Attention Is All You Need
What's New?
Ditched sequential processing (RNNs, LSTMs) for pure attention. All words in a sequence can now "look at" each other simultaneously to understand relationships, rather than processing one word at a time.
What Changed?
Speed: Parallel processing instead of sequential = dramatically faster training
Scale: Faster training enabled much larger models and datasets
Performance: Better results on translation tasks
Impact on How We Build AI Products
The compute-scale breakthrough: This paper proved that throwing more compute at bigger models yields better results. It established the "scaling laws" mindset that drives today's AI development.
Architecture over algorithms: Instead of clever algorithms for sequence processing, the focus shifted to designing architectures that can absorb massive amounts of data efficiently.
Transfer learning foundation: The same architecture could be pre-trained on huge datasets then fine-tuned for specific tasks - the blueprint for GPT, BERT, and modern foundation models.
Why This Matters
Every major language model today (GPT, Claude, Gemini) builds on this architecture. It shifted AI from clever algorithms to scalable architectures, enabling the current AI boom.
TL;DR
Replaced sequential processing with parallel attention. Enabled larger, faster-training models that became the foundation for modern AI.
More paper summaries coming soon. Planning to cover foundational papers in ML, computer vision, and emerging AI research...