The history of natural language processing goes back to the 1950s when computer scientists first began exploring ways to teach machines to understand and produce human language. In 1950, mathematician Alan Turing proposed his famous Turing Test, which pits human speech against machine-generated speech to see which sounds more lifelike. This is also when researchers began exploring the possibility of using computers to translate languages. In its first decade of research, NLP relied on rule-based processing. By the 1960s, scientists had developed new ways to analyze human language using semantic analysis, parts-of-speech tagging, and parsing. They also developed the first corpora, which are large machine-readable documents annotated with linguistic information used to train NLP algorithms. In the 1970s, scientists began using statistical NLP, which analyzes and generates natural language text using statistical models, as an alternative to rule-based approaches. The 1980s saw a focus on developing more efficient algorithms for training models and improving their accuracy. This led to the rise of machine learning algorithms in NLP. Machine learning is the process of using large amounts of data to identify patterns, which are often used to make predictions. Deep learning, neural networks, and transformer models have fundamentally changed NLP research. The emergence of deep neural networks combined with the invention of transformer models and the "attention mechanism" have created technologies like BERT and ChatGPT. The attention mechanism goes a step beyond finding similar keywords to your queries, for example. It weighs each connected term based on its relevance. This is the technology behind some of the most exciting NLP technology in use right now.