Computational intelligence determines effective rationality

Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.     

Herbert A. Simon: AI pioneer

Herbert A. Simon was one of the founding fathers of artificial intelligence. Simon, who, along with Allen Newell and J.C. Shaw, wrote the first AI program in 1956, received many honors in his lifetime, including the Nobel Prize in Economic Science in 1978. Simon was a true renaissance man. He researched human decision-making and problem-solving processes and the implications of those processes for social institutions. Simon's major awards in four areas: psychology, economics, management science and computer science - attest to his breadth of interest and expertise. Simon's impact in the world is no more apparent than at Carnegie Mellon University, where, arguably, he was the most influential person. Simon left behind over 60 years of research and theorems. He also left the computer science world with the heuristic compiler, in which he applied theories and techniques from other disciplines.     

Computability,complexity and economics

Herbert Simon advocates that economists should study procedureal rationality instead of substantive rationality. One approach for studying procedural rationality is to consider algorithmic representations of procedures, which can then be studied using the concepts of computability and complexity. For some time, game theorists have considered the issue of computability and have employed automata to study bounded rationality. Outside game theory very little research has been performed. Very simple examples of the traditional economic optimization models can require transfinite computations. The impact of procedural rationality on economics depends on the computational resources available to economic agents.     

网络时代人工智能研究与发展

50多年来,人工智能在模式识别、知识工程、机器人等领域已经取得重大成就,但是离真正的人类智能还相差甚远.当今网络时代,人工智能科学要在学科交叉研究中实现人工智能的发展与创新,会更加关注认知科学、脑科学、生物智能、物理学、网络科学、计算机科学与人工智能之间的交叉渗透,重视认知物理学的研究;自然语言是人工智能研究知识表示无法回避的直接对象,要对语言中的概念建立起能够定量表示的不确定性转换模型,发展不确定性人工智能;要利用现实生活中复杂网络的小世界模型和无标度特性,把网络拓扑作为知识表示的一种新方法,研究网络拓扑的演化与网络动力学行为,研究网络智能.对这3个重要方向进行了阐述,并提出了具体建议.     

Beauty and the beast: New approaches to teaching computing for humanities students at the University of Aberdeen

This paper reports on the history and development of a new undergraduate course teaching computing for humanities students at the University of Aberdeen, and assesses some new teaching approaches developed in the course. It is noted that teaching computing to humanities students appears to be viewed with suspicion by some Computer Science and Humanities Departments. The two camps seem to fear, for different reasons, that issues and practices important to their disciplines will be compromised or watered down. This paper describes an attempt to reverse any such attitudes on the part of staff and students and to take undergraduates considerably beyond mere word processing and computer literacy. Various methods and techniques used in the course are presented and their value assessed. The importance of using a consistent computer interface to helping students form a stable conceptual model of computers is considered. We reflect on the value of teaching more about Human Computer Interaction and Artificial Intelligence than is usual in Humanities Computing courses. A number of lessons are drawn from the course.Gordon Burgess is a Reader in the Department of German at the University of Aberdeen. Simon Holland is a Lecturer in Computing Science at the University of Aberdeen.For example, Sterling Beckwith (1992) has noted that major contributors to human computer interaction and artificial intelligence such as Marvin Minsky, Alan Kay, Bill Buxton, and Jaron Lanier (we could add Christopher Longuet-Higgins, Terry Winograd, Phil Johnson-Laird and Herbert Simon) are all musicians or have been involved in music at some time as part of their academic research.     

多媒体智能：当多媒体遇到人工智能

过去10年中涌现出大量新兴的多媒体应用和服务,带来了很多可以用于多媒体前沿研究的多媒体数据。多媒体研究在图像/视频内容分析、多媒体搜索和推荐、流媒体服务和多媒体内容分发等方向均取得了重要进展。与此同时,由于在深度学习领域所取得的重大突破,人工智能(artificial intelligence,AI)在20世纪50年代被正式视为一门学科之后,迎来了一次“新”的发展浪潮。因此,一个问题就自然而然地出现了：当多媒体遇到人工智能时会带来什么？为了回答这个问题,本文通过研究多媒体和人工智能之间的相互影响引入了多媒体智能的概念。从两个方面探讨多媒体与人工智能之间的相互影响：一是多媒体促使人工智能向着更具可解释性的方向发展;二是人工智能反过来为多媒体研究注入了新的思维方式。这两个方面形成了一个良性循环,多媒体和人工智能在其中不断促进彼此发展。本文对相关研究及进展进行了讨论,并围绕值得进一步探索的研究方向分享见解。希望可以对多媒体智能的未来发展带来新的研究思路。     

人工智能知识产权问题综述

自20世纪50年代以来,人工智能引领着各行各业发展的风向,在人工智能技术助力各领域发展的同时,制度设计如何在最大程度上反哺技术和经济的发展成为至关重要的问题。不断推动人工智能技术的创新,不仅要重视人工智能技术本身的保护问题,人工智能生成物的保护也不容忽视。本文收集了国内外相关案例,从学理与司法实践的角度分析人工智能知识产权保护的必要性与可行性,以期通过人工智能知识产权保护推动我国技术进步与法律完善齐头并进。     

基于人工智能的自然长寿人群生命信息系统建模

以中国新疆和田地区维吾尔自然长寿人群为例，探索一种基于人工智能(artificial intelligence，AI)的生命信息系统新的建模方法．在建模过程中，由于引入人工智能和数据融合技术，能够高效地提取隐藏于复杂数据中对生命起关键作用的因素，因此成功地建立了一个既非语言表达也非数学公式描述的隐式的自然长寿人群的人工智能模型．此人工智能模型不但更接近实际，而且具有判别、预见等超前功能．该模型可修改、可移植．     

Artificial intelligence: New jobs from old

The age of artificial intelligence (AI) is upon us, and its effect upon society in the coming years will be noteworthy. Artificial intelligence is a field that encompasses such applications as robotics, expert systems, natural language understanding, speech recognition, and computer vision. The effect of these AI systems upon existing and future job occupations will be important. This paper takes a look at artificial intelligence in terms of the creation of new job categories. Also, the introduction of AI into the organization to better familiarize the employees about AI will be discussed.     

人工智能对科研信息化的推动作用

随着科技的不断发展,国家相关部门的重视,人工智能掀起了又一轮热潮。国家相关部门发布了关于人工智能的行动实施方案,明确了人工智能的重点研究领域和相关任务,为人工智能的强力有序发展提供了保障。本文概述了人工智能的国家战略以及主要任务,如加快建设文献、语音、图像、视频、地图等多种类数据的海量训练资源库和基础资源服务公共平台,建设支撑超大规模深度学习的新型计算集群,建立完善产业公共服务平台。本文列举了人工智能在各行各业的应用,包括化学,物理学,生物信息学,地质学,药物设计,建筑领域等方面,其中包括深度学习方法的运用,重点强调了人工智能对于科研信息化的巨大推动作用,包括超级计算领域,科学数据领域,无线网络领域,物联网领域,生物信息领域等。之后从政府、科研院所、企业多个层面详细阐述世界各国以及中国在基于人工智能的科研信息化方面的探索,对中国基于人工智能的科研信息化建设方面提出可行性建议。最后,对人工智能的应用范围以及发展前景做了展望。     

Advancing artificial intelligence research and dissemination through conference series: Benchmark,scientific impact and the MICAI experience

This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained.     

Artificial intelligence applications in the telecommunications industry

Abstract: Artificial intelligence (AI) has been applied to the telecommunications industry for more than a decade. The purpose of this paper is to examine the application of AI in the telecommunications industry sector. Our research finds that AI's first main application in telecommunications is in the network management area. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques which are most promising for the future. The research also finds that different AI techniques have their unique applications in the telecommunications industry.     

AI,anthropocentrism, and the evolution of ‘intelligence’

Intuitive conceptions guide practice, but practice reciprocally reshapes intuition. The intuitive conception of intelligence in AI was originally highly anthropocentric. However, the internal dynamics of AI research have resulted in a divergence from anthropocentric concerns. In particular, the increasing emphasis on commonsense knowledge and peripheral intelligence (perception and movement) in effect constitutes an incipient reorientation of intuitions about the nature of intelligence in a non-anthropocentric direction. I argue that this conceptual shift undermines Joseph Weizenbaum's claim that the project of artificial intelligence is inherently dehumanizing.     

APPLIED COMPUTATIONAL INTELLIGENCE FOR FINANCE AND ECONOMICS

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.     

机制主义人工智能理论——一种通用的人工智能理论

现行人工智能研究取得了许多进展,但存在“深度上浅层化、广度上碎片化和体系上封闭化”的重要缺陷。这不是改进算法或者提高硬件性能所能解决的问题,而是要在科学观方法论上寻找根源。本文依据“科学观→方法论→研究模型→研究途径→基本概念→基本原理”这个顶天立地的研究纲领,总结了信息科学的科学观,提炼了信息生态方法论;在新的科学观和方法论指导下构筑了体现智能生长全过程的研究模型,发现了智能生长的共性机制,确立了机制主义研究途径,进而澄清和匡正了信息（特别是语义信息）、感知、知识、认知、基础意识、情感、理智、综合决策等一系列基础概念,总结了实现信息－知识－智能转换的一组基本原理,创建了机制主义人工智能理论。而且证明了:长期三分而立的结构主义（人工神经网络）、功能主义（专家系统）、行为主义（感知动作系统）三大人工智能理论可在机制主义人工智能理论框架内实现和谐统一;机制主义是生成基础意识、情感、理智三位一体高等人工智能的科学途径;机制主义人工智能理论是通用型的人工智能理论。     

网络时代的人工智能

五十多年来,人工智能在模式识别、知识工程、机器人等领域已经取得重大成就,但是离真正的人类智能还相差甚远。本文强调 在当今的网络时代,作为信息技术的先导,人工智能科学有着非常值得关注的研究方向,要在学科交叉研究中实现人工智能的发展与创新。要关注认知科学、脑科学、生物智能、物理学、复杂网络、计算机科学与人工智能之间的交叉渗透,尤其是重视认知物理学的研究;自然语言是人类思维活动的载体,是人工智能研究知识表示无法回避的直接对象,要对语言中的概念建立起能够定量表示的不确定性转换模型,发展不确定性人工智能;要利用现实生活中复杂网络的小世界模型和无尺度特性,把网络拓扑作为知识表示的一种新方法,研究网络拓扑的演化与网络动力学行为,研究网络化了的智能,从而适应信息时代数据挖掘的普遍要求,迎接人工智能科学与应用新的辉煌。     

Computational Collective Intelligence with Big Data: Challenges and Opportunities

Collective intelligence has been an important research topic in many AI communities. With The big data phenomenon, we have been facing on many research problems on how to integrate the big data with collective intelligence. This special issue has selected 9 high quality papers covering various research issues.     

Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector

ABSTRACT

Robots and chatbots are sophisticated. Artificial intelligence (AI) is increasingly popular in the financial industry due to its ability to provide customers with cheap, efficient and personalised services. This article uses doctrinal sources and a case study to show that many banks and FinTech start-ups are investing in AI. Yet, there are a number of challenges arising from the use of AI which could undermine trust and confidence amongst consumers. This article features the issue of bias and discrimination in banking. There is evidence that algorithms discriminate against certain races and gender. Legislative gaps in the Equality Act 2010 and the General Data Protection Regime will be analysed. Ultimately, human beings are still needed to input, train and help machines to learn. Fortunately, the FCA are leading in regulating technology, from the launch of regulatory sandboxes to their co-operative collaboration with FinTech start-ups on regulatory matters. Augmented intelligence collaboration is needed to enable industry players and regulators to provide seamless regulation and financial stability. The future of AI regulation is inter-disciplinary in approach.