2021地大考博英语专业英语翻译真题

1、2021地大考博英语专业英语翻译真题 2021地大英语专业英语翻译真题 alphago是怎么学会下围棋的 2021北京地大考博群 221616188 where computers defeat humans, and where they cant alphago是怎么学会下围棋的 alphago, the artificial intelligence system built by the google subsidiary deepmind, has just defeated the human champion, lee se-dol, four games to one in t

2、he tournament of the strategy game of go. why does this matter? after all, computers surpassed humans in chess in 1997, when ibms deep blue beat garry kasparov. so why is alphagos victory significant? 由google的子公司deepmind创建的人工智能系统alphago，刚刚在一场围棋竞赛中以四比一的成果战胜了人类冠军李世石(lee se-dol)。此事有何重大意义？究竟在1997年ibm深蓝(

3、deep blue)击败加里卡斯帕罗夫(garry kasparov)后，电脑已经在国际象棋上超越了人类。为什么要对alphago的成功大惊小怪呢？ like chess, go is a hugely complex strategy game in which chance and luck play no role. two players take turns placing white or black stones on a 19-by-19 grid; when stones are surrounded on all four sides by those of the oth

4、er color they are removed from the board, and the player with more stones remaining at the games end wins. 和国际象棋一样，围棋也是一种高度简单的策略性嬉戏，不行能靠巧合和运气取胜。两名棋手轮番将黑色或白色的棋子落在纵横19道线的网格棋盘上；一旦棋子的四周被另一色棋子包围，就要从棋盘上提走，最终在棋盘上留下棋子多的那一方获胜。 unlike the case with chess, however, no human can explain how to play go at the hi

5、ghest levels. the top players, it turns out, cant fully access their own knowledge about how theyre able to perform so well. this self-ignorance is common to many human abilities, from driving a car in traffic to recognizing a face. this strange state of affairs was beautifully summarized by the phi

6、losopher and scientist michael polanyi, who said, “we know more than we can tell.” its a phenomenon that has come to be known as “polanyis paradox.” 然而和国际象棋不一样的是，没有人能解释顶尖水平的围棋是怎么下的。我们发觉，顶级棋手本人也无法解释他们为什么下得那么好。人类的很多力量中存在这样的不自知，从在车流中驾驶汽车，到辨识一张面孔。对于这一怪象，哲学家、科学家迈克尔波兰尼(michael polanyi)有精彩的概括，他说，“我们知道的，比我们

7、可言说的多。”这种现象后来就被称为“波兰尼悖论”。 polanyis paradox hasnt prevented us from using computers to accomplish complicated tasks, like processing payrolls, optimizing flight schedules, routing telephone calls and calculating taxes. but as anyone whos written a traditional computer program can tell you, automating

8、 these activities has required painstaking precision to explain exactly what the computer is supposed to do. 波兰尼悖论并没有阻挡我们用电脑完成一些简单的工作，比如处理工资单、优化航班支配、转送电话信号和计算税单。然而，任何一个写过传统电脑程序的人都会告知你，要想将这些事务自动化，必需极度缜密地向电脑解释要它做什么。 this approach to programming computers is severely limited; it cant be used in the man

9、y domains, like go, where we know more than we can tell, or other tasks like recognizing common objects in photos, translating between human languages and diagnosing diseases all tasks where the rules-based approach to programming has failed badly over the years. 这样的电脑编程方式是有很大局限的；在许多领域无法应用，比如我们知道但不行

10、言说的围棋，或者对比片中寻常物品的识别、人类语言间的转译和疾病的诊断等多年来，基于规章的编程方法在这些事务上几无建树。 deep blue achieved its superhuman performance almost by sheer computing power: it was fed millions of examples of chess games so it could sift among the possibilities to determine the optimal move. the problem is that there are many more po

11、ssible go games than there are atoms in the universe, so even the fastest computers cant simulate a meaningful fraction of them. to make matters worse, its usually far from clear which possible moves to even start exploring. “深蓝”几乎全凭强大的计算力实现了超人表现：它汲取了数百万份棋局实例，在可能选项中搜寻最佳的走法。问题是围棋的可能走法比宇宙间的原子数还多，即使最快的

12、电脑也只能模拟微不足道的一小部分。更糟的是，我们甚至说不清该从哪一步入手进行探究。 what changed? the alphago victories vividly illustrate the power of a new approach in which instead of trying to program smart strategies into a computer, we instead build systems that can learn winning strategies almost entirely on their own, by seeing exam

13、ples of successes and failures. 这次有什么不同？alphago的成功清楚地呈现了一种新方法的威力，这种方法并不是将聪慧的策略编入电脑中，而是建筑了一个能学习制胜策略的系统，系统在几乎完全自主的状况下，通过观看胜败实例来学习。 since these systems dont rely on human knowledge about the task at hand, theyre not limited by the 2021地大英语专业英语翻译真题 fact that we know more than we can tell. 由于这些系统并不依靠人类对这

14、项工作的已有学问，即使我们知道的比可言说的更多，也不会对它构成限制。 alphago does use simulations and traditional search algorithms to help it decide on some moves, but its real breakthrough is its ability to overcome polanyis paradox. it did this by figuring out winning strategies for itself, both by example and from experience. th

15、e examples came from huge libraries of go matches between top players amassed over the games 2,500-year history. to understand the strategies that led to victory in these games, the system made use of an approach known as deep learning, which has demonstrated remarkable abilities to tease out patter

16、ns and understand whats important in large pools of information. alphago的确会在某几步棋中使用模拟和传统搜寻算法来帮助决策，但它真正的突破在于它有力量克服“波兰尼悖论”。它能通过实例和阅历自行得出制胜策略。这些实例来自2500年围棋历史积累下来的高人对局。为了理解这些棋局的制胜策略，系统采纳了一种叫做“深度学习”的方法，经证明这种方法可以对规律进行有效梳理，在大量信息中认清哪些是重要的东西。 learning in our brains is a process of forming and strengthening c

17、onnections among neurons. deep learning systems take an analogous approach, so much so that they used to be called “neural nets.” they set up billions of nodes and connections in software, use “training sets” of examples to strengthen connections among stimuli (a go game in process) and responses (t

18、he next move), then expose the system to a new stimulus and see what its response is. alphago also played millions of games against itself, using another technique called reinforcement learning to remember the moves and strategies that worked well. 在我们的大脑中，学习是神经元间形成和巩固关系的过程。深度学习系统采纳的方法与此类似，以至于这种系统一度

19、被称为“神经网络”。系统在软件中设置了数十亿个节点和连结，使用对弈实例组成的“训练集合”来强化刺激（一盘正在进行的围棋）和反应（下一步棋）的连结，然后让系统接收一次新的刺激，看看它的反应是什么。通过另一种叫做“强化学习”的技术，alphago还和自己下了几百万盘棋，从而记住哪些走法和策略是有效的。 deep learning and reinforcement learning have both been around for a while, but until recently it was not at all clear how powerful they were, and how

20、 far they could be extended. in fact, its still not, but applications are improving at a gallop, with no end in sight. and the applications are broad, including speech recognition, credit card fraud detection, and radiology and pathology. machines can now recognize faces and drive cars, two of the examples that polanyi himself noted as areas where we know more than we can tell. 深度学习和强化学习都是早已提出的技术，但我们直到近年才意识到它们的威力，以及