The Transformer: AI's Brain
How AI understands and processes language, explained simply
What is a Transformer?
The transformer is the "brain" of modern AI like ChatGPT. It's a special type of neural network designed to understand language and generate text.
Unlike older AI models that processed words one after another (like reading a sentence from left to right), transformers can look at all words at once to understand how they relate to each other.
This allows them to grasp complex language patterns, understand context, and generate human-like text.
Main Parts of a Transformer
A transformer has several key components that work together:
Positional Encoding
The Problem: Transformers look at all words at once, but word order matters! "Dog bites man" is different from "Man bites dog."
The Solution: Before processing, each word is tagged with its position in the sentence through "positional encoding." This is like numbering each word so the AI knows their order.
The positional information is actually added as a pattern of numbers to each token's embedding vector, but this simple numbering helps us visualize the concept.
Self-Attention
The Magic: Self-attention is the transformer's superpower. It allows each word to "look at" all other words and figure out which ones are most important to understand its meaning.
For example, in "The bank by the river was muddy," the word "bank" is related to "river" and "muddy," helping the AI understand it's a riverbank, not a financial bank.
When processing the word "bank", the model pays special attention to "river" and "muddy" to understand the meaning correctly.
How Self-Attention Works:
- For each word, the model computes three values: a Query, a Key, and a Value.
- The Query of one word is compared with the Keys of all words to determine how much attention to pay to each.
- The model then uses these attention scores to create a weighted sum of the Values.
- This weighted sum becomes the new representation of the word, now enriched with context from the entire input.
Multiple Attention Heads
Transformers actually use multiple "attention heads" that each focus on different types of relationships between words. This is like having multiple readers who each notice different aspects of the text.
Feed-Forward Networks
After self-attention, each word's representation passes through a simple neural network called a feed-forward network.
Think of this as the model's "thinking time" - it processes the information gathered during attention and refines its understanding of each word.
Unlike attention (which connects words to each other), the feed-forward network processes each word independently, applying the same transformation to each one.
Layer Stacking
Modern transformers don't just have one layer of attention and feed-forward processing — they stack many layers (sometimes over 100) on top of each other!
Each layer refines the understanding from the previous layer, building up a more sophisticated interpretation of the text.
In early layers, the model might understand simple word meanings. In deeper layers, it grasps complex concepts, reasoning, and the overall context.
Residual Connections & Normalization
These are "helper features" that make training deep transformers possible:
Residual Connections: Allow information to skip directly from earlier layers to later ones. This is like taking a shortcut instead of going through every step.
Layer Normalization: Keeps the values flowing through the network in a reasonable range, preventing them from becoming too large or too small. Think of this like keeping the volume at the right level.
Without these features, very deep transformers would be nearly impossible to train effectively.
How Your Input Moves Through a Transformer
Let's see how a simple question moves through the transformer:
Input Preprocessing
Tokenization & Embedding
Positional Encoding
Self-Attention (Layer 1)
The model finds these important relationships:
- "capital" strongly attends to "France"
- "What" connects to "capital" (the thing being asked about)
- "is" connects to both "What" and "capital"
Processing Through Deeper Layers
As the information passes through deeper transformer layers, the model:
- Recognizes this as a geography question
- Understands "capital" means a primary city of a country
- Associates "France" with its attributes including its capital
Final Layer Output
The model has processed all information and is ready to generate a response starting with: "The capital of France is Paris..."
Types of Transformer Models
There are several popular types of transformer models, each with slightly different designs:
GPT Family
Type: Decoder-only
Examples: GPT-3, GPT-4, ChatGPT
Good at: Generating text, conversation, creative writing
GPT models are trained to predict the next word in a sequence, making them excellent at generating coherent text.
BERT Family
Type: Encoder-only
Examples: BERT, RoBERTa
Good at: Understanding text, classification, answering questions
BERT models are trained to fill in missing words anywhere in a sentence, making them great at understanding meaning.
T5 Family
Type: Encoder-Decoder
Examples: T5, BART
Good at: Translation, summarization, rewriting text
These models transform one piece of text into another, like turning English into French or long text into short summaries.
Note:
ChatGPT and modern conversational AI models like Claude are based on the GPT (Generative Pre-trained Transformer) architecture, which is mainly focused on generating text.