英文翻译人工智能
大学人工智能英语教材翻译
大学人工智能英语教材翻译IntroductionIn recent years, artificial intelligence (AI) has become a ubiquitous presence in our lives, revolutionizing various industries and fields. To meet the growing demand for AI professionals, universities have started offering courses and developing textbooks on the subject. This article aims to translate key contents of a university-level AI English textbook into Chinese, providing students with a comprehensive resource to enhance their understanding of this rapidly evolving field.Chapter 1: Introduction to Artificial Intelligence人工智能简介Artificial intelligence, often referred to as AI, is a branch of computer science that focuses on the creation of intelligent machines capable of performing tasks that typically require human intelligence. AI can be divided into two categories: narrow AI, which is designed to perform a specific task, and general AI, which aims to replicate human-level intelligence across a wide range of domains.Chapter 2: Machine Learning机器学习Machine learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. It involves the development of algorithms and models that allow computers to analyze and interpret data, identify patterns, and make predictions or decisions basedon the observed information. Supervised learning, unsupervised learning, and reinforcement learning are the three main types of machine learning techniques.Chapter 3: Neural Networks神经网络Neural networks are a fundamental concept in AI. Inspired by the structure and function of the human brain, neural networks consist of interconnected nodes or artificial neurons. These networks learn from training data by adjusting the connections between nodes to optimize their performance. Deep learning, a subfield of AI, utilizes neural networks with multiple layers to solve complex problems and achieve higher accuracy in tasks such as image recognition and natural language processing.Chapter 4: Natural Language Processing自然语言处理Natural language processing (NLP) focuses on enabling computers to interact and understand human language in a natural and meaningful way. It involves the development of algorithms and models that can process, analyze, and generate human language, enabling tasks such as machine translation, sentiment analysis, and chatbot development. NLP plays a crucial role in bridging the gap between humans and AI systems.Chapter 5: Computer Vision计算机视觉Computer vision is an interdisciplinary field that deals with the extraction, analysis, and understanding of visual information from images or videos. Through the use of AI techniques, computers can recognize objects, detect and track motion, and perform tasks such as facial recognition and image classification. Computer vision has various applications, including autonomous vehicles, surveillance systems, and augmented reality.Chapter 6: Robotics and Artificial Intelligence机器人与人工智能The integration of AI and robotics has led to significant advancements in the field of robotics. AI-powered robots can perceive their environment, make autonomous decisions, and interact with humans and other robots effectively. This chapter explores the role of AI in robotics, discussing topics such as robot perception, robot control, and human-robot interaction.Chapter 7: Ethical and Social Implications of AI人工智能的伦理和社会影响As AI continues to advance, ethical considerations and potential societal impact become increasingly important. This chapter delves into the ethical dilemmas surrounding AI, including privacy concerns, biases in AI systems, and the impact of AI on employment and workforce. It emphasizes the need for responsible development and deployment of AI technologies, ensuring that they benefit humanity and uphold ethical standards.ConclusionIn conclusion, this article has provided a translated overview of key topics in a university-level AI English textbook. By familiarizing themselves with these concepts, students can deepen their understanding of artificial intelligence and its various applications. Moreover, this translation serves as a valuable resource for educators and researchers in the Chinese-speaking community who seek to expand their knowledge in this rapidly advancing field. With the continued development of AI, it is imperative to bridge language barriers and foster global collaboration in order to drive innovation and ensure responsible AI implementation.。
人工智能AI革命外文翻译中英文
人工智能（AI）革命外文翻译中英文英文The forthcoming Artificial Intelligence (AI) revolution:Its impact on society and firmsSpyros MakridakisAbstractThe impact of the industrial and digital (information) revolutions has, undoubtedly, been substantial on practically all aspects of our society, life, firms and employment. Will the forthcoming AI revolution produce similar, far-reaching effects? By examining analogous inventions of the industrial, digital and AI revolutions, this article claims that the latter is on target and that it would bring extensive changes that will also affect all aspects of our society and life. In addition, its impact on firms and employment will be considerable, resulting in richly interconnected organizations with decision making based on th e analysis and exploitation of “big” data and intensified, global competition among firms. People will be capable of buying goods and obtaining services from anywhere in the world using the Internet, and exploiting the unlimited, additional benefits that will open through the widespread usage of AI inventions. The paper concludes that significant competitive advantages will continue to accrue to those utilizing the Internet widely and willing to take entrepreneurial risks in order to turn innovative products/services into worldwide commercial success stories. The greatest challenge facing societies and firms would be utilizing the benefits of availing AI technologies, providing vast opportunities for both new products/services and immense productivity improvements while avoiding the dangers and disadvantages in terms of increased unemployment and greater wealth inequalities.Keywords：Artificial Intelligence (AI)，Industrial revolution，Digital revolution，AI revolution，Impact of AI revolution，Benefits and dangers of AI technologies The rise of powerful AI will be either the best or the worst thing ever to happento humanity. We do not yet know which.Stephen HawkingOver the past decade, numerous predictions have been made about the forthcoming Artificial Intelligence (AI) Revolution and its impact on all aspects of our society, firms and life in general. This paper considers such predictions and compares them to those of the industrial and digital ones. A similar paper was written by this author and published in this journal in 1995, envisioning the forthcoming changes being brought by the digital (information) revolution, developing steadily at that time, and predicting its impact for the year 2015 (Makridakis, 1995). The current paper evaluates these 1995 predictions and their impact identifying hits and misses with the purpose of focusing on the new ones being brought by the AI revolution. It must be emphasized that the stakes of correctly predicting the impact of the AI revolution arefar reaching as intelligent machines may become our “final invention” that may end human supremacy (Barrat, 2013). There is little doubt that AI holds enormous potential as computers and robots will probably achieve, or come close to, human intelligence over the next twenty years becoming a serious competitor to all the jobs currently performed by humans and for the first time raising doubt over the end of human supremacy.This paper is organized into four parts. It first overviews the predictions made in the 1995 paper for the year 2015, identifying successes and failures and concluding that major technological developments (notably the Internet and smartphones) were undervalued while the general trend leading up to them was predicted correctly. Second, it investigates existing and forthcoming technological advances in the field of AI and the ability of computers/machines to acquire real intelligence. Moreover, it summarizes prevailing, major views of how AI may revolutionize practically everything and its impact on the future of humanity. The third section sums up the impact of the AI revolution and describes the four major scenarios being advocated, as well as what could be done to avoid the possible negative consequences of AI technologies. The fourth section discusses how firms will be affected by these technologies that will transform the competitive landscape, how start-up firms are founded and the way success can be achieved. Finally, there is a brief concluding section speculating about the future of AI and its impact on our society, life, firms and employment.1. The 1995 paper: hits and missesThe 1995 paper (Makridakis, 1995) was written at a time when the digital (at that time it was called information) revolution was progressing at a steady rate. The paper predicted that by 2015 “the information revolution should be in full swing” and that “computers/communications” would be in widespread use, whi ch has actually happened, although its two most important inventions (the Internet and smartphones) and their significant influence were not foreseen as such. Moreover, the paper predicted that “a single computer (but not a smartphone) can, in addition to its traditional tasks, also become a terminal capable of being used interactively for the following:” (p. 804–805)• Picture phone and teleconference• Television and videos• Music• Shopping• On line banking and financial services• Reservations• Medic al advice• Access to all types of services• Video games• Other games (e.g., gambling, chess etc.)• News, sports and weather reports• Access to data banksThe above have all materialized and can indeed be accessed by computer,although the extent of their utilization was underestimated as smartphones are now being used widely. For instance, the ease of accessing and downloading scientific articles on one's computer in his/her office or home would have seemed like science fiction back in 1995, when finding such articles required spending many hours in the library (often in its basement for older publications) and making photocopies to keep them for later use. Moreover, having access, from one's smartphone or tablet, to news from anywhere in the world, being able to subscribe to digital services, obtain weather forecasts, purchase games, watch movies, make payments using smartphones and a plethora of other, useful applications was greatly underestimated, while the extensive use of the cloud for storing large amounts of data for free was not predicted at all at that time. Even in 1995 when the implications of Moore's law leading to increasing computer speed and storage while reducing costs were well known, nevertheless, it was hard to imagine that in 2016 there would be 60 trillion web pages, 2.5 billion smartphones, more than 2 billion personal computers and 3.5 billion Google searches a day.The paper correctly predicted “as wireless telecommunications will be possible the above list of capabilities can be accessed from anywhere in the world without the need for regular telephone lines”. What the 1995 paper missed, however, was that in 2015 top smartphones, costing less than €500, would be as powerful as the 1995 supercomputer, allowing access to the Internet and all tasks that were only performed by expensive computers at that time, including an almost unlimited availability of new, powerful apps providing a large array of innovative services that were not imagined twenty years ago. Furthermore, the paper correctly predicted super automation leading to unattended factories stating that “by 2015 there will be little need for people to do repetitive manual or mental tasks”. It also foresaw the decline of large industrial firms, increased global competition and the drop in the percentage of labour force employed in agriculture and manufacturing (more on these predictions in the section The Impact of the AI Revolution on Firms). It missed however the widespread utilization of the Internet (at that time it was a text only service), as well as search engines (notably Google), social networking sites(notably Facebook) and the fundamental changes being brought by the widespread use of Apple's iPhone, Samsung's Galaxy and Google's Android smartphones. It is indeed surprising today to see groups of people in a coffee shop or restaurant using their smartphones instead of speaking to each other and young children as little as three or four years of age playing with phones and tablets. Smartphones and tablets connected to the Internet through Wi-Fi have influenced social interactions to a significant extent, as well as the way we search for information, use maps and GPS for finding locations, and make payments. These technologies were not predicted in the 1995 paper.2. Towards the AI revolutionThe 1995 paper referred to Say, the famous French economist, who wrote in 1828 about the possibility of cars as substitutes for horses:“Nevertheless no machine will ever be able to perform what even the worst horses can - the service of carrying people and goods through the bustle and throng of a great city.” (p. 800)Say could never have dreamed of, in his wildest imagination, self-driving cars, pilotless airplanes, Skype calls, super computers, smartphones or intelligent robots. Technologies that seemed like pure science fiction less than 190 years ago are available today and some like self-driving vehicles will in all likelihood be in widespread use within the next twenty years. The challenge is to realistically predict forthcoming AI technologies without falling into the same short-sighted trap of Say and others, including my 1995 paper, unable to realize the momentous, non-linear advancements of new technologies. There are two observations to be made.First, 190 years is a brief period by historical standards and during this period we went from horses being the major source of transportation to self-driving cars and from the abacus and slide rules to powerful computers in our pockets. Secondly, the length of time between technological inventions and their practical, widespread use is constantly being reduced. For instance, it took more than 200 years from the time Newcomen developed the first workable steam engine in 1707 to when Henry Ford built a reliable and affordable car in 1908. It took more than 90 years between the time electricity was introduced and its extensive use by firms to substantially improve factory productivity. It took twenty years, however, between ENIAC, the first computer, and IBM's 360 system that was mass produced and was affordable by smaller business firms while it took only ten years between 1973 when Dr Martin Cooper made the first mobile call from a handheld device and its public launch by Motorola. The biggest and most rapid progress, however, took place with smartphones which first appeared in 2002 and saw a stellar growth with the release of new versions possessing substantial improvements every one or two years by the likes of Apple, Samsung and several Chinese firms. Smartphones, in addition to their technical features, now incorporate artificial intelligence characteristics that include understanding speech, providing customized advice in spoken language, completing words when writing a text and several other functions requiring embedded AI, provided by a pocket computer smaller in size than a pack of cigarettes.From smart machines to clever computers and to Artificial Intelligence (AI) programs: A thermostat is a simple mechanical device exhibiting some primitive but extremely valuable type of intelligence by keeping temperatures constant at some desired, pre-set level. Computers are also clever as they can be instructed to make extremely complicated decisions taking into account a large number of factors and selection criteria, but like thermostats such decisions are pre-programmed and based on logic, if-then rules and decision trees that produce the exact same results, as long as the input instructions are alike. The major advantage of computers is their lightning speed that allows them to perform billions of instructions per second. AI, on the other hand, goes a step further by not simply applying pre-programmed decisions, but instead exhibiting some learning capabilities.The story of the Watson computer beating Jeopardy's two most successful contestants is more complicated, since retrieving the most appropriate answer out of the 200 million pages of information stored in its memory is not a sign of real intelligence as it relied on its lightning speed to retrieve information in seconds. What is more challenging according to Jennings, one of Jeopardy's previous champions, is“to read clues in a natural language, understand puns and the red herrings, to unpack just the meaning of the clue” (May, 2013). Similarly, it is a sign of intelligence to improve it s performance by “playing 100 games against past winners”. (Best, 2016). Watson went several steps beyond Deep Blue towards AI by being able to understand spoken English and learn from his mistakes (New Yorker, 2016). However, he was still short of AlphaGo that defeated Go Champions in a game that cannot be won simply by using “brute force” as the number of moves in this game is infinite, requiring the program to use learning algorithms that can improve its performance as it plays more and more gamesComputers and real learning: According to its proponents, “the main focus of AI research is in teaching computers to think for themselves and improvise solutions to common problems” (Clark, 2015). But many doubt that computers can learn to think for themselves even though they can display signs of intelligence. David Silver, an AI scientist working at DeepMind, explained that “even though AlphaGo has affectively rediscovered the most subtle concepts of Go, its knowledge is implicit. The computer parse out these concepts –they simply emerge from its statistical comparisons of types of winning board positions at GO” (Chouard, 2016). At the same time Cho Hyeyeon, one of the strongest Go players in Korea commented that “AlphaGo seems like it knows everything!” while others believe that “AlphaGo is likely to start a ‘new revolution’ in the way we play Go”as “it is seeking simply to maximize its probability of reaching winning positions, rather than as human players tend to do –maximize territorial gains” (Chouard, 2016). Does it matter, as Silver said, that AlphaGo's knowledge of the game is implicit as long as it can beat the best players? A more serious issue is whether or not AlphaGo's ability to win games with fixed rules can extend to real life settings where not only the rules are not fixed, but they can change with time, or from one situation to another.From digital computers to AI tools: The Intel Pentium microprocessor, introduced in 1993, incorporated graphics and music capabilities and opened computers up to a large number of affordable applications extending beyond just data processing. Such technologies signalled the beginning of a new era that now includes intelligent personal assistants understanding and answering natural languages, robots able to see and perform an array of intelligent functions, self-driving vehicles and a host of other capabilities which were until then an exclusive human ability. The tech optimists ascertain that in less than 25 years computers went from just manipulating 0 and 1 digits, to utilizing sophisticated neural networkalgorithms that enable vision and the understanding and speaking of natural languages among others. Technology optimists therefore maintain there is little doubt that in the next twenty years, accelerated AI technological progress will lead to a breakthrough, based on deep learning that imitates the way young children learn, rather than the laborious instructions by tailor-made programs aimed for specific applications and based on logic, if-then rules and decision trees (Parloff, 2016).For instance, DeepMind is based on a neural program utilizing deep learning that teaches itself how to play dozens of Atari games, such as Breakout, as well or better than humans, without specific instructions for doing so, but by playing thousands ofgames and improving itself each time. This program, trained in a different way, became the AlphaGo that defeated GO champion Lee Sodol in 2016. Moreover, it will form the core of a new project to learn to play Starcraft, a complicated game based on both long term strategy as well as quick tactical decisions to stay ahead of an opponent, which DeepMind plans to be its next target for advancing deep learning (Kahn, 2016). Deep learning is an area that seems to be at the forefront of research and funding efforts to improve AI, as its successes have sparked a burst of activity in equity funding that reached an all-time high of more than $1 billion with 121 projects for start-ups in the second quarter of 2016, compared to 21 in the equivalent quarter of 2011 (Parloff, 2016).Google had two deep learning projects underway in 2012. Today it is pursuing more than 1000, according to their spokesperson, in all its major product sectors, including search, Android, Gmail, translation, maps, YouTube, and self-driving cars (The Week, 2016). IBM's Watson system used AI, but not deep learning, when it beat the two Jeopardy champions in 2011. Now though, almost all of Watson's 30 component services have been augmented by deep learning. Venture capitalists, who did not even know what deep learning was five years ago, today are wary of start-ups that do not incorporate it into their programs. We are now living in an age when it has become mandatory for people building sophisticated software applications to avoid click through menus by incorporating natural-language processing tapping deep learning (Parloff, 2016).How far can deep learning go? There are no limits according to technology optimists for three reasons. First as progress is available to practically everyone to utilize through Open Source software, researchers will concentrate their efforts on new, more powerful algorithms leading to cumulative learning. Secondly, deep learning algorithms will be capable of remembering what they have learned and apply it in similar, but different situations (Kirkpatrick et al., 2017). Lastly and equally important, in the future intelligent computer programs will be capable of writing new programs themselves, initially perhaps not so sophisticated ones, but improving with time as learning will be incorporated to be part of their abilities. Kurzweil (2005) sees nonbiological intelligence to match the range and subtlety of human intelligence within a quarter of a century and what he calls “Singularity” to occur by 2045, b ringing “the dawning of a new civilization that will enable us to transcend our biological limitations and amplify our creativity. In this new world, there will be no clear distinction between human and machine, real reality and virtual reality”.For some people these predictions are startling, with far-reaching implications should they come true. In the next section, four scenarios associated with the AI revolution are presented and their impact on our societies, life work and firms is discussed.3. The four AI scenariosUntil rather recently, famines, wars and pandemics were common, affecting sizable segments of the population, causing misery and devastation as well as a large number of deaths. The industrial revolution considerably increased the standards of living while the digital one maintained such rise and also shifted employment patterns,resulting in more interesting and comfortable office jobs. The AI revolution is promising even greater improvements in productivity and further expansion in wealth. Today more and more people, at least in developed countries, die from overeating rather than famine, commit suicide instead of being killed by soldiers, terrorists and criminals combined and die from old age rather than infectious disease (Harari, 2016). Table 1 shows the power of each revolution with the industrial one aiming at routine manual tasks, the digital doing so to routine mental ones and AI aiming at substituting, supplementing and/or amplifying practically all tasks performed by humans. The cri tical question is: “what will the role of humans be at a time when computers and robots could perform as well or better andmuch cheaper, practically all tasks that humans do at present?” There are four scenarios attempting to answer this question.The Optimists: Kurzweil and other optimists predict a “science fiction”, utopian future with Genetics, Nanotechnology and Robotics (GNR) revolutionizing everything, allowing humans to harness the speed, memory capacities and knowledge sharing ability of computers and our brain being directly connected to the cloud. Genetics would enable changing our genes to avoid disease and slow down, or even reverse ageing, thus extending our life span considerably and perhaps eventually achieving immortality. Nanotechnology, using 3D printers, would enable us to create virtually any physical product from information and inexpensive materials bringing an unlimited creation of wealth. Finally, robots would be doing all the actual work, leaving humans with the choice of spending their time performing activities of their choice and working, when they want, at jobs that interest them.The Pessimists: In a much quoted article from Wired magazine in 2000, Bill Joy (Joy, 2000) wrote “Our most powerful 21st-century technologies –robotics, genetic engineering, and nanotech –are threatening to make humans an endangered species”. Joy pointed out that as machines become more and more intelligent and as societal problems become more and more complex, people will let machines make all the important decisions for them as these decisions will bring better results than those made by humans. This situation will, eventually, result in machines being in effective control of all important decisions with people dependent on them and afraid to make their own choices. Joy and many other scientists (Cellan-Jones, 2014) and philosophers (Bostrom, 2014) believe that Kurzweil and his supporters vastly underestimate the magnitude of the challenge and the potential dangers which can arise from thinking machines and intelligent robots. They point out that in the utopian world of abundance, where all work will be done by machines and robots, humans may be reduced to second rate status (some saying the equivalent of computer pets) as smarter than them computers and robots will be available in large numbers and people will not be motivated to work, leaving computers/robots to be in charge of making all important decisions. It may not be a bad world, but it will definitely be a different one with people delegated to second rate status.Harari is the newest arrival to the ranks of pessimists. His recent book (Harari, 2016, p. 397) concludes with the following three statements:• “Science is converging to an all-encompassing dogma, which says thatorganisms are algorithm s, and life is data processing”• “Intelligence is decoupling from consciousness”• “Non-conscious but highly intelligent algorithms may soon know us better than we know ourselves”Consequently, he asks three key questions (which are actually answered by the above three statements) with terrifying implications for the future of humanity: • “Are organisms really just algorithms, and is life just data processing?”• “What is more valuable –intelligence or consciousness?”• “What will happen to society, polit ics and daily life when non-conscious but highly intelligent algorithms know us better than we know ourselves?”Harari admits that nobody really knows how technology will evolve or what its impact will be. Instead he discusses the implications of each of his three questions: • If indeed organisms are algorithms then thinking machines utilizing more efficient ones than those by humans will have an advantage. Moreover, if life is just data processing then there is no way to compete with computers that can consult/exploit practically all available information to base their decisions.• The non-conscious algorithms Google search is based on the consultation of millions of possible entries and often surprise us by their correct recommendations. The implications that similar, more advanced algorithms than those utilized by Google search will be developed (bearing in mind Google search is less than twenty years old) in the future and be able to access all available information from complete data bases are far reachi ng and will “provide us with better information than we could expect to find ourselves”.• Humans are proud of their consciousness, but does it matter that self-driving vehicles do not have one, but still make better decisions than human drivers, as can be confirmed by their significantly lower number of traffic accidents?When AI technologies are further advanced and self-driving vehicles are in widespread use, there may come a time that legislation may be passed forbidding or restricting human driving, even though that may still be some time away according to some scientists (Gomes, 2014). Clearly, self-driving vehicles do not exceed speed limits, do not drive under the influence of alcohol or drugs, do not get tired, do not get distracted by talking on the phone or sending SMS or emails and in general make fewer mistakes than human drivers, causing fewer accidents. There are two implications if humans are not allowed to drive. First, there will be a huge labour displacement for the 3.5 million unionized truck drivers in the USA and the 600 thousand ones in the UK (plus the additional number of non-unionized ones) as well as the more than one million taxi and Uber drivers in these two countries. Second, and more importantly, it will take away our freedom of driving, admitting that computers are superior to us. Once such an admission is accepted there will be no limits to letting computers also make a great number of other decisions, like being in charge of nuclear plants, setting public policies or deciding on optimal economic strategies as their biggest advantage is their objectivity and their ability to make fewer mistakes than humans.One can go as far as suggesting letting computers choose Presidents/PrimeMinisters and elected officials using objective criteria rather than having people voting emotionally and believing the unrealistic promises that candidates make. Although such a suggestion will never be accepted, at least not in the near future, it has its merits since people often choose the wrong candidate and later regret their choice after finding out that pre-election promises were not only broken, but they were even reversed. Critics say if computers do eventually become in charge of making all important decisions there will be little left for people to do as they will be demoted to simply observing the decisions made by computers, the same way as being a passenger in a car driven by a computer, not allowed to take control out of the fear of causing an accident. As mentioned before, this could lead to humans eventually becoming computers’ pets.The pragmatists: At present the vast majority of views about the future implications of AI are negative, concerned with its potential dystopian consequences (Elon Musk, the CEO of Tesla, says it is like “summoning the demon” and calls the consequences worse than what nuclear weapons can do). There are fewer optimists and only a couple of pragmatists like Sam Altman and Michio Kaku (Peckham, 2016) who believe that AI technologies can be controlled through “OpenAI” and effective regulation. The ranks of pragmatists also includes John Markoff (Markoff, 2016) who pointed out that the AI field can be distinguished by two categories: The first trying to duplicate human intelligence and the second to augment it by expanding human abilities exploiting the power of computers in order to augment human decision making. Pragmatists mention chess playing where the present world champion is neither a human nor a computer but rather humans using laptop computers (Baraniuk, 2015). Their view is that we could learn to exploit the power of computers to augment our own skills and always stay a step ahead of AI, or at least not be at a disadvantage. The pragmatists also believe that in the worst of cases a chip can be placed in all thinking machines/robots to render them inoperative in case of any danger. By concentrating research efforts on intelligence augmentation, they claim we can avoid or minimize the possible danger of AI while providing the means to stay ahead in the race against thinking machines and smart robots.The doubters: The doubters do not believe that AI is possible and that it will ever become a threat to humanity. Dreyfus (1972), its major proponent, argues that human intelligence and expertise cannot be replicated and captured in formal rules. He believes that AI is a fad promoted by the computer industry. He points out to the many predictions that did not materialize such as those made by Herbert A. Simon in 1958 that “a computer would be the world's chess champion within ten years” and those made in 1965 that “machines will be capable within twenty years, of doing any work a man can do” (Crevier, 1993). Dreyfus claims that Simon's optimism was totally unwarranted as they were based on false assumptions that human intelligence is based on an information processing viewpoint as our mind is nothing like a computer. Although, the doubters’ criticisms may have been valid in the last century, they cannot stand for the new developments in AI. Deep Blue became the world's chess champion in 1997 (missing Simon's forecast by twenty one years) while we are not far today from machines being capable of doing all the work that humans can do (missing。
人工智能技术的英语
人工智能技术的英语artificial intelligence technology常见释义：英[ˌɑːtɪˈfɪʃl ɪnˈtelɪdʒəns tekˈnɒlədʒi]美[ˌɑːrtɪˈfɪʃl ɪnˈtelɪdʒəns tekˈnɑːlədʒi]例句：虽然现在针对选择题和判断正误题的自动评分系统已经非常普遍，但利用人工智能技术对短文进行评分尚未得到教育工作者的广泛认可，而且批评声也很多。
Although automated grading systems for multiple-choice and true-false tests are now widespread, the use of artificial intelligence technology to grade essay answers has not yet received widespread acceptance by educators and has many critics.将CAD和人工智能技术引入组合夹具设计中，可以提高生产效率、减轻劳动强度、缩短生产准备周期和加快产品上市时间。
Introducing CAD and artificial intelligence technology into modular fixture design can improve production efficiency, lighten working intensity, reduce manufacturing lead-time and marketing time.听上去够酷的了，不过该公司已经开始研发包含摄像头和人工智能技术的升级版鞋子，不仅可以探测到障碍物，还可以检测出是何种障碍物。
That sounds impressive enough, but the company is already working on a much more advanced version that incorporates cameras and artificial intelligence to not only detectobstacles but also their nature.Java语言特点及其对人工智能技术的影响和促进Characteristics of Java Language and the Action of Influence and Promotion of it for AI Technology。
人工智能简称什么
人工智能简称什么人工智能简称AI人工智能（Artificial Intelligence）是计算机科学的一个重要领域，旨在使机器具备模拟和表现人类智能的能力。
它涉及到模拟和实现人类认知、学习、推理、判断、交流和问题解决等方面的功能。
在现代社会中，人工智能正扮演着越来越重要的角色。
而人工智能的简称，广泛地被称为AI。
AI，即Artificial Intelligence的缩写，它是英文artificial（人工）和intelligence（智能）两个单词的首字母缩写。
这个简称因其简短、易记、具有时代感而被广泛使用，几乎成为了人工智能领域的代名词。
AI这个缩写的使用可追溯到上世纪50年代，也就是人工智能领域的起源时期。
当时，科学家们开始研究如何模拟人类智能，为此命名了这一领域，并在公开场合使用AI作为缩写。
从此以后，AI逐渐成为人工智能的简称，并广泛流传至今。
AI的简称不仅仅是对人工智能这个领域的简便描述，也具有象征意义。
首先，AI给人以现代、科技、前沿的感觉，与人工智能的概念相吻合。
其次，AI作为一个简短的缩写，易于在技术写作、讲座、会议等场合快速地出现和引用。
此外，AI还成为了各类科幻作品中的常见元素，进一步提升了它的知名度和独特性。
随着现代科技的飞速发展，AI的应用范围也在不断扩大。
人们已经可以看到AI在机器学习、自然语言处理、图像识别、智能助理等各个领域的应用。
AI不仅对人们的日常生活产生了深远影响，也推动了社会经济的进步和创新。
而AI这个简称，则在这个过程中不断得到强化和巩固。
总结起来，人工智能的简称AI成为了人们对于这个领域的代名词，它简短、易记，具有现代科技的感觉，准确地概括了人工智能的精髓。
随着人工智能的广泛应用，AI的影响力不断扩大，将继续成为各行各业不可或缺的关键术语。
人工智能作文题目英文翻译
人工智能作文题目英文翻译1. Artificial intelligence is changing the way we live and work. It's like having a super smart assistant who can analyze data, make predictions, and even learn from experience.2. Some people worry that AI will take over all our jobs, but others believe it will create new opportunities and make our lives easier. It's a hot topic with lots of different opinions.3. Have you ever used a virtual assistant like Siri or Alexa? They're powered by AI and can help you with all sorts of tasks, from setting reminders to playing music.4. AI is also being used in healthcare to diagnose diseases and develop new treatments. It's amazing how it can process huge amounts of medical data and find patterns that humans might miss.5. But there are also concerns about AI's potential to be biased or make mistakes. We need to make sure it's being used ethically and responsibly.6. In the future, AI could revolutionize industrieslike transportation, finance, and agriculture. It'sexciting to think about the possibilities, but we also need to consider the potential risks and challenges.。
人工智能 中英文翻译(升序排列)
B 规则B-ruleF 规则F-ruleNP 完全问题 NP-complete problem本原问题primitive problem博弈game不可解标示过程unsolvable-labeling procedure不可解节点unsolvable node不可满足集unsatisfiable set不确定性uncertainty差别difference产生式production产生式规则production rule冲突解决conflict resolution存在量词existential quantifier代换substitution代换例substitution instance倒退值backed-up value等价equivalence定理证明theorem-proving动作action反演refutation反演树refutation tree费用cost估计费用estimated cost 估值函数evaluation function归结resolution归结反演resolution refutation归结式resolvent归结原理resolution principle归约reduction合取conjunction合取范式conjunctive normal form合取式conjunct合适公式、合式公式well-formed formula (wff)合一unifier回答语句answer statement回溯backtracking机器学习machine learning节点的扩展expansion of node解释器interpreter解树solution tree解图solution graph句子sentence可解标示过程solvable labeling procedure可解节点solvable node 可满足性satisfiability 空子句empty clause控制策略control strategy宽度优先搜索breadth-first search扩展节点expendingnode连词，连接词connective量词quantifier量词辖域scope ofquantifier论域，文字域domainof discourse逻辑logic逻辑连词logicconnective逻辑推理logicreasoning盲目搜索，无信息搜blind search模式匹配match pattern模式识别Patternrecognition母式matrix逆向推理backwardreasoning匹配match启发函数heuristicfunction启发式搜索Heuristicsearch启发搜索heuristicsearch启发信息heuristicinformation前缀prefix全称量词universalquantifier全局数据库Globaldatabase人工神经网络artificialneural network人工智能artificialintelligence,AI人工智能语言AIlanguage深度优先搜索depth-first search事实fact搜索search, searching搜索策略searchingstrategy搜索树searching tree搜索算法searchingalgorithm搜索算法的效率efficiency of searchalgorithm搜索图searching graph算符、算子、操作符operator图graph图表示法graph notation图搜索graph search图搜索控制策略graph-search controlstrategy推导表，引导图derivation graph推理inference推理reasoning推理机reasoning machine谓词predicate谓词逻辑predicatelogic谓词演算predicatecalculus谓词演算公式wffs ofpredicate calculus谓词演算辖域domainin predicate calculus文字literal问题归约problem-reduction问题求解problemsolving析取disjunction析取式disjunct线形输入形策略linear-input formstrategy项term学习learning演绎deduction一阶谓词演算firstorder predicate calculus一致解图consistantsolution graph遗传算法geneticalgorithm永真式validity有向图directed graph有序搜索orderedsearch与或树AND/OR tree与或图AND/OR graph与节点AND node原子公式atomicformula蕴涵，蕴涵式implication正向推理forwardreasoning知识knowledge知识工程knowledgeengineering知识获取knowledgeacquisition知识库knowledge base智能intelligence重言式tautology专家系统Expert system状态state状态空间state space子句clause自动定理证明automatic theoremproving组合爆炸combinatorialexplosion祖先过滤形策略ancestry-filtered formstrategy最一般合一most generalunifier最一般合一者mostgeneral unifier最优解树optimalsolution treeaction 动作AI language 人工智能语言ancestry-filtered form strategy 祖先过滤形策略AND node 与节点AND/OR graph 与或图AND/OR tree 与或树answer statement 回答语句artificial intelligence,AI 人工智能artificial neural network 人工神经网络atomic formula 原子公式automatic theorem proving 自动定理证明backed-up value 倒退值backtracking 回溯backward reasoning 逆向推理blind search 盲目搜索，无信息搜breadth-first search 宽度优先搜索B-rule B 规则clause 子句combinatorial explosion 组合爆炸conflict resolution 冲突解决conjunct 合取式conjunction 合取conjunctive normal form 合取范式connective 连词，连接词consistant solution graph一致解图control strategy 控制策略cost 费用deduction 演绎depth-first search 深度优先搜索derivation graph 推导表，引导图difference 差别directed graph 有向图disjunct 析取式disjunction 析取domain in predicate calculus 谓词演算辖域domain of discourse 论域，文字域efficiency of search algorithm 搜索算法的效率empty clause 空子句equivalence 等价estimated cost 估计费用evaluation function 估值函数existential quantifier 存在量词expansion of node 节点的扩展expending node 扩展节点Expert system 专家系统fact 事实first order predicate calculus一阶谓词演算forward reasoning 正向推理F-rule F 规则game 博弈genetic algorithm 遗传算法Global database 全局数据库graph 图graph notation 图表示法graph search 图搜索graph-search control strategy图搜索控制策略heuristic function 启发函数heuristic information 启发信息Heuristic search 启发式搜索heuristic search 启发搜索implication 蕴涵，蕴涵式inference 推理intelligence 智能interpreter 解释器knowledge 知识knowledge acquisition 知识获取knowledge base 知识库knowledge engineering 知识工程learning 学习linear-input form strategy线形输入形策略literal 文字logic逻辑logic connective 逻辑连词logic reasoning 逻辑推理machine learning 机器学习match 匹配match pattern 模式匹配matrix 母式most general unifier 最一般合一most general unifier 最一般合一者NP-complete problem NP完全问题operator 算符、算子、操作符optimal solution tree 最优解树ordered search 有序搜索Pattern recognition 模式识别predicate 谓词predicate calculus 谓词演算predicate logic 谓词逻辑prefix 前缀primitive problem 本原问题problem solving 问题求解problem-reduction 问题归约production 产生式production rule 产生式规则quantifier 量词reasoning 推理reasoning machine 推理机reduction 归约refutation 反演refutation tree 反演树resolution归结resolution principle 归结原理resolution refutation 归结反演resolvent 归结式satisfiability 可满足性scope of quantifier 量词辖域search, searching 搜索searching algorithm 搜索算法searching graph 搜索图searching strategy 搜索策略searching tree 搜索树sentence 句子solution graph 解图solution tree 解树solvable labeling procedure可解标示过程solvable node 可解节点state 状态state space 状态空间substitution 代换substitution instance 代换例tautology 重言式term 项theorem-proving 定理证明uncertainty 不确定性unifier 合一universal quantifier 全称量词unsatisfiable set 不可满足集unsolvable node 不可解节点unsolvable-labelingprocedure不可解标示过程validity 永真式well-formed formula (wff)合适公式、合式公式wffs of predicate calculus谓词演算公式。
人工智能英语缩写及应用
人工智能英语缩写及应用人工智能（Artificial Intelligence，简称AI）是一门研究如何使计算机系统完成类似于人类智能的任务的领域。
以下是AI的英语缩写及其应用：1.AI - Artificial Intelligence：人工智能，是指通过模拟、延伸人类智能的方式赋予计算机系统学习、理解、推理、规划、感知等能力的科学和工程。
2.ML - Machine Learning：机器学习，是AI的一个分支，致力于开发能够自动学习和改进的算法。
3.DL - Deep Learning：深度学习，是机器学习的一种特殊形式，使用神经网络进行复杂的模式识别和决策任务。
4.NLP - Natural Language Processing：自然语言处理，是一种使计算机能够理解、解释和生成人类语言的技术。
5.CV - Computer Vision：计算机视觉，是一种使计算机系统能够理解和解释图像和视频的技术。
6.ASR - Automatic Speech Recognition：自动语音识别，是一种使计算机能够识别和理解语音的技术。
7.IoT - Internet of Things：物联网，是通过互联网连接各种设备，使它们能够收集和交换数据的概念。
8.AIoT - Artificial Intelligence of Things：物联网中的人工智能，是将人工智能技术应用于物联网设备，使其更加智能化。
9.RPA - Robotic Process Automation：机器人流程自动化，是使用软件机器人或“机器人”自动执行重复性业务流程的技术。
10.A GI - Artificial General Intelligence：人工通用智能，是一种具有与人类相似广泛认知能力的理论AI形态。
11.A IaaS - AI as a Service：人工智能即服务，是通过云服务提供商提供的云端人工智能服务。
《人工智能基础》名词术语
1，AI:AI是人工智能英文单词Artificial Intelligence的缩写。
2，人工智能:人工智能是研究如何制造出人造的智能机器或智能系统，来模拟人类智能活动的能力，以延伸人们智能的科学。
3，产生式系统:产生式系统是Post于1943年提出的一种计算形式体系里所使用的术语，主要是使用类似于文法的规则，对符号串作替换运算。
到了60年代产生式系统成为认知心理学研究人类心理活动中信息加工过程的基础，并用它来建立人类认识的模型。
到现在产生式系统已发展成为人工智能系统中最典型最普遍的一种结构，例如目前大多数的专家系统都采用产生式系统的结构来建造。
产生式系统由综合数据库、一组产生式规则（规则集）和一个控制系统（控制策略）三部分组成，称为产生式系统的三要素。
4，产生式系统的三要素:产生式系统的三要素是综合数据库、一组产生式规则（规则集）和一个控制系统（控制策略）。
5，产生式规则:产生式规则是知识表示的一种形式，其形式如下： IF <前件> THEN <后件> 其中规则的<前件>表达的是该条规则所要满足的条件，规则的<后件>表示的是该规则所得出的结论，或者动作。
规则表达的可以是与待求解的问题有关的客观规律方面的知识，也可以是对求解问题有帮助的策略方面的知识。
6，八数码游戏（八数码问题）:八数码游戏（八数码问题）描述为：在3×3组成的九宫格棋盘上，摆有八个将牌，每一个将牌都刻有1-8八个数码中的某一个数码。
棋盘中留有一个空格，允许其周围的某一个将牌向空格移动，这样通过移动将牌就可以不断改变将牌的布局。
这种游戏求解的问题是：给定一种初始的将牌布局或结构（称初始状态）和一个目标的布局（称目标状态），问如何移动将牌，实现从初始状态到目标状态的转变。
7，传教士和野人问题（M-C问题）:传教士和野人问题描述为：有N个传教士和N个野人来到河边准备渡河，河岸有一条船，每次至多可供k人乘渡。
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【PT】[J].【AU:】shambour,QusaiXu, YisiLin, QingZhang, Guangquan【AB】The web provides excellent opportunities to businesses in various aspects ofdevelopment such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking one-to-one e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users,particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new cold start item problems.【题目】：基于Web的个性化推荐系统使用的业务合作伙伴---模糊语义技术【刊登杂志】: 计算智能【摘要】网站为企业在各方面的发展提供了极好的机会，例如找到一个在线的业务合作伙伴。
然而，随着网络信息的快速增长，商业用户正在和信息过载做斗争，并且在正确的时间找到正确的信息的难度在不断增加。
同时，特别是中小型企业（中小企业），在当前竞争激烈的市场中从政府寻求的是一对一的电子服务。
怎么为企业用户提供他们需要的的信息和服务，而不是一种未分化的海量信息？本文中就为个性化服务发展提出了一个有效的解决方法。
推荐系统是实施个性化的全方位服务的一种有效的方法，近年来在电子商务中得到了广泛的提及。
相应的，本文首先提出了一种混合模糊语义推荐（HFSR）的方法，这种方法结合了基于项目的模糊语义相似度和基于项目的模糊协同过滤（CF）相似的技术。
本文就介绍了在一个智能推荐系统原型中该方法的实现，这个实现方法称为智能bizseeker，它可推荐相关个人商务用户的业务合作伙伴，特别是对中小企业。
实验结果表明，HFSR方法可以帮助克服基于推荐的经典CF语义的限制方法，即稀疏性和冷开始新项目问题。
【PT】[J].【AU】AU Amigoni, FrancescoContinanza, Luca【题目】：基于网格的方法解决多智能体系统中招聘问题【刊登杂志】: 计算智能【摘要】多智能体系统构成的分布式计算和人工智能之间的交叉口的一个独立的课题。
作为算法的技术和多智能体系统的应用已在过去的二十年中持续发展，达到显著的成熟阶段后，许多方法上的问题已经解决了。
本文中我们的目的是通过考虑选择或招聘的问题来帮助该方法的评估，多代理系统代理的一个子集，从一组可用的代理来满足特定的要求。
这个遇到的问题称之为补充的问题，比如在匹配和任务分配中。
我们提出并研究招聘问题的一个新的正式的方法，基于网格的代数形式主义的方法。
由此产生的正式框架可以支持自动招募算法的发展。
【PT】[S].【AU】Zhao, QiangfuBE Madani, KDourado, ARosa, AFilipe, J【摘要】Artificial intelligence (AI) has been a dream of researchers for decades.In 1982, Japan launched the 5th generation computer project,expecting to create AI in computers, but failed. Noting that logic approach alone is not enough, soft computing (e.g. neuro-computing,fuzzy logic and evolutionary computation) has attracted great attention since 1990s. After another 2 decades, however, we have not got any system that is as intelligent as a human, in the sense of "over-all performance". Instead of trying to create intelligence directly, we may try to create "awareness" first, and obtain intelligence "step-by-step".Briefly speaking, awareness is a mechanism for detecting any event which may or may not lead to complete understanding. Depending on the complexity of the events to detect, aware systems can be divided into many levels. Although low level aware systems may not be clever enough to provide understandable knowledge about an observation;they may provide important information for high level aware systems to make understandable decisions. In this paper we do not intend to provide a survey of existing results related to awareness computing. Rather, we will study this field from a new perspective, try to clarify some related terminologies, and propose some problems to solve for creating intelligence through computational awareness.【题目】：计算意识：另一种方式走向智能化【刊登杂志】: 计算智能【SE】Studies in Computational Intelligence【摘要】人工智能(AI)一直是研究人员几十年来的梦想。
1982，日本推出的第五代电子计算机，期待创造人工智能计算机，但失败了。
注意的是，逻辑方法本身是不足够的，自从20世纪90年代以来软计算（例如，神经计算，模糊逻辑和遗传计算）已经引起了极大的关注。
然而又过了20年，我们没有研究出任何向人类智能的智能系统，即意义上的“综合性能”。
我们不是试图去创建直接的智能，而是可能会首先尝试创建“意识”，然后“循序渐进”的获得智能。
简单地说，意识是一种机制，用于检测任何可能的或可能不会完全理解事件。
根据事件的复杂度检测、感知系统可以分成很多层次。
虽然低层次的感知系统可能不足够聪明来提供关于观察可理解的知识，他们可能为高层次感知系统进行理解的决策提供重要的信息。
在本文中，我们不打算提及现有与感知计算相关的成果。
相反，我们将从新的角度对这一领域进行研究，试图阐明一些相关术语，并提出解决的一些通过计算意识来创造智能的问题。
【CT】第三计算智能国际会议【CT】国际复杂分析和潜在的理论会议【CY】月24-26日，2011【CY 】6月20-23日，2011【CL】巴黎，法国【CL】CTR丰富的数学，蒙特利尔，加拿大【PT】[S].【AU】Agarwal, ManishBiswas, Kanad K.Hanmandlu, Madasu【BE】 Madani, KDourado, ARosa, AFilipe, J【AB】 This chapter extends the fuzzy models to the probabilistic domain using the probabilistic fuzzy rules with multiple outputs. The focus has been to effectively model the uncertainty in the real world situations using the extended fuzzy models. The extended fuzzy models capture both the aspects of uncertainty, vagueness and random occurence. We also look deeper into the concepts of fuzzy logic, possibility and probability that sets the background for laying out the mathematical framework for the extended fuzzy models. The net conditional probabilistic possibility is computed that forms the key ingredient in the extension of the fuzzymodels. The proposed concepts are well illustrated through two case-studies of intelligent probabilistic fuzzy systems. The study paves the way for development of computationally intelligent systems that are able to represent the real worldsituations more realistically.【题目】：处理模糊模型的概率域【SE】计算智能研究【刊登杂志】: 计算智能【摘要】本章用具有多个输出的概率模糊规则的模糊模型扩展概率域。