How Transformers Power Modern AI
The transformer architecture and the attention mechanism that revolutionized AI.
Inspired by learning material from Google
The transformer, introduced in the paper Attention Is All You Need, is the architecture behind today's most powerful AI models.
The problem with older models
Earlier sequence models (RNNs) processed text word-by-word, making them slow and forgetful over long passages.
Self-attention
Transformers use self-attention: every word can directly 'look at' every other word and weigh its relevance. This captures long-range relationships and runs in parallel, making training far faster.
Key components
- Embeddings: turn tokens into vectors.
- Positional encoding: tells the model word order.
- Multi-head attention: looks at relationships from several angles.
- Feed-forward layers: transform the attended information.
Why it matters
Transformers scale beautifully. Make them bigger, feed them more data, and they keep improving — the foundation of modern LLMs and many vision models.
Key takeaways
- Attention lets every token relate to every other token.
- Parallel processing makes transformers fast to train.
- Scaling transformers unlocked modern generative AI.