(Transformers & Attention Mechanism)
1. Why Architecture Matters in Generative AI
- Architecture defines how AI processes data and produces outputs.
- In Generative AI, especially models like GPT, the core architecture is based on Transformers.
- Transformers revolutionized AI by enabling models to understand context in language/images efficiently.
2. Transformers – The Backbone of Generative AI
- Introduced in 2017 in the paper “Attention Is All You Need”.
- Key difference from older models:
- No recurrent loops like RNNs.
- Processes all input data in parallel (faster and scalable).
2.1 Key Components of a Transformer
| Component | Purpose |
|---|---|
| Encoder | Reads and understands input data (used in translation, classification). |
| Decoder | Generates output step-by-step (used in text generation). |
| Positional Encoding | Adds order information to data (since Transformers process all data at once). |
| Attention Mechanism | Helps the model decide which parts of input are important. |
3. Attention Mechanism – The Secret Sauce
Definition:
A method that allows the model to focus on relevant parts of input when generating output.
3.1 How It Works
- Assigns weights to each word/token based on importance.
- Words with higher weights get more focus in the output.
- Example: In the sentence
“The cat sat on the mat because it was tired”
The word “it” should focus on “cat”, not “mat”.
3.2 Types of Attention
- Self-Attention – Focuses on relationships between words in the same sentence.
- Cross-Attention – Connects input (encoder) with output (decoder).
- Multi-Head Attention – Multiple attention layers working in parallel to catch different context meanings.
4. Why Transformers + Attention = Game Changer
- Scalability → Handles massive datasets efficiently.
- Context Awareness → Understands meaning over long text sequences.
- Versatility → Works for text, images, audio, and more.
5. Visual Flow of a Transformer Model
Input → [Embedding + Positional Encoding] → Self-Attention → Feed Forward Layer → Output
✅ Key Takeaway:
Transformers with attention mechanisms allow Generative AI to generate human-like, context-aware, and coherent content—the foundation for models like ChatGPT, Bard, Claude, and MidJourney.