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。     

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。     

A comparison study on modeling of clustered and overdispersed count data for multiple comparisons

Data collected in various scientific fields are count data. One way to analyze such data is to compare the individual levels of the factor treatment using multiple comparisons. However, the measured individuals are often clustered – e.g. according to litter or rearing. This must be considered when estimating the parameters by a repeated measurement model. In addition, ignoring the overdispersion to which count data is prone leads to an increase of the type one error rate. We carry out simulation studies using several different data settings and compare different multiple contrast tests with parameter estimates from generalized estimation equations and generalized linear mixed models in order to observe coverage and rejection probabilities. We generate overdispersed, clustered count data in small samples as can be observed in many biological settings. We have found that the generalized estimation equations outperform generalized linear mixed models if the variance-sandwich estimator is correctly specified. Furthermore, generalized linear mixed models show problems with the convergence rate under certain data settings, but there are model implementations with lower implications exists. Finally, we use an example of genetic data to demonstrate the application of the multiple contrast test and the problems of ignoring strong overdispersion.     

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。     

趋近化视角下社交媒体关于人工智能报道的话语建构研究

基于社交媒体关于人工智能的相关报道自建语料库,通过数据搜集和清洗,运用KH Coder文本分析可视化软件,选择其中的名词短语和动词短语,生成关联词频图进行可视化解析,并从趋近化策略的空间趋近、时间趋近和价值趋近维度对自建语料库进行分析。研究结果显示,社交媒体人工智能话语建构了以空间趋近化策略为主,以时间趋近化和价值趋近化策略为辅的正向话语。同时,人工智能对人类具有潜在威胁,公众应采取相应的风险预防对策。     

A new estimator of Kullback–Leibler information and its application in goodness of fit tests*

We propose here a general statistic for the goodness of fit test of statistical distributions. The proposed statistic is constructed based on an estimate of Kullback–Leibler information. The proposed test is consistent and the limiting distribution of the test statistic is derived. Then, the established results are used to introduce goodness of fit tests for the normal, exponential, Laplace and Weibull distributions. A simulation study is carried out for examining the power of the proposed test and to compare it with those of some existing procedures. Finally, some illustrative examples are presented and analysed, and concluding comments are made.     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.