人工智能专业外文翻译-机器人

人工智能的英语解释英文回答：Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI research has been highly successful in developing effective techniques for solving a wide range of problems in many fields, including computer vision, natural language processing, and machine learning.Applications of AI.AI has a wide range of applications in variousindustries and sectors, including:Healthcare: AI-powered systems are used for disease diagnosis, drug discovery, and personalized treatment plans.Finance: AI algorithms are employed for fraud detection, risk assessment, and trading decisions.Transportation: AI is used for autonomous driving, traffic management, and route optimization.Manufacturing: AI techniques are utilized for predictive maintenance, process optimization, and quality control.Retail: AI systems are used for personalized recommendations, demand forecasting, and inventory management.Types of AI.AI can be categorized into different types based on the level of intelligence and autonomy exhibited by the system:Reactive Machines: These AI systems react to the current environment without the ability to learn or remember past experiences.Limited Memory: AI systems with limited memory canstore past experiences and use them to inform current decisions.Theory of Mind: These AI systems can understand the mental states of other entities, such as beliefs, desires, and intentions.Self-Awareness: AI systems with self-awareness are able to reflect on their own thoughts and actions.Benefits of AI.The adoption of AI technology offers numerous potential benefits, such as:Efficiency: AI systems can automate repetitive and time-consuming tasks, freeing up human workers for more complex and creative endeavors.Accuracy: AI algorithms can process large volumes of data and extract patterns and insights that are difficult for humans to identify.Consistency: AI systems can consistently perform tasks without the errors or biases that can affect humandecision-making.Innovation: AI technology drives innovation by enabling the development of new products, services, and processes.Economic Growth: AI has the potential to contribute to economic growth by increasing productivity and creating new job opportunities.Challenges of AI.While AI holds great promise, it also presents several challenges that need to be addressed:Job Displacement: AI systems can displace human workers in certain job roles, raising concerns about unemployment.Bias: AI algorithms can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes.Security: AI systems can be vulnerable to hacking and misuse, potentially leading to security breaches.Ethics: The rapid advancement of AI raises ethical questions about the responsible development and use of AI technology.Future of AI.The future of AI is highly anticipated, with researchers and industry leaders envisioning a world where AI systems seamlessly collaborate with humans to solve complex problems and enhance human capabilities. As AI technology continues to evolve, it is expected to play an increasingly significant role in shaping the future of society and the economy.中文回答：什么是人工智能？人工智能（AI）是指机器（尤其是计算机系统）模拟人类智能过程的能力。

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。

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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。

机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。

Robots机器人中英文翻译Robots机器人With advancements in technology, robots have become an integral part of our daily lives. From manufacturing industries to healthcare and even our homes, robots are taking over various tasks and transforming the way we live. In this article, we will explore the significance of robots and discuss their benefits and drawbacks.机器人在科技的进步下，在我们的日常生活中变得不可或缺。

从制造业到医疗保健，甚至到我们的家庭，机器人正在接管各种任务，改变着我们的生活方式。

本文中，我们将探讨机器人的重要性，并讨论他们的利与弊。

1. The Advantages of Robots 机器人的优势Robots offer numerous benefits in various aspects of our society. Firstly, they improve productivity and efficiency in industries. With their precision and speed, robots can carry out tasks more accurately and faster compared to humans. This leads to increased production rates and reduced errors, ultimately resulting in cost savings for businesses.Secondly, robots minimize the risk to humans in dangerous and hazardous situations. They can be programmed to perform tasks in hazardous environments such as nuclear power plants, mines, or disaster-stricken areas. This reduces the chances of human injuries or fatalities.Thirdly, robots contribute to medical advancements by assisting in surgeries and healthcare. Surgical robots, for example, aid doctors inperforming intricate procedures with enhanced precision and control. Furthermore, robots can also assist with patient care, such as providing support to the elderly or individuals with disabilities.机器人在我们社会的各个方面都提供了众多的优势。

AI（人工智能）术语中英文对照表缩写英语汉语A网课代上Activation Function激活函数Adversarial Networks对抗网络Affine Layer仿射层agent代理/智能体algorithm算法alpha-beta pruningα-β剪枝anomaly detection异常检测approximation近似AGI Artificial General Intelligence通用人工智能AI Artificial Intelligence人工智能association analysis关联分析attention mechanism注意机制autoencoder自编码器ASR automatic speech recognition自动语音识别automatic summarization自动摘要average gradient平均梯度Average-Pooling平均池化BBP backpropagation反向传播BPTT Backpropagation Through Time 通过时间的反向传播BN Batch Normalization分批标准化Bayesian network贝叶斯网络Bias-Variance Dilemma偏差/方差困境Bi-LSTM Bi-directional Long-Short TermMemory双向长短期记忆bias偏置/偏差big data大数据Boltzmann machine玻尔兹曼机CCPU Central Processing Unit中央处理器chunk词块clustering聚类cluster analysis聚类分析co-adapting共适应co-occurrence共现Computation Cost计算成本Computational Linguistics计算语言学computer vision计算机视觉concept drift概念漂移CRF conditional random field条件随机域/场convergence收敛CA conversational agent会话代理convexity凸性CNN convolutional neural network卷积神经网络Cost Function成本函数cross entropy交叉熵DDecision Boundary决策边界Decision Trees决策树DBN Deep Belief Network深度信念网络DCGAN Deep Convolutional GenerativeAdversarial Network深度卷积生成对抗网络DL deep learning深度学习DNN deep neural network深度神经网络Deep Q-Learning深度Q学习DQN Deep Q-Network深度Q网络DNC differentiable neural computer可微分神经计算机dimensionality reduction algorithm降维算法discriminative model判别模型discriminator判别器divergence散度domain adaption领域自适应DropoutDynamic Fusion动态融合EEmbedding嵌入emotional analysis情绪分析End-to-End端到端EM Expectation-Maximization期望最大化Exploding GradientProblem梯度爆炸问题ELM Extreme Learning Machine超限学习机FFAIR Facebook Artificial IntelligenceResearchFacebook人工智能研究所factorization因子分解feature engineering特征工程Featured Learning特征学习Feedforward Neural Networks前馈神经网络Ggame theory博弈论GMM Gaussian Mixture Model高斯混合模型GA Genetic Algorithm遗传算法Generalization泛化GAN Generative Adversarial Networks生成对抗网络Generative Model生成模型Generator生成器Global Optimization全局优化GNMT Google Neural Machine Translation谷歌神经机器翻译Gradient Descent梯度下降graph theory图论GPU graphics processing unit 图形处理单元/图形处理器HHDM hidden dynamic model隐动态模型hidden layer隐藏层HMM Hidden Markov Model隐马尔可夫模型hybrid computing混合计算hyperparameter超参数IICA Independent Component Analysis独立成分分析input输入ICML International Conference for Machine Learning国际机器学习大会language phenomena语言现象latent dirichlet allocation隐含狄利克雷分布JJSD Jensen-Shannon Divergence JS距离KK-Means Clustering K-均值聚类K-NN K-Nearest Neighbours AlgorithmK-最近邻算法Knowledge Representation知识表征KB knowledge base知识库LLatent Dirichlet Allocation隐狄利克雷分布LSA latent semantic analysis潜在语义分析learner学习器Linear Regression线性回归log likelihood对数似然Logistic Regression Logistic回归LSTM Long-Short Term Memory长短期记忆loss损失MMT machine translation机器翻译Max-Pooling最大池化Maximum Likelihood最大似然minimax game最小最大博弈Momentum动量MLP Multilayer Perceptron多层感知器multi-document summarization多文档摘要MLP multi layered perceptron多层感知器multimodal learning多模态学习multiple linear regression多元线性回归NNaive Bayes Classifier朴素贝叶斯分类器named entity recognition命名实体识别Nash equilibrium纳什均衡NLG natural language generation自然语言生成NLP natural language processing自然语言处理NLL Negative Log Likelihood负对数似然NMT Neural Machine Translation神经机器翻译NTM Neural Turing Machine神经图灵机NCE noise-contrastive estimation噪音对比估计non-convex optimization非凸优化non-negative matrix factorization非负矩阵分解Non-Saturating Game非饱和博弈Oobjective function目标函数Off-Policy离策略On-Policy在策略one shot learning一次性学习output输出PParameter参数parse tree解析树part-of-speech tagging词性标注PSO Particle Swarm Optimization粒子群优化算法perceptron感知器polarity detection极性检测pooling池化PPGN Plug and Play Generative Network即插即用生成网络PCA principal component analysis主成分分析Probability Graphical Model概率图模型QQNN Quantized Neural Network量子化神经网络quantum computer量子计算机Quantum Computing量子计算RRBF Radial Basis Function径向基函数Random Forest Algorithm随机森林算法ReLU Rectified Linear Unit 线性修正单元/线性修正函数RNN Recurrent Neural Network循环神经网络recursive neural network递归神经网络RL reinforcement learning强化学习representation表征representation learning表征学习Residual Mapping残差映射Residual Network残差网络RBM Restricted Boltzmann Machine受限玻尔兹曼机Robot机器人Robustness稳健性RE Rule Engine规则引擎Ssaddle point鞍点Self-Driving自动驾驶SOM self organised map自组织映射Semi-Supervised Learning半监督学习sentiment analysis情感分析SLAM simultaneous localization and mapping同步定位与地图构建SVD Singular Value Decomposition奇异值分解Spectral Clustering谱聚类Speech Recognition语音识别SGD stochastic gradient descent随机梯度下降supervised learning监督学习SVM Support Vector Machine支持向量机synset同义词集Tt-SNE T-Distribution Stochastic Neighbour EmbeddingT-分布随机近邻嵌入tensor张量TPU Tensor Processing Units张量处理单元the least square method最小二乘法Threshold阙值Time Step时间步骤tokenization标记化treebank树库transfer learning迁移学习Turing Machine图灵机Uunsupervised learning无监督学习VVanishing Gradient Problem梯度消失问题VC Theory Vapnik–Chervonenkis theory 万普尼克-泽范兰杰斯理论von Neumann architecture 冯·诺伊曼架构/结构WWGAN Wasserstein GANW weight权重word embedding词嵌入WSD word sense disambiguation词义消歧XYZZSL zero-shot learning零次学习zero-data learning零数据学习。

英文原文Artificial IntelligenceAdvanced Idea ,Anticipating Incomparability on Artificial Intelligence.Artificial intelligence(AI) is the field of engineering that builds systems ,primarily computer systems ,to perform tasks requiring intelligence .This field of research has often set itself ambitious goals, seeking to build machines that can outlook humans in particular domains of skill and knowledge ,and has achieved some success in this.The key aspects of intelligence around which AI research is usually focused include expert system ,industrial robotics,systems and languages language understanding ,learning ,and game playing,etc.Expert SystemAn expert system is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise . Typically,the user interacts with an expert system in a consultation dialogue,just as he would interact with a human who had some type of expertise,explaining his problem,performing suggested tests,and asking questions about proposed solutions. Current experimental systems have achieved high levels of performance in consultation tasks like chemical and geological data analysis,computer system configuration,structural engineering,and even medical diagnosis.Expert systems can be viewed as intermediaries between human experts,who interact with the systems in knowledge acquisition mode ,and human users who interact with the systems in consultation mode. Furthermore ,much research in this area of AI has focused on endowing these systems with the ability to explain their reasoning,both to make the consultation more acceptable to the user and to help the human expert find errors in the system´s reasoning when they occur.Here are the features of expert systems:①Expert systems use knowledge rather than data to control the solution process.②The know is encoded and maintained as an entity separated fromthe control program.Furthermore,it is possible in some cases to use differentknowledge bases with the same control programs to producedifferent types of expert system.Such system are known as expert systemshells.③Expert systems are capable of explaining how a particular concl-usion is reached,and why requested information is needed during a consultation.④Expert systems use symbolic representations for knowledge andperform their inference through symbolic computations.⑤Expert systems often reason with metaknowledge.Industrial RoboticsAn industrial robot is a general-purpose computer-controlled manipulator consisting of several rigid links connected in series by revolute or prismatic joints.Research in this field has looked at everything from the optimal movement of robot arms to methods of planning a sequence of actions to achieve a robot´s goals.Although more complex systems have been built,the thousands of robots that are being used today in industrial applications are simple devices that have been programmed to some repetitive task.Robots,when compared to humans,yield more consistent quality,more predictable output,and are more reliable.Robots has been used in industry since 1965.They are usually characterized by the design of the mechanical system.There are six recognizable robot configurations:①Cartesian Robots:A robot whose main frame consist of three Linear axes.②Gantry Robots:A Gantry robot is a type of artesian robot whose structure resembles a gantry.This structure is used to minimize deflection along each axis.③Cylindrical Robots:A cylindrical robot has two linear axes and one rotary axis.④Spherical Robots:A spherical robot has one linear axis and two rotary axes.Spherical Robots are used in a variety of industrial tasks such as welding and material handling.⑤Articulated Robots:An articulated robot has three rotational axes connecting three rigid links and a base.⑥Scara Robots:One style of robot that has recently become quite popular is a combination of the articulated arm and the cylindrical robot.The robot has more than three axes and is widely used in electronic assembly.Systems and LanguagesComputer-systems ideas like time-sharing,list processing,and interactive debugging were developed in the AI research environment.Specialized programming languages and systems,with features designed to facilitate deduction,robot manipulation,cognitive modeling,and so on, have often been rich sources of new ideas.Most recently,reveral knowledge-representation languages,computer languages for encoding knowledge and reasoning methods as data structure and procedures,which have been developed in the last few years to explore a variety of ideas about how to build reasoning programs.Problem SolvingThe first big success in AI was programs that could solve puzzles and play games like chess.Techniques like looking ahead several moves and dividing difficult problems into easier sub-problems evolved into the fundamental AI techniques of search and problem reduction.Today´s programs play championship-level checkers and backgammon,as well as verygood chess.Another problem-solving program that integrates mathematical formulates symbolically has attained very high levels of performance and is being used by scientists and engineers.Some programs can even improve their performance with experience.As discussed above,the open questions in this area involve capabilities that human players have but cannot articulate,like the chess master´s ability to see the board configuration in terms of meaningful patterns.Another basic open question involves the original conceptualization of a problem,called in AI the choice of problem representation.Humans often solve a problem by finding a way of thinking about it that makes the solution easy-AI problems,so far,must be told how to think about the problems they solve.Logical ReasoningClosely related to problem and puzzle solving was early work on logical deduction.Programs were developed that could prove assertions by manipulating a database of facts,each represented by discrete data structures just as they are represented by discrete formulas in mathematical logic.These methods,unlike many other AI techniques,could be shownto be complete and consistent.That is,so long as the original facts were correct,the programs could prove all theorems that followed from the facts,and only those theorems.Logical reasoning has been one of the most persistently investigated subareas of AI research.Of particular interest are the problems of finding ways of focusing on only the relevant facts of a large database and of keeping track of the justifications for beliefs and updating them when new information arrives.Language UnderstandingThe domain of language understanding was also investigated by early AI researchers and has consistently attracted interest.Programs have been written that answer questions posed in English from an internal database,that translate sentences from one language to another,that follow instruction given in English,and that acquire knowledge by reading textual material and building an internal database.Some programs have even achieved limited success in interpreting instructions spoken into a microphone instead of typed into the computer.Although these language systems are not nearly as good as people are at any of these tasks,they are adequate for some applications.Early successes with programs that answered simple queries and followed simple directions,and early failures at machine translation,have resulted in a sweeping change in the whole AI approach to language.The principal themes of current language-understanding research are the importance of vase amounts of general,commonsense world knowledge and the role of expectations,based on the subject matter and the conversational situation,in interpreting sentences.LearningLearning has remained a challenging area for AI.Certainly one of the most salient and significant aspects of human intelligence is the ability to learn.This is a good example of cognitive behavior that is so poorly understood that vary little progress has been made in achieving it in AI system.There have been several interesting attempts,including programs learn from examples,form their own performance,and from being told.An expert system may perform extensive and costly computations to solve a problem.Most expert systems are hindered by the inflexibility of their problem-solving strategies and the difficulty of modifying large amounts of code.The obvious solution to these problems is for programs to learn on their own,either from experience,analogy,and examples or by being told what to do.Game PlayingMuch of the early research in state space search was done using common board games such as checkers,chess,and the 15-puzzle.In addition to their inherent intellectual appeal,board games have certain properties that make them ideal subjects for this early work.Most games are played using a well-defined set of rules,this makes it easy to generate the search space and frees the researcher from many of the ambiguities and complexities inherent in less structured problems.The board configurations used in playing these games are easily represented on a computer,requiring none of the complex formalisms.ConclusionWe have attempted to define artificial intelligence through discussion of its major areas of research and application.In spite of the variety of problems addressed in artificial intelligence research,a number of important features emerge that seem common to all divisions of the field,these include:①The use of computers to do reasoning,learning,or some other form of intelligence.②A focus on problems that do not respond to algorithmic solutions.This underlies the reliance on heuristic search as an AI problem-solving technique.③Reasoning about the significant qualitative features of a situation.④An attempt to deal with issues of semantic meaning as well as syntactic form.⑤The use of large amounts of domain-specific knowledge in solving problems.This is the basis of expert systems.AbstractArtificial intelligence(AI) is the field of engineering that builds systems,primarily computer systems,to perform tasks requiring intelligence .This field of research has often set itself ambitious goals,seeking to build machines that can outlook humans in particular domains of skill and knowledge,and has achieved some success in this.The key aspects of intelligence around which AI research is usually focused include expert systems,industrial robotics,systems and languages,language understanding,learning,and game playing,machine translation,etc.中文译文人工智能先进的想法不断注入到人工智能的发展过程中，使其最新理念无与伦比。

外文资料robotThe industrial robot is a tool that is used in the manufacturing environment to increase productivity. It can be used to do routine and tedious assembly line jobs，or it can perform jobs that might be hazardous to the human worker . For example ，one of the first industrial robot was used to replace the nuclear fuel rods in nuclear power plants. A human doing this job might be exposed to harmful amounts of radiation. The industrial robot can also operate on the assembly line，putting together small components，such as placing electronic components on a printed circuit board. Thus，the human worker can be relieved of the routine operation of this tedious task. Robots can also be programmed to defuse bombs，to serve the handicapped，and to perform functions in numerous applications in our society.The robot can be thought of as a machine that will move an end-of-tool ，sensor ，and/or gripper to a preprogrammed location. When the robot arrives at this location，it will perform some sort of task .This task could be welding，sealing，machine loading ，machine unloading，or a host of assembly jobs. Generally，this work can be accomplished without the involvement of a human being，except for programming and for turning the system on and off.The basic terminology of robotic systems is introduced in the following:1. A robot is a reprogrammable ，multifunctional manipulator designed to move parts，material，tool，or special devices through variable programmed motions for the performance of a variety of different task. This basic definition leads to other definitions，presented in the following paragraphs，that give acomplete picture of a robotic system.2. Preprogrammed locations are paths that the robot must follow to accomplish work，At some of these locations，the robot will stop and perform some operation ，such as assembly of parts，spray painting ，or welding .These preprogrammed locations are stored in the robot’s memory and are recalled later for continuousoperation.Furthermore，these preprogrammed locations，as well as other program data，can be changed later as the work requirements change.Thus，with regard to this programming feature，an industrial robot is very much like a computer ，where data can be stoned and later recalled and edited.3. The manipulator is the arm of the robot .It allows the robot to bend，reach，and twist.This movement is provided by the manipulator’s axes，also called the degrees of freedom of the robot .A robot can have from 3 to 16 axes.The term degrees of freedom will always relate to the number of axes found on a robot.4. The tooling and frippers are not part the robotic system itself;rather，they are attachments that fit on the end of the robot’s arm. These attachments connected to the end of the robot’s arm allow the robot to lift parts，spot-weld ，paint，arc-weld，drill，deburr，and do a variety of tasks，depending on what is required of the robot.5. The robotic system can control the work cell of the operating robot.The work cell of the robot is the total environment in which the robot must perform itstask.Included within this cell may be the controller ，the robot manipulator ，a work table ，safety features，or a conveyor.All the equipment that is required in order for the robot to do its job is included in the work cell .In addition，signals from outside devices can communicate with the robot to tell the robot when it should parts，pick up parts，or unload parts to a conveyor.The robotic system has three basic components: the manipulator，the controller，and the power source.A.ManipulatorThe manipulator ，which does the physical work of the robotic system，consists of two sections:the mechanical section and the attached appendage.The manipulator also has a base to which the appendages are attached.Fig.1 illustrates the connectionof the base and the appendage of a robot.图1.Basic components of a robot’s manipulatorThe base of the manipulator is usually fixed to the floor of the work area. Sometimes，though，the base may be movable. In this case，the base is attached to either a rail or a track，allowing the manipulator to be moved from one location to anther.As mentioned previously ，the appendage extends from the base of the robot. The appendage is the arm of the robot. It can be either a straight ，movable arm or a jointed arm. The jointed arm is also known as an articulated arm.The appendages of the robot manipulator give the manipulator its various axes of motion. These axes are attached to a fixed base ，which，in turn，is secured to a mounting. This mounting ensures that the manipulator will in one location.At the end of the arm ，a wrist（see Fig 2）is connected. The wrist is made up of additional axes and a wrist flange. The wrist flange allows the robot user to connect different tooling to the wrist for different jobs.图2.Elements of a work cell from the topThe manipulator’s axes allow it to perform work within a certain area. The area is called the work cell of the robot ，and its size corresponds to the size of the manipulator.（Fid2）illustrates the work cell of a typical assembly ro bot.As the robot’s physical size increases，the size of the work cell must also increase.The movement of the manipulator is controlled by actuator，or drive systems.The actuator，or drive systems，allows the various axes to move within the work cell. The drive system can use electric，hydraulic，or pneumatic power.The energy developed by the drive system is converted to mechanical power by various mechanical power systems.The drive systems are coupled through mechanical linkages.These linkages，in turn，drive the different axes of the robot.The mechanical linkages may be composed of chain，gear，and ball screws.B.ControllerThe controller in the robotic system is the heart of the operation .The controller stores preprogrammed information for later recall，controls peripheral devices，and communicates with computers within the plant for constant updates in production.The controller is used to control the robot manipulator’s movements as well as to control peripheral components within the work cell. The user can program the movements of the manipulator into the controller through the use of a hard-held teach pendant.This information is stored in the memory of the controller for later recall.The controller stores all program data for the robotic system.It can store several differentprograms，and any of these programs can be edited.The controller is also required to communicate with peripheral equipment within the work cell. For example，the controller has an input line that identifies when a machining operation is completed.When the machine cycle is completed，the input line turn on telling the controller to position the manipulator so that it can pick up the finished part.Then ，a new part is picked up by the manipulator and placed into the machine.Next，the controller signals the machine to start operation.The controller can be made from mechanically operated drums that step through a sequence of events.This type of controller operates with a very simple robotic system.The controllers found on the majority of robotic systems are more complex devices and represent state-of-the-art eletronoics.That is，they are microprocessor-operated.these microprocessors are either 8-bit，16-bit，or 32-bit processors.this power allows the controller to be very flexible in its operation.The controller can send electric signals over communication lines that allow it to talk with the various axes of the manipulator. This two-way communication between the robot manipulator and the controller maintains a constant update of the end the operation of the system.The controller also controls any tooling placed on the end of the robot’s wrist.The controller also has the job of communicating with the different plant computers. The communication link establishes the robot as part a computer-assisted manufacturing （CAM）system.As the basic definition stated，the robot is a reprogrammable，multifunctional manipulator.Therefore，the controller must contain some of memory stage. The microprocessor-based systems operates in conjunction with solid-state devices.These memory devices may be magnetic bubbles，random-access memory，floppy disks，or magnetic tape.Each memory storage device stores program information fir or for editing.C.power supplyThe power supply is the unit that supplies power to the controller and the manipulator. The type of power are delivered to the robotic system. One type of power is the AC power for operation of the controller. The other type of power isused for driving the various axes of the manipulator. For example，if the robot manipulator is controlled by hydraulic or pneumatic drives，control signals are sent to these devices causing motion of the robot.For each robotic system，power is required to operate the manipulator .This power can be developed from either a hydraulic power source，a pneumatic power source，or an electric power source.There power sources are part of the total components of the robotic work cell.中文翻译机器人工业机器人是在生产环境中用以提高生产效率的工具，它能做常规乏味的装配线工作，或能做那些对于工人来说是危险的工作，例如，第一代工业机器人是用来在核电站中更换核燃料棒，如果人去做这项工作，将会遭受有害放射线的辐射。

机器人外文翻译(中英文翻译)机器人外文翻译(中英文翻译)With the rapid development of technology, the use of robots has become increasingly prevalent in various industries. Robots are now commonly employed to perform tasks that are dangerous, repetitive, or require a high level of precision. However, in order for robots to effectively communicate with humans and fulfill their intended functions, accurate translation between different languages is crucial. In this article, we will explore the importance of machine translation in enabling robots to perform translation tasks, as well as discuss current advancements and challenges in this field.1. IntroductionMachine translation refers to the use of computer algorithms to automatically translate text or speech from one language to another. The ultimate goal of machine translation is to produce translations that are as accurate and natural as those generated by human translators. In the context of robots, machine translation plays a vital role in allowing them to understand and respond to human commands, as well as facilitating communication between robots of different origins.2. Advancements in Machine TranslationThe field of machine translation has experienced significant advancements in recent years, thanks to breakthroughs in artificial intelligence and deep learning. These advancements have led to the development of neural machine translation (NMT) systems, which have greatly improved translation quality. NMT models operate by analyzinglarge amounts of bilingual data, allowing them to learn the syntactic and semantic structures of different languages. As a result, NMT systems are capable of providing more accurate translations compared to traditional rule-based or statistical machine translation approaches.3. Challenges in Machine Translation for RobotsAlthough the advancements in machine translation have greatly improved translation quality, there are still challenges that need to be addressed when applying machine translation to robots. One prominent challenge is the variability of language use, including slang, idioms, and cultural references. These nuances can pose difficulties for machine translation systems, as they often require a deep understanding of the context and cultural background. Researchers are currently working on developing techniques to enhance the ability of machine translation systems to handle such linguistic variations.Another challenge is the real-time requirement of translation in a robotic setting. Robots often need to process and translate information on the fly, and any delay in translation can affect the overall performance and efficiency of the robot. Optimizing translation speed without sacrificing translation quality is an ongoing challenge for researchers in the field.4. Applications of Robot TranslationThe ability for robots to translate languages opens up a wide range of applications in various industries. One application is in the field of customer service, where robots can assist customers in multiple languages, providing support and information. Another application is in healthcare settings, where robots can act as interpreters between healthcare professionals and patientswho may speak different languages. Moreover, in international business and diplomacy, robots equipped with translation capabilities can bridge language barriers and facilitate effective communication between parties.5. ConclusionIn conclusion, machine translation plays a crucial role in enabling robots to effectively communicate with humans and fulfill their intended functions. The advancements in neural machine translation have greatly improved translation quality, but challenges such as language variability and real-time translation requirements still exist. With continuous research and innovation, the future of machine translation for robots holds great potential in various industries, revolutionizing the way we communicate and interact with technology.。

智能机器人翻译器的说明书1.简介智能机器人翻译器是一款先进的语言翻译工具，采用最新的人工智能技术，为您提供高效准确的语言翻译服务。

本说明书将详细介绍智能机器人翻译器的功能及使用方法，以帮助您更好地使用该产品。

2.功能介绍2.1.语言识别功能智能机器人翻译器内置强大的语音识别功能，能够自动识别您输入的语言并将其转化为目标语言。

无论您说的是英语、汉语还是其他语种，智能翻译器都能够准确识别并展示相应的翻译结果。

2.2.多语言翻译功能智能机器人翻译器支持多种主流语言间的翻译，包括但不限于英语、汉语、法语、德语、日语、西班牙语等。

您只需将待翻译的语句输入翻译器，并选择目标语言，即可获得高质量、准确的翻译结果。

2.3.实时翻译功能智能机器人翻译器具备实时翻译的特点，它能够迅速将您的语音或文字输入转化为目标语言，使您能够实时地进行交流、沟通或阅读外文资料，大大提升您的工作效率和生活质量。

2.4.离线翻译功能智能机器人翻译器支持离线翻译，您可以下载相应语言的离线包，即使在没有网络的情况下，也能够使用翻译功能，方便您在旅行或偏远地区使用。

2.5.语音输入与文字输入功能智能机器人翻译器支持语音输入和文字输入两种方式，方便用户根据实际需求选择合适的输入方式。

只需按住语音按钮进行语音输入或者通过键盘输入文字，即可轻松完成语言翻译。

3.使用方法3.1.语音输入方式步骤1：打开智能机器人翻译器，并确保设备连接到网络。

步骤2：点击语音输入按钮，开始语音输入。

步骤3：说出您想要翻译的语句。

步骤4：松开按钮，翻译器会迅速显示出翻译结果。

3.2.文字输入方式步骤1：打开智能机器人翻译器，并确保设备连接到网络。

步骤2：选择输入框，并通过键盘输入待翻译的文字。

步骤3：点击翻译按钮，翻译器会迅速显示出翻译结果。

4.注意事项4.1.确保网络连接稳定智能机器人翻译器需要连接网络才能提供准确的翻译结果，请确保设备连接的网络稳定，并且网络延迟较低，以免影响翻译质量。

人工智能介绍英文版Key Information:1、 Definition of Artificial Intelligence: ＿___________________________2、 History and Development of AI: ＿___________________________3、 Applications of AI: ＿___________________________4、 Benefits of AI: ＿___________________________5、 Challenges and Risks of AI: ＿___________________________1、 Introduction11 Artificial intelligence （AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans12 The field of AI encompasses a wide range of techniques and technologies, including machine learning, natural language processing, computer vision, and robotics2、 History and Development of AI21 The roots of AI can be traced back to ancient times, but it was not until the mid-20th century that significant progress was made22 In the 1950s, the concept of AI was formally introduced, and early research focused on developing algorithms for problemsolving and decisionmaking23 Over the years, advancements in computing power, data availability, and algorithmic improvements have led to significant breakthroughs in AI3、 Applications of AI31 Healthcare311 AI is used in medical diagnosis, drug discovery, and patient monitoring312 Machine learning algorithms can analyze large amounts of medical data to identify patterns and predict diseases32 Finance321 In the financial sector, AI is employed for fraud detection, risk assessment, and investment decisionmaking322 Automated trading systems use AI to make rapid and informed trading decisions33 Transportation331 Selfdriving cars and intelligent transportation systems rely on AI for navigation and traffic management332 AI can optimize routes and improve the efficiency of public transportation4、 Benefits of AI41 Increased Efficiency and Productivity411 AIpowered systems can perform tasks faster and more accurately than humans, leading to improved operational efficiency412 Automation of repetitive tasks frees up human resources for more complex and creative work42 Improved DecisionMaking421 By analyzing large amounts of data, AI can provide valuable insights and predictions to support decisionmaking processes422 Businesses and organizations can make more informed and strategic decisions based on AIdriven analytics43 Enhanced Customer Experience431 AIpowered chatbots and virtual assistants offer 24/7 customer service, providing quick and accurate responses432 Personalized recommendations based on AI algorithms enhance the customer experience and increase customer satisfaction5、 Challenges and Risks of AI51 Ethical and Moral Concerns511 Issues such as bias in algorithms, data privacy, and the potential for autonomous weapons raise ethical questions512 Ensuring that AI is developed and used in an ethical and responsible manner is crucial52 Job Displacement521 The automation of certain jobs by AI may lead to unemployment and the need for workforce reskilling and upskilling522 However, new job opportunities are also emerging in the field of AI and related technologies53 Security Risks531 AI systems can be vulnerable to cyberattacks and malicious use532 Safeguarding AI infrastructure and data is essential to prevent security breaches6、 Conclusion61 AI has the potential to bring significant benefits and transform various aspects of our lives62 However, it is essential to address the challenges and risks associated with its development and use to ensure a positive impact on society63 Continued research and ethical considerations will be crucial in shaping the future of AI。

Introduction to robotics technologyIn the manufacturing field, robot development has focused on engineering robotic arms that perform manufacturing processes. In the space industry, robotics focuses on highly specialized, one-of-kind planetary rovers. Unlike a highly automated manufacturing plant, a planetary rover operating on the dark side of the moon -- without radio communication -- might run into unexpected situations. At a minimum, a planetary rover must have some source of sensory input, some way of interpreting that input, and a way of modifying its actions to respond to a changing world. Furthermore, the need to sense and adapt to a partially unknown environment requires intelligence (in other words, artificial intelligence).Mechanical platforms -- the hardware baseA robot consists of two main parts: the robot body and some form of artificial intelligence (AI) system. Many different body parts can be called a robot. Articulated arms are used in welding and painting; gantry and conveyor systems move parts in factories; and giant robotic machines move earth deep inside mines. One of the most interesting aspects of robots in general is their behavior, which requires a form of intelligence. The simplest behavior of a robot is locomotion. Typically, wheels are used as the underlying mechanism to make a robot move from one point to the next. And some force such as electricity is required to make the wheels turn under command.MotorsA variety of electric motors provide power to robots, allowing them to move material, parts, tools, or specialized devices with variousprogrammed motions. The efficiency rating of a motor describes how much of the electricity consumed is converted to mechanical energy. Let's take a look at some of the mechanical devices that are currently being used in modern robotics technology.Driving mechanismsGears and chains:Gears and chains are mechanical platforms that provide a strong and accurate way to transmit rotary motion from one place to another, possibly changing it along the way. The speed change between two gears depends upon the number of teeth on each gear. When a powered gear goes through a full rotation, it pulls the chain by the number of teeth on that gear.Pulleys and belts:Pulleys and belts, two other types of mechanical platforms used in robots, work the same way as gears and chains. Pulleys are wheels with a groove around the edge, and belts are the rubber loops that fit in that groove.Gearboxes:A gearbox operates on the same principles as the gear and chain, without the chain. Gearboxes require closer tolerances, since instead of using a large loose chain to transfer force and adjust for misalignments, the gears mesh directly with each other. Examples of gearboxes can be found on the transmission in a car, the timing mechanism in a grandfather clock, and the paper-feed of your printer.Power suppliesPower supplies are generally provided by two types of battery. Primary batteries are used once and then discarded; secondary batteries operate from a (mostly) reversible chemical reaction and can be recharged several times. Primary batteries have higher density and a lower self-dischargerate. Secondary (rechargeable) batteries have less energy than primary batteries, but can be recharged up to a thousand times depending on their chemistry and environment. Typically the first use of a rechargeable battery gives 4 hours of continuous operation in an application or robot.SensorsRobots react according to a basic temporal measurement, requiring different kinds of sensors.In most systems a sense of time is built-in through the circuits and programming. For this to be productive in practice, a robot has to have perceptual hardware and software, which updates quickly. Regardless of sensor hardware or software, sensing and sensors can be thought of as interacting with external events (in other words, the outside world). The sensor measures some attribute of the world. The term transducer is often used interchangeably with sensor. A transducer is the mechanism, or element, of the sensor that transforms the energy associated with what is being measured into another form of energy. A sensor receives energy and transmits a signal to a display or computer. Sensors use transducers to change the input signal (sound, light, pressure, temperature, etc.) into an analog or digital form capable of being used by a robot.Microcontroller systemsMicrocontrollers (MCUs) are intelligent electronic devices used inside robots. They deliver functions similar to those performed by a microprocessor (central processing unit, or CPU) inside a personal computer. MCUs are slower and can address less memory than CPUs, but are designed for real-world control problems. One of the major differences between CPUs and MCUs is the number of external components needed tooperate them. MCUs can often run with zero external parts, and typically need only an external crystal or oscillator.Utilities and toolsROBOOP (A robotics object oriented package in C++):This package is an object-oriented toolbox in C++ for robotics simulation. Technical references and downloads are provided in the Resources.CORBA: A real-time communications and object request broker software package for embedding distributed software agents. Each independent piece of software registers itself and its capabilities to the ORB, by means of an IDL (Interface Definition Language). Visit their Web site (see Resources) for technical information, downloads, and documentation for CORBA.TANGO/TACO:This software might be useful for controlling a robotics system with multiple devices and tools. TANGO is an object oriented control system based on CORBA. Device servers can be written in C++ or Java. TACO is object oriented because it treats all(physical and logical) control points in a control system as objects in a distributed environment. All actions are implemented in classes. New classes can be constructed out of existing classes in a hierarchical manner, thereby ensuring a high level of software reuse. Classes can be written in C++, in C (using a methodology called Objects in C), in Python or in LabView (using the G programming language).ControllersTask Control Architecture: The Task Control Architecture (TCA) simplifies building task-level control systems for mobile robots. "Task-level" refers to the integration and coordination of perception, planning, andreal time control to achieve a given set of goals (tasks). TCA provides a general control framework, and is intended to control a wide variety of robots. TCA provides a high-level machine-independent method for passing messages between distributed machines (including between Lisp and C processes). TCA provides control functions, such as task decomposition, monitoring, and resource management, that are common to many mobile robot applications. The Resources section provides technical references and download information for Task Control Architecture.EMC (Enhanced Machine Controller): The EMC software is based on the NIST Real time Control System (RCS) methodology, and is programmed using the NIST RCS Library. The RCS Library eases the porting of controller code to a variety of UNIX and Microsoft platforms, providing a neutral application programming interface (API) to operating system resources such as shared memory, semaphores and timers. The EMC software is written in C and C++, and has been ported to the PC Linux, Windows NT, and Sun Solaris operating systems.Darwin2K: Darwin2K is a free, open source toolkit for robot simulation and automated design. It features numerous simulation capabilities and an evolutionary algorithm capable of automatically synthesizing and optimizing robot designs to meet task-specific performance objectives.LanguagesRoboML (Robotic Markup Language): RoboML is used for standardized representation of robotics-related data. It is designed to support communication language between human-robot interface agents, as well as between robot-hosted processes and between interface processes, and to provide a format for archived data used by human-robot interface agents.ROSSUM: A programming and simulation environment for mobile robots. The Rossum Project is an attempt to help collect, develop, and distribute software for robotics applications. The Rossum Project hopes to extend the same kind of collaboration to the development of robotic software.XRCL (Extensible Robot Control Language): XRCL (pronounced zircle) is a relatively simple, modern language and environment designed to allow robotics researchers to share ideas by sharing code. It is an open source project, protected by the GNU Copyleft.SummaryThe field of robotics has created a large class of robots with basic physical and navigational competencies. At the same time, society has begun to move towards incorporating robots into everyday life, from entertainment to health care. Moreover, robots could free a large number of people from hazardous situations, essentially allowing them to be used as replacements for human beings. Many of the applications being pursued by AI robotics researchers are already fulfilling that potential. In addition, robots can be used for more commonplace tasks such as janitorial work. Whereas robots were initially developed for dirty, dull, and dangerous applications, they are now being considered as personal assistants. Regardless of application, robots will require more rather than less intelligence, and will thereby have a significant impact on our society in the future as technology expands to new horizons.外文出处:Robotic technology / edited by A. Pugh./P. Peregrinus, c1993.附件1：外文资料翻译译文机器人技术简介在制造业领域，机器人的开发集中在执行制造过程的工程机器人手臂上。

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

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

RobotRobot is a type of mechantronics equipment which synthesizes the last research achievement of engine and precision engine, micro-electronics and computer, automation control and drive, sensor and message dispose and artificial intelligence and so on. With the development of economic and the demand for automation control, robot technology is developed quickly and all types of the robots products are come into being. The practicality use of robot products not only solves the problems which are difficult to operate for human being, but also advances the industrial automation program. At present, the research and development of robot involves several kinds of technology and the robot system configuration is so complex that the cost at large is high which to a certain extent limit the robot abroad use. To development economic practicality and high reliability robot system will be value to robot social application and economy development.With the rapid progress with the control economy and expanding of the modern cities, the let of sewage is increasing quickly: With the development of modern technology and the enhancement of consciousness about environment reserve, more and more people realized the importance and urgent of sewage disposal. Active bacteria method is an effective technique for sewage disposal，The lacunaris plastic is an effective basement for active bacteria adhesion for sewage disposal. The abundance requirement for lacunaris plastic makes it is a consequent for the plastic producing with automation and high productivity. Therefore, it is very necessary to design a manipulator that can automatically fulfill the plastic holding.With the analysis of the problems in the design of the plastic holding manipulator and synthesizing the robot research and development condition in recent years, a economic scheme is concluded on the basis of the analysis of mechanical configuration, transform system, drive device and control system and guided by the idea of the characteristic and complex of mechanical configuration, electronic, software and hardware. In this article, the mechanical configuration combines the character of direction coordinate and the arthrosis coordinate which can improve the stability and operation flexibility of the system. The main function of the transmission mechanism is to transmit power to implement department and complete the necessary movement. In this transmission structure, the screw transmission mechanism transmits the rotary motion into linear motion. Worm gear can give vary transmissionratio. Both of the transmission mechanisms have a characteristic of compact structure. The design of drive system often is limited by the environment condition and the factor of cost and technical lever. 'The step motor can receive digital signal directly and has the ability to response outer environment immediately and has no accumulation error, which often is used in driving system. In this driving system, open-loop control system is composed of stepping motor, which can satisfy the demand not only for control precision but also for the target of economic and practicality. on this basis, the analysis of stepping motor in power calculating and style selecting is also given.The analysis of kinematics and dynamics for object holding manipulator is given in completing the design of mechanical structure and drive system. Kinematics analysis is the basis of path programming and track control. The positive and reverse analysis of manipulator gives the relationship between manipulator space and drive sp ace in position and speed. The relationship between manipulator’s tip position and arthrosis angles is concluded by coordinate transform method. The geometry method is used in solving inverse kinematics problem and the result will provide theory evidence for control system. The f0unction of dynamics is to get the relationship between the movement and force and the target is to satisfy the demand of real time control. in this chamfer, Newton-Euripides method is used in analysis dynamic problem of the cleaning robot and the arthrosis force and torque are given which provide the foundation for step motor selecting and structure dynamic optimal ting.Control system is the key and core part of the object holding manipulator system design which will direct effect the reliability and practicality of the robot system in the division of configuration and control function and also will effect or limit the development cost and cycle. With the demand of the PCL-839 card, the PC computer which has a. tight structure and is easy to be extended is used as the principal computer cell and takes the function of system initialization, data operation and dispose, step motor drive and error diagnose and so on. A t the same time, the configuration structure features, task principles and the position function with high precision of the control card PCL-839 are analyzed. Hardware is the matter foundation of the control. System and the software is the spirit of the control system. The target of the software is to combine all the parts in optimizing style and to improve the efficiency and reliability of the control system. The software design of the object holding manipulator control system is divided into several blocks such assystem initialization block, data process block and error station detect and dispose model and so on. PCL-839 card can solve the communication between the main computer and the control cells and take the measure of reducing the influence of the outer signal to the control system.The start and stop frequency of the step motor is far lower than the maximum running frequency. In order to improve the efficiency of the step motor, the increase and decrease of the speed is must considered when the step motor running in high speed and start or stop with great acceleration. The increase and decrease of the motor’s speed can be controlled by the pulse frequency sent to the step motor drive with a rational method. This can be implemented either by hardware or by software. A step motor shift control method is proposed, which is simple to calculate, easy to realize and the theory means is straightforward. The motor' s acceleration can fit the torque-frequency curve properly with this method. And the amount of calculation load is less than the linear acceleration shift control method and the method which is based on the exponential rule to change speed. The method is tested by experiment.At last, the research content and the achievement are sum up and the problems and shortages in main the content are also listed. The development and application of robot in the future is expected.机器人机器人是典型的机电一体化装置，它综合运用了机械与精密机械、微电子与计算机、自动控制与驱动、传感器与信息处理以及人工智能等多学科的最新研究成果，随着经济的发展和各行各业对自动化程度要求的提高，机器人技术得到了迅速发展，出现了各种各样的机器人产品。

毕业设计（论文）外文文献翻译文献、资料中文题目：机器人文献、资料英文题目：Robots文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：班级：姓名：学号：指导教师：翻译日期：2017.02.14外文翻译外文资料：RobotsFirst, I explain the background robots, robot technology development. It should be said it is a common scientific and technological development of a comprehensive results, for the socio-economic development of a significant impact on a science and technology. It attributed the development of all countries in the Second World War to strengthen the economic input on strengthening the country's economic development. But they also demand the development of the productive forces the inevitable result of human development itself is the inevitable result then with the development of humanity, people constantly discuss the natural process, in understanding and reconstructing the natural process, people need to be able to liberate a slave. So this is the slave people to be able to replace the complex and engaged in heavy manual labor, People do not realize right up to the world's understanding and transformation of this technology as well as people in the development process of an objective need.Robots are three stages of development, in other words, we are accustomed to regarding robots are divided into three categories. is a first-generation robots, also known as teach-type robot, it is through a computer, to control over one of a mechanical degrees of freedom Through teaching and information stored procedures, working hours to read out information, and then issued a directive so the robot can repeat according to the people at that time said the results show this kind of movement again, For example, the car spot welding robots, only to put this spot welding process, after teaching, and it is always a repeat of a work It has the external environment is no perception that the force manipulation of the size of the work piece there does not exist, welding 0S It does not know, then this fact from the first generation robot, it will exist this shortcoming, it in the 20th century, the late 1970s, people started to study the second-generation robot, called Robot with thefeeling that This feeling with the robot is similar in function of a certain feeling, for instance, force and touch, slipping, visual, hearing and who is analogous to that with all kinds of feelings, say in a robot grasping objects, In fact, it can be the size of feeling out, it can through visual, to be able to feel and identify its shape, size, color Grasping an egg, it adopted a acumen, aware of its power and the size of the slide. Third-generation robots, we were a robotics ideal pursued by the most advanced stage, called intelligent robots, So long as tell it what to do, not how to tell it to do, it will be able to complete the campaign, thinking and perception of this man-machine communication function and function Well, this current development or relative is in a smart part of the concept and meaning But the real significance of the integrity of this intelligent robot did not actually exist, but as we continued the development of science and technology, the concept of intelligent increasingly rich, it grows ever wider connotations.Now I have a brief account of China's robot development of the basic profiles. As our country there are many other factors that problem. Our country in robotics research of the 20th century the late 1970s. At that time, we organized at the national, a Japanese industrial automation products exhibition. In this meeting, there are two products, is a CNC machine tools, an industrial robot, this time, our country's many scholars see such a direction, has begun to make a robot research But this time, are basically confined to the theory of phase .Then the real robot research, in 7500 August 5, 1995, 15 nearly 20 years of development, The most rapid development, in 1986 we established a national plan of 863 high-technology development plan, As robot technology will be an important theme of the development of The state has invested nearly Jiganyi funds begun to make a robot, We made the robot in the field quickly and rapid development.At present, units like the CAS ShenYng Institute of Automation, the original machinery, automation of the Ministry, as of Harbin Industrial University, Beijing University of Aeronautics and Astronautics, Qinghua University, Chinese Academy of Sciences, also includes automation of some units, and so on have done a very important study, also made a lot of achievements Meanwhile, in recent years, we end up in college, a lot of flats in robot research, Many graduate students and doctoralcandidates are engaged in robotics research, we are more representative national study Industrial robots, underwater robots, space robots, robots in the nuclear industry are on the international level should be taking the lead .On the whole of our country Compared with developed countries, there is still a big gap, primarily manifested in the We in the robot industry, at present there is no fixed maturity product, but in these underwater, space, the nuclear industry, a number of special robots, we have made a lot of achievements characteristics.Now, I would like to briefly outline some of the industrial robot situation. So far, the industrial robot is the most mature and widely used category of a robot, now the world's total sales of 1.1 million Taiwan, which is the 1999 statistics, however, 1.1 million in Taiwan have been using the equipment is 75 million, this volume is not small. Overall, the Japanese industrial robots in this one, is the first of the robots to become the Kingdom, the United States have developed rapidly. Newly installed in several areas of Taiwan, which already exceeds Japan, China has only just begun to enter the stage of industrialization, has developed a variety of industrial robot prototype and small batch has been used in production.Spot welding robot is the auto production line, improve production efficiency and raise the quality of welding car, reduce the labor intensity of a robot. It is characterized by two pairs of robots for spot welding of steel plate, bearing a great need for the welding tongs, general in dozens of kilograms or more, then its speed in meters per second a 5-2 meter of such high-speed movement. So it is generally five to six degrees of freedom, load 30 to 120 kilograms, the great space, probably expected that the work of a spherical space, a high velocity, the concept of freedom, that is to say, Movement is relatively independent of the number of components, the equivalent of our body, waist is a rotary degree of freedom We have to be able to hold his arm, Arm can be bent, then this three degrees of freedom, Meanwhile there is a wrist posture adjustment to the use of the three autonomy, the general robot has six degrees of freedom. We will be able to space the three locations, three postures, the robot fully achieved, and of course we have less than six degrees of freedom. Have more than six degrees of freedom robot, in different occasions the need to configure.The second category of service robots, with the development of industrialization,especially in the past decade, Robot development in the areas of application are continuously expanding, and now a very important characteristic, as we all know, Robot has gradually shifted from manufacturing to non-manufacturing and service industries, we are talking about the car manufacturer belonging to the manufacturing industry, However, the services sector including cleaning, refueling, rescue, rescue, relief, etc. These belong to the non-manufacturing industries and service industries, so here is compared with the industrial robot, it is a very important difference. It is primarily a mobile platform, it can move to sports, there are some arms operate, also installed some as a force sensor and visual sensors, ultrasonic ranging sensors, etc. It’s surrounding environment for the conduct of identification, to determine its campaign to complete some work, this is service robot’s one of the basic characteristics.For example, domestic robot is mainly embodied in the example of some of the carpets and flooring it to the regular cleaning and vacuuming. The robot it is very meaningful, it has sensors, it can furniture and people can identify, It automatically according to a law put to the ground under the road all cleaned up. This is also the home of some robot performance.The medical robots, nearly five years of relatively rapid development of new application areas. If people in the course of an operation, doctors surgery, is a fatigue, and the other manually operated accuracy is limited. Some universities in Germany, which, facing the spine, lumbar disc disease, the identification, can automatically use the robot-aided positioning, operation and surgery Like the United States have been more than 1,000 cases of human eyeball robot surgery, the robot, also including remote-controlled approach, the right of such gastrointestinal surgery, we see on the television inside. a manipulator, about the thickness fingers such a manipulator, inserted through the abdominal viscera, people on the screen operating the machines hand, it also used the method of laser lesion laser treatment, this is the case, people would not have a very big damage to the human body.In reality, this right as a human liberation is a very good robots, medical robots it is very complex, while it is fully automated to complete all the work, there are difficulties, and generally are people to participate. This is America, the development of such a surgery Lin Bai an example, through the screen, through a remote controloperator to control another manipulator, through the realization of the right abdominal surgery A few years ago our country the exhibition, the United States has been successful in achieving the right to the heart valve surgery and bypass surgery. This robot has in the area, caused a great sensation, but also, AESOP's surgical robot, In fact, it through some equipment to some of the lesions inspections, through a manipulator can be achieved on some parts of the operation Also including remotely operated manipulator, and many doctors are able to participate in the robot under surgery Robot doctor to include doctors with pliers, tweezers or a knife to replace the nurses, while lighting automatically to the doctor's movements linked, the doctor hands off, lighting went off, This is very good, a doctor's assistant.We regard this country excel, it should be said that the United States, Russia and France, in our nation, also to the international forefront, which is the CAS ShenYang Institute of Automation of developing successful, 6,000 meters underwater without cable autonomous underwater robot, the robot to 6,000 meters underwater, can be conducted without cable operations. His is 2000, has been obtained in our country one of the top ten scientific and technological achievements. This indicates that our country in this underwater robot, have reached the advanced international level, 863 in the current plan, the development of 7,000 meters underwater in a manned submersible to the ocean further development and operation, This is a great vote of financial and material resources.In this space robotics research has also been a lot of development. In Europe, including 16 in the United States space program, and the future of this space capsule such a scheme, One thing is for space robots, its main significance lies in the development of the universe and the benefit of mankind and the creation of new human homes, Its main function is to scientific investigation, as production and space scientific experiments, satellites and space vehicles maintenance and repair, and the construction of the space assembly. These applications, indeed necessary, for example, scientific investigation, as if to mock the ground some physical and chemical experiments do not necessarily have people sitting in the edge of space, because the space crew survival in the day the cost is nearly one million dollars. But also very dangerous, in fact, some action is very simple, through the ground, via satellitecontrol robot, and some regularly scheduled completion of the action is actually very simple. Include the capsule as control experiments, some switches, buttons, simple flange repair maintenance, Robot can be used to be performed by robots because of a solar battery, then the robot will be able to survive, we will be able to work, We have just passed the last robot development on the application of the different areas of application, and have seen the robots in industry, medical, underwater, space, mining, construction, service, entertainment and military aspects of the application .Also really see that the application is driven by the development of key technologies, a lack of demand, the robot can not, It is because people in understanding the natural transformation of the natural process, the needs of a wide range of robots, So this will promote the development of key technologies, the robot itself for the development of From another aspect, as key technology solutions, as well as the needs of the application, on the promotion of the robot itself a theme for the development of intelligent, and from teaching reappearance development of the current local perception of the second-generation robot, the ultimate goal, continuously with other disciplines and the development of advanced technology, the robot has become rich, eventually achieve such an intelligent robot mainstream.Robot is mankind's right-hand man; friendly coexistence can be a reliable friend. In future, we will see and there will be a robot space inside, as a mutual aide and friend. Robots will create the jobs issue. We believe that there would not be a "robot appointment of workers being laid off" situation, because people with the development of society, In fact the people from the heavy physical and dangerous environment liberated, so that people have a better position to work, to create a better spiritual wealth and cultural wealth.译文资料：机器人首先我介绍一下机器人产生的背景，机器人技术的发展，它应该说是一个科学技术发展共同的一个综合性的结果，同时，为社会经济发展产生了一个重大影响的一门科学技术，它的发展归功于在第二次世界大战中各国加强了经济的投入，就加强了本国的经济的发展。