【精品推荐】2016年人工智能Artificial Intelligence 精品学习课件 完整版ppt课件【ppt版可编辑】
《人工智能导论》期末复习知识点
《人工智能导论》期末复习知识点选择题知识点1.人工智能、人工神经网络、机器学习等人工智能中常用词的英文及其英文缩写。
人工智能Artificial Intelligence,AI人工神经网络Artificial Neural Network，ANN机器学习Machine Learning，ML深度学习Deep Learning,DL2.什么是强人工智能？强人工智能观点认为有可能制造出真正能推理（Reasoning）和解决问题（Problem_solving）的智能机器，并且，这样的机器将被认为是有知觉的，有自我意识的。
可以独立思考问题并制定解决问题的最优方案，有自己的价值观和世界观体系。
有和生物一样的各种本能，比如生存和安全需求。
在某种意义上可以看作一种新的文明。
3.回溯算法的基本思想是什么？能进则进。
从一条路往前走，能进则进，不能进则退回来，换一条路再试。
4.面向对象、产生式系统、搜索树的定义？面向对象(Object Oriented)是软件开发方法，一种编程范式。
面向对象的概念和应用已超越了程序设计和软件开发，扩展到如数据库系统、交互式界面、应用结构、应用平台、分布式系统、网络管理结构、CAD技术、人工智能等领域。
面向对象是一种对现实世界理解和抽象的方法，是计算机编程技术发展到一定阶段后的产物。
面向对象是相对于面向过程来讲的，面向对象方法，把相关的数据和方法组织为一个整体来看待，从更高的层次来进行系统建模，更贴近事物的自然运行模式。
把一组产生式放在一起，让它们相互配合，协同工作，一个产生式生成的结论可以供另一个产生式作为前提使用，以这种方式求得问题的解决的系统就叫作产生式系统。
对于需要分析方法，诸如深度优先搜索和广度优先搜索（穷尽的方法）以及启发式搜索（例如最佳优先搜索和A*算法），这样的问题使用搜索树表示最合适。
5.机器学习的基本定义是什么？机器学习是一门研究及其获取新知识和新技能，并识别现有知识的学问。
人工智能1
3 机器学习（Machine Learning） 研究如何使用计算机模拟和实现人类的学习活动。 如果一个系统能够通过执行某种过程而改进它的 性能，这就是学习。
4 自动定理证明（Automatic Theorem Proving） 利用计算机证明非数值性的结果，即确定它的真 假。主要方法有：自然演绎法、判断法、定理证明器、 人机交互进行定理证明。
人工智能
（Artificial Intelligence，AI ）
刘春阳
智能机器人研究所
第1章 人工智能概述
1.1 什么是人工智能(Artificial Intelligence，AI)
1 自然智能：人类所具有的智能行为。 2 智能行为：包括感知、推理、判断、识别、理解、学习 和问题求解等思维活动。 3 人工智能：关于人造物的智能行为。 4 人工智能（学科）： AI的本质问题 研究如何制造出人造的智能机器或系统，来模拟人类 智能活动，以延伸人类智能的科学。
• 人工智能的发展是以硬件与 软件为基础。它的发展经历
了漫长的发展历程。人们从 很早就已开始研究自身的思 维形成，早在亚里士多德(公
元前384-322年)在着手解释和
编注他称之为三段论的演绎 推理时就迈出了向人工智能 发展的早期步伐，可以看作 为原始的知识表达规范。
亚里士多德(公元前384-322年)
– 新的动向——构造化方法
• 第五阶段（90年代初～现在） 数据与网络时代
– 网络给AI带来无限的机会 – 知识发现与数据挖掘 – AI走向实用化
三个重要事件
1
1956年召开人类历史上第一次人工智能研讨会， 标志着人工智能学科的诞生； 1969年召开了第一届人工智能联合会议；
1970年，《人工智能》国际杂志创刊。
ARTIFICIAL INTELLIGENCE——人工智能(英文)
ARTIFICIAL INTELLIGENCE——人工智能1 Artificial intelligence (AI) is, in theory, the ability of an artificial mechanism to demonstrate some form of intelligent behavior equivalent to the behaviors observed in intelligent living organisms. Artificial intelligence is also the name of the field of science and technology in which artificial mechanisms that exhibit behavior resembling intelligence are developed and studied.2 The term AI itself, and the phenomena actually observed, invite --- indeed demand --- philosophical speculation about what in fact constitutes the mind or intelligence. These kinds of questions can be considered separately, however, from a description of the various endeavors to construct increasingly sophisticated mechanisms that exhibit “intelligence.”3 Research into all aspects of AI is vigorous. Some concern exists among workers in the field, however, that both the progress and expectations of AI have been overstated. AI programs are primitive when compared to the kinds of intuitive reasoning and induction of which the human brain or even the brains of much less advanced organisms are capable. AI has indeed shown great promise in the area of expert systems --- that is, knowledge-based expert programs --- but while these programs are powerful when answering questions within a specific domain, they are nevertheless incapable of any type of adaptable, or truly intelligent, reasoning.4 Examples of AI systems include computer programs that perform such tasks as medical diagnoses and mineral prospecting. Computers have also been programmed to display some degree of legal reasoning, speech understanding, vision interpretation, natural-language processing, problem solving, and learning. Although most of these systems have proved valuable either as research vehicles or in specific, practical applications, most of them are also still very far from being perfected.5 CHARACTERISTICS OF AI: No generally accepted theories have yet emerged within the field of AI, owing in part to the fact that AI is a very young science. It is assumed, however, that on the highest level an AI system must receive input from its environment, determine an action or response, and deliver an output to its environment. A mechanism for interpreting the input is needed. This need has led to research in speech understanding, vision, and natural language. The interpretation must be represented in some form that can be manipulated by the machine.6 In order to achieve this goal, techniques of knowledge representation are invoked. The AI interpretation of this, together with knowledge obtained previously, ismanipulated within the system under study by means of some mechanism or algorithm. The system thus arrives at an internal representation of the response or action. The development of such processes requires techniques of expert reasoning, common-sense reasoning, problem solving, planning, signal interpretation, and learning. Finally, the system must网construct an effective response. This requires techniques of natural-language generation.7 THE FIFTH-GENERATION ATTEMPT: In the 1980s, in an attempt to develop an expert system on a very large scale, the Japanese government began building powerful computers with hardware that made logical inferences in the computer language PROLOG. (Following the idea of representing knowledge declaratively, the logic programming PROLOG had been developed in England and France. PROLOG is actually an inference engine that searches declared facts and rules to confirm or deny a hypothesis. A drawback of PROLOG is that it cannot be altered by the programmer.) The Japanese referred to such machines as “fifth-generation” computers.8 By the early 1990s, however, Japan had forsaken this plan and even announced that they were ready to release its software. Although they did not detail reasons for their abandonment of the fifth-generation program, U.S scientists faulted their efforts at AI as being too much in the direction of computer-type logic and too little in the direction of human thinking processes. The choice of PROLOG was also criticized. Other nations were by then not developing software in that computer language and were showing little further enthusiasm for it. Furthermore, the Japanese were not making much progress in parallel processing, a kind of computer architecture involving many independent processors working together in parallel—a method increasingly important in the field of computer science. The Japanese have now defined a “sixth-generation” goal instead, called the Real World Computing Project, that veers away from the expert-systems approach that works only by built-in logical rules.9 THE FUTURE OF AI RESEARCH: One impediment to building even more useful expert systems has been, from the start, the problem of input---in particular, the feeding of raw data into an AI system. To this end, much effort has been devoted to speech recognition, character recognition, machine vision, and natural-language processing. A second problem is in obtaining knowledge. It has proved arduous toextract knowledge from an expert and then code it for use by the machine, so a great deal of effort is also being devoted to learning and knowledge acquisition.10 One of the most useful ideas that has emerged from AI research, however, is that facts and rules (declarative knowledge) can be represented separately from decision-making algorithms (procedural knowledge). This realization has had a profound effect both on the way that scientists approach problems and on the engineering techniques used to produce AI systems. By adopting a particular procedural element, called an inference engine, development of an AI system is reduced to obtaining and codifying sufficient rules and facts from the problem domain. This codification process is called knowledge engineering. Reducing system development to knowledge engineering has opened the door to non-AI practitioners. In addition, business and industry have been recruiting AI scientists to build expert systems.11 In particular, a large number of these problems in the AI field have been associated with robotics. There are, first of all, the mechanical problems of getting a machine to make very precise or delicate movements. Beyond that are the much more difficult problems of programming sequences of movements that will enable a robot to interact effectively with a natural environment, rather than some carefully designed laboratory setting. Much work in this area involves problem solving and planning.12 A radical approach to such problems has been to abandon the aim of developing “reasoning” AI systems and to produce, instead, robots that function “reflexively”. A leading figure in this field has been Rodney Brooks of the Massachusetts Institute of Technology. These AI researchers felt that preceding efforts in robotics were doomed to failure because the systems produced could not function in the real world. Rather than trying to construct integrated networks that operate under a centralizing control and maintain a logically consistent model of the world, they are pursuing a behavior-based approach named subsumption architecture.13 Subsumption architecture employs a design technique called “layering,”---a form of parallel processing in which each layer is a separate behavior-producing network that functions on its own, with no central control. No true separation exists, in these layers, between data and computation. Both of them are distributed over the same networks. Connections between sensors and actuators in these systems are kept short as well. The resulting robots might be called “mindless,” but in fact they have demonstrated remarkable abilities to learn and to adapt to real-life circumstances.14 The apparent successes of this new approach have not convinced many supporters of integrated-systems development that the alternative is a valid one for drawing nearer to the goal of producing true AI. The arguments that have arisen between practitioners of the two different methodologies are in fact profound ones. They have implications about the nature of intelligence in general, whether natural or artificial。
Artificial intelligence人工智能PPT
Here are some wonderful movies about artificial intelligence.
Ex Machina
In 2016
AlphaGo defeated man
Application
You absolutely can not think of, artificial intelligence has been able to do these things
What do you think of artificial intelligence?
人工智能将会给人类带来挑 战。第一，人工智能代替人 类做各种事情，那人类失业 率就无限增高，人类就无依 靠可生存。第二，人工智能 如果被坏人利用在犯罪上， 那么人类将陷入恐慌。第三， 如果我们不能很好地控制利 用人工智能，反而被人工智 能控制与利用，那么人类将 走向灭亡。
We should treat it in a proper way.
First, the artificial intelligence to do all sorts of things instead of humans, the human higher unemployment rate is unlimited, human beings have no rely on to survive. Second, if you are bad people use artificial intelligence on the crime, then human will be panic. Third, if we can't control very well using artificial intelligence, it is artificial intelligence control and utilization, then humans will end.
人工智能ArtificialIntelligenceAI2006级研究生-精品文档
人工智能是研究那些使理解、推理和行为成
为可能的计算（Winston, 1992）
2019/2/25
从拟人行为角度的定义：
人工智能是一种能够执行需要人的智能的创
造性机器的技术（Kurzwell, 1990）
人工智能研究如何通过使计算机做事而让人
过得更好（Rich & Knight, 1991）
2019/2/25
人
工
智
能
Artificial Intelligence (AI)
（2019级研究生）
许建华
南京师范大学计算机科学系
2006年9-12月
2019/2/25
人工智能成果的例子：
智能天线 国际象棋的人机大战 单机或者网上棋类游戏（中国象棋、围棋、 五子棋、跳棋等）
第五、Shannon（香侬）发表了计算机能够下棋的文章点
2019/2/25
1946年由美国人Mauchly（毛奇莱）和Eckert（艾 克特）在宾夕法尼亚大学莫尔电工学院成功地研制 出世界上第一台电子计算机 ENIC (Electronic Numerical Integrator and Computer)
比赛结果：2.5（人）： 3.5（机）
正式交锋（2019年2月）
计算机：小深蓝
比赛结果：3 : 3
2019/2/25
例3：国际象棋人机大战 前苏联国际象棋世 界冠军卡斯帕洛夫
比赛时间：2019年11月
比赛结果：2（人）: 2实是展示人工智能的研究水平与成果
2019/2/25
门边缘学科
人工智能诞生于1956年
成果多、应用广、波折多、争议大
2019/2/25
自然智能：人类所具有的智力和行为能力，具
人工智能ArtificialIntelligenceAI2006级研究生-精品
Artificial Intelligence (AI)
（2019级研究生）
2019/12/1
许建华 南京师范大学计算机科学系
2006年9-12月
人工智能成果的例子：
智能天线 国际象棋的人机大战 单机或者网上棋类游戏（中国象棋、围棋、
五子棋、跳棋等）
1.2.1 智能信息处理系统的假设 1.2.2 人类智能的计算机模拟
1.3 人工智能各学派的认知观 1.4 人工智能的研究与应用领域 1.5 本课程讲授的主要内容及课程要求 1.6 人工智能课程中的一些常用例子
2019/12/1
1.1 人工智能的定义与发展 1.1.1 人工智能的定义 人工智能 (Artificial Intelligence) ，又称机器智
人机大战其实是展示人工智能的研究水平与成果
2019/12/1
例4：各种下棋程序（人机对垒），计算机方 就是一个人工智能程序
五子棋 中国象棋
2019/12/1
第 1 章 绪论 1.1 人工智能的定义与发展
1.1.1 人工智能的定义 1.1.2 人工智能的起源与发展（发展历史）
1.2 人类智能与人工智能（符号主义的观点）
能 (Machine Intelligence) 是计算机科学中的一 门边缘学科 人工智能诞生于1956年 成果多、应用广、波折多、争议大
2019/12/1
自然智能：人类所具有的智力和行为能力，具
体包括判断、理解、推断、学习、适应性等等 如果机器（计算机）能够执行这样的任务，就可
以认为机器已具有某种性质的“人工智能”
人工智能是研究那些使理解、推理和行为成
为可能的计算（Winston, 1992）
机器人(Robotics)与人工智能( Artificial Intelligence)到底是个啥呢？
机器人（Robotics）与人工智能（Artificial Intelligence）到底是个啥呢？大数据的浪潮开始没多久，机器人和人工智能专业就以迅雷不及掩耳之势占据了留学的热门专业大榜，工程类专业的留学意向者中有一半左右都说“老师，我想申请美国的机器人专业或者人工智能”，那么问题来了：请问你知道美国的机器人/人工智能是什么专业呢？他们有什么区别？有哪些学校设置这类专业的学位课程？今天，小编将带你揭开机器人和人工智能的神秘面纱。
什么是人工智能（Artificial Intelligence）？人工智能这个术语最初是由约翰.麦卡锡（John McCarthy）编写的一种名为LISPAI编程语言信息来源：/technology/difference-between-robots-and-artificial-intellige nce/生硬的文字或许很难理解这两个根本上的差异，在此小编以美国西北大学为例详细讲解，希McCormick School of Engineering & Applied Science 麦考克工程与应用科学学院Electrical Engineering and Computer Science电子工程和计算机科学下设3个大部：ElectricalEngineeringDivisionComputerEngineeringDivisionComputerScienceDivisionComputer Engineeringdivision：Computer architectureComputer-aided designMobile systemsParallel processingHardware softwareinteractionVLSI designEmbedded systemsSystems simulationRoboticsLarge-scale systems翻译：计算机工程方向：计算机架构计算机辅助设计移动系统并行处理硬件软件交互VLSI设计嵌入式系统系统仿真机器人大型系统http://www.mccormick.northwester/eecs/computer-engineering/graduate/Computer Science division：Systems and NetworkingTheoryArtificial Intelligence andMachine LearningHuman-Computer InteractionGraphicsRoboticsCS+X翻译：计算机科学方向：系统和网络理论人工智能和机器学习人机交互图像学机器人计算机科学+ 其他学科http://www.mccormick.northwester/eecs/computer-science/graduate/美国西北大学的麦考克工程与应用科学学院是美国的顶尖工程学院之一，2019年USNEWS排第20位，学院致力于用创新的教育计划激发学生的全脑性思维，促进教育和研究。
人工智能Artificial Intelligence
人工智能的发展历史
• 第一阶段：史前期(1956年以前） • 第二阶段：诞生期（1956-1980） • 第三阶段：发展期（1980年以后）
第1阶段：史前期
• 许多学科的发展为人工智能的发展奠定了基础， 其中包括：
– – – – – – – – 数理逻辑 计算理论 电子数字计算机 脑科学：神经元学说、遗传基因 心理学：认知心理学 语言学： 信息论： 自动化理论
史前阶段
• 数字计算机的发展。 • 关于计算机械的研究有很长的历史。
– 法国数学和物理学家帕斯卡（Pascal，16231662）于1647年制造了一台机械加法器 – 莱布尼茨进一步对此进行了改进，可以进行全 部的四则运算。 – 英国数学家巴贝奇（Babbage）于1821年发明 了差分机和分析机。他当时提出的计算机的五 大组成部分，为今天的计算机的发展奠定了基 础。
– 有限状态控制器 – 读写头 – 纸带
…
有限状态控制器 q1 q2 q3 q4 q5
A
T
A
B
D
图灵机(续)
• 图灵将其作为模拟人的思维活动的模型， 凡是可以用图灵机来计算的函数都是可计 算的。 • 图灵用这个模型证明了不可计算数（也就 是不可用图灵机的算法来表达）的存在。 • 由此建立了可计算性理论。
• 大卫(David): 一个具有来自情 的机器人剧情介绍• Cybertronics Manufacturing制作公司制造出了第一个具有 感情的机器人。他的名字叫大卫David • 作为第一个被输入情感程序的机器男孩，大卫是这个公司 的员工和他的妻子的一个试验品，他们夫妻俩收养了大卫。 而他们自己的孩子却最终因病被冷冻起来，以期待有朝一 日，有一种能治疗这种病的方法会出现。尽管大卫逐渐成 了他们的孩子，拥有了所有的爱，成为了家庭的一员。但 是，一系列意想不到的事件的发生，使得大卫的生活无法 进行下去。 • 人类与机器最终都无法接受他，大卫只有唯一的一个伙伴 机器泰迪(Teddy)----他的超级玩具泰迪熊，也是他的保护 者。大卫开始踏上了旅程，去寻找真正属于自己的地方。 他发现在那个世界中，机器人和机器之间的差距是那么的 巨大，又是那么的脆弱。他要找寻自我、探索人性，成为 一个真正意义上的人。
人工智能ArtificialIntelligence;简称AI
6
1.1.1 AI的定义
智能（自然智能）
• 自然智能
• 指人类和一些动物所具有的智力和行为能力
11
1.1.1 AI的定义
何谓人工智能（1/2）
• 综合各种不同观点，可从能力和学科两个方面讨论 • 能力方面 • 人工智能就是用人工的方法在机器（计算机）上实现的
智能，或称机器智能
• 学科方面 • 是一门研究如何构造智能机器或智能系统，以模拟、延
伸和扩展人类智能的学科
• Turing测试 • 如下图所示。能分辨出人和机器的概率小于50% • Turing测试存在的问题 • 仅反映了结果的比较，没涉及思维过程 • 没指出是什么人
去处理问题，能够模拟人类的智能行为。 • 相互关系 • 远期目标为近期目标指明了方向 • 近期目标则为远期目标奠定了理论和技术基础
14
第1章 人工智能概述
• 1.1 AI的定义及其研究目标
• 1.2 AI的产生与发展 • 孕育期（1956年以前） • 形成期（1956----1970年） • 知识应用期（1970---- 20世纪80年代末） • 从学派分离走向综合（20世纪80年代末到本世纪初） • 智能科学技术学科的兴起（本世纪初以来） • 1.3 AI研究的基本内容 • 1.4 AI研究的不同学派 • 1.5 AI的主要研究和应用领域 • 1.6 AI近期发展分析 • 1.7 我国智能科学技术教育体系
• 人类的自然智能（简称智能）
• 指人类在认识客观世界中，由思维过程和脑力活动所 表现出的综合能力。
人工智能(Artificial Intelligence)
威尔森的行动
• 他开始寻找机会跳出传统的AI圈子，了解 更多信息，并重新开始考虑努力的方向， 在开发一种简单的机器人。 • 他于1987年把他的人造生物取名为 “animations”，后来又简化为“animat （动化物）”。 • 在较短的时间内，在美国甚至欧洲的人工 智能学者都开始纷纷议论起这个“人工智 能动化物”。
从模拟人的思想的角度来考虑
• 当时有的学者把AI的研究途径概括为以符号处理 为核心的传统方法及网络连接为主的连接机制 （Connectionism）方法。 • 人的两种主要思维方式是逻辑思维和形象思维 （直感思维）。 • 符号处理可以认为主要在于模拟人的逻辑思维， 连接机制主要致力于模拟人的形象思维。 • 关于形象思维虽然人们认识到它的重要性，但用 现在的计算机来模拟形象思维是很困难的，需要 在计算机的体系结构上有新的突破。
四个概念：智能与涌现
• ③ 智能（intelligence) ：机器人看起来有 智能行为。智能的来源不仅仅限于计算装 置，也来自周围的情景、敏感器之间的信 息传送以及机器人与周围环境的交互作用。 对于智能的来源与传统的说法不大一样。 • ④ 涌现（emergence) ：智能是由很多部 件交互作用、与环境交互作用所产生的系 统涌现出来的总的行为。
Artificial Intelligence 人 工 智 能
第4章 适应性智能系统
• 4.1人工智能发展的几个阶段 • 4.2智能系统 • 4.3智能控制
– 智能交通 – 智能家居 – 智能楼宇
4.1人工智能发展的几个阶段
• 早期人工智能（AI）的起源是基于心理学 的研究，寻求启发式知识在人类思维过程 中的作用，把这类知识表达成逻辑形式加 以利用。 • 这是AI最早的模型。早期以逻辑为基础的 AI研究，可以概括为符号表达、启发式编 程、逻辑推理或者称为“深思熟虑”的思 维的模型，这可以说是AI研究的最初阶段， 或称传统的AI时期。
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人工智能的利与弊的英文高考作文
人工智能的利与弊的英文高考作文精选五篇【篇一】Title: Pros and Cons of Artificial IntelligenceArtificial Intelligence (AI) has become a ubiquitous presence in our modern world, offering both benefits and challenges that shape our future.On the positive side, AI revolutionizes industries by automating tasks that were once labor-intensive, thus increasing efficiency and productivity. For instance, AI-powered systems in healthcare assist doctors in diagnosing diseases more accurately and quickly, potentially saving lives.Moreover, AI enhances convenience in everyday life through smart devices like virtual assistants and personalized recommendation systems. These technologies learn from user interactions, tailoring experiences and services to individual preferences, thereby improving user satisfaction and efficiency.In the realm of education, AI facilitates personalized learning experiences, adapting content and pacing to students'abilities and learning styles. This adaptive learning approach fosters better engagement and knowledge retention among learners.However, alongside its benefits, AI presents challenges and concerns. One major issue is job displacement, as automation reduces the demand for certain types of labor, potentially leading to unemployment and economic inequality.Furthermore, ethical considerations around AI usage, such as data privacy breaches and algorithmic bias, raisesignificant concerns. Algorithms, if not properly designed or monitored, can perpetuate societal inequalities and infringe upon individuals' rights to privacy and autonomy.Additionally, the rapid advancement of AI raisesexistential questions about its long-term implications for humanity. Debates about AI's potential to surpass human intelligence and its ethical implications continue to fuel discussions among scientists, philosophers, and policymakers worldwide.In conclusion, while AI holds immense promise in transforming industries, enhancing daily life, and advancingknowledge, it also necessitates careful consideration of its ethical, social, and economic impacts. Balancing innovation with responsibility is crucial to harnessing the full potential of AI while mitigating its risks and ensuring a sustainable future for all.【篇二】Title: Pros and Cons of Artificial IntelligenceArtificial Intelligence (AI) stands as a double-edged sword, offering both profound advantages and significant challenges as it integrates further into our lives.On the positive front, AI streamlines operations across various sectors, from healthcare to manufacturing, by automating repetitive tasks, thereby boosting productivity and efficiency. For instance, in healthcare, AI-driven diagnostics systems can swiftly analyze vast amounts of medical data to assist doctors in accurate and timely diagnoses, potentially saving lives.Furthermore, AI fosters convenience and personalization in our daily interactions through the proliferation of smart devices and applications. Virtual assistants like Siri andAlexa anticipate our needs, while recommendation algorithms on platforms like Netflix and Amazon cater content to our preferences, enhancing user experience and satisfaction.In education, AI promises tailored learning experiences, catering to individual student needs and learning styles through adaptive algorithms. This personalized approach can enhance student engagement and academic performance by providing targeted support and feedback.However, amidst these benefits, AI presents formidable challenges. One pressing concern is the potential displacement of jobs due to automation. As AI systems take over routine tasks, there is a risk of job loss and economic inequality, necessitating proactive measures to reskill the workforce and ensure equitable distribution of opportunities.Ethical considerations also loom large in the AI landscape, particularly concerning issues of privacy, bias, and accountability. AI algorithms rely on vast amounts of data, raising concerns about data privacy and the potential for misuse or unauthorized access. Moreover, algorithmic bias can perpetuate societal inequalities, while opaque decision-makingprocesses undermine transparency and accountability.Moreover, the rapid advancement of AI technology raises existential questions about its long-term impact on humanity. Discussions about the implications of AI surpassing human intelligence, known as the "singularity," provoke debates about control, autonomy, and the very essence of what it means to be human.In conclusion, while AI holds immense promise in revolutionizing industries, enhancing personalization, and advancing knowledge, it demands careful consideration of its ethical, social, and economic ramifications. Striking a balance between innovation and responsibility is imperative to harnessing the full potential of AI while safeguarding against its pitfalls, ensuring a future that benefits all members of society.【篇三】Title: The Pros and Cons of Artificial IntelligenceArtificial Intelligence (AI) represents a transformative force in our world, bringing about both remarkable advantages and significant challenges.On the bright side, AI revolutionizes various industries by automating tasks that were once time-consuming and resource-intensive. This automation not only increases productivity but also frees up human resources for more creative and strategic endeavors. In fields like healthcare, AI-driven technologies assist doctors in diagnosing illnesses more accurately and efficiently, potentially saving countless lives.Moreover, AI enhances convenience and personalization in our daily lives through the proliferation of smart devices and applications. Virtual assistants like Siri and Google Assistant anticipate our needs, while recommendation algorithms on platforms like Netflix and Spotify cater content to our preferences, enriching our user experience.In the realm of education, AI offers tailored learning experiences, adapting content and pacing to students'individual needs and learning styles. This personalized approach fosters better engagement and comprehension among learners, ultimately leading to improved academic outcomes.However, alongside these benefits, AI presents formidable challenges. One of the most pressing concerns is the potentialdisplacement of jobs due to automation. As AI systems take over routine tasks, there is a risk of widespread unemployment and economic inequality, requiring proactive measures to retrain and reskill the workforce.Furthermore, ethical considerations loom large in the development and deployment of AI technologies. Issues such as data privacy, algorithmic bias, and accountability raise significant concerns about the fair and equitable use of AI. Ensuring transparency, fairness, and accountability in AI systems is essential to building trust and safeguarding against potential abuses.Moreover, the rapid advancement of AI technology raises existential questions about its long-term impact on humanity. Debates about the implications of AI surpassing human intelligence, commonly referred to as the "singularity," provoke profound philosophical and ethical inquiries about the nature of consciousness, autonomy, and morality.In conclusion, while AI holds immense promise in transforming industries, enhancing personalization, and advancing knowledge, it demands careful consideration of itsethical, social, and economic implications. Striking a balance between innovation and responsibility is essential to harnessing the full potential of AI while mitigating its risks and ensuring a future that benefits all members of society.【篇四】Title: The Benefits and Drawbacks of ArtificialIntelligenceArtificial Intelligence (AI) has become an integral part of our modern world, bringing with it a host of advantages and challenges that shape our society in profound ways.On the positive side, AI has revolutionized industries and businesses by automating tasks and processes, leading to increased efficiency and productivity. In fields such as healthcare, AI-powered technologies have enabled faster and more accurate diagnosis of diseases, resulting in improved patient outcomes and even the discovery of new treatment options.Moreover, AI has enhanced convenience and personalized experiences in our daily lives through smart devices and services. Virtual assistants like Siri and Google Assistanthave become valuable tools in assisting us with tasks, while recommendation algorithms on platforms like social media and e-commerce sites make tailored suggestions based on our preferences, enriching our online experiences.In the realm of education, AI offers innovative solutions for personalized learning experiences. Adaptive learning platforms can cater to individual learning styles and needs, providing students with tailored support and help them achieve better academic outcomes.Despite these advantages, AI also presents significant challenges. One of the foremost concerns is the potential impact on employment, as automation driven by AI technology may lead to job displacement in various sectors. This calls for concerted efforts in retraining the workforce and creating new opportunities in the digital economy.Ethical considerations surrounding AI are also critical, particularly in areas such as data privacy, algorithmic bias, and accountability. The use of AI algorithms in decision-making processes raises questions about transparency, fairness, and the potential reinforcement of social inequalities.Furthermore, there are philosophical debates about the implications of advanced AI surpassing human intelligence, sparking discussions on ethics, control, and the very nature of consciousness and autonomy.In conclusion, while AI offers immense benefits in improving efficiency, personalization, and innovation, it is crucial to address the challenges it poses in terms of employment, ethics, and societal impact. Striking a balance between harnessing the potential of AI for progress while addressing its drawbacks is essential to ensure a future where artificial intelligence serves the well-being of allindividuals and society as a whole.【篇五】Title: The Pros and Cons of Artificial IntelligenceArtificial Intelligence (AI) has become a ubiquitous presence in our modern world, presenting both advantages and disadvantages that demand careful consideration.On the positive side, AI has revolutionized numerous industries, streamlining processes and boosting productivity. In healthcare, for example, AI-powered systems facilitatequicker and more accurate diagnoses, ultimately improving patient care and outcomes. Additionally, AI enhances our daily lives through personalized experiences, with virtual assistants and recommendation algorithms tailoring services and products to our preferences.In education, AI offers promising avenues for personalized learning, catering to individual needs and learning styles through adaptive platforms.However, the rise of AI also raises concerns. Foremost among these is the potential impact on employment, as automation threatens to displace human workers in various sectors. Addressing this challenge requires proactive measures to retrain the workforce and create new opportunities in emerging industries.Ethical considerations loom large in the development and deployment of AI. Issues such as data privacy, algorithmic bias, and accountability demand careful attention to ensure fairness and transparency in decision-making processes.Furthermore, there are existential questions about the implications of AI surpassing human intelligence, promptingdebates on ethics, control, and the very essence of consciousness.In conclusion, while AI offers undeniable benefits in efficiency and innovation, its drawbacks necessitate thoughtful management. Striking a balance between leveraging AI'spotential for progress and mitigating its negative consequences is crucial for shaping a future where artificial intelligence serves humanity's best interests.。
高中论述类阅读：作遇上人工智能人工智能(ArtificialIntelligence,缩写为AI
高中论述类阅读 2019.111,阅读下面的文字，完成下列小题。
当文艺创作遇上人工智能人工智能（Artificial Intelligence，缩写为AI）不仅出现在《终结者》之类的科幻电影之中，也开始走进我们的现实生活。
比如，谷歌旗下公司开发的人工智能程序AlphaGo战胜了围棋世界冠军。
就连人类引以为傲的文艺创作，也开始遭遇人工智能的挑战。
日前，清华大学语音与语言实验中心（CSLT）作诗机器人“薇薇”通过了“图灵测试”（“图灵测试”是著名科学家图灵在1950年提出的一个观点，即将人与机器隔开后，如果有30%以上的机器行为被人误会为是“人”而不是“机器”所为，则机器应被视为拥有智能。
），机器人“薇薇”创作的诗歌令社科院的唐诗专家无法分辨，有31%的作品被认为是人写的。
文艺创作，是通过人脑进行的一种与情感、知觉、记忆与思维相关的复杂的精神活动，这本是人类的骄傲。
面临人工智能，人类传统的文艺创作又会面临怎样的挑战？机器人“薇薇”开启数据库诗歌写作模式。
有的诗一看就是机器人笨拙的模仿，但有的诗判断的难度要大一点，比如这一首《落花》：红湿胭艳逐零蓬/一片春风细雨濛/燕子不知无处去/东流犹有杜鹃声。
要想甄别就需要推敲，但只要认真思考，“细雨濛”之类别扭的用法还是可以被识别出的。
那么文学创作上，人工智能在模仿什么？人工智能的写作本质上是一种“数据库写作”，其对于文学的模仿高度依赖数据库，越是海量数据，越有助于人工智能的学习，转载请保留此链接！。
“薇薇”这类写诗的人工智能程序，学习过的古诗，估计是《全唐诗》五万首的几何倍数之上，故而可以在表面上，进行一些有模有样的模仿。
虽说如此，但诗歌所展现的语言的优美与丰富的人类内心世界，永远无法被量化、被标准化。
人工智能在“阅读”上可以远远超出所有诗人，它可以按照基本的诗歌规则组合出一首诗，但这种组合不是创作。
其实我们今天讨论人工智能与文艺的关系，真正要担心的不是人工智能开始文艺创作，而是我们对于文艺的理解趋向人工智能化。
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• 中层智能
• 以丘脑（感觉中枢）为主，主要完成感知活动。
• 低层智能
• 以小脑、脊髓为主，主要完成动作反应活动。
• 不同观点在层次结构中的对应关系
•
思维理论
•
知识阈值理论
•
进化理论
中层智能高和层低智层能智能
• 包含哪些能力？
10
智能包含的能力（一）
• 感知能力
• 通过感知器官感知外界的能力。是人类获得外界信息的基本途径，其处理方式有以下两种：
3
物质、能量、信息和智能
• 构成宇宙的三大要素：
• 三大要素：物质、能量与信息
• 信息：是物质和能量的表现形式，是以物质和能量为载体的客观存在
• 三大要素的基本关系：
• 能量与物质之间的相互转换
•
能量之间的转换（电能--热能），物质之间的转换（粮食--酒）
•
能量转换为物质 （电--光），物质转换为能量 （煤--电）
•
感知--动作方式：对简单、紧急信息
•
感知--思维--动作方式：对复杂信息
• 记忆和思维能力
•
记忆：对感知到的外界信息和由思维产生的内部知识的存储过程
•
思维：对已存储信息或知识的本质属性、内部知识的认识过程
•
思维方式：
•
抽象思维（逻辑思维）：根据逻辑规则对信息和知识进行处理的理性思维方式。例如，
逻辑推理等
•
形象思维（直感思维）：基于形象概念，根据感性形象认识材料对客观现象进行处理
的一种思维方式。例如，图像、景物识别等
•
灵感思维（顿悟思维）：是一种显意识和潜意识相互作用的思维方式。例如，因灵感
而顿时开窍
11
智能包含的能力（二）
• 学习和自适应能力
人工智能 Artificial Intelligence；简称AI
人工智能诞生59年 1956----2015
1
人工智能的基本内容
• 人工智能基本概念、方法和技术
•
基本技术：知识表示、推理、搜索、规划
• 人工智能的主要研究、应用领域
•
机器感知：机器视觉；机器听觉；自然语言理解；机器翻译；模式识别
•
AI的定义
• 形式化定义 • 目前还没有 • 一般解释 • 人工智能就是用人工的方法在机器（计算机）上实现的智能，或称机器智能 • 无形式化定义的理由 • 人工智能的严格定义依赖于对智能的定义 • 即要定义人工智能，首先应该定义智能 • 但智能本身也还无严格定义 • 因此，应先对人类的自然智能进行讨论
• 信息是物质与能量的表现形式
•
物质与能量表现为信息，或者产生信息
•
信息可控制物质与能量的转换
•
信息能够控制物质、能量自身及相互之间的转换
• 三大要素与智能
• 人类的智能：物质(碳)+能量(生物电)→(生物)信息
• 人造的智能：物质(硅)+能量(物理电)→(电子)信息
• 产业革命及其意义
•
是物质与能量领域的革命，放大了人的体能
其智能就会越高。 • 进化理论 • 是美国MIT的Brooks在对人造机器虫研究的基础上提出来的。智能取决于感
知和行为，取决于对外界复杂环境的适应，智能不需要知识、不需要表示、不 需要推理，智能可由逐步进化来实现。 • 不一致，从层次结构再认识
9
智能的层次结构
• 高层智能
• 以大脑皮层（抑制中枢）为主，主要完成记忆、思维等活动。
4
信息、知识和智能
• 信息、知识和智能
• 信息：是由数据表达的客观事实
• 知识：是由智力对信息进行加工后所形成的对客观世界规
•
律性的认识
• 智能：是指人类在认识客观世界中，由思维过程和脑力活
•
动所表现出的综合能力
• 三者之间的关系
• 信息：是形成知识的原料，是智能的加工对象
• 知识：是信息的关联，是由智能加工后的产品
7
何谓智能（自然智能）
• 自然智能
• 指人类和一些动物所具有的智力和行为能力
• 人类的自然智能（简称智能）
• 指人类在认识客观世界中，由思维过程和脑力活动所表现出的综合能力。
• 人类大脑是如何实现智能的
• 两大难题之一：宇宙起源、人脑奥秘
• 对人脑奥秘知之甚少
• 对人脑奥秘知道什么
•
结构：1011-12 量级的神经元，分布并行
• 智能：是信息到知识的一个加工器
• 信息革命及其意义
• 是信息与智能领域的革命，需要放大人的智能
5
第1章 人工智能概述
• AI的定义及其研究目标
• AI的产生与发展 • AI研究的基本内容 • AI研究的不同学派 • AI的主要研究和应用领域 • AI近期发展分析
6
1.1 AI的定义及其研究目标
机器思维：机器推理；机器搜索；机器规划
•
机器学习：符号学习；连接学习
•
机器行为：智能控制；智能制造；智能检索
•
智能机器：智能机器人；机器智能
•
智能应用：博弈；自动定理证明；自动程序设计
•
专家系统；智能决策；智能检索；智能CAD；智能CAI
•
智能交通；智能电力；智能产品；智能建筑等
• 人工智能新技术
•
• 功能：记忆、思维、观察、分析 等
• 对智能的严格定义
• 有待于人脑奥秘的揭示，进一步认识
8
认识智能的观点
• 思维理论 • 智能来源于思维活动，智能的核心是思维，人的一切知识都是思维的产物。
可望通过对思维规律和思维方法的研究，来揭示智能的本质。 • 知识阈值理论 • 智能取决于知识的数量及其可运用程度。一个系统所具有的可运用知识越多，
计算智能：神经计算；模糊计算；进化计算；自然计算
•
人工生命：人工脑；细胞自动机
•
分布智能：分布式问题求解；多Agent系统；群体智能
•
数据挖掘：知识发现；数据挖掘
• 一个新兴的“智能科学与技术学科”正在兴起
2
本课程的主要内容
• 第1章：人工智能概述 • 定义，产生过程，基本内容，不同学派，研究和应用领域，近期发展分析 • 第2章：知识表示方法 • 谓词，产生式，语义网络、框架等 • 第3章：确定性推理 • 自然演绎推理，归结推理，基于规则的演绎推理 • 第4章：搜索策略 • 状态空间的盲目搜索，状态空间的启发式搜索 • 第5章：计算智能 • 神经计算，进化计算， 模糊计算 • 第6章：非确定性推理 • 确定性理论，主观Bayese方法，证据理论，模糊推理 • 第7章：机器学习 • 符号学习，连接学习 • 第8章：自然语言理解 • 词法分析，句法分析，语义分析 • 第9章： 分布智能 • 多Agent技术，移动Agent技术 • 第10章：高级专家系统 • 模糊专家系统，神经网络专家系统，基于Web的专家系统，分布式和协同式专家系统