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000 nam5i
001 2210080934925
003 DE-He213
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007 cr nn 008mamaa
008 240912s2024 sz | s |||| 0|eng d
020 a97830316741989978-3-031-67419-8
024 a10.1007/978-3-031-67419-82doi
040 a221008
050 aHD45
072 aKJD2bicssc
072 aBUS0410002bisacsh
072 aKJD2thema
082 a658.4062223
082 a658.514223
100 aMiao, Qinghai.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
245 00 aArtificial Intelligence for Science (AI4S)h[electronic resource] :bFrontiers and Perspectives Based on Parallel Intelligence /cby Qinghai Miao, Fei-Yue Wang.
250 a1st ed. 2024.
264 aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.
300 aX, 113 p. 36 illus., 35 illus. in color.bonline resource.
336 atextbtxt2rdacontent
337 acomputerbc2rdamedia
338 aonline resourcebcr2rdacarrier
347 atext filebPDF2rda
490 aSpringerBriefs in Service Science,x2731-3751
505 aAI4S based on Parallel Intelligence -- AI for Mathematics -- AI for Physics -- AI for Biology -- AI for Health and Medicine -- AI for Chemistry -- AI for Material Science -- AI for Astronomy -- Toward a Sustainable AI4S Ecosystem.
520 aThis book presents a comprehensive framework for analyzing, evaluating, and guiding AI for Sciences (AI4Sci) research, offering a unified approach that facilitates analysis across various academic fields through a shared set of dimensions and indicators. It provides a systematic overview of recent AI4Sci advances in various disciplines and offers insights into the latest issues in and prospects of AI4Sci. The book is based on the theory of Parallel Intelligence (PI), which forms the foundation for the general AI4Sci framework. By analyzing multiple cases in various academic fields, this framework integrates key elements of AI4Sci, such as real scientific problems, datasets, virtual systems, AI methods, human roles, and organizational mechanisms, from a multidimensional perspective. It also assesses and summarizes the limitations of AI4Sci, incorporating the latest advances in AI for fundamental models. Lastly, it explores the impact of DeSci and DAO, as well as TAO, on AI4Sci ecosystem development and prospects. Through its balanced approach, the book offers readers a goal-oriented perspective, focusing on a concise presentation of the core ideas and reducing detailed descriptions of specific AI4Sci cases to a minimum.
650 aTechnological innovations.
650 aArtificial intelligence.
650 aMachine learning.
650 aService industries.
650 aInnovation and Technology Management.
650 aArtificial Intelligence.
650 aMachine Learning.
650 aServices.
700 1 aWang, Fei-Yue.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
710 aSpringerLink (Online service)
773 tSpringer Nature eBook
776 iPrinted edition:z9783031674181
776 iPrinted edition:z9783031674204
830 aSpringerBriefs in Service Science,x2731-3751
856 uhttps://doi.org/10.1007/978-3-031-67419-8
912 aZDB-2-BUM
912 aZDB-2-SXBM
950 aBusiness and Management (SpringerNature-41169)
950 aBusiness and Management (R0) (SpringerNature-43719)
Artificial Intelligence for Science (AI4S)[electronic resource] :Frontiers and Perspectives Based on Parallel Intelligence /by Qinghai Miao, Fei-Yue Wang
종류
전자책
서명
Artificial Intelligence for Science (AI4S)[electronic resource] :Frontiers and Perspectives Based on Parallel Intelligence /by Qinghai Miao, Fei-Yue Wang
저자명
판 사항
1st ed. 2024.
형태사항
X, 113 p 36 illus, 35 illus in color online resource.
주기사항
This book presents a comprehensive framework for analyzing, evaluating, and guiding AI for Sciences (AI4Sci) research, offering a unified approach that facilitates analysis across various academic fields through a shared set of dimensions and indicators. It provides a systematic overview of recent AI4Sci advances in various disciplines and offers insights into the latest issues in and prospects of AI4Sci. The book is based on the theory of Parallel Intelligence (PI), which forms the foundation for the general AI4Sci framework. By analyzing multiple cases in various academic fields, this framework integrates key elements of AI4Sci, such as real scientific problems, datasets, virtual systems, AI methods, human roles, and organizational mechanisms, from a multidimensional perspective. It also assesses and summarizes the limitations of AI4Sci, incorporating the latest advances in AI for fundamental models. Lastly, it explores the impact of DeSci and DAO, as well as TAO, on AI4Sci ecosystem development and prospects. Through its balanced approach, the book offers readers a goal-oriented perspective, focusing on a concise presentation of the core ideas and reducing detailed descriptions of specific AI4Sci cases to a minimum.
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