What Exactly Powers the Operation of ChatGPT?
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ChatGPT is a new-generation natural language processing model. As the name suggests, a language model is designed to process human language. When a piece of text is input into the model, it analyzes and processes it to produce a corresponding output. For example, there are translation models for foreign languages, text classification models for categorizing content, and text matching models for retrieving information. ChatGPT, however, is a generative language model. Simply put, a generative language model is a chatbot. When you input text, it generates a response based on its understanding of the text's meaning. Similar chatbots existed when the internet first emerged, and at first glance, ChatGPT might not seem particularly special. However, what sets ChatGPT apart is that its generated text is not pre-programmed "standard answers" by humans. Each response is the result of "thinking" and "creating" after processing the input.
ChatGPT's impressive "thinking" and "creating" capabilities are built on the foundation of big data, large models, and massive computational power. It is estimated that ChatGPT has learned from billions of words sourced from books, articles, news, web pages, blogs, Wikipedia, and other resources, covering all aspects of human society—politics, economics, culture, society, military, history, and more. But ChatGPT doesn't merely copy and record what it learns. It trains itself using deep learning techniques and self-attention mechanisms. Deep learning involves simulating a human-like neural network through computational models, continuously updating model parameters through learning. During its training, ChatGPT refines its understanding by predicting the next part of a sentence based on the preceding context, repeatedly adjusting its approximately 175 billion parameters until the predicted output aligns with statistical patterns. In other words, it identifies the "inherent patterns" of the learned content, completing a mature language model. Notably, ChatGPT stands out among language models due to its use of an improved self-attention mechanism. This mechanism allows it to determine which parts of a sentence require focus, better connecting context, questions, and learned content to provide more appropriate responses. Processing such vast amounts of data, updating and adjusting hundreds of billions of parameters with each learning cycle, and responding swiftly to countless queries post-deployment all rely on immense computational power. Estimates suggest ChatGPT's total computational consumption is around 3640 PF-days (assuming 10^15 calculations per second, requiring 3640 days of computation), necessitating 7 to 8 data centers with investments of $3 billion and 500P computational power to sustain operations. A single training session costs over $10 million.
What is the practical use of such a natural language model?
We know that natural language is a crucial medium for human communication, emotional expression, knowledge dissemination, and abstract thinking. It is language that enables the continuous inheritance, accumulation, and development of human civilization. ChatGPT's emergence allows computers to directly analyze and process human language, making activities based on natural language no longer exclusive to humans. While no research has shown that ChatGPT possesses human-like intelligence, it undeniably simulates human discourse in form and logic. The latest version of ChatGPT has outperformed the majority of humans in many professional tests, such as scoring in the top 10% on simulated bar exams and ranking in the top 7% in reading comprehension tests. Based on this, ChatGPT can play a significant role in various production and daily life activities centered around language. It can quickly generate high-quality copy, greatly enhancing productivity in advertising and marketing; rapidly analyze vast amounts of data and information to provide valuable market insights and recommendations for businesses; serve as an intelligent customer service agent to effectively address user inquiries and improve satisfaction; and act as a personalized tutor, helping students better understand and master learning materials through interactive Q&A.
Notably, as a language-processing "expert," ChatGPT holds great potential for application in computer programming languages, potentially becoming a bridge for direct communication between humans and machines. Currently, ChatGPT can already write some program code as needed. With continuous optimization and upgrades, ChatGPT may achieve the ability to directly translate natural language into machine-readable programming languages, automatically writing code to operate machines based on human instructions. This means that in the future, we could use natural language to command machines to perform various complex, customized tasks.
Of course, ChatGPT is far from perfect. As a language model, its learning and creative content are still based on the knowledge and information accumulated by humans, meaning it does not yet possess the ability to create new knowledge. At the same time, being a text-based learning model means it 'knows the words but not the meaning,' often leading to misinterpretations or 'hallucinations.' It may also make errors in understanding highly logical problems.