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问答ChatGPT之后: 超大预训练模型的机遇和挑战

卢经纬 郭超 戴星原 缪青海 王兴霞 杨静 王飞跃

卢经纬, 郭超, 戴星原, 缪青海, 王兴霞, 杨静, 王飞跃. 问答ChatGPT之后: 超大预训练模型的机遇和挑战. 自动化学报, 2023, 49(4): 705−717 doi: 10.16383/j.aas.c230107
引用本文: 卢经纬, 郭超, 戴星原, 缪青海, 王兴霞, 杨静, 王飞跃. 问答ChatGPT之后: 超大预训练模型的机遇和挑战. 自动化学报, 2023, 49(4): 705−717 doi: 10.16383/j.aas.c230107
Lu Jing-Wei, Guo Chao, Dai Xing-Yuan, Miao Qing-Hai, Wang Xing-Xia, Yang Jing, Wang Fei-Yue. The ChatGPT after: Opportunities and challenges of very large scale pre-trained models. Acta Automatica Sinica, 2023, 49(4): 705−717 doi: 10.16383/j.aas.c230107
Citation: Lu Jing-Wei, Guo Chao, Dai Xing-Yuan, Miao Qing-Hai, Wang Xing-Xia, Yang Jing, Wang Fei-Yue. The ChatGPT after: Opportunities and challenges of very large scale pre-trained models. Acta Automatica Sinica, 2023, 49(4): 705−717 doi: 10.16383/j.aas.c230107

问答ChatGPT之后: 超大预训练模型的机遇和挑战

doi: 10.16383/j.aas.c230107
基金项目: 国家自然科学基金 (U1811463), 行动元联合研究项目: 伺服驱动系统的基础建模和平行驱控研究资助
详细信息
    作者简介:

    卢经纬:青岛智能产业技术研究院副研究员. 2022年获得中国科学院大学计算机应用技术博士学位. 主要研究方向为最优控制, 自适应动态规划, 深度强化学习和自动驾驶. E-mail: lujingweihh@gmail.com

    郭超:中国科学院自动化研究所复杂系统管理与控制国家重点实验室助理研究员. 主要研究方向为机器艺术创作, 智能机器人系统, 深度学习, 强化学习. E-mail: guochao2014@ia.ac.cn

    戴星原:中国科学院自动化研究所复杂系统管理与控制国家重点实验室助理研究员. 2022年获得中国科学院大学控制理论与控制工程专业博士学位. 主要研究方向为人工智能, 强化学习, 智能交通系统. E-mail: xingyuan.dai@ia.ac.cn

    缪青海:中国科学院大学人工智能学院副教授. 2007年获得中国科学院自动化研究所博士学位. 主要研究方向为智能系统, 机器学习, 计算机视觉. E-mail: miaoqh@ucas.ac.cn

    王兴霞:中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生. 2021年获得南开大学工学硕士学位. 主要研究方向为平行控制, 平行油田和多智能体系统. E-mail: wangxingxia2022@ia.ac.cn

    杨静:中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生. 2020年获得北京化工大学自动化学士学位. 主要研究方向为平行制造, 社会制造, 人工智能和社会物理信息系统. E-mail: yangjing2020@ia.ac.cn

    王飞跃:中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员. 主要研究方向为智能系统, 复杂系统建模, 分析与控制. 本文通信作者. E-mail: feiyue.wang@ia.ac.cn

The ChatGPT After: Opportunities and Challenges of Very Large Scale Pre-trained Models

Funds: Supported by National Natural Science Foundation of China (U1811463) and Motion G, Inc. Collaborative Research Project for Foundation Modeling and Parallel Driven/Control for Servo-Drive Systems
More Information
    Author Bio:

    LU Jing-Wei Associate professor at the Qingdao Academy of Intelligent Industries. He received his Ph.D. degree in computer application technology from University of Chinese Academy of Sciences. His research interest covers optimal control, adaptive dynamic programming, deep reinforcement learning, and autonomous driving

    GUO Chao Assistant professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers AI art, intelligent robotic systems, deep learning, and reinforcement learning

    DAI Xing-Yuan Assistant professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences. His research interest covers artificial intelligence, reinforcement learning, and intelligent transportation systems

    MIAO Qing-Hai Associate professor at the School of Artificial Intelligence, University of Chinese Academy of Sciences. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2007. His research interest covers intelligent systems, machine learning, and computer vision

    WANG Xing-Xia Ph.D. candidate at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. She received her master degree in engineering from Nankai University in 2021. Her research interest covers parallel control, parallel oilfields, and multi-agent systems

    YANG Jing Ph.D. candidate at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. She received her bachelor degree in automation from Beijing University of Chemical Technology in 2020. Her research interest covers parallel manufacturing, social manufacturing, artificial intelligence, and cyber-physical-social systems

    WANG Fei-Yue Professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper

  • 摘要: 超大预训练模型(Pre-trained model, PTM)是人工智能领域近年来迅速崛起的研究方向, 在自然语言处理(Natural language processing, NLP)和计算机视觉等多种任务中达到了有史以来的最佳性能, 促进了人工智能生成内容(Artificial intelligence-generated content, AIGC)的发展和落地. ChatGPT作为当下最火热的PTM, 更是以优异的表现获得各界的广泛关注. 本文围绕ChatGPT展开. 首先概括PTM的基本思想并对其发展历程进行梳理; 接着, 详细探讨ChatGPT的技术细节, 并以平行智能的视角阐述ChatGPT; 最后, 从技术、范式以及应用等多个方面对PTM的发展趋势进行展望.
    1)  1 引号内文字由ChatGPT生成(https://chat.openai.com/chat/)
    2)  2 https://openai.com/blog/chatgpt/
    3)  3 https://openai.com/blog/chatgpt/
    4)  4 https://openai.com/research/language-model-safety-and-misuse
    5)  5 https://openai.com/blog/ai-and-compute
  • 图  1  典型超大预训练模型的发展历程

    Fig.  1  Typical very large scale PTMs

    图  2  ChatGPT的功能

    Fig.  2  The functions of ChatGPT

    图  3  ChatGPT采用的Transformer解码器结构

    Fig.  3  The Transformer decoder for ChatGPT

    图  4  ChatGPT的实现流程

    Fig.  4  The implementation process of ChatGPT

    图  5  强化学习视角下的ChatGPT

    Fig.  5  ChatGPT from the perspective of RL

    图  6  社会化大闭环下的ChatGPT

    Fig.  6  ChatGPT in the grand socialization closed-loop

    图  7  PTM研究范式

    Fig.  7  Research paradigms of PTMs

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