应用伦理学视阈下人工智能教育的反思与应对

人工智能的深入发展和广泛应用,深刻影响着教育领域的变革与创新。人工智能教育伦理是对技术伦理视角下教育伦理的再塑,主要面向解决人工智能教育的应用困境。应用伦理学的理论基础来源于五种规范伦理学进路：功利论、义务论、契约论、正义论和美德论,以该理论为基础分析人工智能教育当前主要伦理困境,呈现出功利主义技术观与非功利主义教育观的矛盾、技术与教育多重主体行为的矛盾、技术正当性与个人权利的矛盾、技术引发教育平等与社会不稳定性的矛盾、技术动机与教育本质的矛盾,需从制度、个体和组织三个层面建构人工智能教育生态。     

人工智能时代城市的“韧性”与应急管理

在城市应急管理中,强化城市的“承灾能力”和“自恢复能力”,有利于完善现代城市治理体系,提升城市治理现代化水平。从“韧性城市”理论的视角,分析传统城市应急管理的方法和措施在事前预测预警能力、事中处置应对能力和事后恢复重建能力上面临的新问题：应急预警能力有待提高、应急处置措施有待改进、协同应对能力有待加强。我们应抓住人工智能时代科技发展的新机遇,努力做到智能化的预测预警及应急响应,全过程的实时感知及趋势分析,多方面的协同应对与恢复重建,以提高城市应急管理的智能化水平和抵御各种灾害的能力。     

加快推进人工智能劳动法治保障建设的对策研究

在当今人工智能从体力劳动工作岗位向脑力劳动工作岗位不断延伸扩展的态势下,“十九大”报告前瞻性地提出了加快人工智能和实体经济深度融合的战略目标。为此,有必要审视人工智能对劳动安全、就业促进、劳动法律关系、工伤保险、反就业歧视等劳动法治构成要素的挑战,并以“十九大”报告精神及国务院2017年《新一代人工智能发展规划》为指导,从确立新的劳动安全观、保障劳动者的充分就业、明确人工智能在劳动法律关系中的主体地位、在人工智能赋权革命的基础上建构人工智能工伤保险制度等方面采取对策措施,更新劳动法治观念,完善人工智能发展的政策法规、标准体系。     

信息技术课程多元智力教学模式探究

多元智力理论超越了传统以语言——数理逻辑能力为中心的智力观念,它的发展对传统学科教育产生了深刻的影响,本文就信息技术课程学科特点,从多元智力角度对教学方法改革进行探讨和研究。     

名词化结构在商务英语信函中的应用和翻译

着重分析了在商务英语信函中出现的名词化结构,指出名词化是商务英语信函的主要文体特点之一,在商务英语信函的表达中起着重要的作用,并讨论了其翻译方法。     

论我省非公有制经济中的家族企业制度创新--以我省晋江地区家族企业的现状及发展为例

改革开放后逐步发展起来的我省非公有制经济,尤其是民营经济的企业制度具有一个突出的特征:其通常与家族制联系在一起,家族企业成为民营企业普遍实行的内部组织形式。当前,在促进非公有制经济进一步发展中,改革创新家族企业制度,使之适应现代市场经济竞争势所必然。必须根据我省非公有制经济中的家族企业制度的特点,针对其制度创新的需求诱致与供给制约,探寻其制度创新的演进路径及环境改进。     

人工智能生成物的著作权保护路径探析——兼评人工智能生成物著作权第一案

人工智能技术的发展对传统著作权制度带来了显著的冲击。以人工智能生成物著作权第一案为例,通过审视作品构成要件判断标准发现,独创性始终贯彻以人类作者为中心的评价标准,同时,智力成果要件则是认定人工智能生成物构成著作权法意义上作品与否的重要标准。从人类是否实际参与作品的创作过程来看,多数人工智能生成物不构成作品,而保护人工智能生成物的价值追求与邻接权制度的基本理念相适应,且邻接权制度的扩张趋势也为把人工智能生成物纳入其保护范畴创造了机会。建议立法通过扩张邻接权制度的权利内容与客体类型以对人工智能生成物提供必要保护。而在当前阶段,反不正当竞争法可以为人工智能生成物相关利益主体提供一定限度的过渡保护。     

人工智能背景下医疗法律责任的现实困境与完善路径

医疗人工智能利用深度学习和计算机算法等现代化技术,实现辅助诊疗、医学影像、药物发掘、健康诊疗等功能,是人工智能应用最具前景的领域之一。随着人工智能技术发展,具备自主思维和决策能力的智能机器人医生等人工智能将对现有的医疗法律责任制度带来颠覆性影响。在人工智能背景下,医疗人工智能是否具有独立法律主体地位、传统的医疗过错责任归责能否适用、因果关系如何认定等成为现代民事法律责任制度的严峻挑战,也成为实践中的必答之问。因此,在借鉴欧美人工智能领域的探索经验的基础上,在当前阶段通过修正和完善我国民事法律责任制度,提出针对不同的人工智能侵权情形适用差别化归责原则,并尝试建立人工智能强制保险或储备金制度,设置人工智能监管机构及行业标准,以实现既保障患者合法公平权益,又鼓励人工智能技术有效发展之间的平衡。     

Public relations and artificial intelligence: It’s not (just) about robots

Organizations of all kinds, as well as their in-house or agency public relations teams, increasingly co-opt Artificial Intelligence (AI) to enhance their capabilities. This paper examines a relatively new topic that has received little scholarly attention: the growing relationship between AI and public relations. It outlines several key roles that AI may play in future, based on trends in other industries, and considers the implications for public relations practitioners, their clients and employers. It therefore launches a dialogue about the diversity and extent of AI’s uses in public relations practice. The paper argues that, to date, commentators have placed too much emphasis on AI’s potential for task automation; AI’s broader technological, economic and societal implications for public relations warrant greater critical attention. This does not imply that practitioners need become expert technologists; rather, they should develop a sufficient understanding of AI’s present and potential uses to be able to offer informed counsel.     

Automating argument construction

Over the past five years the Artificial Intelligence Center at SRI has been developing a new technology to address the problem of automated information management within real- world contexts. The result of this work is a body of techniques for automated reasoning from evidence that we call evidential reasoning. The techniques are based upon the mathematics of belief functions developed by Dempster and Shafer and have been successfully applied to a variety of problems including computer vision, multisensor integration, and intelligence analysis.

We have developed both a formal basis and a framework for implementating automated reasoning systems based upon these techniques. Both the formal and practical approach can be divided into four parts: (1) specifying a set of distinct propositional spaces, (2) specifying the interrelationships among these spaces, (3) representing bodies of evidence as belief distributions, and (4) establishing paths of the bodies for evidence to move through these spaces by means of evidential operations, eventually converging on spaces where the target questions can be answered. These steps specify a means for arguing from multiple bodies of evidence toward a particular (probabilistic) conclusion. Argument construction is the process by which such evidential analyses are constructed and is the analogue of constructing proof trees in a logical context.

This technology features the ability to reason from uncertain, incomplete, and occasionally inaccurate information based upon seven evidential operations: fusion, discounting, translation, projection, summarization, interpretation, and gisting. These operation are theoretically sound but have intuitive appeal as well.

In implementing this formal approach, we have found that evidential arguments can be represented as graphs. To support the construction, modification, and interrogation of evidential arguments, we have developed Gister. Gister provides an interactive, menu-driven, graphical interface that allows these graphical structures to be easily manipulated.

Our goal is to provide effective automated aids to domain experts for argument construction. Gister represents our first attempt at such an aid.