Attention Is All You Need

The transformer paper. Reading it in 2024 knowing what came after is surreal—every major AI breakthrough since traces back to this architecture.

The key innovation is self-attention: allowing every token to attend to every other token, enabling parallelization and capturing long-range dependencies. Simple in retrospect, revolutionary in impact.

What’s remarkable is how the paper undersells itself. The authors focus on machine translation benchmarks, treating the architecture as an incremental improvement. They had no idea they’d created the foundation for GPT, BERT, and everything that followed.

Essential reading for anyone wanting to understand modern AI. The math is accessible, and the intuitions transfer to understanding any transformer-based model.