AI Deep Dive EP11: Attention Is All You Need — The Paper That Changed Everything

The Transformer: From RNNs to Revolution

Published in June 2017, "Attention Is All You Need" introduced the Transformer architecture that underpins every modern AI system.

The Problem with RNNs

Sequential processing: step t requires step t-1
Information degradation over long distances
Fixed-length encoding bottleneck
Cannot parallelize across GPUs

Self-Attention Mechanism

For every position, compute three vectors:

Query (Q): What this position is looking for
Key (K): What this position can offer
Value (V): The actual content

Attention(Q,K,V) = softmax(QK^T / sqrt(d_k)) * V

The entire operation is fully parallel — every position computes relationships with every other position simultaneously.

Multi-Head Attention

Eight parallel attention operations, each learning different relationship patterns (syntax, coreference, proximity). Results are concatenated and projected.

Key Innovations

Positional Encoding: Sinusoidal functions to inject position information
Feed-Forward Networks: Process gathered information through nonlinear transformations
Residual Connections + LayerNorm: Enable training of deep networks