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

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

缪青海^1,,
王兴霞^{1, 2,},
杨静^{1, 2,},
赵勇^3,,
王雨桐^2,,
陈圆圆^2,,
田永林^2,,
俞怡^4,,
林懿伦^4,,
鄢然^5,,
马嘉琪^6,,
那晓翔^7,,
王飞跃^{1, 2,}

1.
中国科学院大学人工智能学院北京 100049 中国
2.
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北京 100190 中国
3.
国防科技大学系统工程学院长沙 410000 中国
4.
上海人工智能实验室上海 200232 中国
5.
南洋理工大学土木与环境工程学院新加坡 639798 新加坡
6.
加州大学洛杉矶分校萨穆埃利工程学院洛杉矶 90095 美国
7.
剑桥大学工程系剑桥 CB2 1TN 英国

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

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出版历程
- 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

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

MIAO Qing-Hai^1
,,
WANG Xing-Xia^{1, 2
,},
YANG Jing^{1, 2
,},
ZHAO Yong^3
,,
WANG Yu-Tong^2
,,
CHEN Yuan-Yuan^2
,,
TIAN Yong-Lin^2
,,
YU Yi^4
,,
LIN Yi-Lun^4
,,
YAN Ran^5
,,
MA Jia-Qi^6
,,
NA Xiao-Xiang^7
,,
WANG Fei-Yue^{1, 2
,}

1.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
2.
The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.
College of Systems Engineering, National University of Defense Technology, Changsha 410000, China
4.
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
5.
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
6.
UCLA Samueli School of Engineering, Los Angeles 90095, U.S.
7.
Department of Engineering, University of Cambridge, Cambridge CB2 1TN, U.K.

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

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.
- 生成式人工智能 /
- 大语言模型 /
- 基础模型 /
- 通用人工智能 /
- 基础智能 /
- 平行智能 /
- Sora
Abstract: This article provides an overview of the current development status of generative artificial intelligence (GenAI) both in China and abroad, focusing on the analysis of the gap between China and the United States in terms of computing power, data, algorithms, and ecosystems. To change the backward situation of China in the field of generative AI, perspectives such as constructing high-energy-efficient computing, federated data, specialized domain models, and a federated ecosystem based on TAO are proposed. It also discusses AI security governance in the era of foundation intelligence and prospects for the future development of artificial general intelligence (AGI).
- Generative artificial intelligence (GenAI) /
- large language model /
- foundation model /
- artificial general intelligence (AGI) /
- foundation intelligence /
- parallel intelligence /
- Sora
注释:

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/

HTML全文

图 1 国产大模型发展全景

Fig. 1 Panorama of the development of domestic large models

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

图 3 国内人工智能安全评估体系

Fig. 3 Artificial intelligence safety evaluation system in China

下载: 全尺寸图片幻灯片

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
模型	发布时间	开发者	文本	语音	图像	视频	文本	语音	图像	视频
GPT-1	2018年6月	OpenAI	√				√
BERT	2018年10月	Google	√				√
GPT-2	2019年2月	OpenAI	√				√
RoBERTa	2019年7月	Meta	√				√
T5	2019年10月	Google	√				√
GPT-3	2020年5月	OpenAI	√				√
GPT-3.5	2022年3月	OpenAI	√				√
GPT-4	2023年3月	OpenAI	√	√	√		√	√	√
PaLM 2	2023年5月	Google	√				√
Llama 2	2023年6月	Meta	√				√
Claude 3	2024年3月	Anthropic	√				√
MusicLM	2023年5月	Google	√					√
MusicGen	2023年6月	Meta	√					√
Voicebox	2023年6月	Meta	√	√				√
DALL·E	2021年1月	OpenAI	√						√
DALL·E 2	2022年4月	OpenAI	√						√
Stable Diffusion	2022年8月	Stability AI	√		√				√
Midjourney	2022年7月	Midjourney	√						√
Firefly	2023年3月	Adobe	√						√
DALL·E 3	2023年9月	OpenAI	√						√
Imagen 2	2023年12月	Google	√						√
Make-A-Video	2022年9月	Meta	√	√		√				√
Gen-2	2023年2月	Runway	√	√		√				√
Lumiere	2024年1月	Google	√	√						√
Sora	2024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910	H100 SXM
	速度 (FP16)	280 TFLOPS	1979 TFLOPS
	显存	16 G	80 G
	互联带宽	—	900 GB/s
	并行平台API	CANN	CUDA
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^#	70/1.3T	315/5.9T
	高引论文^#	60+	220+
	机构/人才占比^*	14%/13.85%	55%/56.55%
	创新城市数量^*	19	33
注: ^# State of AI Report 2023, ^*Aminer

下载: 导出CSV

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