非标准格式引用，仅用作列出本书相关内容以及参考内容，望悉知。
[1] Wiener, N. (1948). Cybernetics: or Control and Communication in the Animal and the Machine [《控制论：或动物和机器中的控制与通信》]. MIT Press.——[1.1]
[2] Turing, A. M. (1950). Computing Machinery and Intelligence [《计算机与智能》]. Mind, 49, 433-460.——[1.2]
[3] Scott, R. (导演). (1982). Blade Runner [《银翼杀手》] [电影]. Warner Bros.——[1.2]
[4] Tyldum, M. (导演). (2014). The Imitation Game [《模仿游戏》] [电影]. The Weinstein Company.——[1.2]
[5] Eugene Goostman [尤金·古斯特曼] (2014). 聊天程序首次通过图灵测试.——[1.2]
[6] Russell, B. & Whitehead, A. N. Principia Mathematica [《数学原理》]. Cambridge University Press.——[2.1]
[7] McCarthy, J. (1955). A Proposal for The Dartmouth Summer Research Project on Artificial Intelligence [达特茅斯人工智能夏季研究项目提案].——[2.1]
[8] Lighthill, J. (1973). Artificial Intelligence A General Survey [人工智能通用调查 or 莱特希尔报告]. Science Research Council.——[2.4]
[9] Minsky, M. & Papert, S. (1969). Perceptrons: An Introduction to Computational Geometry [《感知机：计算几何学》]. MIT Press.——[3.1]
[10] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors [《通过反向传播学习表示》]. Nature, 323, 533-536.——[3.2]
[11] Brooks, R. A. (1990). Elephants don't play chess [《大象不下棋》]. Robotics and Autonomous Systems, 6, 3-15.——[3.3]
[12] Vapnik, V. N. & Chervonenkis, A. Y. (1971). On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities [《论事件相对频率对其概率的一致收敛》]. Theory of Probability and its Applications, 16, 264-280.——[3.4]
[13] Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets [一种深度信念网络的快速学习算法]. Science, 313, 504-507.——[4.1]
[14] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks [使用深度卷积神经网络进行ImageNet分类]. Advances in Neural Information Processing Systems, 25.——[4.2]
[15] Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory [长短期记忆网络]. Neural Computation, 9(8), 1735-1780.——[4.3]
[16] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need [注意力就是全部所需]. Advances in Neural Information Processing Systems, 30.——[5.1]
[17] Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training [通过生成预训练提高语言理解能力]. OpenAI.——[5.2]
[18] Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [BERT: 基于深度双向Transformer的语言理解预训练]. arXiv preprint arXiv:1810.04805.——[5.2]
[19] Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021). Finetuned Language Models Are Zero-Shot Learners [微调语言模型是零样本学习者]. arXiv preprint arXiv:2109.01652.——[6.1]
[20] Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep Reinforcement Learning from Human Preferences [基于人类偏好的深度强化学习]. Advances in Neural Information Processing Systems, 30.——[6.2]
[21] Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Hume, T., Johnston, S., Kravec, S., Lovitt, L., Nanda, N., Olsson, C., Amodei, D., Brown, T., Clark, J., McCandlish, S., Olah, C., Mann, B., & Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback [宪法式人工智能：来自AI反馈的无害性]. arXiv preprint arXiv:2212.08073.——[6.2]
[22] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks [知识密集型NLP任务的检索增强生成]. Advances in Neural Information Processing Systems, 33.——[7.1]
[23] OpenAI (2023). Function Calling and other API updates [函数调用和其他API更新]. OpenAI Blog.——[7.2]
[24] Anthropic (2024). Introducing the Model Context Protocol [模型上下文协议]. Anthropic Blog.——[7.3]
[25] Google (2025). Introducing the Agent-to-Agent protocol [Agent到Agent协议]. Google Blog.——[7.5]