人工智能伦理问题的现状分析与对策

人工智能(AI)是第四次产业革命的核心,但也为伦理道德规范和社会治理带来了挑战。文章在阐释当前人工智能伦理风险的基础上,分析了当前对人工智能伦理准则、治理原则和治理进路的一些共识,提出了以"共建共治共享"为指导理论,逐渐建设形成包含教育改革、伦理规范、技术支撑、法律规制、国际合作在内的多维度伦理治理体系等对策建议。     

适应人工智能快速发展的知识产权制度研究

基于深度学习算法的进步,人工智能逐渐有能力独立进行发明创造和文艺作品创作。本文主要探讨现行专利及著作权制度中规定的保护对象、权利人资格、专利及著作权的权属、侵权判定、侵权责任主体等对人工智能技术快速发展的适应及协调程度,研究指出：现有的专利和版权制度应当对人工智能的发明和作品持鼓励的态度,在排除不适宜作为专利或著作权保护对象的同时,人工智能的发明或作品的权利授予标准应当与人类的有所区分;相关权利人仍须对应自然人或法人,而非人工智能本身;相关专利侵权行为应包括间接侵权,同时应对人工智能作品安排“登记-授权”的著作权制度、参考临摹作品为人工智能绘画作品提供相应的授权使用制度等。本文还探讨了当前的专利法及著作权法在人工智能时代符合公平原则的程度,并提出解决方案：在“强人工智能时代”将人工智能的发明创造或作品作为公共财产,授予相应的开发者“数据处理权”作为一种新的邻接权,赋予人工智能创造物新的特别权利（Sui Generis）,修改专利法与著作权法中关于主要权利的相关规定等。     

通用模型的伦理与治理：挑战及对策

通用模型是近年来人工智能发展的重要方向之一。随着模型研发应用的增多，模型的社会和伦理影响受到广泛关注。文章从通用模型的特性出发、分析了模型在算法、数据和算力3个层面潜在的伦理挑战，包括不确定性、真实性、可靠性，偏见、毒性、公平、隐私及环境问题。进一步从技术哲学的视角分析了数据驱动的模型在人与世界关系中的中介性作用及所产生的“镜像”效应问题和透明性问题，提出了人与世界关系的新形态是以模型（数据）为中介的，即“人-模型（数据）-世界”关系。最后，从治理技术和治理机制两方面反思了当前的应对措施及局限性。建议建立开放式、全流程、价值嵌入的伦理规约机制，保障通用模型在合规、合伦理的框架下发展。     

我国人工智能政策特征与PMC模型量化评价研究

探讨我国人工智能政策特征以优化政策设计思路，以中央部委与地方政府印发的31份人工智能政策文件为研究样本，综合运用文本挖掘、内容分析和PMC指数模型等方法，提炼人工智能领域的研究前沿与热点分布、人工智能的政策特征以及评价结果。研究发现：我国人工智能政策以智能化为主要目标，重点关注科技研发与创新环节，并对“卡脖子”的芯片、机器人产品提出政策规范；政策工具应用不平衡，对政府采购、对外贸易管制、反他国管制、购置补贴四项需求型政策工具重视度不够；总体政策效力较好，但政策之间具有效力差异性，相关政策条款亟待加强和完善。基于以上特征和问题，建议进一步促进人工智能前沿政策的设计与前瞻，加大需求端政策工具的组合性和灵活性应用、优化供需政策工具包，加强对人工智能领域的监管政策力度，开展人工智能政策系统的整体性评估。     

The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot

The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self-regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self-determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self-directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations.

Practitioner notes

What is already known about this topic

• Artificial intelligence (AI) technologies can potentially support students' self-regulated learning (SRL) of disciplinary knowledge through chatbots.
• Needs satisfaction in Self-determination theory (SDT) can explain the directive process required for SRL.
• Technical and disciplinary knowledge would affect SRL with technologies.

What this paper adds

• This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot.
• Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot.
• Autonomy and competence were mediators, but relatedness was not.

Implications for practice and/or policy

• Teachers should use chatbots for knowledge consolidation rather than exploration.
• Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL.
• School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.

     

新时代中国高等音乐教育发展的路径

新时代的中国高等音乐教育已经走到一个重要且关键的历史拐点。面对全球人才竞争的巨大压力,面对人类进入信息时代的良好机遇,中国高等音乐教育要通过如下路径实现快速发展:努力做好高等音乐教育普及和质量提升工作,不断加快高等音乐教育的现代化步伐,全面提升高等音乐教育对外开放水平。     

The future of scientific journals: The rise of UniAI

• The enormous amount of scientific data produced each second will make it difficult to analyze them.
• In near future, a universal AI-based (UniAI) system with ready access to a collective database will be formed to analyze the gigantic amount of data being created.
• There will be no ready articles, no scientific journal, no indexing system, no peer review, no research or publication ethical concerns, and no editor.
• UniAI, a self-organized self-sufficient AI system will assume most part of the research and its publication.

     

产业链韧性在人工智能产业链评估中的应用探讨

基于对韧性理论、产业链理论研究现状的回顾，论述将产业链韧性引入人工智能产业链韧性评估中的应用价值。在此基础上提出人工智能产业链韧性模型，并从产业链分布、识别扰动因素、产业链韧性评估与测度方面构建人工智能产业链韧性的研究框架，最后总结人工智能产业链韧性评估的重要研究方向。其中，人工智能产业链韧性指标体系构建、基于关键指标体系和韧性曲线的产业韧性测度是人工智能产业链韧性评估需解决的重要问题。     

基于专利计量的全球人工智能技术在医疗健康领域应用发展态势分析

借助ISS信息检索和分析平台,对德温特数据库的医疗健康与人工智能领域的专利信息进行综合分析,梳理1963-2020年间相关领域专利的演进,对专利申请国分布及其所聚焦的研究热点、技术点进行探查分析.较为清晰地判定出全球人工智能技术在医疗健康领域专利数加速攀升,热点领域以专门用于处置或处理医疗或健康数据的信息和通信技术类居...     

Learning theories for artificial intelligence promoting learning processes

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

Practitioner notes

What is already known about this topic

• Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
• Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
• Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.

What this paper adds

• A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
• A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
• The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
• Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.

Implications for practice and policy

• Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
• Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.