立足科技情报知识服务视角,梳理AI for Science (AI4S)推动的“平台科研”范式内涵与框架。根据库恩范式理论论述了AI4S推动科研范式革新的必然性,采用培根归纳法总结的科学研究流程作为框架线索,阐明创新知识服务与“平台科研”范式的...立足科技情报知识服务视角,梳理AI for Science (AI4S)推动的“平台科研”范式内涵与框架。根据库恩范式理论论述了AI4S推动科研范式革新的必然性,采用培根归纳法总结的科学研究流程作为框架线索,阐明创新知识服务与“平台科研”范式的互促共进关系并作为理论指导。创新知识服务视角下的“平台科研”范式以服务科研创新活动为宗旨,主要内容包括知识表示视角下的科学数据管理、知识融合视角下的通用知识库构建、知识推理视角下的科学假设预测、知识发现视角下的科学实验执行和知识应用视角下的工业赋能。本文提出了一种创新知识服务视角下的“平台科研”范式框架,旨在从创新知识服务角度理解“平台科研”范式,厘清各主要环节创新知识服务的核心研究内容,以期成为科技情报研究领域的新兴知识生长点,为我国抢抓AI4S科研范式革新机遇提供参考思路。展开更多
人工智能驱动的科学研究(AI for Science)被视为科学发现的第五范式的曙光。依循演绎主义的科学研究逻辑,梳理了人工智能在科学假设生成、数据收集以及分析挖掘中的应用。人工智能“数据算法算力”三原则,对科学数据的质量、算法的复杂...人工智能驱动的科学研究(AI for Science)被视为科学发现的第五范式的曙光。依循演绎主义的科学研究逻辑,梳理了人工智能在科学假设生成、数据收集以及分析挖掘中的应用。人工智能“数据算法算力”三原则,对科学数据的质量、算法的复杂性以及计算能力提出了更高的要求。AI for Science时代预计会出现科技巨头、AI专家、软硬件工程师、政府以及教育机构等紧密协同的新型科研模式。然而,AI算法的黑箱特性对科学研究的可解释性和可重复性构成潜在威胁。因此,在推进人工智能驱动的科学研究的发展过程中,必须坚持伦理优先的原则,注重科学数据的安全性管理,防范化解大模型分布外泛化带来的解释性弱等问题。展开更多
近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分...近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分析维度高、尺度跨度大以及局限性科研实验制约大规模跨学科科研活动的瓶颈问题,促进科学研究迈向以“平台协作”为主要特征的新模式。分析了AI4S的国际态势,梳理了当前我国农业数字化发展现状及现实困境,将文献、统计数据、调研案例分析相结合,提出推动AI4S赋能我国农业发展的实践路径。AI4S将成为撬动农业生产从“看天、看地、看庄稼”的传统模式向智能感知、智能决策、可视化管理等模式转变的强力引擎,推动科学研究从单打独斗的“小农作坊模式”迈向“安卓模式”的平台科研。在此平台上,科研人员共享算力、模型、算法、数据库和知识库等基础设施,围绕农业全产业链全生命周期研发应用,通过“滚雪球效应”加速科研创新和成果应用。利用AI技术赋能农业生产数字化、网络化和智能化,为支撑理论-实验的在线迭代,还需要完善高质量农业科学数据资源体系、适度超前推进AI关键技术与基础设施、优化新范式下的交叉创新科研生态、加强农业数据安全监管、制定完善的配套政策和激励机制等措施来打通数据壁垒,推动AI+农业落地,从源头强化农业科技创新,推动农业强国建设。展开更多
Since its arrival in late November 2022,ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned,conducted,and published using a generative artificial intelligence approach.ChatGPT-...Since its arrival in late November 2022,ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned,conducted,and published using a generative artificial intelligence approach.ChatGPT-4 was released four months later and became more popular in November 2023.However,there is little study about the perception of scientists of these chatbots,especially in soil science.This article presents the new findings of a brief research investigating soil scientists’responses and perceptions towards chatbots in Indonesia.This artificial intelligence application facilitates conversation-based interactions in text format.The study evaluated ten ChatGPT answers to fundamental questions in soil science,which has developed into a normal science with a mutually agreed-upon paradigm.The evaluation was carried out by seven soil scientists recognized for their expertise in Indonesia,using a scale of 1-100.In addition,a questionnaire was distributed to soil scientists at the National Research and Innovation Agency of the Republic of Indonesia(BRIN),universities,and Indonesian Soil Science Society(HITI)members to gauge their perception of ChatGPT’s presence in the research field.The study results indicate that the scores of ChatGPT answers range from 82.99 to 92.24.ChatGPT-4 is better than both the paid and free versions of ChatGPT-3.5.There is no significant difference between the English and Indonesian versions of ChatGPT-4.0.However,the perception of general soil scientists about the level of trust is only 55%.Furthermore,80%of soil scientists believe that chatbots can only be used as digital tools to assist in soil science research and cannot be used without the involvement of soil scientists.展开更多
There is no patina of doubt that the central philosophical theories of Karl Popper and Thomas Kuhn concerning the nature, substance and method for acquiring scientific knowledge constitute milestones in 20th century p...There is no patina of doubt that the central philosophical theories of Karl Popper and Thomas Kuhn concerning the nature, substance and method for acquiring scientific knowledge constitute milestones in 20th century philosophy of science. Just as Popper＇s fundamental work on the subject, The Logic of Scientific Discovery, marked a decisive break with inductivist epistemologies, Kuhn＇s magnum opus, The Structure of Scientific Revolutions （1962, enlarged ed. 1970）, inaugurated the coming of age of the historical turn in the philosophy of science. Some scholars seem to consider the main doctrines of both philosophers as irreconcilables or contradictories. This explains why, for example Popper and Popperians such as Imre Lakatos and John Watkins describe themselves as ＂critical rationalists＂, whereas they refer to Kuhn as an ＂irrationalist＂ or ＂relativist＂-appellations that the latter has consistently rejected. The debate between Popper and Kuhn, especially as contained in an important work, Criticism and the Growth of Knowledge （1970）, highlights some of the knotty problems connected with philosophical appraisals of science. It also demonstrates the strengths and weaknesses of logistic approaches in the philosophy of science, on the one hand, and of historically informed socio-psychological analysis of science, on the other. In this paper, we reexamine the Popper-Kuhn controversy from an experimentalist perspective. In other words, we argue that the ideas of testing and normal science can be systematically accommodated by fine-structure dissection of empirical research through which scientists learn about the world, based on the assumption that the progress of science is the growth of experimental knowledge-a fact often neglected in theory-dominated philosophies of science. Taking discovery of the cosmic background radiation by Arno Penzias and Robert Wilson as example, the paper argues that important scientific discoveries have been accomplished even in the absence of theory in any obvious sense, a situation that conflicts with the theory-dominated models of Popper and Kuhn. Thus, it offers an account of how practicing scientists learn from research to control errors and avoid blind alleys. The paper affirms, in conclusion, that going beyond the theories of Popper and Kuhn requires that philosophers of science should take what scientists learn from experiments seriously when theorising about science, by taking into account normal testing or error detection and control strategies through which scientific knowledge is acquired and extended展开更多
Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and futur...Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.展开更多
Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent an...Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent and mathematical foundations of autonomous systems.It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems.It explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive process.The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering.Emerging paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are described.Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.展开更多
In a recently published article Sydney Brenner argued that the most relevant scientific revolution in biology at his time was the breakthrough of the role of "information" in biology.The fundamental concept ...In a recently published article Sydney Brenner argued that the most relevant scientific revolution in biology at his time was the breakthrough of the role of "information" in biology.The fundamental concept that integrates this new biological "information" with matter and energy is the universal Turing machine and von Neumann's self-reproducing machines.In this article we demonstrate that in contrast to Turing/von Neumann machines living cells can really reproduce themselves.Additionally current knowledge on the roles of noncoding RNAs indicates a radical violation of the central dogma of molecular biology and opens the way to a new revolution in life sciences.展开更多
The presented paper is dedicated to a new ret-rospective view on the history of natural sci-ences in XX-XXI cc, partially including the sci-ence philosophy (mainly, the problems of the scientific realism, i.e. the cor...The presented paper is dedicated to a new ret-rospective view on the history of natural sci-ences in XX-XXI cc, partially including the sci-ence philosophy (mainly, the problems of the scientific realism, i.e. the correspondence of science to reality) and also a novel scheme for different classes of sciences with different ob-jects and paradigms. There are analyzed the chosen “great” and “grand” problems of phys-ics (including the comprehension of quantum mechanics, with a recently elaborated new chapter, connected with time as a quantum obs- ervable and time analysis of quantum processes) and also of natural sciences as a whole. The particular attention is paid to the interpretation questions and slightly to the aspects, inevitably connected with the world- views of the res- earchers (which do often constitute a part of the interpretation questions).展开更多
Thomas S. Kuhn＇s theory of normal science （NS）, aside from being a provocative philosophical reconstruction of the relatively conservative phase of scientific research, contains useful ideas for systematic analysis...Thomas S. Kuhn＇s theory of normal science （NS）, aside from being a provocative philosophical reconstruction of the relatively conservative phase of scientific research, contains useful ideas for systematic analysis of specific episodes in the history of science. Therefore, although the theory has been looked at from different angles since the first edition of The Structure of Scientific Revolutions （TSSR） was published in 1962, its detailed exploration of the cumulative phase of research in mature science is of abiding relevance in the philosophy of science. This is because NS provides a compelling account of how and why members of scientific communities succeed, largely, to produce reliable knowledge about an incompletely known phenomenal world. Again, the theory elucidates special features of scientific research that differentiate it from other creative enterprises. In that regard, this paper reconstructs Arthur Compton＇s research into x-ray scattering as a good instantiation of NS. Discussion of Compton＇s convincing demonstration of the particulate properties of electromagnetic radiation within the framework of NS showcases the elucidatory power of Kuhn＇s theory with respect to selected episodes in science, and corroborates the notion that the bulk of scientific work is a conservative puzzle-solving activity with the potential for precipitating scientific revolutions. To the best of my knowledge, this is the first time that Compton＇s groundbreaking work on x-ray scattering has been analysed within the framework of Kuhn＇s philosophy of science.展开更多
[目的/意义]本研究旨在在AI4S(AI for science)的科研范式变革背景下,探讨AI在赋能智库发展中的内涵、模式与实践路径。[方法/过程]本文在梳理历次科研范式演进的基础上,探索性地厘定了AI4S的内涵,并对国内外AI4S的发展现状进行了总结;...[目的/意义]本研究旨在在AI4S(AI for science)的科研范式变革背景下,探讨AI在赋能智库发展中的内涵、模式与实践路径。[方法/过程]本文在梳理历次科研范式演进的基础上,探索性地厘定了AI4S的内涵,并对国内外AI4S的发展现状进行了总结;进而,结合我国智库特征与功能定位,选取智库决策咨询报告撰写作为典型应用场景,从智库报告“选题、破题、解题”等写作流程入手,探索构建AI4S赋能智库的理论机理和流程机制。[结果/结论]研究认为,在特定需求和应用场景下,AI推动的科研范式变革亦适用于智库研究和工作,将有助于赋能我国新型智库建设与发展。展开更多
文摘立足科技情报知识服务视角,梳理AI for Science (AI4S)推动的“平台科研”范式内涵与框架。根据库恩范式理论论述了AI4S推动科研范式革新的必然性,采用培根归纳法总结的科学研究流程作为框架线索,阐明创新知识服务与“平台科研”范式的互促共进关系并作为理论指导。创新知识服务视角下的“平台科研”范式以服务科研创新活动为宗旨,主要内容包括知识表示视角下的科学数据管理、知识融合视角下的通用知识库构建、知识推理视角下的科学假设预测、知识发现视角下的科学实验执行和知识应用视角下的工业赋能。本文提出了一种创新知识服务视角下的“平台科研”范式框架,旨在从创新知识服务角度理解“平台科研”范式,厘清各主要环节创新知识服务的核心研究内容,以期成为科技情报研究领域的新兴知识生长点,为我国抢抓AI4S科研范式革新机遇提供参考思路。
文摘人工智能驱动的科学研究(AI for Science)被视为科学发现的第五范式的曙光。依循演绎主义的科学研究逻辑,梳理了人工智能在科学假设生成、数据收集以及分析挖掘中的应用。人工智能“数据算法算力”三原则,对科学数据的质量、算法的复杂性以及计算能力提出了更高的要求。AI for Science时代预计会出现科技巨头、AI专家、软硬件工程师、政府以及教育机构等紧密协同的新型科研模式。然而,AI算法的黑箱特性对科学研究的可解释性和可重复性构成潜在威胁。因此,在推进人工智能驱动的科学研究的发展过程中,必须坚持伦理优先的原则,注重科学数据的安全性管理,防范化解大模型分布外泛化带来的解释性弱等问题。
文摘近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分析维度高、尺度跨度大以及局限性科研实验制约大规模跨学科科研活动的瓶颈问题,促进科学研究迈向以“平台协作”为主要特征的新模式。分析了AI4S的国际态势,梳理了当前我国农业数字化发展现状及现实困境,将文献、统计数据、调研案例分析相结合,提出推动AI4S赋能我国农业发展的实践路径。AI4S将成为撬动农业生产从“看天、看地、看庄稼”的传统模式向智能感知、智能决策、可视化管理等模式转变的强力引擎,推动科学研究从单打独斗的“小农作坊模式”迈向“安卓模式”的平台科研。在此平台上,科研人员共享算力、模型、算法、数据库和知识库等基础设施,围绕农业全产业链全生命周期研发应用,通过“滚雪球效应”加速科研创新和成果应用。利用AI技术赋能农业生产数字化、网络化和智能化,为支撑理论-实验的在线迭代,还需要完善高质量农业科学数据资源体系、适度超前推进AI关键技术与基础设施、优化新范式下的交叉创新科研生态、加强农业数据安全监管、制定完善的配套政策和激励机制等措施来打通数据壁垒,推动AI+农业落地,从源头强化农业科技创新,推动农业强国建设。
文摘Since its arrival in late November 2022,ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned,conducted,and published using a generative artificial intelligence approach.ChatGPT-4 was released four months later and became more popular in November 2023.However,there is little study about the perception of scientists of these chatbots,especially in soil science.This article presents the new findings of a brief research investigating soil scientists’responses and perceptions towards chatbots in Indonesia.This artificial intelligence application facilitates conversation-based interactions in text format.The study evaluated ten ChatGPT answers to fundamental questions in soil science,which has developed into a normal science with a mutually agreed-upon paradigm.The evaluation was carried out by seven soil scientists recognized for their expertise in Indonesia,using a scale of 1-100.In addition,a questionnaire was distributed to soil scientists at the National Research and Innovation Agency of the Republic of Indonesia(BRIN),universities,and Indonesian Soil Science Society(HITI)members to gauge their perception of ChatGPT’s presence in the research field.The study results indicate that the scores of ChatGPT answers range from 82.99 to 92.24.ChatGPT-4 is better than both the paid and free versions of ChatGPT-3.5.There is no significant difference between the English and Indonesian versions of ChatGPT-4.0.However,the perception of general soil scientists about the level of trust is only 55%.Furthermore,80%of soil scientists believe that chatbots can only be used as digital tools to assist in soil science research and cannot be used without the involvement of soil scientists.
文摘There is no patina of doubt that the central philosophical theories of Karl Popper and Thomas Kuhn concerning the nature, substance and method for acquiring scientific knowledge constitute milestones in 20th century philosophy of science. Just as Popper＇s fundamental work on the subject, The Logic of Scientific Discovery, marked a decisive break with inductivist epistemologies, Kuhn＇s magnum opus, The Structure of Scientific Revolutions （1962, enlarged ed. 1970）, inaugurated the coming of age of the historical turn in the philosophy of science. Some scholars seem to consider the main doctrines of both philosophers as irreconcilables or contradictories. This explains why, for example Popper and Popperians such as Imre Lakatos and John Watkins describe themselves as ＂critical rationalists＂, whereas they refer to Kuhn as an ＂irrationalist＂ or ＂relativist＂-appellations that the latter has consistently rejected. The debate between Popper and Kuhn, especially as contained in an important work, Criticism and the Growth of Knowledge （1970）, highlights some of the knotty problems connected with philosophical appraisals of science. It also demonstrates the strengths and weaknesses of logistic approaches in the philosophy of science, on the one hand, and of historically informed socio-psychological analysis of science, on the other. In this paper, we reexamine the Popper-Kuhn controversy from an experimentalist perspective. In other words, we argue that the ideas of testing and normal science can be systematically accommodated by fine-structure dissection of empirical research through which scientists learn about the world, based on the assumption that the progress of science is the growth of experimental knowledge-a fact often neglected in theory-dominated philosophies of science. Taking discovery of the cosmic background radiation by Arno Penzias and Robert Wilson as example, the paper argues that important scientific discoveries have been accomplished even in the absence of theory in any obvious sense, a situation that conflicts with the theory-dominated models of Popper and Kuhn. Thus, it offers an account of how practicing scientists learn from research to control errors and avoid blind alleys. The paper affirms, in conclusion, that going beyond the theories of Popper and Kuhn requires that philosophers of science should take what scientists learn from experiments seriously when theorising about science, by taking into account normal testing or error detection and control strategies through which scientific knowledge is acquired and extended
文摘Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.
基金supported in part by the Department of National Defence’s Innovation for Defence Excellence and Security(IDEa S)Program,Canadathrough the Project of Auto Defence Towards Trustworthy Technologies for Autonomous Human-Machine Systems,NSERCthe IEEE SMC Society Technical Committee on Brain-Inspired Systems(TCBCS)。
文摘Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent and mathematical foundations of autonomous systems.It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems.It explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive process.The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering.Emerging paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are described.Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.
文摘In a recently published article Sydney Brenner argued that the most relevant scientific revolution in biology at his time was the breakthrough of the role of "information" in biology.The fundamental concept that integrates this new biological "information" with matter and energy is the universal Turing machine and von Neumann's self-reproducing machines.In this article we demonstrate that in contrast to Turing/von Neumann machines living cells can really reproduce themselves.Additionally current knowledge on the roles of noncoding RNAs indicates a radical violation of the central dogma of molecular biology and opens the way to a new revolution in life sciences.
文摘The presented paper is dedicated to a new ret-rospective view on the history of natural sci-ences in XX-XXI cc, partially including the sci-ence philosophy (mainly, the problems of the scientific realism, i.e. the correspondence of science to reality) and also a novel scheme for different classes of sciences with different ob-jects and paradigms. There are analyzed the chosen “great” and “grand” problems of phys-ics (including the comprehension of quantum mechanics, with a recently elaborated new chapter, connected with time as a quantum obs- ervable and time analysis of quantum processes) and also of natural sciences as a whole. The particular attention is paid to the interpretation questions and slightly to the aspects, inevitably connected with the world- views of the res- earchers (which do often constitute a part of the interpretation questions).
文摘Thomas S. Kuhn＇s theory of normal science （NS）, aside from being a provocative philosophical reconstruction of the relatively conservative phase of scientific research, contains useful ideas for systematic analysis of specific episodes in the history of science. Therefore, although the theory has been looked at from different angles since the first edition of The Structure of Scientific Revolutions （TSSR） was published in 1962, its detailed exploration of the cumulative phase of research in mature science is of abiding relevance in the philosophy of science. This is because NS provides a compelling account of how and why members of scientific communities succeed, largely, to produce reliable knowledge about an incompletely known phenomenal world. Again, the theory elucidates special features of scientific research that differentiate it from other creative enterprises. In that regard, this paper reconstructs Arthur Compton＇s research into x-ray scattering as a good instantiation of NS. Discussion of Compton＇s convincing demonstration of the particulate properties of electromagnetic radiation within the framework of NS showcases the elucidatory power of Kuhn＇s theory with respect to selected episodes in science, and corroborates the notion that the bulk of scientific work is a conservative puzzle-solving activity with the potential for precipitating scientific revolutions. To the best of my knowledge, this is the first time that Compton＇s groundbreaking work on x-ray scattering has been analysed within the framework of Kuhn＇s philosophy of science.
文摘[目的/意义]本研究旨在在AI4S(AI for science)的科研范式变革背景下,探讨AI在赋能智库发展中的内涵、模式与实践路径。[方法/过程]本文在梳理历次科研范式演进的基础上,探索性地厘定了AI4S的内涵,并对国内外AI4S的发展现状进行了总结;进而,结合我国智库特征与功能定位,选取智库决策咨询报告撰写作为典型应用场景,从智库报告“选题、破题、解题”等写作流程入手,探索构建AI4S赋能智库的理论机理和流程机制。[结果/结论]研究认为,在特定需求和应用场景下,AI推动的科研范式变革亦适用于智库研究和工作,将有助于赋能我国新型智库建设与发展。
