This chapter introduces Chat Generative Pre-trained Transformer (ChatGPT) and its history, discusses its advantages and disadvantages, and points out potential applications. It also analyzes its impact on the development of trustworthy artificial intelligence, conversational search engine, and artificial general intelligence.

1 Introduction

This chapter introduces the rapid growth of ChatGPT as a consumer application and its ability to deliver high-quality conversations. It discusses how ChatGPT obtains emergent abilities through large language models (LLMs) and self-supervised learning. It also touches on the time-consuming and labor-intensive nature of training LLMs. Additionally, it mentions that LLMs offer a possible approach to artificial general intelligence (AGI) and the exploration of LLMs by various organizations.

2 Potential and prospects

ChatGPT obtains many emergent abilities compared with previous generation models. The main advantages of ChatGPT are: generalization, correction, safety, and creativity. It can generate responses that match the user’s intent with multiple turns, admit its own mistakes, reject unsafe questions, and show strong performance in creative writing tasks.

3 Preliminaries of ChatGPT

1. Code pre-training is a commonly used strategy for LLMs and not only improves code understanding and generation abilities, but also long-range context understanding and CoT reasoning. It helps the model accurately answer questions with few examples.

2. Instruction tuning is a popular technique for LLMs that aligns model behavior with human intent, enabling zero-shot task generalization. It reduces the problem of scaling law and allows for adaptations to new tasks without changing model parameters.

3. Reinforcement learning from human feedback is used to align the model behavior with human feedback, optimize the generation model, and ensure the model generates informative, helpful, correct, and harmless responses. Deployment strategies include safety evaluations, beta testing, and retrospective reviews to reduce risks associated with the model.

4 Limitations

This chapter discusses the limitations of ChatGPT, including its inability to effectively handle logic problems, tendency to generate factually incorrect or biased responses, inability to learn new knowledge in real time, and vulnerability to attacks. Despite its powerful conversation abilities, ChatGPT still has room for improvement in these areas.

5 Potential applications

ChatGPT has potential applications in improving production effectiveness and efficiency in various industries such as education, mobile, search engine, content production, and medicine. However, it may also bring negative effects, including making academic misconduct and misinformation imperceptible, ethical issues, and the need for AI governance to regulate its legal and reasonable utilization.

6 Discussions and conclusions

This chapter discusses the impact of ChatGPT on AI development, raising points related to trustworthy AI, conversational search engines, and artificial general intelligence. It emphasizes the need for attention to trustworthy AI, the potential impact of ChatGPT on traditional search engines, and the challenges and potential for ChatGPT in approaching AGI. The potential, prospects, and limitations of ChatGPT are analyzed, highlighting its potential to change traditional AI research directions and offer a possible approach to AGI.

1 绪论

This chapter introduces the recent release of Chat Generative Pre-trained Transformer (ChatGPT) by OpenAI, discussing its capabilities, advantages, limitations, and potential applications. It highlights how ChatGPT has become the fastest-growing consumer application in history and analyzes its impact on trusted artificial intelligence, conversation search engines, and the development of artificial general intelligence. The chapter also explains how large language models like ChatGPT acquire their impressive abilities through pre-training on large-scale data using massive neural network models, such as the Transformer. Additionally, it discusses the emergence of various advanced capabilities in ChatGPT, including high-quality conversation, complex reasoning, context learning, and touts the potential of large language models as a pathway towards artificial general intelligence. Furthermore, it mentions other organizations exploring large language models and their potential for advancing the field of artificial intelligence in the future.

2 潜力和前景

This chapter discusses the potential and prospects of ChatGPT. It highlights its strengths in generating contextually relevant multi-turn responses, acknowledging and correcting its own mistakes, ensuring safety in responses, and showcasing creativity in creative writing tasks. It also outlines the use of fine-tuning and reinforcement learning to enhance its learning and generalization capabilities.

3 ChatGPT背景

This chapter discusses the background of ChatGPT, its origin from GPT-3, and the three basic strategies for deriving ChatGPT from GPT-3. These strategies include code pretraining, instruction fine-tuning, and human feedback-based reinforcement learning. Code pretraining involves adding code to the pretraining corpus to enhance language model capabilities. Instruction fine-tuning ensures model behavior consistency with human intent through a diverse set of instruction templates. Human feedback-based reinforcement learning helps the model produce helpful, correct, and non-harmful responses, while avoiding illegal questions. Additionally, the deployment process for ChatGPT involves various strategies such as safety assessment, beta testing, and retrospective review to mitigate associated risks.

4 限制

ChatGPT has limitations in logical reasoning, reliability, knowledge learning, and robustness. It often gives incorrect answers to logical questions, may produce biased or inaccurate responses, lacks real-time knowledge learning and updating capabilities, and is vulnerable to attacks such as instruction injection and prompt injection. Additionally, it needs development for other languages and cultures based on relevant dataset backgrounds.

5 潜在应用

This chapter discusses the potential applications of ChatGPT and how it will significantly change human life in many aspects in the coming years. It is expected to impact various industries such as education, mobile, search engines, content creation, and medicine. However, it is also noted that ChatGPT may bring some negative impacts, such as making it difficult to detect academic dishonesty or false information, raising ethical concerns regarding its use in authorship, and the need for better governance of its usage.

6 讨论和结论

This chapter discusses the potential impact of ChatGPT in several areas. It emphasizes the need for trustworthy AI, especially in the field of natural language generation. ChatGPT has been integrated into search engines by companies like Microsoft and Google, changing the way users interact with search results. While ChatGPT shows potential for approaching general AI, it still faces challenges related to learning methods and the understanding of common sense, suggesting the need for further research in combining AI with human intelligence. Ultimately, ChatGPT represents a significant advancement in AI and has the potential to change the direction of traditional AI research and lead to various applications.