In 2017, a single Google paper quietly ignited an AI revolution. Titled “Consideration Is All You Want,” it proposed a radical thought: substitute conventional neural networks with a mechanism known as “self-attention.” Little did the authors know, their work would start ChatGPT, energy AlphaFold’s biology breakthroughs, and even assist Hollywood administrators dream up alien worlds.
However the transformer was only the start. Over the following eight years, researchers reimagined its potential — scaling it to trillion-parameter fashions, adapting it for imaginative and prescient and science, and confronting its moral flaws. Listed below are the 10 papers that outlined this period, ranked by their tectonic impression on AI and society.
Authors: Ashish Vaswani, Google Mind
TL;DR: Killed RNNs, made parallelization king.
Earlier than transformers, AI struggled with lengthy sentences. Recurrent Neural Networks (RNNs) processed phrases one after the other, like a sluggish conveyor belt. Vaswani’s workforce scrapped this for multi-head consideration — a mechanism letting fashions weigh all phrases concurrently.
