What are Transformers?
Transformers are a type of neural network architecture introduced in 2017 that revolutionized AI. They power ChatGPT, Claude, Gemini, BERT, and most modern language models.
The key innovation: they can process entire sequences of text at once (not word by word) and understand relationships between distant words using a mechanism called "attention."
Historical Significance
The 2017 paper "Attention Is All You Need" by Google researchers is one of the most influential AI papers ever. It enabled the current AI boom.
The Attention Mechanism
The core idea: when processing a word, the model looks at all other words and decides how much "attention" to pay to each one.
Consider: "The animal didn't cross the street because it was too tired." What does "it" refer to? A Transformer learns to pay more attention to "animal" than "street" when processing "it."
This "self-attention" is computed mathematically: each word is converted to three vectors (Query, Key, Value), and attention scores are calculated through matrix multiplication.
Why Transformers Beat Earlier Approaches
Before: RNNs and LSTMs
- Processed text sequentially, word by word
- Struggled with long-distance relationships
- Slow—couldn't parallelize well on GPUs
Transformers
- Process all words simultaneously
- Direct connections between any two words
- Highly parallelizable—train faster on modern hardware
Transformer Architecture
Encoder-Decoder
The original Transformer had two parts: an encoder (understands input) and decoder (generates output). Used for translation.
Encoder-Only (BERT-style)
Just the encoder. Great for understanding text: classification, sentiment analysis, search ranking. Processes text bidirectionally.
Decoder-Only (GPT-style)
Just the decoder. Great for generating text. Predicts the next word, one at a time. Powers ChatGPT, Claude, and most LLMs.
Key Components
- Embeddings — Convert words to vectors
- Positional encoding — Add information about word order
- Multi-head attention — Multiple attention mechanisms in parallel
- Feed-forward layers — Process each position individually
- Layer normalization — Keep values in stable ranges
Beyond Text
Transformers now power more than language:
- Vision Transformers (ViT) — Image recognition
- DALL-E, Stable Diffusion — Image generation
- Whisper — Speech recognition
- AlphaFold — Protein structure prediction
Limitations
- Context length — Can only process limited text at once (though improving)
- Compute cost — Attention scales quadratically with sequence length
- Training data needs — Require massive datasets
Summary
- • Transformers use attention to understand relationships in text
- • They process sequences in parallel, enabling much faster training
- • Introduced in 2017, they now power nearly all modern AI
- • Three variants: encoder-decoder, encoder-only, decoder-only