The Transformer & Large Model Era (2017+)

In 2017, researchers at Google published a paper called "Attention Is All You Need," introducing an architecture called the transformer. It's not an exaggeration to say this single technical innovation underpins almost everything in modern AI. Transformers process information differently from previous approaches - instead of reading data sequentially, they can attend to all parts of an input simultaneously, identifying which pieces are most relevant to each other. This made them exceptionally good at understanding language, and they scaled beautifully: make them bigger, give them more data, and they reliably got better. This led to the era of large language models - GPT, BERT and their descendants - systems trained on vast portions of the internet's text that could generate remarkably fluent prose, answer questions, summarise documents and write code. The "scaling" insight was crucial and controversial: these models seemed to develop new capabilities simply by getting larger, without being explicitly taught. Whether this represents genuine understanding or sophisticated pattern matching remains one of AI's most heated debates. What's not debatable is the practical impact - these models changed what computers could do with language almost overnight.