Transformers
The architecture that revolutionized AI — from language to vision and beyond
What Are Transformers?
Transformers are a type of neural network architecture that powers almost all modern AI systems — from ChatGPT to image generators to code assistants.
The key innovation: Instead of processing data sequentially (one word after another), transformers can look at everything at once and figure out what's important.
The "Attention" Idea
Imagine reading a sentence:
"The cat sat on the mat because it was tired."
What does "it" refer to? You instantly know it's "the cat" — not "the mat." How?
You paid attention to the right words.
Transformers do exactly this, but mathematically. They compute "attention scores" that tell the model which parts of the input are relevant to each other.
Why Transformers Changed Everything
| Before Transformers | After Transformers |
|---|---|
| Processed one word at a time | Processes all words in parallel |
| "Forgot" earlier context | Remembers entire context |
| Slow to train | Massively parallelizable |
| Limited to ~100 words | Handles millions of tokens |
The Building Blocks
- Self-Attention: Each word "looks at" every other word to understand context
- Position Encoding: Tells the model where each word is in the sequence
- Feed-Forward Networks: Transforms the attention outputs
- Layer Stacking: Multiple layers of attention for deeper understanding
Where You'll See Transformers
- ChatGPT, Claude, Gemini: Large language models
- DALL-E, Midjourney: Image generation
- GitHub Copilot: Code completion
- Google Search: Query understanding
- AlphaFold: Protein structure prediction
References
Citation Note: All referenced papers are open access. We encourage readers to explore the original research for deeper understanding. If you notice any citation errors, please let us know.