改进导向下人工智能产业政策比较研究

政府主导推动人工智能产业发展已经成为全球趋势。基于比较公共政策理论,把政策改进作为人工智能产业政策比较研究的目的导向,有助于为政策制定部门进一步完善现行政策文本具体内容提供有益参考。根据政策比较研究一般范式和逻辑,将政策改进导向下的比较分析框架细分为五大模块,对每个模块分别提出基本要求,即政策比较文本先进性、政策比较主体标杆性、政策比较方法科学性、政策比较结果分析因果性、政策比较对策建议适宜性。将人工智能产业政策比较研究现有成果按照基本要求进行系统梳理,研判对每个模块基本要求的满足情况。研究发现,现有成果均不能充分满足各个模块相应的基本要求。鉴于此,针对每个模块提出了下一步改进的主要研究方向。     

用神经网络对开式冷挤压极限变形程度的预测

给出了基于 BP网络的极限变形程度预测的分析方法 ,得到了开式冷挤压εmax神经网络计算结果 ,与实验结果比较 ,其精度较高 ,证明了神经网络模型在开式冷挤压工艺中应用的可行性。     

电力供需实验室的开发与建设

国家电网公司电力供需实验室是"十一五"期间国家电网公司重点建设的八大实验室之一。电力实验室由数据信息平台、业务支撑平台和应用平台3层平台构成。三者既相互独立,又相互联系。数据信息平台是电力供需实验室的基础。应用平台是直接面向用户、实现电力供需分析预测以及实验等功能的窗口,业务支撑平台是连接数据信息平台和应用平台的桥梁。目前,电力供需实验室已经基本具备了电力市场分析预测功能以及实验功能,可以应用于年度电力市场分析预测工作中。     

一种基于ANN的市场营销组合策略模型

应用人工神经网络,充分考虑影响产品市场占有率的各种因素,建立相应的神经网络模型。借此对市场营销组合策略进行前瞻性分析,有助于企业根据市场特点,有针对性的选择市场营销组合策略,从而提高企业产品的市场占有率。     

意大利发展人工智能的主要举措及启示

意大利重视人工智能及数字化发展,将其作为疫后经济复苏的优先方向和重要支柱,颁布了相关战略和计划,启动大型超级计算机建设。人工智能在筛选新冠肺炎药物方面取得重要进展。意大利对人工智能赋能未来产业发展和改善社会公共服务寄予厚望,其相关思路值得借鉴。     

Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.     

不同利用方式下草地CH4、CO2及N2O通量研究

[目的]在全球气候变化背景下,研究天然草地和人工草地的温室气体通量以及与部分环境因子的相关关系,进而明确不同草地对温室气体通量的影响情况,为当地相关部门合理开发利用草地提供理论依据,推进草业绿色发展、高质量发展。[方法]文章以内蒙古自治区的阿鲁科尔沁旗选取退化的天然牧草地、混播型放牧人工草地以及刈割型人工牧草地为研究对象,利用目前较为普遍的箱法进行了野外实验,对比分析不同草地利用类型的主要温室气体CO₂、CH₄和N₂O的通量变化情况,并进一步探讨了温室气体与土壤含水量、箱内温度、地表温度以及其他土壤养分指标的相关性。[结果](1)不同利用方式下草地的CO₂通量在整个生长季均表现为排放,其中刈割人工草地的CO₂排放量为67.00±9.15 mg/(m²·h),显著高于放牧人工草地35.50±5.37 mg/(m²·h)和天然草地30.17±4.57 mg/(m²·h)(P<0.05),且CO₂     

Enterprise knowledge graphs with applications to continuous monitoring systems

Continuous monitoring systems generate continuous business intelligence by gathering text from a range of sources and typically storing that text information using snippets of text. Further, continuous monitoring systems derive information from those snippets, such as sentiment and new concepts emerging in the text, beyond the existing ontology and dictionaries. However, it is difficult for users to gather much additional information from large quantities of text snippets. As a result, this paper investigates introducing the notion of “knowledge graphs” into such systems and analyzes some of the potential applications and capabilities in business intelligence settings. As part of that analysis, this paper extends current-generation continuous monitoring systems to include knowledge graphs.     

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Human–computer interaction (HCI) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI, more in particular the capacity of individuals and firms to apply state‐of‐the‐art techniques and to prevent an artificial divide, thereby increasing social sustainability.     

Effectiveness of Q-learning as a tool for calibrating agent-based supply network models

This paper examines effectiveness of Q-learning as a tool for specifying agent attributes and behaviours in agent-based supply network models. Agent-based modelling (ABM) has been increasingly employed to study supply chain and supply network problems. A challenging task in building agent-based supply network models is to properly specify agent attributes and behaviours. Machine learning techniques, such as Q-learning, can be a useful tool for this purpose. Q-learning is a reinforcement learning technique that has been shown to be an effective adaptation and searching mechanism in distributed settings. In this study, Q-learning is employed by supply network agents to search for ‘optimal’ values for a parameter in their operating policies simultaneously and independently. Methods are designed to identify the ‘optimal’ parameter values against which effectiveness of the learning is evaluated. Robustness of the learning's effectiveness is also examined through consideration of different model settings and scenarios. Results show that Q-learning is very effective in finding the ‘optimal’ parameter values in all model settings and scenarios considered.