人工智能外文翻译文献

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。

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Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. 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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。

原创人工智能技术论文参考文献引言人工智能（Artificial Intelligence，简称AI）是一种模拟人类智能的科学和技术。

近年来，随着计算机技术和大数据的不断发展，人工智能技术正在迅速应用于各个领域，包括机器学习、计算机视觉、自然语言处理等。

在人工智能研究与应用的过程中，参考文献是不可或缺的资源，它不仅提供了前人的研究成果和方法，还为当前的研究提供了理论基础和思路。

本文将提供一些原创人工智能技术论文参考文献，为人工智能研究者和开发者提供参考。

机器学习1.Bishop, C. M. (2006). Pattern recognition and machine learning.Springer.2.Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements ofstatistical learning: data mining, inference, and prediction (2nd ed.). Springer.3.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MITPress.计算机视觉1.Szeliski, R. (2010). Computer vision: algorithms and applications.Springer.2.Forsyth, D. A., & Ponce, J. (2012). Computer vision: a modernapproach (2nd ed.). Prentice Hall.3.Hartley, R., & Zisserman, A. (2003). Multiple view geometry incomputer vision (2nd ed.). Cambridge University Press.自然语言处理1.Jurafsky, D., & Martin, J. H. (2019). Speech and language processing(3rd ed.). Pearson.2.Manning, C. D., & Schütze, H. (1999). Foundations of statistical naturallanguage processing. MIT Press.3.Goldberg, Y. (2017). Neural network methods for natural languageprocessing. Morgan & Claypool.强化学习1.Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: anintroduction (2nd ed.). MIT Press.2.Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcementlearning: a survey. Journal of Artificial Intelligence Research.3.Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare,M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S.,Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcementlearning. Nature.结论本文列举了一些关于机器学习、计算机视觉、自然语言处理和强化学习等人工智能技术方面的原创参考文献，这些文献为人工智能研究者和开发者提供了宝贵的资料和灵感。

附件四英文文献原文Artificial Intelligence"Artificial intelligence" is a word was originally Dartmouth in 1956 to put forward. From then on, researchers have developed many theories and principles, the concept of artificial intelligence is also expands. Artificial intelligence is a challenging job of science, the person must know computer knowledge, psychology and philosophy. Artificial intelligence is included a wide range of science, it is composed of different fields, such as machine learning, computer vision, etc, on the whole, the research on artificial intelligence is one of the main goals of the machine can do some usually need to perform complex human intelligence. But in different times and different people in the "complex" understanding is different. Such as heavy science and engineering calculation was supposed to be the brain to undertake, now computer can not only complete this calculation, and faster than the human brain can more accurately, and thus the people no longer put this calculation is regarded as "the need to perform complex human intelligence, complex tasks" work is defined as the development of The Times and the progress of technology, artificial intelligence is the science of specific target and nature as The Times change and development. On the one hand it continues to gain new progress on the one hand, and turning to more meaningful, the more difficult the target. Current can be used to study the main material of artificial intelligence and artificial intelligence technology to realize the machine is a computer, the development history of artificial intelligence is computer science and technology and the development together. Besides the computer science and artificial intelligence also involves information, cybernetics, automation, bionics, biology, psychology, logic, linguistics, medicine and philosophy and multi-discipline. Artificial intelligence research include: knowledge representation, automatic reasoning and search method, machine learning and knowledge acquisition and processing of knowledge system, natural language processing, computer vision, intelligent robot, automatic program design, etc.Practical application of machine vision: fingerprint identification,face recognition, retina identification, iris identification, palm, expert system, intelligent identification, search, theorem proving game, automatic programming, and aerospace applications.Artificial intelligence is a subject categories, belong to the door edge discipline of natural science and social science.Involving scientific philosophy and cognitive science, mathematics, neurophysiological, psychology, computer science, information theory, cybernetics, not qualitative theory, bionics.The research category of natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combined scheduling problem, perception, pattern recognition, logic design program, soft calculation, inaccurate and uncertainty, the management of artificial life, neural network, and complex system, human thinking mode of genetic algorithm.Applications of intelligent control, robotics, language and image understanding, genetic programming robot factory.Safety problemsArtificial intelligence is currently in the study, but some scholars think that letting computers have IQ is very dangerous, it may be against humanity. The hidden danger in many movie happened.The definition of artificial intelligenceDefinition of artificial intelligence can be divided into two parts, namely "artificial" or "intelligent". "Artificial" better understanding, also is controversial. Sometimes we will consider what people can make, or people have high degree of intelligence to create artificial intelligence, etc. But generally speaking, "artificial system" is usually significance of artificial system.What is the "smart", with many problems. This involves other such as consciousness, ego, thinking (including the unconscious thoughts etc. People only know of intelligence is one intelligent, this is the universal view of our own. But we are very limited understanding of the intelligence of the intelligent people constitute elements are necessary to find, so it is difficult to define what is "artificial" manufacturing "intelligent". So the artificial intelligence research often involved in the study of intelligent itself. Other about animal or other artificial intelligence system is widely considered to be related to the study of artificial intelligence.Artificial intelligence is currently in the computer field, the moreextensive attention. And in the robot, economic and political decisions, control system, simulation system application. In other areas, it also played an indispensable role.The famous American Stanford university professor nelson artificial intelligence research center of artificial intelligence under such a definition: "artificial intelligence about the knowledge of the subject is and how to represent knowledge -- how to gain knowledge and use of scientific knowledge. But another American MIT professor Winston thought: "artificial intelligence is how to make the computer to do what only can do intelligent work." These comments reflect the artificial intelligence discipline basic ideas and basic content. Namely artificial intelligence is the study of human intelligence activities, has certain law, research of artificial intelligence system, how to make the computer to complete before the intelligence needs to do work, also is to study how the application of computer hardware and software to simulate human some intelligent behavior of the basic theory, methods and techniques.Artificial intelligence is a branch of computer science, since the 1970s, known as one of the three technologies (space technology, energy technology, artificial intelligence). Also considered the 21st century (genetic engineering, nano science, artificial intelligence) is one of the three technologies. It is nearly three years it has been developed rapidly, and in many fields are widely applied, and have made great achievements, artificial intelligence has gradually become an independent branch, both in theory and practice are already becomes a system. Its research results are gradually integrated into people's lives, and create more happiness for mankind.Artificial intelligence is that the computer simulation research of some thinking process and intelligent behavior (such as study, reasoning, thinking, planning, etc.), including computer to realize intelligent principle, make similar to that of human intelligence, computer can achieve higher level of computer application. Artificial intelligence will involve the computer science, philosophy and linguistics, psychology, etc. That was almost natural science and social science disciplines, the scope of all already far beyond the scope of computer science and artificial intelligence and thinking science is the relationship between theory and practice, artificial intelligence is in the mode of thinking science technology application level, is one of its application. From the view of thinking, artificial intelligence is not limited to logicalthinking, want to consider the thinking in image, the inspiration of thought of artificial intelligence can promote the development of the breakthrough, mathematics are often thought of as a variety of basic science, mathematics and language, thought into fields, artificial intelligence subject also must not use mathematical tool, mathematical logic, the fuzzy mathematics in standard etc, mathematics into the scope of artificial intelligence discipline, they will promote each other and develop faster.A brief history of artificial intelligenceArtificial intelligence can be traced back to ancient Egypt's legend, but with 1941, since the development of computer technology has finally can create machine intelligence, "artificial intelligence" is a word in 1956 was first proposed, Dartmouth learned since then, researchers have developed many theories and principles, the concept of artificial intelligence, it expands and not in the long history of the development of artificial intelligence, the slower than expected, but has been in advance, from 40 years ago, now appears to have many AI programs, and they also affected the development of other technologies. The emergence of AI programs, creating immeasurable wealth for the community, promoting the development of human civilization.The computer era1941 an invention that information storage and handling all aspects of the revolution happened. This also appeared in the U.S. and Germany's invention is the first electronic computer. Take a few big pack of air conditioning room, the programmer's nightmare: just run a program for thousands of lines to set the 1949. After improvement can be stored procedure computer programs that make it easier to input, and the development of the theory of computer science, and ultimately computer ai. This in electronic computer processing methods of data, for the invention of artificial intelligence could provide a kind of media.The beginning of AIAlthough the computer AI provides necessary for technical basis, but until the early 1950s, people noticed between machine and human intelligence. Norbert Wiener is the study of the theory of American feedback. Most familiar feedback control example is the thermostat. It will be collected room temperature and hope, and reaction temperature compared to open or close small heater, thus controlling environmental temperature. The importance of the study lies in the feedback loop Wiener:all theoretically the intelligence activities are a result of feedback mechanism and feedback mechanism is. Can use machine. The findings of the simulation of early development of AI.1955, Simon and end Newell called "a logical experts" program. This program is considered by many to be the first AI programs. It will each problem is expressed as a tree, then choose the model may be correct conclusion that a problem to solve. "logic" to the public and the AI expert research field effect makes it AI developing an important milestone in 1956, is considered to be the father of artificial intelligence of John McCarthy organized a society, will be a lot of interest machine intelligence experts and scholars together for a month. He asked them to Vermont Dartmouth in "artificial intelligence research in summer." since then, this area was named "artificial intelligence" although Dartmouth learn not very successful, but it was the founder of the centralized and AI AI research for later laid a foundation.After the meeting of Dartmouth, AI research started seven years. Although the rapid development of field haven't define some of the ideas, meeting has been reconsidered and Carnegie Mellon university. And MIT began to build AI research center is confronted with new challenges. Research needs to establish the: more effective to solve the problem of the system, such as "logic" in reducing search; expert There is the establishment of the system can be self learning.In 1957, "a new program general problem-solving machine" first version was tested. This program is by the same logic "experts" group development. The GPS expanded Wiener feedback principle, can solve many common problem. Two years later, IBM has established a grind investigate group Herbert AI. Gelerneter spent three years to make a geometric theorem of solutions of the program. This achievement was a sensation.When more and more programs, McCarthy busy emerge in the history of an AI. 1958 McCarthy announced his new fruit: LISP until today still LISP language. In. "" mean" LISP list processing ", it quickly adopted for most AI developers.In 1963 MIT from the United States government got a pen is 22millions dollars funding for research funding. The machine auxiliary recognition from the defense advanced research program, have guaranteed in the technological progress on this plan ahead of the Soviet union. Attracted worldwide computer scientists, accelerate the pace of development of AI research.Large programAfter years of program. It appeared a famous called "SHRDLU." SHRDLU "is" the tiny part of the world "project, including the world (for example, only limited quantity of geometrical form of research and programming). In the MIT leadership of Minsky Marvin by researchers found, facing the object, the small computer programs can solve the problem space and logic. Other as in the late 1960's STUDENT", "can solve algebraic problems," SIR "can understand the simple English sentence. These procedures for handling the language understanding and logic.In the 1970s another expert system. An expert system is a intelligent computer program system, and its internal contains a lot of certain areas of experience and knowledge with expert level, can use the human experts' knowledge and methods to solve the problems to deal with this problem domain. That is, the expert system is a specialized knowledge and experience of the program system. Progress is the expert system could predict under certain conditions, the probability of a solution for the computer already has. Great capacity, expert systems possible from the data of expert system. It is widely used in the market. Ten years, expert system used in stock, advance help doctors diagnose diseases, and determine the position of mineral instructions miners. All of this because of expert system of law and information storage capacity and become possible.In the 1970s, a new method was used for many developing, famous as AI Minsky tectonic theory put forward David Marr. Another new theory of machine vision square, for example, how a pair of image by shadow, shape, color, texture and basic information border. Through the analysis of these images distinguish letter, can infer what might be the image in the same period. PROLOGE result is another language, in 1972. In the 1980s, the more rapid progress during the AI, and more to go into business. 1986, the AI related software and hardware sales $4.25 billion dollars. Expert system for its utility, especially by demand. Like digital electric company with such company XCON expert system for the VAX mainframe programming. Dupont, general motors and Boeing has lots of dependence of expert system for computer expert. Some production expert system of manufacture software auxiliary, such as Teknowledge and Intellicorp established. In order to find and correct the mistakes, existing expert system and some other experts system was designed，such as teach users learn TVC expert system of the operating system.From the lab to daily lifePeople began to feel the computer technique and artificial intelligence. No influence of computer technology belong to a group of researchers in the lab. Personal computers and computer technology to numerous technical magazine now before a people. Like the United States artificial intelligence association foundation. Because of the need to develop, AI had a private company researchers into the boom. More than 150 a DEC (it employs more than 700 employees engaged in AI research) that have spent 10 billion dollars in internal AI team.Some other AI areas in the 1980s to enter the market. One is the machine vision Marr and achievements of Minsky. Now use the camera and production, quality control computer. Although still very humble, these systems have been able to distinguish the objects and through the different shape. Until 1985 America has more than 100 companies producing machine vision systems, sales were us $8 million.But the 1980s to AI and industrial all is not a good year for years. 1986-87 AI system requirements, the loss of industry nearly five hundred million dollars. Teknowledge like Intellicorp and two loss of more than $6 million, about one-third of the profits of the huge losses forced many research funding cuts the guide led. Another disappointing is the defense advanced research programme support of so-called "intelligent" this project truck purpose is to develop a can finish the task in many battlefield robot. Since the defects and successful hopeless, Pentagon stopped project funding.Despite these setbacks, AI is still in development of new technology slowly. In Japan were developed in the United States, such as the fuzzy logic, it can never determine the conditions of decision making, And neural network, regarded as the possible approaches to realizing artificial intelligence. Anyhow, the eighties was introduced into the market, the AI and shows the practical value. Sure, it will be the key to the 21st century. "artificial intelligence technology acceptance inspection in desert storm" action of military intelligence test equipment through war. Artificial intelligence technology is used to display the missile system and warning and other advanced weapons. AI technology has also entered family. Intelligent computer increase attracting public interest. The emergence of network game, enriching people's life.Some of the main Macintosh and IBM for application software such as voice and character recognition has can buy, Using fuzzy logic,AI technology to simplify the camera equipment. The artificial intelligence technology related to promote greater demand for new progress appear constantly. In a word ，Artificial intelligence has and will continue to inevitably changed our life.附件三英文文献译文人工智能“人工智能”一词最初是在1956 年Dartmouth在学会上提出来的。

人工智能发展论文英文Artificial Intelligence: A Journey from Concept to RealityThe dawn of the 21st century has witnessed a remarkable surge in the development and application of artificial intelligence (AI), a field that has evolved from a mere theoretical concept to a transformative force in various sectors of society. This paper delves into the history, current state, and future prospects of AI, exploring its impact on humanity and the ethical considerations that accompany its rapid advancement.The Genesis of Artificial IntelligenceThe concept of artificial intelligence traces back to antiquity, with myths and stories of artificial beings endowed with consciousness or intelligence. However, the modern field of AI research began in the mid-20th century. The term "artificial intelligence" was coined during the Dartmouth Conference in 1956, where the first AI research program was initiated. Early AI research focused on problem-solving and symbolic methods, with the development of the Logic Theorist and General Problem Solver being significant milestones.Evolution of AI: From Expert Systems to Machine LearningThe 1980s saw the rise of expert systems, which were designedto mimic the decision-making abilities of a human expert in a specific domain. These systems were rule-based and relied on a knowledge base to provide solutions. However, their limited scope and inflexibility led to a period of skepticism known as AI winter.The advent of machine learning in the 1990s marked a resurgence in AI research. Machine learning algorithms, such as decision trees and neural networks, allowed computers to learn from and make decisions based on data. The introduction of deep learning, a subset of machine learning inspired by the human brain's structure, further propelled AI capabilities, enabling breakthroughs in image and speech recognition.Current Landscape of AIToday, AI is pervasive, influencing everything from healthcare, where it assists in diagnostics and personalized medicine, to finance, where it optimizes trading algorithms and fraud detection. In the consumer market, AI powersvirtual assistants, recommendation systems, and autonomous vehicles. The integration of AI with the Internet of Things (IoT) is creating smart environments that can predict and respond to human needs.AI's impact on the workforce is also profound. Automation and intelligent systems are transforming job roles, necessitating a shift in skills and lifelong learning. While AI has the potential to augment human capabilities, it also raises concerns about job displacement and the widening gap betweenthe technologically advanced and those left behind.Ethical Considerations and Societal ImpactThe ethical implications of AI are multifaceted. Issues such as privacy, bias in algorithms, and the accountability of AI systems are at the forefront of public discourse. The development of AI must be guided by ethical principles that prioritize transparency, fairness, and the well-being of all individuals. Moreover, the governance of AI, including regulations and international cooperation, is crucial to ensure its beneficial and responsible use.The Future of AILooking ahead, AI is poised to become even more integrated into daily life. Advancements in quantum computing and neuromorphic engineering could lead to a new generation of AI systems with unprecedented capabilities. The potential for AI to solve complex problems, from climate change to disease eradication, is immense. However, this potential must be balanced with a commitment to social good and the prevention of misuse.ConclusionThe journey of AI from a theoretical concept to a practical reality has been remarkable. It has transformed industries, redefined human-computer interaction, and challenged our understanding of intelligence. As we stand on the precipice of new breakthroughs, it is imperative that we approach AI'sfuture with a keen eye on its ethical and societal implications. The collective effort of researchers, policymakers, and society at large will determine the trajectory of AI, ensuring that it serves as a catalyst for positive change and a beacon of human ingenuity.In conclusion, artificial intelligence is not just a technological revolution; it is a profound shift in the way we perceive and interact with the world. As we continue to explore its possibilities, we must do so with a sense of responsibility and foresight, ensuring that AI's development is aligned with the values that define our humanity.。

人工智能技术论文英文Artificial Intelligence: A Comprehensive Exploration of Modern Technologies and Their ApplicationsThe advent of artificial intelligence (AI) has revolutionized the way we interact with technology, transforming industries and shaping the future of human-computer interaction. This paper delves into the realm of AI, exploring its evolution, current technologies, applications, and the ethical considerations that accompany its rapid advancement.IntroductionArtificial intelligence, a term coined in 1956, has come along way from its initial conceptualization to its current state, where AI systems are capable of performing tasks that typically require human intelligence. The field of AI encompasses a wide range of disciplines, including machine learning, natural language processing, computer vision, and robotics. The integration of AI into various sectors has ledto significant breakthroughs in efficiency, accuracy, and innovation.Historical ContextThe history of AI is marked by periods of optimism and skepticism. The first AI programs were developed in the 1950s, with the Dartmouth Conference in 1956 being a pivotal momentthat set the stage for AI research. The 1960s and 1970s saw the development of the first AI programs, including ELIZA and SHRDLU. However, the field faced a period of stagnation known as the "AI winter" due to unfulfilled promises and lack of funding. It wasn't until the late 1990s and early 2000s that AI research gained momentum again, with the advent of machine learning and the availability of big data.Fundamental ConceptsAt the core of AI are algorithms and computational modelsthat enable machines to learn from data, make decisions, and perform tasks autonomously. Machine learning, a subset of AI, involves the development of algorithms that can learn from and make predictions or decisions based on data. Deep learning, a subset of machine learning, uses neural networks with many layers to model complex patterns in data.Current TechnologiesThe current landscape of AI technologies is diverse and includes:1. Machine Learning Platforms: These platforms provide the tools and frameworks for developers to build and train AI models.2. Natural Language Processing (NLP): NLP enables machines to understand, interpret, and generate human language.3. Computer Vision: This technology allows machines to interpret and analyze visual information from the world.4. Robotics: AI-powered robots can perform tasks that requirephysical manipulation and movement.5. Expert Systems: These systems use AI to simulate the decision-making ability of a human expert.ApplicationsAI has found its way into numerous applications across various industries:1. Healthcare: AI is used for diagnosis, treatment planning, and personalized medicine.2. Finance: AI technologies are employed for fraud detection, algorithmic trading, and risk management.3. Transportation: Autonomous vehicles and smart traffic systems are powered by AI.4. Retail: AI enhances customer experience through personalized recommendations and inventory management.5. Education: Adaptive learning systems powered by AI cater to individual learning needs.Challenges and Ethical ConsiderationsAs AI continues to advance, it brings with it a set of challenges and ethical considerations:1. Bias and Fairness: AI systems can inherit and amplify the biases present in their training data, leading to unfair outcomes.2. Privacy: The use of AI in data analysis raises concerns about individual privacy and data protection.3. Job Displacement: The automation of tasks by AI has thepotential to displace jobs, leading to economic and social implications.4. Transparency and Explainability: The complexity of AI models can make it difficult to understand how they arrive at certain decisions.The Future of AILooking ahead, AI is poised to become more integrated intoour daily lives, with advancements in areas such as general AI, which aims to create machines that can perform any intellectual task that a human being can. The development ofAI also calls for a collaborative approach between technologists, policymakers, and society to ensure its responsible and beneficial use.ConclusionArtificial intelligence stands as a testament to human ingenuity and our relentless pursuit of innovation. While it presents numerous opportunities for societal advancement, it also poses significant challenges that must be addressed. As we move forward, it is crucial to foster a balanced approach that harnesses the potential of AI while mitigating its risks. The journey of AI is not just about creating intelligent machines; it is about shaping a future that is inclusive, ethical, and beneficial for all.In conclusion, the field of AI is dynamic and ever-evolving.It holds the promise of transforming our world in ways we are only beginning to understand. As we continue to explore anddevelop AI technologies, it is imperative that we do so with a keen eye on their societal impact, ensuring that they serve to enhance and enrich our lives in a manner that is responsible and sustainable.。

人工智能与专家系统外文文献译文和原文AI研究仍在继续，但与MIS和DDS等计算机应用相比，研究热情的减弱使人工智能的研究相对落后。

然而，在研究方面的不断努力一定会推动计算机向人工智能化方向发展。

2．AI领域AI现在已经以知识系统的形式应用于商业领域，既利用人类知识来解决问题。

专家系统是最流行的基于知识的系统，他是应用计算机程序以启发方式替代专家知识。

Heuritic术语来自希腊eureka，意思是“探索”。

因此，启发方式是一种良好猜想的规则。

启发式方法并不能保证其结果如同DSS系统中传统的算法那样绝对化。

但是启发式方法提供的结果非常具体，以至于能适应于大部分情况启发式方法允许专家系统能像专家那样工作，建议用户如何解决问题。

因为专家系统被当作顾问，所以，应用专家系统就可以被称为咨询。

除了专家系统外，AI还包括以下领域：神经网络系统、感知系统、学习系统、机器人、AI硬件、自然语言处理。

注意这些领域有交叉，交叉部分也就意味着这个领域可以从另一个领域中收益。

3．专家系统的吸引力专家系统的概念是建立在专家知识能够存储在计算机中并能被其他人应用这一假设的基础上的。

专家系统作为一种决策支持系统提供了独无二的能力。

首先，专家系统为管理者提供了超出其能力的决策机会。

比如，一家新的银行投资公司可以应用先进的专家系统帮助他们进行选择、决策。

其次，专家系统在得到一个解决方案的同时给出一步步的推理。

在很多情况下，推理本身比决策的结果重要的多。

4．专家系统模型专家系统模型主要由4个部分组成：用户界面使得用户能与专家系统对话；推理引擎提供了解释知识库的能力；专家和工程师利用开发引擎建立专家系统。

1．用户界面用户界面能够方便管理者向专家系统中输入命令、信息，并接受专家系统的输出。

命令中有具体化的参数设置，引导专家系统的推理过程。

信息以参数形式赋予某些变量。

（1）专家系统输入现在流行的界面格式是图形化用户界面格式，这种界面与Window有些相同的特征。

人工智能英文文献译文在计算机科学里许多现代研究都致于两个方面:一是怎样制造智能计算机,二是怎样制造超高速计算机.硬件成本的降低,大规模集成电路技术(VLSI)不可思议的进步以及人工智能(AI)所取得的成绩使得设计面向AI应用的计算机结构极为可行,这使制造智能计算机成了近年来最”热门”的方向.AI 提供了一个崭新的方法,即用计算技术的概念和方法对智能进行研究,因此,它从根本上提供了一个全新的不同的理论基础.作为一门科学,特别是科学最重要的部分,AI的上的是了解使智能得以实现的原理.作为一种技术和科学的一部分,AI的最终目的是设计出能完全与人类智能相媲美的智能计算机系统.尽管科学家们目前尚未彀这个目的,但使计算机更加智能化已取得了很大的进展,计算机已可用来下出极高水平的象棋,用来诊断某种疾病,用来发现数学概念,实际上在许多领域已超出了高水平的人类技艺.许多AI计算机应用系统已成功地投入了实用领域.AI是一个正在发展的包括许多学科在内的领域,AI的分支领域包括:知识表达,学习,定理证明,搜索,问题的求解以及规划,专家系统,自然语言(文本或语音)理解,计算机视觉,机器人和一些其它方面/(例如自动编程,AI教育,游戏,等等).AI是使技术适应于人类的钥匙,将在下一代自动化系统中扮演极为关键的角色.据称AI应用已从实验室进入到实用领域,但是传统的冯·诺依曼计算机中,有更大的存储容量与处理能力之比,但最终效率也不是很高.无论使处理器的速度多快也无法解决这个问题,这是因为计算机所花费的时间主要取决于数据的处理器和存储器之间传送所需的时间,这被称之为冯·诺依曼瓶颈.制造的计算机越大,这个问题就越严重.解决的方法是为AI应用设计出不同于传统计算机的特殊结构.在未来AI结构的研究中,我们可以在计算机结构中许多已有的和刚刚出现的新要领的优势,比如数据流计算,栈式计算机,特征,流水线,收缩阵列,多处理器,分布式处理,数据库计算机和推理计算机.无需置疑,并行处理对于AI应用是至关重要的.根据AI中处理问题的特点,任何程序,哪怕只模拟智能的一小部分都将是非常复杂的.因此,AI仍然要面对科学技术的限制,并且继续需要更快更廉价的计算机.AI的发展能否成为主流在很大程度上取决于VLSI技术的发展.另一方面,并行提供了一个在更高性能的范围内使用廉价设备的方法.只要使简单的处理单元完全构成标准模式,构成一个大的并行处理系统就变得轻而易举,由此而产生的并行处理器应该是成本低廉的.在计算机领域和AI中,研究和设计人员已投入大量精力来考查和开发有效的并行AI结构,它也越来越成为吸引人的项目.目前,AI在表达和使用大量知识以及处理识别问题方面仍然没有取得大的进展,然而人脑在并行处理中用大量相对慢的(与目前的微电子器件比较)神经元却可十分出色地完成这些任务.这启发了人们或许需要某种并行结构来完成这些任务.将极大地影响我们进行编程的方法.也许,一旦有了正确的结构,用程序对感觉和知识表达进行处理将变得简单自然.研究人员因此投入大量努力来寻求并行结构.AI中的并行方法不仅在廉价和快速计算机方面,而且在新型计算方法方面充满希望.两种流行的AI语言是函数型编程语言,即基于λ算子的和逻辑编程语言,即基于逻辑的.此外,面向对象的编程正在引起人们的兴趣.新型计算机结构采用了这些语言并开始设计支持一种或多种编程形式的结构.一般认为结合了这三种编程方式可为AI应用提供更好的编程语言,在这方面人们已经作了大量的研究并取得了某些成就.人工智能的发展1 经典时期:游戏和定理证明人工智能比一般的计算机科学更年轻,二战后不久出现的游戏程序和解迷宫程序可以看作是人工智能的开始,游戏和解迷宫看起来距专家系统甚远,也不能为实际应用提供理论基础.但是,基于计算机的问题的最基本概念可以追溯到早期计算机完成这些任务的程序设计方法.(1)状态空间搜索早期研究提出的基本叫做状态空间搜索,实质非常简单.很多问题都可以用以下三个组成部分表述:1. 初始状态,如棋盘的初始态;2. 检查最终状态或问题解的终止测试;3. 可用于改变问题当前状态的一组操作,如象棋的合法下法.这种概念性状态空间的一种思路是图,图中节点表示状态, 弧表示操作.这种空间随着思路的发展而产生,例如,可以从棋盘的初始状态开始构成图的第一个节,白子每走一步都产生连向新状态的一条弧,黑子对白子每步棋的走法,可以认为是改变了棋盘状态的情况下连向这些新节点的操作,等等.(2)启发式搜索如果除小范围搜索空间以外,彻底的搜索不可能的话,就需要某些指导搜索的方法.用一个或多项域专门知识去遍历状态空间图的搜索叫做启发式搜索.启发是凭经验的想法,它不像算法或决策程序那样保证成功,它是一种算法或过程,但大多数情况下是有用的.2 现代时期:技术与应用所谓现代时期是从70年代半期延续到现在,其特征是日益发展的自意识和自批判能力以及对于技术和应用的更强的定位.与理解的心理学概念相联系似已不占据核心地位.人们也渐渐不再对一般问题方法(如启发式搜索)心存幻想,研究者们已经认识到,这种方法过高估计了”一般智能”的概念,这一概念一向为心理学家喜欢,其代价是未考虑人类专家所具有的某一领域内的能力.这种方法也过低地估计了人的简单常识,特别是人能够避免,认识和纠正错误的能力.解决问题的启发能力程序能够处理的相关知识的清晰表达,而非某些复杂的推理机制或某些复杂的求值函数,这一观点已被证实并接受.研究者已经研制出以模块形式对人的知识进行编码的技术,此种编码可用模式启动.这些模式可以代表原始的或处理过的数据,问题说明或问题的部分解.早期模拟人们解决问题的努力试图达到知识编码的一致性和推理机制的简单性.后来将该结果应用于万家系统的尝试主要是允许自身的多样性.INTRODCTION TO ARTIFICIALMuch modern research effort in computer science goes along two directions. One is how to make intelligent computers,the other how to make ultraly high-speed computers. The former has become the newest “hot ” direction in recent years because the decreasing hardware costs, the marvelous progress in VLSI technology,and the results achieved in Artificial Intelligence(AI) have made it feasible to design AI applications oriented computer architectures.AI,which offers a mew methodology, is the study of intelligence using the idead and methods of computation, thus offering a radically new and different basis for theory formation. As a science, essentially part of Cognitive Science, the goal of AI is to understand the principles thatmake intelligence possible. As a technology and as a part of computer science,the final goal of AI is to design intelligent computer systems that behave with the complete intelligence of human mind.although scientists are far from achieving this goal, great progress dose hae been made in making computers more intelligent . computers can be made to play excellint chess, to diagnose certain types of diseases, to discover mathematical comcepts, and if fact , to excel in many other areas requiring a high level of human expertise. Many Aiapplication computer systems have been successfully put into practical usages.AI is a growing field that covers many disciplines. Subareas of AI include knowledge representation ,learning, theorem proving,search,problem solving, and planning, expert systems, natural-language(text or speech)understanding,computer vision,robotics, and several others (such as automatic programming ,AI education,game playing, etc.) .AI is the key for making techmology adaptable to people. It will play a crucial role in the next generation of automated systems.It is a growing field that covers many disciplines.subbareas of AI include knowledge representation,learing,theorem proving,search,prroblem solving, and planning,expert systems,natural_language(text or speech ) understanding,computer vision,robotics , and severalothers (such as automatic programming, AI education, game playing,etc.).AI is the key for making technology adaptable to people. It will play a crucial role in the next generation of automated systems.It is claimed that AI applications have moved from laboratories to the real wortld. However ,conventional von Neumann computers are unsuitable for AI applications,because they are designed mainly for numerical processing. In a larger von Neumann computer, there is a larger tatio of memory to processing power and consequently it is even less efficient. This inefficiency remains no matter how fast we make the processor because the length of the computation becomes dominated by the time required to move data between processor and memory. This is called the von Neumann bottleneck. The bigger we build machines, the worse it gets. The way to solve the problem is to diverse from the traditional architectures and to design special ones for AI applications. In the research of future AI architectures, we can take advantages of many existing or currentlyemerging concepts in computer architecture, such as dataflow computation, stack machines, tagging,pipelining, systolic array,multiprocessing,distrbuted processing,database machines ,and inference machines.No doubt, parallel processing is of crucial importance for AI applications.due to the nature of problems dealt with in AI, any program that will successfully simulate even a small part of intelligence will be very complicated. Therefor,AI continuously confronts the limits of computer science technology,and there id an instatiable demand for fastert and cheaper computers.the movement of AI into mainstream is largely owned to the addevent of VLSI technology.parallel architectures,on the other han,provide a way of using the inexpensive device technology at much higher performance ranges.it ix becoming easier and cheaper to construc large parallel processing systems as long as they are made of fairly regular patterns of simpl processwing elements,and thus parallel processors should become cost effective.a great amount of effort has been devoted to inverstigating and developing effictive parallel AI architectures,ans this topic id becoming more and more attractive for reaseachers and designersin the areas of computers and AI.Currently, very little success has been achieved in AI in representing and using large bodies of knowledge and in dealing with recognition problems. Whereas human brain can perform these tasks temarkably well using a large number of relatively slow (in comparison with todays microelectronic devices) neurons in parallel. This suggests that for these tasks some kind of parllel architecture may be needed. Architectures can significantly influence the way we programming it for perception and knowledge representation would be easy and natural. This has led researchers to look into massively parallel architectures. Parallelism holds great promise for AI not only in terms of cheaper and faster computers. But also as a novel way of viewingcomputation.Two kinds of popular AI languages are functoional programming languages, which are lambda-based ,and logic programming is attracting a growing interest. Novel computer architects have considered these languages seriously and begun to design architectures supporting one or more of the programming styles. It has been recognized that a combination of the three programming styles mingt provide a better language for AI applications. There have already been a lot of research effort and achievements on this topic.Development of AI1 the classical period: game playing and theorem provingartificial inteligence is scarcely younger than conventional computer science;the bebinnings of AI can be seen in the first game-playing and puzzle-solving programs written shortly after World War Ⅱ. Gameplaying and puzzle-solving may seem somewhat remote from espert systems, and insufficiently serious to provide a theoretical basis for real applications. However, a rather basic notion about computer-based problem solving can be traced back to early attempts to program computers to perform shuch tasks.(1)state space searchThe fundamental idea that came out of early research is called state space search,and it is essentially very simple. Many kinds of problem can be formulated in terms of three important ingredients:（1）a starting state,such as the initial state of the chess board;（2）a termination test for detecing final states or sulutions to the problem,such as the simple rule for detecting checkmate in chess;（3）a set of operations that can be applied to change the current state of theproblem,such as the legal moves of chess.One way of thinking of this conceptual space of states is as a graph in which the states are nodes and the operations are arcs. Such spaces can be generated as you go . gor exampe, you coule gegin with the starting state of the chess board and make it the first node in the graph. Each of White’s possilbe first moves would then be an arc connecting this node to a new state of the board. Each of Black’s legal replies to each of these f irst moves could then be considered as operations which connect each of these new nodes to a changed statd of the board , and so on .(2)Heuristic searchHiven that exhaustive search is mot feasible for anything other than small search spaces, some means of guiding the search is required. A search that uses one or more items of domain-specific knowledge to traverse a state space graphy is called a heuristic search. Aheuristic is best thought of as a rule of thumb;it is not guaranteed to succeed,in the way that an algorithm or decision procedure is ,but it is useful in the majority of cases .2 the romantic period: computer understandingthe mid-1960s to the mid-1970s represents what I call the romantic period in artificial intelligence reserch. Atthis time, people were very concerned with making machines “understand”, by which they usually meant the understanding of natural language, especially stories and dialogue. Winograd’s (1972)SHRDLU system was arguably the climax of this epoch : a program which was capable of understanding a quite substantial subset of english by representing and reasoning about a very restricted domain ( a world consisting of children’s toy blocks).The program exhibited understanding by modifying its “blocksworld” represent ation in respinse to commands , and by responding to questions about both the configuration of blocks and its “actions” upon them. Thus is could answer questions like:What is the colour of the block supporting the red pyramid?And derive plans for obeying commands such as :Place the blue pyramid on the green block.Other researchers attempted to model human problem-solving behaviour on simple tasks ,such as puzzles, word games and memory tests. The aim war to make the knowledge and strategy used by the program resemble the knowledge and strategy of the human subject as closely as possible. Empirical studies compared the performance of progran and subject in an attempt to see how successful the simulation had been.。

外文翻译范例在全球化日益加深的今天，外文翻译的重要性愈发凸显。

无论是学术研究、商务交流，还是文化传播，准确而流畅的外文翻译都起着至关重要的桥梁作用。

下面为大家呈现几个不同领域的外文翻译范例，以帮助大家更好地理解和掌握外文翻译的技巧与要点。

一、科技文献翻译原文：The development of artificial intelligence has brought about revolutionary changes in various fields, such as healthcare, finance, and transportation译文：人工智能的发展给医疗保健、金融和交通运输等各个领域带来了革命性的变化。

在这个范例中，翻译准确地传达了原文的意思。

“artificial intelligence”被准确地翻译为“人工智能”，“revolutionary changes”翻译为“革命性的变化”，“various fields”翻译为“各个领域”，用词准确、贴切，符合科技文献严谨、客观的语言风格。

二、商务合同翻译原文：This Agreement shall commence on the effective date and shall continue in force for a period of five years, unless earlier terminated in accordance with the provisions herein译文：本协议自生效日起生效，并将持续有效五年，除非根据本协议的规定提前终止。

商务合同的翻译需要格外注重准确性和专业性。

上述译文中，“commence”翻译为“生效”，“in force”翻译为“有效”，“terminated”翻译为“终止”，清晰准确地表达了合同条款的含义，避免了可能的歧义。

三、文学作品翻译原文：The sun was setting, painting the sky with hues of orange and pink, as if nature were a master artist at work译文：太阳正在西沉，把天空涂成了橙色和粉色，仿佛大自然是一位正在创作的艺术大师。

人工智能英文文献原文及译文附件四英文文献原文Artificial Intelligence"Artificial intelligence" is a word was originally Dartmouth in 1956 to put forward. From then on, researchers have developed many theories and principles, the concept of artificial intelligence is also expands. Artificial intelligence is a challenging job of science, the person must know computer knowledge, psychology and philosophy. Artificial intelligence is included a wide range of science, it is composed of different fields, such as machine learning, computer vision, etc, on the whole, the research on artificial intelligence is one of the main goals of the machine can do some usually need to perform complex human intelligence. But in different times and different people in the "complex" understanding is different. Such as heavy science and engineering calculation was supposed to be the brain to undertake, now computer can not only complete this calculation, and faster than the human brain can more accurately, and thus the people no longer put this calculation is regarded as "the need to perform complex human intelligence, complex tasks" work is defined as the development of The Times and the progress of technology, artificial intelligence is the science of specific target and nature as The Times change and development. On the one hand it continues to gain new progress on the one hand, and turning to more meaningful, the more difficult the target. Current can be used to study the main material of artificial intelligence and artificial intelligence technology to realize the machine is a computer, the development history of artificial intelligence is computer science and technology and the development together. Besides the computer science and artificial intelligence also involves information, cybernetics, automation, bionics, biology, psychology, logic, linguistics, medicine and philosophy and multi-discipline. Artificial intelligence research include: knowledge representation, automatic reasoning and search method, machine learning and knowledge acquisition and processing of knowledge system, natural language processing, computer vision, intelligent robot, automatic program design, etc.Practical application of machine vision: fingerprint identification,face recognition, retina identification, iris identification, palm, expert system, intelligent identification, search, theorem proving game, automatic programming, and aerospace applications.Artificial intelligence is a subject categories, belong to the door edge discipline of natural science and social science.Involving scientific philosophy and cognitive science, mathematics, neurophysiological, psychology, computer science, information theory, cybernetics, not qualitative theory, bionics.The research category of natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combined scheduling problem, perception, pattern recognition, logic design program, soft calculation, inaccurate and uncertainty, the management of artificial life, neural network, and complex system, human thinking mode of genetic algorithm.Applications of intelligent control, robotics, language and image understanding, genetic programming robot factory.Safety problemsArtificial intelligence is currently in the study, but some scholars think that letting computers have IQ is very dangerous, it may be against humanity. The hidden danger in many movie happened.The definition of artificial intelligenceDefinition of artificial intelligence can be divided into two parts, namely "artificial" or "intelligent". "Artificial" better understanding, also is controversial. Sometimes we will consider what people can make, or people have high degree of intelligence to create artificial intelligence, etc. But generally speaking, "artificial system" is usually significance of artificial system.What is the "smart", with many problems. This involves other such as consciousness, ego, thinking (including the unconscious thoughts etc. People only know of intelligence is one intelligent, this is the universal view of our own. But we are very limited understanding of the intelligence of the intelligent people constitute elements are necessary to find, so it is difficult to define what is "artificial" manufacturing "intelligent". So the artificial intelligence research often involved in the study of intelligent itself. Other about animal or other artificial intelligence system is widely considered to be related to the study of artificial intelligence.Artificial intelligence is currently in the computer field, the moreextensive attention. And in the robot, economic and political decisions, control system, simulation system application. In other areas, it also played an indispensable role.The famous American Stanford university professor nelson artificial intelligence research center of artificial intelligence under such a definition: "artificial intelligence about the knowledge of the subject is and how to represent knowledge -- how to gain knowledge and use of scientific knowledge. But another American MIT professor Winston thought: "artificial intelligence is how to make the computer to do what only can do intelligent work." These comments reflect the artificial intelligence discipline basic ideas and basic content. Namely artificial intelligence is the study of human intelligence activities, has certain law, research of artificial intelligence system, how to make the computer to complete before the intelligence needs to do work, also is to study how the application of computer hardware and software to simulate human some intelligent behavior of the basic theory, methods and techniques.Artificial intelligence is a branch of computer science, since the 1970s, known as one of the three technologies (space technology, energy technology, artificial intelligence). Also considered the 21st century (genetic engineering, nano science, artificial intelligence) is one of the three technologies. It is nearly three years it has been developed rapidly, and in many fields are widely applied, and have made great achievements, artificial intelligence has gradually become an independent branch, both in theory and practice are already becomes a system. Its research results are gradually integrated into people's lives, and create more happiness for mankind.Artificial intelligence is that the computer simulation research of some thinking process and intelligent behavior (such as study, reasoning, thinking, planning, etc.), including computer to realize intelligent principle, make similar to that of human intelligence, computer can achieve higher level of computer application. Artificial intelligence will involve the computer science, philosophy and linguistics, psychology, etc. That was almost natural science and social science disciplines, the scope of all already far beyond the scope of computer science and artificial intelligence and thinking science is the relationship between theory and practice, artificial intelligence is in the mode of thinking science technology application level, is one of its application. From the view of thinking, artificial intelligence is notlimited to logical thinking, want to consider the thinking in image, the inspiration of thought of artificial intelligence can promote the development of the breakthrough, mathematics are often thought of as a variety of basic science, mathematics and language, thought into fields, artificial intelligence subject also must not use mathematical tool, mathematical logic, the fuzzy mathematics in standard etc, mathematics into the scope of artificial intelligence discipline, they will promote each other and develop faster.A brief history of artificial intelligenceArtificial intelligence can be traced back to ancient Egypt's legend, but with 1941, since the development of computer technology has finally can create machine intelligence, "artificial intelligence" is a word in 1956 was first proposed, Dartmouth learned since then, researchers have developed many theories and principles, the concept of artificial intelligence, it expands and not in the long history of the development of artificial intelligence, the slower than expected, but has been in advance, from 40 years ago, now appears to have many AI programs, and they also affected the development of other technologies. The emergence of AI programs, creating immeasurable wealth for the community, promoting the development of human civilization.The computer era1941 an invention that information storage and handling all aspects of the revolution happened. This also appeared in the U.S. and Germany's invention is the first electronic computer. Take a few big pack of air conditioning room, the programmer's nightmare: just run a program for thousands of lines to set the 1949. After improvement can be stored procedure computer programs that make it easier to input, and the development of the theory of computer science, and ultimately computer ai. This in electronic computer processing methods of data, for the invention of artificial intelligence could provide a kind of media.The beginning of AIAlthough the computer AI provides necessary for technical basis, but until the early 1950s, people noticed between machine and human intelligence. Norbert Wiener is the study of the theory of American feedback. Most familiar feedback control example is the thermostat. It will be collected room temperature and hope, and reaction temperature compared to open or close small heater, thus controlling environmental temperature. The importance of the study lies in the feedback loop Wiener:all theoretically the intelligence activities are a result of feedback mechanism and feedback mechanism is. Can use machine. The findings of the simulation of early development of AI.1955, Simon and end Newell called "a logical experts" program. This program is considered by many to be the first AI programs. It will each problem is expressed as a tree, then choose the model may be correct conclusion that a problem to solve. "logic" to the public and the AI expert research field effect makes it AI developing an important milestone in 1956, is considered to be the father of artificial intelligence of John McCarthy organized a society, will be a lot of interest machine intelligence experts and scholars together for a month. He asked them to Vermont Dartmouth in "artificial intelligence research in summer." since then, this area was named "artificial intelligence" although Dartmouth learn not very successful, but it was the founder of the centralized and AI AI research for later laid a foundation.After the meeting of Dartmouth, AI research started seven years. Although the rapid development of field haven't define some of the ideas, meeting has been reconsidered and Carnegie Mellon university. And MIT began to build AI research center is confronted with new challenges. Research needs to establish the: more effective to solve the problem of the system, such as "logic" in reducing search; expert There is the establishment of the system can be self learning.In 1957, "a new program general problem-solving machine" first version was tested. This program is by the same logic "experts" group development. The GPS expanded Wiener feedback principle, can solve many common problem. Two years later, IBM has established a grind investigate group Herbert AI. Gelerneter spent three years to make a geometric theorem of solutions of the program. This achievement was a sensation.When more and more programs, McCarthy busy emerge in the history of an AI. 1958 McCarthy announced his new fruit: LISP until today still LISP language. In. "" mean" LISP list processing ", it quickly adopted for most AI developers.In 1963 MIT from the United States government got a pen is 22millions dollars funding for research funding. The machine auxiliary recognition from the defense advanced research program, have guaranteed in the technological progress on this plan ahead of the Soviet union. Attracted worldwide computer scientists, accelerate the pace of development of AI research.Large programAfter years of program. It appeared a famous called "SHRDLU." SHRDLU "is" the tiny part of the world "project, including the world (for example, only limited quantity of geometrical form of research and programming). In the MIT leadership of Minsky Marvin by researchers found, facing the object, the small computer programs can solve the problem space and logic. Other as in the late 1960's STUDENT", "can solve algebraic problems," SIR "can understand the simple English sentence. These procedures for handling the language understanding and logic.In the 1970s another expert system. An expert system is a intelligent computer program system, and its internal contains a lot of certain areas of experience and knowledge with expert level, can use the human experts' knowledge and methods to solve the problems to deal with this problem domain. That is, the expert system is a specialized knowledge and experience of the program system. Progress is the expert system could predict under certain conditions, the probability of a solution for the computer already has. Great capacity, expert systems possible from the data of expert system. It is widely used in the market. Ten years, expert system used in stock, advance help doctors diagnose diseases, and determine the position of mineral instructions miners. All of this because of expert system of law and information storage capacity and become possible.In the 1970s, a new method was used for many developing, famous as AI Minsky tectonic theory put forward David Marr. Another new theory of machine vision square, for example, how a pair of image by shadow, shape, color, texture and basic information border. Through the analysis of these images distinguish letter, can infer what might be the image in the same period. PROLOGE result is another language, in 1972. In the 1980s, the more rapid progress during the AI, and more to go into business. 1986, the AI related software and hardware sales $4.25 billion dollars. Expert system for its utility, especially by demand. Like digital electric company with such company XCON expert system for the VAX mainframe programming. Dupont, general motors and Boeing has lots of dependence of expert system for computer expert. Some production expert system of manufacture software auxiliary, such as Teknowledge and Intellicorp established. In order to find and correct the mistakes, existing expert system and some other experts system was designed，such as teach users learn TVC expert system of the operating system.From the lab to daily lifePeople began to feel the computer technique and artificial intelligence. No influence of computer technology belong to a group of researchers in the lab. Personal computers and computer technology to numerous technical magazine now before a people. Like the United States artificial intelligence association foundation. Because of the need to develop, AI had a private company researchers into the boom. More than 150 a DEC (it employs more than 700 employees engaged in AI research) that have spent 10 billion dollars in internal AI team.Some other AI areas in the 1980s to enter the market. One is the machine vision Marr and achievements of Minsky. Now use the camera and production, quality control computer. Although still very humble, these systems have been able to distinguish the objects and through the different shape. Until 1985 America has more than 100 companies producing machine vision systems, sales were us $8 million.But the 1980s to AI and industrial all is not a good year for years. 1986-87 AI system requirements, the loss of industry nearly five hundred million dollars. Teknowledge like Intellicorp and two loss of more than $6 million, about one-third of the profits of the huge losses forced many research funding cuts the guide led. Another disappointing is the defense advanced research programme support of so-called "intelligent" this project truck purpose is to develop a can finish the task in many battlefield robot. Since the defects and successful hopeless, Pentagon stopped project funding.Despite these setbacks, AI is still in development of new technology slowly. In Japan were developed in the United States, such as the fuzzy logic, it can never determine the conditions of decision making, And neural network, regarded as the possible approaches to realizing artificial intelligence. Anyhow, the eighties was introduced into the market, the AI and shows the practical value. Sure, it will be the key to the 21st century. "artificial intelligence technology acceptance inspection in desert storm" action of military intelligence test equipment through war. Artificial intelligence technology is used to display the missile system and warning and other advanced weapons. AI technology has also entered family. Intelligent computer increase attracting public interest. The emergence of network game, enriching people's life.Some of the main Macintosh and IBM for application software such as voice and character recognition has can buy, Using fuzzy logic,AI technology to simplify the camera equipment. The artificial intelligence technology related to promote greater demand for new progress appear constantly. In a word ，Artificial intelligence has and will continue to inevitably changed our life.附件三英文文献译文人工智能“人工智能”一词最初是在1956 年Dartmouth在学会上提出来的。