Artificial intelligence

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。

人工智能（Artificial Intelligence），英文缩写为AI。

它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。

人工智能是计算机科学的一个分支，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器，该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。

人工智能从诞生以来，理论和技术日益成熟，应用领域也不断扩大，可以设想，未来人工智能带来的科技产品，将会是人类智慧的“容器”。

人工智能可以对人的意识、思维的信息过程的模拟。

人工智能不是人的智能，但能像人那样思考、也可能超过人的智能。

人工智能是一门极富挑战性的科学，从事这项工作的人必须懂得计算机知识，心理学和哲学。

人工智能是包括十分广泛的科学，它由不同的领域组成，如机器学习，计算机视觉等等，总的说来，人工智能研究的一个主要目标是使机器能够胜任一些通常需要人类智能才能完成的复杂工作。

但不同的时代、不同的人对这种“复杂工作”的理解是不同的。

人工智能（Artificial Intelligence），英文缩写为AI。

它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。

人工智能是计算机科学的一个分支，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器，该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。

人工智能从诞生以来，理论和技术日益成熟，应用领域也不断扩大，可以设想，未来人工智能带来的科技产品，将会是人类智慧的“容器”。

人工智能可以对人的意识、思维的信息过程的模拟。

人工智能不是人的智能，但能像人那样思考、也可能超过人的智能。

人工智能是一门极富挑战性的科学，从事这项工作的人必须懂得计算机知识，心理学和哲学。

人工智能是包括十分广泛的科学，它由不同的领域组成，如机器学习，计算机视觉等等，总的说来，人工智能研究的一个主要目标是使机器能够胜任一些通常需要人类智能才能完成的复杂工作。

人工智能的英语作文_artificial intelligence 4篇导读：关于”人工智能“的英语作文范文4篇，作文题目：artificial intelligence。

以下是关于人工智能的三年级英语范文，每篇作文均为万能范文带翻译。

关于”人工智能“的英语作文范文4篇，作文题目：artificial intelce。

以下是关于人工智能的xx年级英语范文，每篇作文均为万能范文带翻译。

高分英语作文1：artificial intelceCan machines really think of artificial intelce? For example, a computer that thinks like human beings is terrible. It is possible that we are getting closer to building an artificial intelce that thinks like human beings. When it comes to this problem, different people will put forward different views.Some people think that the machine has human interest, it may be human Class is a better server, while another person thinks it's erous, it may cause resistance.中文翻译：机器真的能认为人工智能吗，比如一台像人类一样思考的计算机是可怕的正在建造一个像人类一样思考的机器真的有可能我们越来越接近于建立一个像人类一样思考的人工智能当涉及到这个问题时，不同的人会提出不同的观点，有人认为机器有人情趣，它可能是人类更好的服务者，而另一个人认为它危险，它可能会引起反抗。

人工智能（AI）的英语作文及译文（精选5篇）篇一：artificial intelligence can make our life more interesting. for example, if we have no company when playing games, artificial intelligence can accompany you.at the same time, artificial intelligence has the ability of self-learning and can be your little assistant in life.artificial intelligence must be the most popular and potential industry in the future. you can earn money to support your family without leaving home.you can completely release yourself, including st udents‘ learning, through artificial intelligence.译文：人工智能可以让我们在生活中更加有趣，比如说我们在玩游戏的时候没有人陪伴，那么人工智能可以陪你，同时人工智能有自我学习能力，可以做你的生活小助手。

篇二：future trends in computer science is one of the artificial intelligence，it is the research and artificial simulation of human thought and eventually be able to make a human like to think the same machine.for human services andto help people solve problems.after all, people thought it was unique, there are feelings, there are a variety of character, this will be very difficult to achieve in the machine.in fact, to do the same as the human thinking machine, the only one of the artificial intelligence, is by no means all. through the study of artificial intelligence, can resolve all kinds of scientific problems, and promote the development of other science, the artificial intelligence is the best!i believe that the science of artificial intelligence is waiting for humanity to explore it step by step the real connotation.译文：计算机科学的未来趋势是人工智能之一，它是对人类思维的研究和人工模拟，最终能够使人类喜欢思考的同一台机器。

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。

人工智能名词解释人工智能（Artificial Intelligence，简称AI），是指模拟、延伸和扩展人类智能的一门科学与技术。

它旨在研究和开发能够模仿、执行人类智能任务的智能系统。

人工智能的发展涉及多个子领域，包括机器学习、自然语言处理、计算机视觉和专家系统等。

下面将逐个解释这些与人工智能相关的名词。

1. 机器学习（Machine Learning）机器学习是人工智能的一个重要分支，它涉及让计算机通过从大量数据中学习、识别模式并进行预测和决策的能力。

机器学习算法通过对训练数据进行分析和学习，从而能够自主地改善和适应新数据，实现模型的自动调整和优化。

2. 自然语言处理（Natural Language Processing，简称NLP）自然语言处理是人工智能领域关注的一个重要方向，它涉及让计算机能够理解、分析和生成人类语言。

通过使用自然语言处理技术，计算机可以实现自动的文本理解、问答系统、机器翻译和情感分析等任务。

3. 计算机视觉（Computer Vision）计算机视觉是人工智能中的一个子领域，研究和开发让计算机能够理解和解释图像和视频的能力。

计算机视觉技术可以实现图像识别、目标检测、人脸识别和图像生成等任务，打开了计算机与视觉世界之间的交互通道。

4. 专家系统（Expert System）专家系统是一类基于知识和推理的人工智能系统，它通过模拟和应用人类专家的知识和经验来解决复杂的问题。

专家系统通过与用户的交互，推理和提供问题解决方案，可广泛用于医疗、金融、工业等领域的决策支持和问题求解。

5. 深度学习（Deep Learning）深度学习是机器学习领域中一种特殊的算法，其核心思想是构建和训练具有多个层次和参数的神经网络模型。

深度学习通过模拟人脑神经元之间的连接方式，实现了对复杂数据的高级抽象和表征，广泛应用于图像和语音识别、自动驾驶和自然语言处理等领域。

6. 强化学习（Reinforcement Learning）强化学习是一种机器学习的方法，通过建立智能体与环境的交互模型，以试错的方式逐步学习和改进行为策略。

人工智能ai英文介绍Artificial Intelligence (AI): An IntroductionIn the era of rapid technological advancements, artificial intelligence (AI) stands as one of the most exciting and transformative fields. AI refers to the simulation of human intelligence in machines that are programmed to think and learn, enabling them to perform tasks typically requiring human cognition. This article serves as an introduction to AI, discussing its definition, applications, and potential impact on various industries.1. Definition of Artificial IntelligenceAI is a branch of computer science that focuses on creating intelligent machines capable of mimicking human behavior. It involves developing algorithms and models that enable computers to process information, reason, learn, and make decisions. The ultimate goal of AI is to build machines that not only perform tasks but also possess a level of intelligence similar to or surpassing human intelligence.2. History of Artificial IntelligenceThe concept of AI emerged in the 1950s when researchers began exploring the idea of creating machines that can imitate human thinking. The field progressed through various stages, from early rule-based systems to modern machine learning algorithms. Significant milestones in AI history include the development of expert systems, neural networks, and the recent breakthroughs in deep learning.3. Types of Artificial IntelligenceAI can be categorized into two main types: Narrow AI and General AI. Narrow AI, also known as Weak AI, refers to AI systems designed for specific tasks, such as voice assistants or autonomous vehicles. General AI, on the other hand, represents a hypothetical form of AI that possesses the ability to understand, learn, and perform any intellectual task that a human being can do.4. Applications of Artificial IntelligenceAI has found applications across various industries and domains. In healthcare, AI is utilized for medical diagnosis, drug discovery, and personalized treatment plans. In finance, AI is used for algorithmic trading, fraud detection, and risk assessment. Other sectors benefiting from AI include transportation, manufacturing, customer service, and agriculture.5. Impact of Artificial Intelligence on SocietyThe widespread adoption of AI brings both opportunities and challenges. On one hand, AI has the potential to enhance productivity, automate mundane tasks, and improve decision-making. On the other hand, concerns arise regarding job displacement, ethical implications, and biases in AI systems. Striking a balance between technological progress and societal well-being is a crucial consideration for the future of AI.6. Future Trends in Artificial IntelligenceThe future of AI holds immense potential for advancements. Some emerging trends include the integration of AI with other technologies like Internet of Things (IoT) and robotics, the development of explainable AI for transparency, and the focus on ethical AI design. Continued research anddevelopment will drive further innovation and push the boundaries of what AI can achieve.In conclusion, artificial intelligence is a fascinating field that revolutionizes how machines interact and respond to tasks, rivalling human intelligence. This article provided an overview of AI, discussing its definition, history, types, applications, societal impact, and future trends. As AI continues to evolve, it is essential to ensure its ethical and responsible adoption in order to harness its full potential for the benefit of society.。