基于人工意识概念的人工智能科学的重构

本文回顾了人工智能科学发展的历史与现状,通过分析指出,人工智能科学的困境的出路是回到人的智能自身自然实现的仿生道路上来.进而,我们试图用一种统一观点-人工意识及其相关系统的概念来连接人工智能科学的各个分支,使之系统化.我们还初步考虑了人工意识的实现问题,考察了操作系统、专家系统、人工生命与Agent理论相结合来完成这一目标的可能性.     

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.     

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。     

浅析网络时代人工智能的研究与发展

人工智能是建立在多门学科知识基础之上的,研究的主要方向是机器智能化的发展,它涉及到网络技术与计算机的程序问题.当今社会已进入了网络时代,人工智能的技术有了更加广泛的运用空间,在学习生活的各个领域发挥着极其重要的作用,并且成为未来工业发展的重要技术,得到了学界的大力认可.在本文中对于网络时代人工智能的创新发展与研究进行了分析.     

人工智能概述

该文介绍了人工智能当前的研究概况、研究热点,展望了人工智能未来的发展方向,同时分析了广义人工智能概念涵义、理论基础、学科体系、科学方法、科学意义及应用价值。     

人工智能及其在计算机网络技术中的运用

人工智能是近代以来新型起的科学产业,这个产业对社会生活的各个方面都产生了深远的影响。人工智能的发展使得工厂的机器进入到半智能半人工的状态。这种现状在产品生产和计算机网络技术发展中提到了极其重要的作用。本文从人工智能的概念入手,对人工智能在计算机网络技术中的作用进行了深入的分析,希望可以为读者提供一定的帮助。     

研究人工智能的一条新途径

计算机的所有功能都是由指令组成的程序实现的,从计算机功能角度去研究人工智能的基本要素具有探索意义。在区分了人类智力表示和人工智能问题的概念及描述了计算机功能序的概念后,证明了人类智力表示不能都由计算机实现;证明了从计算机功能研究已解决和将得到解决的人工智能问题的可行性,并找到了这些人工智能问题与计算机功能序之间的关系。在基于文中提出的指令集功能完全性和独立性概念描述了极小指令集概念之后,证明了通用计算机所有计算功能的实现只需要5种基本指令。由于指令集代表了计算机所具有的全部功能,从而建立了极小指令集与人工智能问题之间的联系。文中的讨论表明从研究极小指令集中的指令的特性和深入探讨指令有限重复排列的行为规律去研究人工智能是一条可行的途径。     

人工智能视域下机器学习应用与创新探索

能够在不同情况下单独或联机执行模拟人类任务的机器被成为人工智能,其主要目的在于使机器能够模拟人类的思维和智力,并进行相应的思维活动。人工智能以硬件为基础,软件为核心,是计算机科学中技能机器的一个分支,其经过不断改良,虽然已经逐渐形成了较为成熟的人工智能技术,但当前的人工智能还需要不断完善,使其具有自我学习以及独立思考的能力,摆脱编程算法的限制,才能满足社会发展的需求。基于此,笔者首先分析了机器学习的概念和发展,随后从4个方面讲述了机器学习在教育领域中的创新应用与探索,希望能够相关人士提供借鉴。     

文字识别视域下的人工智能机器学习的文字识别方法研究

人工智能的出现为人们的生活和生产方式带来了巨大的改变,加之科技和网络技术的发展,人们已经成功地步入到了信息时代,在人们生活中的各个方面都离不开信息技术.人工智能机器在众多的智能化产物中由于自身具备的独特特点得到了人们的关注,这也是自动驾驶技术中不可或缺的关键技术,对其进行研究具有显著的现实意义.基于此种发展背景,从文字识别角度出发,对人工智能机器学习中文字识别发展的背景和主要方法进行分析,旨在促进其发展过程中问题的解决.     

Applying machine consciousness models in autonomous situated agents

This paper briefly describes the most relevant current approaches to the implementation of scientific models of consciousness. Main aspects of scientific theories of consciousness are characterized in sight of their possible mapping into artificial implementations. These implementations are analyzed both theoretically and functionally. Also, a novel pragmatic functional approach to machine consciousness is proposed and discussed. A set of axioms for the presence of consciousness in agents is applied to evaluate and compare the various models.     

伦理智能体及其设计:现状和展望

智能体一直是人工智能的主要研究领域之一,任何独立的能够同环境交互并自主决策的实体都可以抽象为智能体.随着人工智能从计算智能到感知智能,再到认知智能的发展,智能体已逐步渗透到无人驾驶、服务机器人、智能家居、智慧医疗、战争武器等人类生活密切相关的领域.这些应用中,智能体与环境、尤其是与人类和社会的交互愈来愈突出,其中的伦理...     

Hypercalculia for the mind emulation

By imitating the high-speed computational behavior of a machine through a consciousness of the future, we suggest a reverse artificial intelligence in an attempt to achieve the computational whole mind emulation of high level thoughts. The methodology, using such reverse artificial intelligence which we run with control on the mind instead of a machine, is disclosed. We then generalize this ability to enable the proposed mind emulation through high-speed mental computation processes. We suggest a set of theoretical and empirical principles and methods for the mind transfer, which leads to an almost unlimited potential for the human beings and society. In this paper, we present the most basic case of experimenting with the inactive behaviors, then a new hypothesis.     

人工智能在计算机网络技术中的应用分析

随着信息技术的快速发展,人工智能已成为引领新一轮科技革命和产业变革的战略性技术。现阶段,各个国家都在争先布局和发展人工智能,以期能在未来科技革命中抢占高点和先机。人工智能是一种模拟人脑工作的技术形式,它包含系统推荐、人工神经网络、语言处理、机器学习等方面的内容。将人工智能应用于计算机网络技术,可以节省人力资源、提升效率,可较好地弥补当前计算机网络技术在运用过程中存在的不足,进一步提升计算机网络技术水平。     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.     

A hypothesis of consciousness or intelligent neuronet information systems

This paper deals with the problematics of artificial intelligence and is devoted to problems of investigation of natural systems and synthesis of artificial intelligence systems. The specificity of functioning of biological intelligence systems is illustrated by simple examples. These examples allow one to disclose a probable mechanism that provides the formation of consciousness inherent in living beings and whose explicit manifestation is thinking. The natural-science aspect of the content of fundamental concepts such as semantics and knowledge is also considered as applied to the functioning of these systems. Based on paradigms of several scientific schools and that of system approach, general concepts and approaches to the solution of the problem of artificial intelligence are presented.Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 73–86, November–December 2004.This revised version was published online in April 2005 with a corrected cover date.     

From biological consciousness to machine consciousness: An approach to make smarter machines

Consciousness research has been of great concern to philosophers, psychologists and neuroscientists in recent years. At the same time, consciousness has also attracted more and more interest of artificial intelligence （AI） researchers. In order to make more intelligent machines, many computing models of machine consciousness have been presented. Furthermore, self-consciousness has relevance to the level of intelligent functions. Hence, it is necessary to study self-consciousness in AI. This thesis, starting from biological consciousness, discusses some viewpoints of machine consciousness. Based on the discussions, we present a way to emulate self-consciousness and test this method via simulation experiments. Our results indicate that self-consciousness, which belongs to organisms, can he imitated by machines.     

人工智能的逆向工程——反向智能研究综述

在大数据时代,人工智能得到了蓬勃发展,尤其以机器学习、深度学习为代表的技术更是取得了突破性进展.随着人工智能在实际场景中的广泛应用,人工智能的安全和隐私问题也逐渐暴露出来,并吸引了学术界和工业界的广泛关注.以机器学习为代表,许多学者从攻击和防御的角度对模型的安全问题进行了深入的研究,并且提出了一系列的方法.然而,当前对机器学习安全的研究缺少完整的理论架构和系统架构.从训练数据逆向还原、模型结构反向推演、模型缺陷分析等角度进行了总结和分析,建立了反向智能的抽象定义及其分类体系.同时,在反向智能的基础上,将机器学习安全作为应用对其进行简要归纳.最后探讨了反向智能研究当前面临的挑战以及未来的研究方向.建立反向智能的理论体系,对于促进人工智能健康发展极具理论意义.     

Advancing an in-memory computing for a multi-accent real-time voice frequency recognition modeling: a comprehensive study of models & mechanism

Multimedia Tools and Applications - In this age of pervasive computing, numerous scientific accomplishments, such as artificial intelligence and machine learning [ML], have conveyed exciting...     

Interview with the drone: experimenting with post-anthropocentric art practice

ABSTRACT

The main idea behind the development of the art project discussed in this artistic contribution, called MNEMODRONE, is to have people share private memories with a drone and then use those memories to create an artificial intelligence. Is it possible for a machine to act based on collective memories? Although there is not a definitive answer, I discuss the ideas behind the project: the problem of quantifying nonhuman forms of consciousness, a model for the drone's artificial intelligence and the advantages of a post-anthropocentric artistic investigation. The second half of my contribution discusses the limits of post-anthropocentric creativity and proceeds more speculatively, proposing a theoretical model of metamorphosis that could be useful for understanding technology and also for artificial intelligent systems to understand humans. My contribution advocates for a creative analysis of future scenarios, both technological and cultural, beyond the boundaries of traditional methodologies. I conclude with a fictional interview that could eventually take place between an interviewer and MNEMODRONE. This fictional interview allows the artwork to speak for itself, and is based on the data collected by the drone in its first year of activities, mimicking its discursive capabilities once the artificial intelligence is fully developed.