Language Technology NLP

Natural Language Processing (NLP) — the branch of AI focused on enabling computers to understand, generate, and translate human language — has undergone a revolutionary transformation since 2017. Before transformers (the architecture behind modern NLP, introduced in 'Attention is All You Need' by Google researchers, 2017), machine translation used rule-based systems and then statistical models; the quality was limited. After transformers and large language models (LLMs), the field exploded. LLM milestones: BERT (Google, 2018 — bidirectional encoder for understanding); GPT-2 (OpenAI, 2019 — OpenAI initially withheld it as 'too dangerous to release'); GPT-3 (2020 — 175 billion parameters, demonstrated in-context learning); ChatGPT (November 2022 — 100M users in 2 months, fastest growing consumer application in history); GPT-4, Claude, Gemini (2023-24 — multimodal, code, reasoning). These systems have demonstrated capabilities that surprised their creators: chain-of-thought reasoning, emergent capabilities at scale, and performance approaching or exceeding human experts on professional exams (bar exam, medical licensing, GRE). The economic implications are enormous: Goldman Sachs estimates LLMs could automate 25% of all work tasks across the US economy.