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从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望

缪青海 王兴霞 杨静 赵勇 王雨桐 陈圆圆 田永林 俞怡 林懿伦 鄢然 马嘉琪 那晓翔 王飞跃

缪青海, 王兴霞, 杨静, 赵勇, 王雨桐, 陈圆圆, 田永林, 俞怡, 林懿伦, 鄢然, 马嘉琪, 那晓翔, 王飞跃. 从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望. 自动化学报, 2024, 50(4): 674−687 doi: 10.16383/j.aas.c240156
引用本文: 缪青海, 王兴霞, 杨静, 赵勇, 王雨桐, 陈圆圆, 田永林, 俞怡, 林懿伦, 鄢然, 马嘉琪, 那晓翔, 王飞跃. 从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望. 自动化学报, 2024, 50(4): 674−687 doi: 10.16383/j.aas.c240156
Miao Qing-Hai, Wang Xing-Xia, Yang Jing, Zhao Yong, Wang Yu-Tong, Chen Yuan-Yuan, Tian Yong-Lin, Yu Yi, Lin Yi-Lun, Yan Ran, Ma Jia-Qi, Na Xiao-Xiang, Wang Fei-Yue. From foundation intelligence to general intelligence: The state-of-art and perspectives of GenAI and AGI based on foundation models. Acta Automatica Sinica, 2024, 50(4): 674−687 doi: 10.16383/j.aas.c240156
Citation: Miao Qing-Hai, Wang Xing-Xia, Yang Jing, Zhao Yong, Wang Yu-Tong, Chen Yuan-Yuan, Tian Yong-Lin, Yu Yi, Lin Yi-Lun, Yan Ran, Ma Jia-Qi, Na Xiao-Xiang, Wang Fei-Yue. From foundation intelligence to general intelligence: The state-of-art and perspectives of GenAI and AGI based on foundation models. Acta Automatica Sinica, 2024, 50(4): 674−687 doi: 10.16383/j.aas.c240156

从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望

doi: 10.16383/j.aas.c240156
基金项目: 国家自然科学基金(62271485, 61903363, U1811463)资助
详细信息
    作者简介:

    缪青海:中国科学院大学人工智能学院副教授. 主要研究方向为智能系统, 智能交通, 平行智能. E-mail: miaoqh@ucas.ac.cn

    王兴霞:中国科学院自动化研究所复制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智能, 平行油田, 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专业学士学位. 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

    赵勇:国防科技大学系统工程学院博士研究生. 2021年获国防科技大学控制科学与工程专业硕士学位. 主要研究方向为移动群智感知, 空间众包, 人机交互. E-mail: zhaoyong15@nudt.edu.cn

    王雨桐:中国科学院自动化研究所助理研究员. 2021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. 主要研究方向为计算机视觉. E-mail: yutong.wang@ia.ac.cn

    陈圆圆:中国科学院自动化研究所副研究员. 2018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. 主要研究方向为交通数据分析, 社会交通, 平行交通管理与控制系统. E-mail: yuanyuan.chen@ia.ac.cn

    田永林:中国科学院自动化研究所博士后. 2022年获得中国科学技术大学控制理论与控制工程专业博士学位. 主要研究方向为平行智能, 自动驾驶, 智能交通. E-mail: yonglin.tian@ia.ac.cn

    俞怡:上海人工智能实验室助理研究员. 主要研究方向为智能交通系统, 数据要素化, 城市计算. E-mail: yuyi@pjlab.org.cn

    林懿伦:上海人工智能实验室副研究员. 2019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. 主要研究方向为社会计算, 平行智能, 深度学习, 智能交通系统与人工智能安全. E-mail: linyilun@pjlab.org.cn

    鄢然:新加坡南洋理工大学土木与环境工程学院助理教授. 主要研究方向为海事研究中的数据分析, 海运大数据, 绿色航运管理, 海事风险管理以及港口和航运优化. E-mail: ran.yan@ntu.edu.sg

    马嘉琪:加州大学洛杉矶分校萨穆埃利工程学院副教授, 加州大学洛杉矶分校交通研究所副所长. 2014年获得弗吉尼亚大学交通运输工程博士学位. 主要研究方向为联网和自动化车辆, 网络物理运输系统, 运输系统的弹性, 分布式多智能体系统的协同控制, 智能交通系统, 动态运输系统建模和控制, 网络优化, 出行行为建模和需求预测, 人工智能和先进计算在交通领域的应用. E-mail: jiaqima@ucla.edu

    那晓翔:英国剑桥大学工程系长聘助理教授. 2014年获英国剑桥大学机械工程博士学位. 主要研究方向为重型商用车智能车载信息系统开发与车辆能耗特性评价. E-mail: xnhn2@cam.ac.uk

    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和复杂系统的建模、分析与控制. 本文通信作者. E-mail: feiyue.wang@ia.ac.cn

From Foundation Intelligence to General Intelligence: The State-of-Art and Perspectives of GenAI and AGI Based on Foundation Models

Funds: Supported by National Natural Science Foundation of China (62271485, 61903363, U1811463)
More Information
    Author Bio:

    MIAO Qing-Hai Associate professor at School of Artificial Intelligence, University of Chinese Academy of Sciences. His research interest covers intelligent systems, intelligent transportation systems, parallel intelligence

    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 intelligence, 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

    ZHAO Yong Ph.D. candidate at the College of Systems Engineering, National University of Defense Technology. He received his master degree in control science and engineering from National University of Defense Technology in 2021. His research interest covers mobile crowdsensing, spatial crowdsourcing, and human computer interaction

    WANG Yu-Tong Assistant professor at the Institute of Automation, Chinese Academy of Sciences. She received her Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences in 2021. Her main research interest is computer vision

    CHEN Yuan-Yuan Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in control theory and control engineering from University of Chinese Academy of Sciences in 2018. His research interest covers traffic data analytics, social transportation, and parallel traffic management and control systems

    TIAN Yong-Lin Postdoctor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in control theory and control engineering from the University of Science and Technology of China, in 2022. His research interest covers parallel intelligence, autonomous driving, and intelligent transportation systems

    YU Yi Assistant professor at Shanghai Artificial Intelligence Laboratory. Her research interest covers intelligent transportation systems, data trading, and urban computing

    LIN Yi-Lun Associate professor at Shanghai Artificial Intelligence Laboratory. He received his Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences, in 2019. His research interest covers social computing, parallel intelligence, deep learning, intelligent transportation systems and AI safety

    YAN Ran Assistant professor at the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. Her research interest covers data analytics in maritime studies, big data in maritime transport, green-shipping management, maritime risk management, and port and shipping optimization

    MA Jia-Qi Associate professor at the UCLA Samueli School of Engineering and associate director of UCLA Institute of Transportation Studies. He received his Ph.D. degree of transportation engineering from University of Virginia, 2014. His research interest covers connected and automated vehicles; cyber-physical transportation systems; transportation systems resilience; cooperative control of distributed multi-agent systems; intelligent transportation systems; dynamic transportation systems modeling and control; network optimization; travel behavior modeling and demand forecasting; artificial intelligence and advanced computing applications in transportation

    NA Xiao-Xiang University assistant professor in the Department of Engineering, University of Cambridge, U.K.. He received his Ph.D. degree in mechanical engineering from University of Cambridge, U.K.. His research interest covers development of intelligent telematics systems for heavy goods vehicles and assessment of vehicle energy performance

    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: 本文对生成式AI (Generative artificial intelligence, GenAI)的国内外发展现状进行了概述, 重点分析了中美之间在算力、数据、算法、生态等方面存在的差距. 为改变我国在生成式AI领域的落后现状, 提出高能效算力建设、联邦数据、专业领域模型、基于TAO的联邦生态等应对策略, 对大模型时代AI安全治理进行了论述, 对通用人工智能(Artificial general intelligence, AGI)的未来发展进行了展望.
    1)  11 https://hunyuan.tencent.com/2 https://xinghuo.xfyun.cn/3 https://kimi.moonshot.cn/4 https://taichu-web.ia.ac.cn/5 https://www.baai.ac.cn/6 https://www.lingyiwanwu.com/7 https://zhipuai.cn/8 https://www.ecnu.edu.cn/info/1426/65145.htm
    2)  29 https://www.seiee.sjtu.edu.cn/index_news/8667.html10 https://github.com/blcuicall/taoli11 https://www.mathgpt.com/12 https://ziyue.youdao.com//home13 http://dicp.cas.cn/xwdt/kyjz/202403/t20240324_7050498.html14 http://www.ciictec.com/ciigpt15 http://www.cctegxian.com/html/news/2023-12-18/4185.html16 https://github.com/CMKRG/QiZhenGPT17 http://web-qa.medlinker.com/pc/product/medgpt18 http://www.dajingtcm.com/node/2119 https://github.com/ywjawmw/TCMEB20 https://github.com/SupritYoung/Zhongjing21 https://github.com/jerry1993-tech/Cornucopia-LLaMA-Fin-Chinese.git
    3)  322 https://www.hundsun.com/lightgpt23 https://www.langboat.com/portal/mengzi-gpt24 https://github.com/AbaciNLP/InvestLM
    4)  425 https://resources.nvidia.com/en-us-tensor-core/nvidia-tensorcore-gpu-datasheet26 https://e.huawei.com/cn/products/computing/ascend27 https://www.nvidia.com/en-us/data-center/h200/28 https://www.nvidia.com/en-us/data-center/gb200-nvl72/29 阿里研究院《中美大模型的竞争之路: 从训练数据讲起》报告.30 https://pubmed.ncbi.nlm.nih.gov/31 https://pile.eleuther.ai/
    5)  532 https://www.stateof.ai/33 https://www.aminer.org/
  • 图  1  国产大模型发展全景

    Fig.  1  Panorama of the development of domestic large models

    图  2  人工智能全生命周期四阶段主要风险, 风险影响范围随技术发展逐渐增大

    Fig.  2  The main risks of the four stages in the artificial intelligence lifecycle, and the risk impact gradually increases with technological development

    图  3  国内人工智能安全评估体系

    Fig.  3  Artificial intelligence safety evaluation system in China

    表  1  国外主要GenAI模型

    Table  1  Typical foreign GenAI models

    模型发布时间开发者输入模态输出模态
    文本语音图像视频文本语音图像视频
    GPT-12018年6月OpenAI
    BERT2018年10月Google
    GPT-22019年2月OpenAI
    RoBERTa2019年7月Meta
    T52019年10月Google
    GPT-32020年5月OpenAI
    GPT-3.52022年3月OpenAI
    GPT-42023年3月OpenAI
    PaLM 22023年5月Google
    Llama 22023年6月Meta
    Claude 32024年3月Anthropic
    MusicLM2023年5月Google
    MusicGen2023年6月Meta
    Voicebox2023年6月Meta
    DALL·E2021年1月OpenAI
    DALL·E 22022年4月OpenAI
    Stable Diffusion2022年8月Stability AI
    Midjourney2022年7月Midjourney
    Firefly2023年3月Adobe
    DALL·E 32023年9月OpenAI
    Imagen 22023年12月Google
    Make-A-Video2022年9月Meta
    Gen-22023年2月Runway
    Lumiere2024年1月Google
    Sora2024年2月OpenAI
    下载: 导出CSV

    表  2  中美生成式AI领域现状

    Table  2  Current status of generative AI fields in China and the United States

    对比条目国内国外
    算力处理器昇腾910H100 SXM
    速度 (FP16)280 TFLOPS1979 TFLOPS
    显存16 G80 G
    互联带宽900 GB/s
    并行平台APICANNCUDA
    数据政府部门有限开放能开尽开
    社会力量碎片、孤立联合、开源
    数据生态尚未形成趋于完善
    算法基本算法Transformer
    语言模型文心一言等GPT/Llama
    文生图秒画等DALL·E/Imagen
    文本生成视频Sora
    多模态悟道等GPT-4等
    生态独角兽量/值#70/1.3T315/5.9T
    高引论文#60+220+
    机构/人才占比*14%/13.85%55%/56.55%
    创新城市数量*1933
    注: # State of AI Report 2023, *Aminer
    下载: 导出CSV
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  • 收稿日期:  2024-03-29
  • 录用日期:  2024-04-07
  • 网络出版日期:  2024-04-15
  • 刊出日期:  2024-04-26

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