The architecture that revolutionized artificial intelligence
"Attention Is All You Need" — Vaswani et al., 2017
Transformers are the neural network architecture behind every major AI breakthrough of the last 5 years. From ChatGPT to BERT, from language translation to image generation, transformers are the engine powering modern AI systems.
Unlike previous architectures that processed data sequentially, transformers can look at all parts of the input simultaneously using a mechanism called "attention". This parallel processing made them faster to train and dramatically more powerful.
"Attention Is All You Need" paper published by Google researchers
First major applications: understanding (BERT) and generation (GPT-1)
Scaling up: 1.5B → 175B parameters, emergent capabilities
Transformers power conversational AI, reaching mainstream adoption
Here's the full transformer architecture. Click on different components to highlight them and see how data flows through the system.
First, text is broken into tokens (words or subwords). Try typing a sentence below:
Each token is converted to a dense vector (embedding) that captures its meaning in high-dimensional space. Similar words have similar embeddings.
Since transformers process all tokens in parallel, we need to add positional information so the model knows word order. This is done with sine and cosine functions.
// Positional encoding formula
PE(pos, 2i) = sin(pos / 10000^(2i/d_model))
PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))
where:
- pos: position in sequence
- i: dimension index
- d_model: embedding dimension
The encoder processes the input to build a rich understanding of the context. It consists of multiple identical layers, each with two main components:
Allows each word to attend to all other words simultaneously, using multiple attention "heads" to capture different relationships.
Processes each position independently, transforming the attention output through learned non-linear transformations.
Skip connections and layer normalization ensure stable training and prevent vanishing gradients in deep networks.
Watch as different attention heads focus on different aspects of the sentence. Each head can specialize in different types of relationships.
The decoder generates output one token at a time, using masked self-attention to prevent looking ahead, and cross-attention to focus on relevant parts of the encoder output.
Only attends to previous positions, ensuring the model can't "cheat" by seeing future tokens during training.
Allows decoder to attend to encoder outputs, connecting generated text back to the input context.
Converts decoder outputs to probability distributions over the vocabulary to predict the next token.
See how masking prevents the model from looking at future tokens. Each position can only attend to itself and previous positions.
See the autoregressive generation process in action. The model predicts one token at a time, each prediction informed by all previous tokens through attention.
Transformers come in three main architectural variants, each optimized for different tasks:
Encoder-Only
Decoder-Only
Encoder-Decoder
Configure your own transformer and see how different parameters affect its capabilities and computational requirements.
Transformers power virtually every major AI application you interact with today:
Conversational AI powered by massive decoder-only transformers trained on internet-scale text data.
Encoder-decoder transformers translate between 100+ languages with near-human quality.
BERT-based models understand query intent and rank results based on semantic meaning.
Code-specialized transformers suggest completions and generate entire functions from comments.
Vision transformers combined with diffusion models create images from text descriptions.
Smart compose, grammar correction, and summarization all use transformer architectures.
// The transformer revolution in pseudocode:
function Transformer(input_sequence):
# Embed tokens and add positional info
embeddings = Embed(input_sequence) + PositionalEncoding()
# Process through encoder layers
encoder_output = embeddings
for layer in encoder_layers:
encoder_output = layer.MultiHeadAttention(encoder_output)
encoder_output = layer.FeedForward(encoder_output)
# Process through decoder layers (for generation)
decoder_output = target_embeddings
for layer in decoder_layers:
decoder_output = layer.MaskedAttention(decoder_output)
decoder_output = layer.CrossAttention(decoder_output, encoder_output)
decoder_output = layer.FeedForward(decoder_output)
# Predict next token
return Softmax(Linear(decoder_output))