The goal of this research paper was to explore the possibilities of the GPT-3 language model on the platform GPT-3 Playground and to see how the company OpenAI brings the implementation of artificial intelligence closer to the public. The theoretical part of the research paper analyses human intelligence and behaviour in comparison to artificial intelligence and rational behaviour. It also shows the transformer technology and the way that it changes the approach to natural language processing. The paper presents the development of the GPT model from the first to the third generation. The research part of the paper analyses three different implementations of the GPT-3 transformer model: text simplification and summarization, translating Python code to natural text, and a Q&A chatbot. Each of the implementations is analysed on four models that were trained on different amounts of data. The results show that the GPT transformer model is a highly advanced technology and that the possibilities of the implementation of this technology are diverse. The research shows that the model Davinci, which had the most training data and the largest number of parameters, was the most successful at completing the three tasks. The model Curie was nearly as successful as Davinci, however it ran into some problems with understanding the context of a text, but it proved to be successful at generating friendly chat. The model Babbage had more problems with generating natural language text because it repeated phrases and it did not use some grammatical forms that would make the text sound more natural. The model Ada had large problems with understanding sentences if it was not given enough context.