AI人工智能论文翻译（AI人工智能网站写论文），老铁们想知道有关这个问题的分析和解答吗，相信你通过以下的文章内容就会有更深入的了解，那末接下来就随着我们的小编一起看看吧。

AI人工智能论文翻译（AI人工智能网站写论文）

随着人工智能技术的日趋发展，愈来愈多的人开始关注AI在各个领域的利用与研究。在这篇论文中，我们将通过翻译一篇AI人工智能论文，来介绍AI的一些基本概念和它在现实世界中的利用。

人工智能，简称AI，是一种触及到机器摹拟人类智能行动的技术。它不单单是简单的机器学习或模式辨认，而是通过学习和理解人类思惟和决策的方式来摹拟人类智能。AI技术触及到许多领域，包括机器学习、自然语言处理、计算机视觉等。

在现实世界中，AI技术已被广泛利用于各个领域。在医疗领域，AI可以帮助医生进行疾病诊断和医治方案的制定。通过分析大量的医疗数据和病例，AI可以提供更精确的诊断结果，减少误诊和漏诊的情况。AI还可以帮助医生制定个性化的医治方案，根据患者的具体情况和需求来调剂医治方案，提高医治效果。

在交通领域，AI技术也发挥侧重要作用。自动驾驶汽车就是一个典型的利用案例。自动驾驶汽车通过使用各种传感器和算法来感知周围环境，并自动控制汽车的行驶和操作。这类技术不但可以提高交通安全性，减少交通事故的产生，还可以提高行驶效力，减少交通拥堵。

AI技术还可以利用于金融、教育、文娱等各个领域。在金融领域，AI可以帮助银行和金融机构进行风险评估和数据分析，提高投资决策的准确性和效力。在教育领域，AI可以帮助教师进行个性化教学，根据学生的学习情况和需求来制定教学计划。在文娱领域，AI可以通过分析用户的兴趣和偏好，为用户提供个性化的文娱内容和推荐。

AI人工智能是一门前沿的技术，正在改变我们生活和工作的各个方面。通过翻译本篇论文，我们对AI的基本概念和利用进行了介绍。随着技术的不断进步和利用的拓展，相信AI技术将在未来发挥更大的作用，为我们带来更多的便利和创新。

AI人工智能论文翻译（AI人工智能网站写论文）

Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it. Textbooks define the field as "the study and design of intelligent agents,"[1] where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.[2] John McCarthy, who coined the term in 1956,[3] defines it as "the science and engineering of making intelligent machines."[4]The field was founded on the claim that a central property of human beings, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.[5] This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity.[6] Artificial intelligence has been the subject of breathtaking optimism,[7] has suffered stunning setbacks[8] and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.[9]AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other.[10] Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.[11] General intelligence (or "strong AI") is still a long-term goal of (some) research.[12]Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the golden robots of Hephaestus and Pygmalions Galatea.[13] Human likenesses believed to have intelligence were built in every major civilization: animated statues were worshipped in Egypt and Greece[14] and humanoid automatons were built by Yan Shi,[15] Hero of Alexandria,[16] Al-Jazari[17] and Wolfgang von Kempelen.[18] It was also widely believed that artificial beings had been created by Jābir ibn Hayyān,[19] Judah Loew[20] and Paracelsus.[21] By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as in Mary Shelleys Frankenstein or Karel apeks R.U.R. (Rossums Universal Robots).[22] Pamela McCorduck argues that all of these are examples of an ancient urge, as she describes it, "to forge the gods".[6] Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.The problem of simulating (or creating) intelligence has been broken down into a number of specific sub-problems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The traits described below have received the most attention.[11][edit] Deduction, reasoning, problem solving

Early AI researchers developed algorithms that imitated the step-by-step reasoning that human beings use when they solve puzzles, play board games or make logical deductions.[39] By the late 80s and 90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[40]For difficult problems, most of these algorithms can require enormous computational resources — most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research.[41]Human beings solve most of their problems using fast, intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able to model.[42] AI has made some progress at imitating this kind of "sub-symbolic" problem solving: embodied approaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside human and animal brains that gives rise to this skill.

General intelligence

Main articles: Strong AI and AI-complete

Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them.[12] A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.[74]Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation requires that the machine follow the authors argument (reason), know what is being talked about (knowledge), and faithfully reproduce the authors intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.[75][edit] Approaches

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[76] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence, by studying psychology or neurology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[77] Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[78] Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?[79][edit] Cybernetics and brain simulation

Main articles: Cybernetics and Computational neuroscienceThere is no consensus on how closely the brain should be simulated.In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walters turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[24] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.How can one determine if an agent is intelligent? In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.The broad classes of outcome for an AI test are:Optimal: it is not possible to perform better

Strong super-human: performs better than all humans

Super-human: performs better than most humans

Sub-human: performs worse than most humans

For example, performance at draughts is optimal,[143] performance at chess is super-human and nearing strong super-human,[144] and performance at many everyday tasks performed by humans is sub-human.A quite different approach is based on measuring machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of this kind of tests start in the late nineties devising intelligence tests using notions from Kolmogorov Complexity and compression [145] [146]. Similar definitions of machine intelligence have been put forward by Marcus Hutter in his book Universal Artificial Intelligence (Springer 2005), which was further developed by Legg and Hutter [147]. Mathematical definitions have, as one advantage, that they could be applied to nonhuman intelligences and in the absence of human testers.AI is a common topic in both science fiction and in projections about the future of technology and society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues and the potential power of the technology inspires both hopes and fears.Mary Shelleys Frankenstein,[160] considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human being? The idea also appears in modern science fiction: the film Artificial Intelligence: A.I. considers a machine in the form of a small boy which has been given the ability to feel human emotions, including, tragically, the capacity to suffer. This issue, now known as "robot rights", is currently being considered by, for example, Californias Institute for the Future,[161] although many critics believe that the discussion is premature.[162]Another issue explored by both science fiction writers and futurists is the impact of artificial intelligence on society. In fiction, AI has appeared as a servant (R2D2 in Star Wars), a law enforcer (K.I.T.T. "Knight Rider"), a comrade (Lt. Commander Data in Star Trek), a conqueror (The Matrix), a dictator (With Folded Hands), an exterminator (Terminator, Battlestar Galactica), an extension to human abilities (Ghost in the Shell) and the saviour of the human race (R. Daneel Olivaw in the Foundation Series). Academic sources have considered such consequences as: a decreased demand for human labor,[163] the enhancement of human ability or experience,[164] and a need for redefinition of human identity and basic values.[165]Several futurists argue that artificial intelligence will transcend the limits of progress and fundamentally transform humanity. Ray Kurzweil has used Moores law (which describes the relentless exponential improvement in digital technology with uncanny accuracy) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and that by 2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer Vernor Vinge named the "technological singularity".[164] Edward Fredkin argues that "artificial intelligence is the next stage in evolution,"[166] an idea first proposed by Samuel Butlers "Darwin among the Machines" (1863), and expanded upon by George Dyson in his book of the same name in 1998. Several futurists and science fiction writers have predicted that human beings and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil.[164] Transhumanism has been illustrated in fiction as well, for example in the manga Ghost in the Shell and the science fiction series Dune. Pamela McCorduck writes that these scenarios are expressions of the ancient human desire to, as she calls it, "forge the gods."[6]

AI人工智能写论文

ai写论文是不可靠的。人工智能的发展非常迅速，经过测试ai写出的文章非常流畅。但是用ai写文章会不会相当于学生做弊而对学生自己的水平来讲让ai代替写，自己的水平并没有得到很好的展现。论文是每位大学生毕业之前都要写的东西。

AI会成为学生做弊的工具，它们也能够成为强大的助教，或提高我们创造力的工具。重点在于学生使用的会不会恰当。如果学生本身甚么都没学到却用ai论文蒙混过关，这是不靠谱的。当用ai写论文被发现以后后果也是很严重的，可能会影响到自己会不会能正常毕业。

AI人工智能论文范文

人工智能是20世纪计算机科学发展的重大成绩，在许多领域有着广泛的利用。以下是我整理的人工智能的毕业论文范文的相关资料，欢迎浏览! 　　人工智能的毕业论文范文篇一 摘要：人工智能是20世纪计算机科学发展的重大成绩，在许多领域有着广泛的利用。论述了人工智能的定义，分析了目前在管理、教育、工程、技术、等领域的利用，总结了人工智能研究现状，分析了其发展方向。 关键词：人工智能;计算机科学;发展方向 中图分类号：TP18 文献标识码：A 文章编号：1672⑻198(2009)13-0248-02 1　人工智能的定义 人工智能(Artificial Intelligence，AI)，是一门综合了计算机科学、生理学、哲学的交叉学科。“人工智能”一词最初是在1956年美国计算机协会组织的达特莫斯(Dartmouth)学会上提出的。自那以后，研究者们发展了众多理论和原理，人工智能的概念也随之扩大。由于智能概念的不肯定，人工智能的概念一直没有一个统一的标准。著名的美国斯坦福大学人工智能研究中心尼尔逊教授对人工智能下了这样一个定义“人工智能是关于知识的学科――怎样表示知识和怎样取得知识并使用知识的科学。”而美国麻省理工学院的温斯顿教授认为“人工智能就是研究如何使计算机去做过去只有人材能做的智能工作。”童天湘在《从“人机大战”到人机共生》中这样定义人工智能：“虽然现在的机器不能思惟也没有“直觉的方程式”，但可以把人处理问题的方式编入智能程序，是不能思惟的机器也有智能，使机器能做那些需要人的智能才能做的事，也就是人工智能。”诸如此类的定义基本都反应了人工智能学科的基本思想和基本内容。即人工智能是研究人类智能活动的规律，构造具有一定智能的人工系统，研究怎么让计算机去完成以往需要人的智力才能胜任的工作，也就是研究如何利用计算机的软硬件来摹拟人类某些智能行动的基本理论、方法和技术。 2　人工智能的利用领域 2.1　人工智能在管理及教学系统中的利用 人工智能在企业管理中的利用。刘玉然在《谈谈人工智能在企业管理中的利用》一文中提到把人工智能利用于企业管理中，认为要做的工作就是弄清楚人的智能和人工智能的关系，了解人工智能的外延和内涵，搭建人工智能的利用平台，弄好企业智能化软件的开发工作，人工智能就可以在企业决策中起到关键的作用。 人工智能在智能教学系统中的利用。焦加麟，徐良贤，戴克昌(2003)在总结国际上相关研究成果的基础上，结合其在开发智能多媒体汉德语言教学系统《二十一世纪汉语》的进程中积累的实践经验，介绍了智能教学系统的历史、结构和主要技术，侧重讨论了人工智能技术与方法在其中的利用，并指出了现今这个领域上存在的一些问题。 2.2　人工智能专家系统在工程领域的利用 人工智能专家系统在医学中的利用。海外最早将人工智能利用于医疗诊断的是MYCIN专家系统。1982年，美国Pittsburgh大学Miller发表了著名的作为内科医生咨询的Internist 2I内科计算机辅助诊断系统的研究成果，1977年改进为Internist 2Ⅱ，经过改进后成为现在的CAU-CEUS，1991年美国哈佛医学院Barnett等开发的DEX-PLAIN，包括有2200种疾病和8000种症状。我国研制基于人工智能的专家系统始于上世纪70年代末，但是发展很快。初期的有北京中医学院研制成“关幼波肝炎医疗专家系统”，它是摹拟著名老中医关幼波大夫对肝病诊治的程序。上世纪80年代初，福建中医学院与福建计算机中心研制的林如高骨伤计算机诊疗系统。其他如厦门大学、重庆大学、河南医科大学、长春大学等高等院校和其他研究机构开发了基于人工智能的医学计算机专家系统，并成功利用于临床。 人工智能在矿业中的利用。与矿业有关的第一个人工智能专家系统是1978年美国斯坦福国际研究所的矿藏勘探和评价专家系统PROSPECTOR，用于勘探评价、区域资源估值和钻井井位选择等。20世纪80年代以来，美国矿山局匹兹堡研究中心与其它单位合作开发了预防煤矿巷道底臌、瓦斯治理和煤尘控制的专家系统;弗尼吉亚理工学院及州立大学研制了摹拟连续开采进程中开采、装载、运输、顶板锚固和装备检查专家系统Consim;阿拉斯加大学编写了地下煤矿采矿方法选择专家系统。 2.3　人工智能在技术研究中的利用 人工智能在超声无损检测中的利用。在超声无损检测(NDT)与无损评价(NDE)领域中，目前主要广泛采取专家系统方法对超声损伤(UT)中缺点的性质，形状和大小进行判断和归类;专家在传统超声无损检测与智能超声无损检测之间架起了一座桥梁，它能把一般的探伤人员变成技术熟练。经验丰富的专家。所以在实际利用中这类智能超声无损检测有很大的价值。 人工智能在电子技术方面的利用。沈显庆认为可以把人工智能和仿真技术相结合，以单片机硬件电路为专家系统的知识来源，建立单片机硬件配置专家系统，进行故障诊断，以提高纠错能力。人工智能技术也被引入到了计算机网络领域，计算机网络安全管理的经常使用技术是防火墙技术，而防火墙的核心部份就是入侵检测技术。随着网络的迅速发展，各种入侵手段也在层见叠出，单凭传统的防范手段已远远不能满足现实的需要，把人工智能技术利用到网络安全管理领域，大大提高了它的安全性。马秀荣等在《简述人工智能技术在网络安全管理中的利用》一文中具体介绍了如何把人工智能技术利用于计算机网络安全管理中，起到了很好的安全防范作用。 3　人工智能的发展方向 3.1　人工智能的发展现状 海外发展现状。AI技术在美国、欧洲和日本发展很快。在AI技术领域十分活跃的IBM公司。已为加州劳伦斯利佛摩尔国家实验室制造了号称具有人脑的千分之一的智力能力的“ASCII White”电脑，而且正在开发的更加强大的新超级电脑――“蓝色牛仔(blue jean)”，据其研究主任保罗霍恩称，预计“蓝色牛仔”的智力水平将大致与人脑相当。麻省理工学院的AI实验室进行一个的代号为cog的项目。cog计划意图赋予机器人以人类的行动，该实验的一个项目是让机器人捕捉眼睛的移动和脸部表情，另外一个项目是让机器人捉住从它眼前经过的东西，还有一个项目则是让机器人学会凝听音乐的节奏并将其在鼓上演奏出来。由于人工智能有着广大的发展前景，巨大的发展市场被各国和各公司所看好。除IBM等公司继续在AI技术上大量投入，以保证其领先地位外，其他公司在人工智能的分支研究方面，也保持着一定的投入比例。微软公司总裁比尔盖茨在美国华盛顿召开的AI(人工智能)国际会议上进行了主题演讲，称微软研究院目前正致力于AI的基础技术与利用技术的研究，其对象包括自我决定、表达知识与信息、信息检索、机械学习、数据收集、自然语言、语音字迹辨认等。 我国人工智能的研究现状。很长机械 和自动控制专家们都把研制具有人的行动特点的类人性机器人作为奋斗目标。中国国际科技大学在国家863计划和自然科学基金支持下，一直从事两足步行机器人、类人性机器人的研究开发，在1990年成功研制出我国第一台两足步行机器人的基础上，经过科研10年攻关，于2000年11月，又成功研制成我国第一台类人性机器人。它有人一样的身躯、四肢、头颈、眼睛，并具有了一定的语言功能。它的行走频率从过去的每六秒一步，加快到每秒两步;从只能平静地静态不行，到能快速自若的动态步行;从只能在已知的环境中步行，到可在小偏差、不肯定环境中行走，获得了机器人神经网络系统、生理视觉系统、双手调和系统、手指控制系统等多项重大研究成果。 3.2　人工智能发展方向 在信息检索中的利用。人工智能在网络信息检索中的利用，主要表现在：①如何利用计算机软硬件系统模仿、延伸与扩大人类智能的理论、方法和技术，包括机器感知、机器思惟、机器行动，即知识获得、知识处理、知识利用的进程。②由于网络知识信息既包括规律性的知识，如一般原理概念，也包括大量的经验知识，这些知识不可避免地带有模糊性、随机性、不可靠性等不肯定性因素，对其进行推理，需要利用人工智能的研究成果。 基于专家系统的入侵检测方法。入侵检测中的专家系统是网络安全专家对可疑行动的分析后得到的一套推理规则。一个基于规则的专家系统能够在专家的指点下，随着经验的积累而利用自学习能力进行规则的扩充和修正，专家系统对历史记录的依赖性相对统计方法较小，因此适应性较强，可以较灵活地适应广普的安全策略和检测要求。这是人工智能发展的一个主要方向。 人工智能在机器人中的利用。机器人足球系统是目前进行人工智能体系统研究的热门，其即高科技和文娱性于一体的特点吸引了国内外大批学者的兴趣。决策系统主要解决机器人足球比赛进程中机器人之间的协作和机器人运动计划问题，在机器人足球系统设计中需要将人工智能中的决策树、神经网络、遗传学的等算法综合应用，随着人工智能理论的进一步发展，将使机器人足球有长足的发展。 4　结语 由上述的讨论我们可以看到，目前人工智能的利用领域相当广泛。不管是学术界或者利用领域对人工智能都高度重视。人工智能良好的发展和利用前景，要求我们一定要加大研究和投入力度，以令人工智能的发展能为人类服务。 下一页分享更优秀的<<<人工智能的毕业论文范文

AI人工智能网站写论文

ai写论文是不可靠的。人工智能的发展非常迅速，经过测试ai写出的文章非常流畅。但是用ai写文章会不会相当于学生做弊而对学生自己的水平来讲让ai代替写，自己的水平并没有得到很好的展现。论文是每位大学生毕业之前都要写的东西。

AI会成为学生做弊的工具，它们也能够成为强大的助教，或提高我们创造力的工具。重点在于学生使用的会不会恰当。如果学生本身甚么都没学到却用ai论文蒙混过关，这是不靠谱的。当用ai写论文被发现以后后果也是很严重的，可能会影响到自己会不会能正常毕业。

人工智能AI技术利用论文

人工智能技术的利用以下：

随着数字化时期的到来，人工智能被广泛利用。特别是在家居、制造、金融、医疗、安防、交通、零售、教育和物流等多领域。

1、智能制造随着工业制造4.0时期的推动，传统的制造业在人工智能的推动下迅速爆发。人工智能在制造的利用领域主要分为三个方面：（1） 智能设备：主要包括自动辨认装备、人机交互系统、工业机器人和数控机床等。（2） 智能工厂：包括智能设计、智能生产、智能管理及集成优化等。（3） 智能服务：个性化定制、远程运维及预测性保护等。2、智能家居智能家居主要是援用物联网技术，通过智能硬件、软件、云计算平台等构成一套完全的家居生态系统。这些家居产品都有一个智能AI你可以设置口令指挥产品自主运行，同时AI还可以搜索你的使用数据，最后到达不需要指挥的效果。3、智慧金融人工智能在金融方面可以进行自动获客、身份辨认、大数据风控、智能投顾、智能客服和金融云等。4、智能医疗智能医疗主要是通过大数据、5G、云计算、大数据、AR/VRh和人工智能等技术与医疗行业进行深度融会等。智能医疗主要是起到辅助诊断、医疗影象及疾病检测、药物开发等作用。

AI人工智能论文翻译（AI人工智能网站写论文）的介绍，今天就讲到这里吧，感谢你花时间浏览本篇文章，更多关于AI人工智能论文翻译（AI人工智能网站写论文）的相关知识，我们还会随时更新，敬请收藏本站。