利用人工智能语言Prolog解决汉诺塔问题的研究

汉诺塔问题是经典递归程序设计案例,一直以来,大家多利用面向对象、过程等编程语言来实现。当今人工智能发展迅速,能否利用人工智能语言解决汉诺塔问题成为一个新的研究领。此次研究利用结构式分析方法,借助人工智能语言推理强的特征,解决了汉诺塔问题。     

AI for Science：智能化科学设施变革基础研究

近年来，人工智能（AI）在前沿科技领域取得了诸如AlphaFold2、核聚变智能控制、新冠药物设计等诸多令人瞩目成果，表明AI for Science正在成为一种新的研究范式。实现智能时代的基础科学源头创新及其下游重大技术创新，需破解2个方面的核心问题：（1）如何利用新一代AI（特别是生成式AI及大模型）的通用性和创造性推动新范式的形成；（2）如何利用AI实现对传统科学设施的赋能与改造。文章提出一种智能化科学设施的建设构想，兼顾“高度智能化的科学新设施”和“AI赋能已有科学大设施”2个层面的需求，构筑AI for Science的科学设施体系，形成科学领域大模型、生成式模拟与反演、自主智能无人实验及大规模可信科研协作等创新功能，加速重大科学发现、变革性物质合成，以及重大工程技术应用。     

人工智能驱动的生命科学研究新范式

生物技术和信息技术的迅速发展,使生命科学进入了数据爆发的新时代,传统生命科学研究范式难以在日益增长的生物大数据中揭示生命复杂系统的本质规律。随着人工智能（AI）在生命科学研究领域持续取得颠覆性突破,AI驱动的生命科学研究新范式呼之欲出。文章通过深入剖析AI驱动的生命科学研究的典型范例,提出了生命科学研究新范式的内涵和关键要素,阐述并讨论了新范式下的生命科学研究前沿和我国面临的挑战。     

数字科学家与平行科学：AI4S和S4AI的本源与目标

围绕人工智能（AI）大模型技术的最新进展，从AI4S （人工智能驱动的科学研究）到S4AI （面向人工智能的科学研究），讨论人工与自然平行的智能科技与数字人科学家的作用及其对科研范式和社会形态变革的可能冲击；认为范式与形态的变革刻不容缓，必须积极应对。     

The 2011 Benjamin Franklin Medal in computer and cognitive science presented to John R. Anderson

John R. Anderson is an international leader in the computational representation and simulation of human cognition. He is credited as having developed the first “cognitive architecture”—a computational framework for specifying both the abilities and limitations of human behavior. Anderson has also made fundamental contributions in applying cognitive architectures to practical problems, most notably in the development of intelligent tutoring systems: computer-based tutors that continually infer the cognitive state of the student, striving to determine what the student knows and does not know, and targeting further instruction accordingly. For these achievements, Anderson was awarded the 2011 Benjamin Franklin Medal in Computer and Cognitive Science.     

Inspired,but not mimicking: a conversation between artificial intelligence and human intelligence

How intelligent is artificial intelligence (AI)? How intelligent will it become in the future? What is the relationship between AI and human intelligence (HI)? These questions have been a hot topic of discussion in recent years, but no consensus has yet been reached. To discuss these issues, we should first understand the concept of intelligence as well as the underlying mechanisms for both HI and AI. In this NSR Forum, experts from both disciplines gathered to discuss these issues; in particular, the similarities and differences between AI and HI, how these two disciplines could benefit from each other, and the emerging social and ethical challenges of AI. Xiaolan Fu Professor at the Institute of Psychology, Chinese Academy of Sciences (CAS) Yong Gu Professor at the Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, CAS Sheng He Professor at the Institute of Biophysics, CAS Zhuojun Liu Professor at the Academy of Mathematics and Systems Science, CAS Tieniu Tan Professor at the Institute of Automation, CAS Zhi-Hua Zhou Professor at National Key Laboratory for Novel Software Technology, Nanjing University Huimin Lin (Chair) Professor at the Institute of Software, CAS

Lin: Welcome to this panel discussion. I am a computer scientist, but not specialised in AI. AI, especially deep learning, has achieved great success in the past decade and many people in and out of this field have begun to think about the relationship between AI and HI. Here, we invite experts in neuroscience, cognitive science, psychology and AI to discuss various relevant issues.     

人工智能计算机的发展困境

上个世纪八十年代，第五代计算机，也就是人工智能计算机的研究曾热极一时，然而时至今日，已经鲜有人提起第五代计算机了。本文对第五代计算机的主要特征进行了描述，从第五代计算机的核心技术…人工智能入手，对第五代计算机研究陷于困境的原因进行了探讨。在此基础上，对于人工智能的发展前景做出了自己的判断。     

基于Eclipse插件的Scheme语言解释器设计

软件演化是解决软件产品需求变化的重要方法，但传统的手工演化方式已不能适用于日益复杂的演化过程，基于人工智能的软件演化方法则可以较好地解决这方面的问题。通过对人工智能语言Scheme特点的分析，设计并实现了一个基于Eclipse平台的Scheme语言解释器。该解释器为智能化软件演化系统的设计与实现提供了可靠的解决方法。     

人工智能赋能对创新绩效的影响路径研究——基于模糊定性比较分析

以15家国家新一代人工智能开放创新平台依托企业为研究样本,利用多元化数据,采用模糊集定性比较分析方法探索人工智能赋能对创新绩效的影响路径.研究发现,人工智能赋能对创新绩效的影响是技术层面、数据层面和企业层面多因素作用的结果;人工智能赋能对创新绩效影响路径为技术支撑型、数据驱动型、组织变革型、服务生态型和营销整合型5种,并对每条路径的使用特征进行详细解析.从理论上丰富人工智能赋能和创新绩效影响路径的相关研究,同时为多数企业加快实现数字化和智能化转型,实现高质量发展提供有益启示.     

Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders; and (iii) enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The number of humanitarian relief actions is growing due to the increased frequency of natural and man-made crises. Various streams of research across different disciplines have focused on ICT and AI solutions for enhancing disaster management processes. However, most of the existing research is fragmented without a common vision towards a converging paradigm. Recognizing this, this paper presents the Disaster City Digital Twin as a unifying paradigm. The four main components of the proposed Digital Twin paradigm include: multi-data sensing for data collection, data integration and analytics, multi-actor game-theoretic decision making, and dynamic network analysis. For each component, the current state of the art related to AI methods and approaches are examined and gaps are identified.     

Human-aligned artificial intelligence is a multiobjective problem

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility (MEU) paradigm provides insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action–selection can overcome many of the issues which limit MEU’s effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents.     

Is chess the drosophila artificial intelligence? A social history of an algorithm

Since the mid 1960s, researchers in computer science have famously referred to chess as the 'drosophila' of artificial intelligence (AI). What they seem to mean by this is that chess, like the common fruit fly, is an accessible, familiar, and relatively simple experimental technology that nonetheless can be used productively to produce valid knowledge about other, more complex systems. But for historians of science and technology, the analogy between chess and drosophila assumes a larger significance. As Robert Kohler has ably described, the decision to adopt drosophila as the organism of choice for genetics research had far-reaching implications for the development of 20th century biology. In a similar manner, the decision to focus on chess as the measure of both human and computer intelligence had important and unintended consequences for AL research. This paper explores the emergence of chess as an experimental technology, its significance in the developing research practices of the AI community, and the unique ways in which the decision to focus on chess shaped the program of AI research in the decade of the 1970s. More broadly, it attempts to open up the virtual black box of computer software--and of computer games in particular--to the scrutiny of historical and sociological analysis.     

The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users

There is an exponential growth of the use of AI applications in organisations. Due to the machine learning capability of artificial intelligence (AI) applications, it is critical that such systems are used continuously in order to generate rich use data that allow them to learn, evolve and mature into a better fit for their user and organisational context. This research focuses on the actual use of conversational AI, in particular AI chatbot, as one type of workplace AI application to answer the research question: how do employees experience the use of an AI chatbot in their day-to-day work? Through a qualitative case study of a large international organisation and by performing an inductive analysis, the research uncovers the different ways in which users appropriate the AI chatbot and identifies two key dimensions that determine their type of use: the dominant mode of interaction and the understanding of the AI chatbot technology. Based on these dimensions, a taxonomy of users is presented, which classifies users of AI chatbots into four types: early quitters, pragmatics, progressives, and persistents. The findings contribute to the understanding of how conversational AI, particularly AI chatbots, is used in organisations and pave the way for further research in this regard. The implications for practice are also discussed.     

Big data governance of personal health information and challenges to contextual integrity

Pervasive digitization and aggregation of personal health information (PHI), along with artificial intelligence (AI) and other advanced analytical techniques, hold promise of improved health and healthcare services. These advances also pose significant data governance challenges for ensuring value for individual, organizational, and societal stakeholders as well as individual privacy and autonomy. Through a case study of a controversial public-private partnership between Royal Free Trust, a National Health Service hospital system in the United Kingdom, and Alphabet’s AI venture DeepMind Health, we investigate how forms of data governance were adapted, as PHI data flowed into new use contexts, to address concerns of contextual integrity, which is violated when personal information collected in one use context moves to another use context with different norms of appropriateness.     

人工智能对中国区域经济高质量发展影响的理论机理与实证分析----以工业机器人为例

随着工业4.0时代的到来,在技术变革的浪潮中,以全球视角布局人工智能技术,引领未来科技发展,为我国推动经济高质量发展、建设创新型国家提供有力动力。本文分析了人工智能对经济高质量发展影响的理论机理,并采用2009-2019年中国省级面板数据,构建面板固定效应模型实证分析了人工智能对中国区域经济高质量发展的影响,研究发现：人工智能可以为经济高质量发展提供动力支持,在不同区域中,人工智能对西部地区经济高质量发展的促进作用较大,东北、东部地区次之,中部地区较小；在人工智能实现经济高质量发展的传导效应中,资本形成累积效应和技术创新效应传导效果明显,人力资本提升效应传导效果不明显。为了实现我国区域经济高质量发展,要加快人工智能领域的人才培养、加强新型基础设施建设以及鼓励人工智能核心技术的研发。     

分布式自主科学：科学多样可持续性发展的新范式

人工智能驱动的科学（artificial intelligence for science, AI4S）的兴起,使得如何确保科学系统的公开性、公平性、公正性和多样可持续性变得尤为重要和迫切。这关系到各国在全球创新和产业革新中的话语权和领导地位,同时也影响人类命运共同体的安全、稳定与可持续发展。为了应对这些挑战,AI4S需要引入新的科学组织和运营方式。基于Web3和分布式自主组织与运营（DAOs）等智能技术之上的分布式自主科学（decentralized science,DeSci）与AI4S相辅相成,为AI4S提供强有力的支撑。DeSci可以有效解决现有科研体系中的信息孤岛、偏见、不公平分配和垄断等问题,进而促进多学科、跨学科和交叉学科合作。文章首先从理论层面对DeSci的基本概念、特征和框架进行界定,其次分析DeSci的研究现状与应用现状,最后探讨和总结DeSci对于科学系统进一步发展的启示与意义。     

Artificial intelligence innovation in education: A twenty-year data-driven historical analysis

Reflecting on twenty years of educational research, we retrieved over 400 research article on the application of artificial intelligence (AI) and deep learning (DL) techniques in teaching and learning. A computerised content analysis was conducted to examine how AI and DL research themes have evolved in major educational journals. By doing so, we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade, due to the discipline’s dynamism. By examining the major research themes and historical trends from 2000 to 2019, we demonstrate that, as advanced technologies in education evolve over time, some areas of research topics seem have stood the test of time, while some others have experienced peaks and valleys. More importantly, our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational research. For instance, the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning analytics. Furthermore, this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.     

The emergence of cognitive science in France

A comparison between the development of cognitive science in France and the USA enables us to analyze some national differences linked to specific connections between the scientific, military, economic and political worlds. The influence of new practices and tools developed during World War II and the Cold War appears to be of crucial importance in understanding the development of this new field, as well as that of cybernetics, computer science, artificial intelligence and molecular biology. This paper can be considered as a study in how the differing contexts in France and the USA shaped the history of the construction of cognitive science in each of these two countries. In spite of various differences, some common aspects may be pointed out: in both cases, computer experts and psychologists using a computational modelling approach were those first engaged in the construction of cognitive science. If in France neuroscience-oriented cognitive science research was stronger than in the USA, it seems that the artificial intelligence orientation is also of growing importance in France.     

低年级本科生人工智能通识选修课教学方法研究

随着人工智能教育的普及,各高校纷纷开设人工智能课程。文章根据人工智能课程的特点,分析面向低年级本科生开设人工智能选修课时遇到的问题,提出了一套人工智能通识课的教学改革方法,比如:优化教学内容;借助AI Studio学习平台,利用深度学习框架PaddlePaddle进行编程实践,并采用合理的课程考核方式;在传授理论知识的同时,注重实际动手环节,培养学生的学习兴趣。