Как работает ChatGPT: объясняем нейросети просто

Pavel Komarovsky introduces the topic of neural networks, specifically focusing on language models like ChatGPT. He explains that these models are essentially advanced versions of predictive text technology, aiming to guess the next word in a sequence based on the given context. The analogy of T9 from old mobile phones is used to explain the evolution of language models, which have become more sophisticated over time by incorporating punctuation, context, and word prediction. The script sets the stage for a deeper dive into how these models operate and their potential impact on jobs.

This paragraph delves into the concept of language modeling, emphasizing the predictive nature of neural networks in guessing the next word in a sentence. It compares the functionality of T9 predictive text on smartphones to modern language models like ChatGPT, highlighting the evolution from simple word completion to context-aware prediction. The script discusses the importance of probability in language models, explaining how they use statistical patterns to offer sensible word predictions, and touches on the creative aspect of these models, which can generate a variety of text outputs.

The script discusses the evolution of neural networks, particularly the introduction of the transformer architecture by Google researchers in 2017. It explains how transformers have become a pivotal technology in AI, revolutionizing various fields including text, image, and audio processing. The transformer's ability to handle large datasets and its scalability due to its simple modular design is highlighted, as well as its impact on overcoming the so-called 'AI winter' and propelling the industry forward.

This section of the script explores the process of training large language models like GPT-2, emphasizing the need for vast amounts of unlabeled text data. It describes how OpenAI researchers used Reddit as a source for training data, collecting millions of links and gigabytes of text to train their models. The script also touches on the concept of model complexity, measured by the number of parameters, and how it affects the model's ability to understand and generate text.

The script introduces GPT-2, a significant leap in language modeling with 1.5 billion parameters. It discusses the model's ability to generate coherent and contextually relevant text, such as essays on complex topics. The paragraph also highlights the ethical considerations and potential risks associated with releasing such powerful models into the public domain, as they could be misused to generate misinformation.

This paragraph explores the unexpected capabilities of GPT-2, which include understanding ambiguity in text and solving reasoning tasks without specific training for these skills. The script discusses the model's ability to answer questions correctly at a rate higher than chance, indicating a qualitative leap in its reasoning abilities. It also touches on the non-linear progression of model capabilities as the number of parameters increases.

The script introduces GPT-3, a model with 175 billion parameters, which is a 100-fold increase in size over its predecessor. It discusses the model's ability to perform a wide range of tasks, from translation to basic arithmetic, without being specifically trained for them. The paragraph also highlights the model's capacity for extrapolation and its ability to understand and generate text at a level comparable to human performance.

This section discusses the importance of prompt engineering in effectively communicating with AI models like ChatGPT. It explains how the way a question or task is framed can significantly impact the quality of the AI's response. The script also mentions the role of 'prompt engineers' who specialize in formulating queries that elicit the best performance from AI models.

The final paragraph touches on the future of AI, particularly the potential of models with even greater parameters and the new abilities they might develop. It also raises ethical considerations in aligning AI with human values, discussing the challenges of creating AI that is both accurate and harmless. The script concludes by highlighting the importance of feedback from human evaluators in refining AI models to better meet societal expectations.