Article(id=1242140686363992314, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140662385156262, articleNumber=null, orderNo=14, doi=10.3981/j.issn.1000-7857.2023.19.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1689782400000, receivedDateStr=2023-07-20, revisedDate=1694534400000, revisedDateStr=2023-09-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1698393547290, onlineDateStr=2023-10-27, pubDate=1697126400000, pubDateStr=2023-10-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1698393547290, onlineIssueDateStr=2023-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774079017483, creator=sys-migrate, updateTime=1774079017483, updator=sys-migrate, issue=Issue{id=1242140662385156262, tenantId=1146029695717560320, journalId=1146031591421210625, year='2023', volume='41', issue='19', pageStart='1', pageEnd='188', issueExtLink='null', onlineDate='null', pubDate='1697126400000', pubDateStr='2023-10-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774079011768, creator='sys-migrate', updateTime=1774079011768, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=103, endPage=112, ext={EN=ArticleExt(id=1242140688675058545, articleId=1242140686363992314, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Foundation intelligence: From federated intelligence to TAO-based intelligent systems federation, columnId=1242140672887698205, journalTitle=Science & Technology Review, columnName=Innovation leads selfreliance and selfimprovement—creating a source of high-quality technological, runingTitle=null, highlight=null, articleAbstract=From the early use of mathematical tools to explore the mechanism of neural activity, to the use of artificial neural network models to simulate human intelligence, to the solution of specific problems based on deep networks, AI has developed over the past 80 years and has now entered the era of foundation intelligence (FI), which is characterized by its foothold in large models, serving the broad social groups. The development of large models faces a series of challenges. First, the quality and quantity of data necessary for large models training need to be improved. Second, high-end computing resource is limited, and model training has high power consumption and high carbon emissions, which brings environmental problems. Third, it is the socialization of large models that brings about social development problems such as discrimination and prejudice, the spread of false information, invasion of privacy, and impact on employment. In order to overcome the aforementioned challenges, it is essential to build a federal ecosystem (including federal data, federal control, federal management and federal services) with support of block chain and decentralized autonomous organizations (TRUE+DAO, TAO), and provide solutions for the development of artificial intelligence in the new era., authors=WANG Fei-Yue1,2 , MIAO Qinghai1* , authorsList=WANG Fei-Yue, MIAO Qinghai, authorCompany=1. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China1,2 ，缪青海1* , authorsList=王飞跃，缪青海, authorCompany=1. 中国科学院大学人工智能学院，北京 100049
科技导报
| 专题：创新引领 自立自强——打造高质量科技创新策源地 2023, 41(19): 103-112
基础智能：从联邦智能到基于TAO的智能系统联邦
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王飞跃1,2 ，缪青海1*
作者信息
1. 中国科学院大学人工智能学院，北京 100049
通讯作者:
缪青海（通信作者），副教授，研究方向为智能系统、平行智能、机器学习等，电子信箱：miaoqh@ucas.ac.cn
Foundation intelligence: From federated intelligence to TAO-based intelligent systems federation
Affiliations
出版时间: 2023-10-13
doi: 10.3981/j.issn.1000-7857.2023.19.012
文章导航
从早期使用数学工具探索神经活动机理，到采用人工神经网络模型模拟人类智能，再到基于深度网络解决特定问题，人工智能历经80年发展，当前已进入大模型时代，其特点是立足基础大模型、服务广大社会群体。基础大模型的发展面临一系列挑战：一是大模型训练所必需的数据，质与量有待提高；二是高端算力设备受限制，模型训练高电耗、高碳排放带来环境问题；三是大模型的社会化带来歧视与偏见、虚假信息传播、隐私侵犯、影响就业等社会发展问题。在当今去全球化的国际形势下，为克服以上挑战，健全包含联邦数据、联邦控制、联邦管理和联邦服务的联邦生态系统，构建基于区块链保真（trustable+reliable+usable+effective/efficient，TRUE）和去中心化自治组织和分布式自治运营（DAO）的智能系统联邦，发展基于TAO（TRUE DAO）的基础智能（foundation intelligence，FI），为大模型时代人工智能的发展提供必要和有力的支撑。
大模型
/
基础智能
/
联邦智能
/
TAO
/
智能系统联邦
From the early use of mathematical tools to explore the mechanism of neural activity, to the use of artificial neural network models to simulate human intelligence, to the solution of specific problems based on deep networks, AI has developed over the past 80 years and has now entered the era of foundation intelligence (FI), which is characterized by its foothold in large models, serving the broad social groups. The development of large models faces a series of challenges. First, the quality and quantity of data necessary for large models training need to be improved. Second, high-end computing resource is limited, and model training has high power consumption and high carbon emissions, which brings environmental problems. Third, it is the socialization of large models that brings about social development problems such as discrimination and prejudice, the spread of false information, invasion of privacy, and impact on employment. In order to overcome the aforementioned challenges, it is essential to build a federal ecosystem (including federal data, federal control, federal management and federal services) with support of block chain and decentralized autonomous organizations (TRUE+DAO, TAO), and provide solutions for the development of artificial intelligence in the new era.
large models
/
foundation intelligence
/
federated intelligence
/
TAO
/
intelligent systems federation
王飞跃，缪青海.
基础智能：从联邦智能到基于TAO的智能系统联邦.
科技导报,
2023
, 41
(19)
: 103
-112
.
DOI: 10.3981/j.issn.1000-7857.2023.19.012
WANG Fei-Yue, MIAO Qinghai.
Foundation intelligence: From federated intelligence to TAO-based intelligent systems federation[J].
Science & Technology Review ,
2023
, 41
(19)
: 103
-112
.
DOI: 10.3981/j.issn.1000-7857.2023.19.012
2023年第41卷第19期
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文章信息
doi: 10.3981/j.issn.1000-7857.2023.19.012
接收时间：2023-07-20
首发时间：2023-10-27
出版时间：2023-10-13
收稿日期：2023-07-20
修回日期：2023-09-13
通讯作者:
缪青海（通信作者），副教授，研究方向为智能系统、平行智能、机器学习等，电子信箱：miaoqh@ucas.ac.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2023.19.012
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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