engaging conversationalists. As we continue our journey into the world of AI, remember that the line between technology and magic is just a matter of understanding.