排序方式： 共有157条查询结果，搜索用时 15 毫秒
151.
科学研究的目的是发现基本原理和解决实际问题。尽管人类在发现基本原理和解决实际问题上已经取得了巨大成就,但有效工具和有效科研组织模式的缺乏仍然是制约科研效率的主要瓶颈。人工智能（AI）的迅速发展为改变这种状况提供了新的可能。近年来,深度学习方法在科学研究领域大放异彩,不仅助力解决了一些核心科学问题,扩展了科学方法,也开始带动科学研究从传统的“作坊模式”转向“平台模式”。目前,我国已在人工智能驱动的科学（AI for Science）领域打下良好基础,应把握机遇,争取引领科技创新,为人类的科技发展作出贡献。 相似文献
152.
Although AI-enabled customer relationship management (CRM) systems have gained momentum in healthcare to enhance performance, there is a striking dearth of knowledge on how such capabilities are formed and affect service innovation. The study adopted a mixed-method approach to investigate the underlying phenomena. This research infused resource-based theory, dynamic capability theory, and theory of productivity paradox to investigate how healthcare in India acquires AI-enabled CRM capabilities and enhances service innovation. We identified the facets of AI-enabled CRM capabilities using a case study and developed a framework for AI-enabled CRM capability and service innovation. This study noticed that customer service flexibility (CSF) is a missing link in this relationship. The findings of the quantitative study employing PLS-SEM reveal the linear relationships between AI-enabled CRM capability, CSF, and service innovation. This study explains the formation of AI-enabled CRM capabilities to fill the research gap and direct innovative performance in healthcare, which is an immediate need to sustain in a volatile environment. This study provides theoretical implications to enhance the research stream and practical implications for decision-makers. 相似文献
153.
AI赋能教师教育评价的内涵是AI与教师教育评价进行深度有机融合,在特定教师教育价值观的指导下,依据明确的教师教育目标,使用AI技术和方法,对所实施的各种教育活动、教育过 程和教育结果进行科学判定。与以往教师教育评价相比,AI赋能教师教育评价在评价模型、参与主体、获取数据、分析诊断、评价反馈等方面具有鲜明特征。AI赋能教师教育评价的框架由五个层面和一个保障体系构成。在实践中,AI赋能教师教育评价可通过更新观念、规范标准、挖掘数据、支持专业等路径整体推进,以构建智能高效的教师教育评价体系。 相似文献
154.
王佳 《科学技术与辩证法》2010,27(5):36-40
“中文屋”思想实验蕴涵的论证结构是其有效性的根本保证,而基于“语法不等同于语义”,“模拟不等同于复制”两个逻辑真理,又可以从中分离出两种论证形式：逻辑论证和经验论证,它们分别支撑“中文屋论证”的两个要点。文章试图对“中文屋”两种论证形式进行考察,证明它在批判强人工智能上的有效性,同时揭示两种论证形式之间存在的不融贯问题。 相似文献
155.
[目的/意义]人工智能已成为推动新一轮科技革命和产业变革的重要技术力量,世界各国加紧出台了相关政策。通过对当前研究进行及时梳理,可为今后国内人工智能政策的理论推进及政策出台和完善等提供指导。[方法/过程]以国外SSCI和国内CSSCI期刊数据库收录的395篇研究论文为样本,采用文献计量分析和比较研究法对中外人工智能政策研究的高共被引文献、热点主题及演进趋势等进行深入探索。[结果/结论]与国外相比,国内研究起步较晚但势头迅猛;高共被引文献反映了人工智能领域存在的问题和风险、应用前景、技术革新及对社会的影响;国外研究热点涵盖了知识管理等十二类主题,而国内研究热点则包括国家治理背景下人工智能政策发展路径等三类主题;国外研究的演进特征体现在三个方面,而国内研究则体现在两个方面。最后,从加快形成和构建人工智能政策研究的理论框架体系等三个方面提出对国内研究的启示。 相似文献
156.
[目的/意义]产业变革快速演进,技术创新成为驱动社会经济发展、提高国家和企业科技竞争力的关键所在,如何对前沿技术进行识别和预测,成为国家科技政策研究和企业技术创新活动关注的热点。[方法/过程]以人工智能作为重点研究领域,首先以LDA模型进行技术主题抽取,并结合K-means算法进行专利文本聚类;在此基础上,以Z分数表示技术主题创新度,以Sen's斜率估计技术主题授权趋势,两个指标结合形成技术主题前沿度并将二者映射到二维空间,识别前沿技术主题以及划分技术主题类型;再,计算前沿技术主题的新颖度和关注度,二者融合形成技术主题趋势度指标;最后,采用三次指数平滑法对前沿技术主题的发展趋势进行预测。[结果/结论]人工智能领域的前沿技术主题有“智能家居”“电动汽车”和“自动化控制系统”,其中“智能家居”在未来3年的发展呈下降态势,而“电动汽车”和“自动化控制系统”的发展呈明显上升趋势。 相似文献
157.
Margaret Bearman Rola Ajjawi 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1160-1173
Artificial intelligence (AI) is increasingly integrating into our society. University education needs to maintain its relevance in an AI-mediated world, but the higher education sector is only beginning to engage deeply with the implications of AI within society. We define AI according to a relational epistemology, where, in the context of a particular interaction, a computational artefact provides a judgement about an optimal course of action and that this judgement cannot be traced. Therefore, by definition, AI must always act as a ‘black box’. Rather than seeking to explain ‘black boxes’, we argue that a pedagogy for an AI-mediated world involves learning to work with opaque, partial and ambiguous situations, which reflect the entangled relationships between people and technologies. Such a pedagogy asks learners locate AI as socially bounded, where AI is always understood within the contexts of its use. We outline two particular approaches to achieve this: (a) orienting students to quality standards that surround AIs, what might be called the tacit and explicit ‘rules of the game’; and (b) providing meaningful interactions with AI systems.
Practitioner notes
What is already known about this topic- Artificial intelligence (AI) is conceptualised in many different ways but is rarely defined in the higher education literature.
- Experts have outlined a range of graduate capabilities for working in a world of AI such as teamwork or ethical thinking.
- The higher education literature outlines an imperative need to respond to AI, as underlined by recent commentary on ChatGPT.
- A definition of an AI that is relational: A particular interaction where a computational artefact provides a judgement about an optimal course of action, which cannot be easily traced.
- Focusing on working with AI black boxes rather than trying to see inside the technology.
- Describing a pedagogy for an AI-mediated world that promotes working in complex situations with partial and indeterminate information.
- Focusing on quality standards helps learners understand the social regulating boundaries around AI.
- Promoting learner interactions with AI as part of a sociotechnical ensemble helps build evaluative judgement in weighting AI's contribution to work.
- Asking learners to work with AI systems prompts understanding of the evaluative, ethical and practical necessities of working with a black box.