Talking AI: ChatGPT and Co.

The history of natural language processing (NLP) began in the mid-twentieth century with early attempts at machine translation, went through phases of rule-based and statistical methods, made significant progress with machine learning and word representation techniques, and was revolutionized by deep learning techniques and advanced architectures, leading to the development of large language models (LLM) like BERT and GPT. Representation techniques such as word vectors are crucial for natural language processing as they convert text data into numerical formats. Word2Vec is a technique for generating word embeddings that is based on neural networks and learns unsupervised from large text corpora. The Transformer architecture, the basis of all modern large language models like DeepL and ChatGPT, has revolutionized natural language processing with its innovative attention mechanism, which enables parallel processing and effective detection of long-range dependencies, thus leading to shorter training times. The history of OpenAI’s GPT series reflects the rapid advances in natural language processing. ChatGPT, an advanced large language model based on GPT-3, can generate human-like responses in dialogues and masters a variety of tasks such as generating text, answering questions, summarizing, rephrasing, or translating texts in many languages, and has been refined into an efficient conversational AI tool through an iterative process of supervised learning and reinforcement learning, including human feedback fine-tuning and a trained reward model.