Question

英语翻译急救

Daniel Bump, a mathematics professor at Stanford, works on a program called GNU Go in his spare time. “You can very quickly look at a chess game and see if there's some major issue,” he said. But to make a decision in Go, he said, players must learn to combine their pattern-matching abilities with the logic and knowledge they have accrued in years of playing.
One measure of the challenge the game poses is the performance of Go computer programs. The past five years have yielded incremental improvements but no breakthroughs, said David Fotland, a programmer and chip designer in San Jose, California, who created and sells The Many Faces of Go, one of the few commercial Go programs.
Part of the challenge has to do with processing speed. The typical chess program can evaluate about 300,000 positions in a second, and Deep Blue was able to evaluate some 200 million positions in a second. By mid-game, most Go programs can evaluate only a couple of dozen positions each second, said Anders Kierulf, who wrote a program called SmartGo.
In the course of a chess game, a player has an average of 25 to 35 moves available. In Go, on the other hand, a player can choose from an average of 240 moves. A Go-playing computer would need about 30,000 years to look as far ahead as Deep Blue can with chess in three seconds, said Michael Reiss, a computer scientist in London. But the obstacles go deeper than processing power. Not only do Go programs have trouble evaluating positions quickly; they have trouble evaluating them correctly. Nonetheless, the allure of computer Go increases as the difficulties it poses encourages programmers to advance basic work in artificial intelligence.
“We think we have the basics of what we do as humans down pat,” Bump said. “We get up in the morning and make breakfast, but if you tried to program a computer to do that, you’d quickly find that what’s simple to you is incredibly difficult for a computer.”
The same is true for Go. “When you’re deciding what variations to consider, your subconscious mind is pruning,” he said. “It’s hard to say how much is going on in your mind to accomplish this pruning, but in a position on the board where I’d look at 10 variations, the computer has to look at thousands, maybe a million positions to come to the same conclusions, or to wrong conclusions.”
Reiss, an expert in neural networks, compared a human being’s ability to recognize a strong or weak position in Go with the ability to distinguish between an image of a chair and one of a bicycle. Both tasks, he said are hugely difficult for a computer. For that reason, Fotland said, “writing a strong Go program will teach us more about making computers think like people than writing a strong chess program.”
我就7分啊!!!!

Answer 1

Daniel Bump,斯坦福大学数学教授,在业余时间致力于一个称做"GNU Go"的项目研究."你可以快速浏览一下象棋游戏看看里面是否存在什么问题"他说,但是要玩"GO"就没那么容易了,玩家在玩的过程中要运用他们在游戏中不断积累起来的逻辑思维力及相关知识,并要结合自身的模式匹配能力.
"GO"这个电脑程序的性能是评价该游戏挑战性的标准之一.David Fotland,加利福尼亚圣何塞的编程师及芯片设计师,设计并卖出了许多GO程序的界面,他说,过去的五年里这方面改进了许多但是没有新的突破,
其中一个很大的难题在于处理速度,像一个典型的象棋程序一秒钟可以计算出大约300,000位置,Deep Blue可以计算大约两亿个位置.By mid-game,(不知道啥意思)Go程序每秒钟只能够计算出几十个位置.Anders Kierulf如是说,他曾编写过一个SmartGo程序.
玩一盘象棋游戏,玩家平均可以走25到35步,但是在Go中,可以多至240步.Michael Reiss,伦敦的一位计算机科学家,说,如果要像Deep Blue程序处理象棋时那样计算到那么远的情况的话,他三秒钟处理的事可能Go要花30,000年才能处理出来.但是问题也不仅仅是处理能力的问题.Go程序不仅不能很快计算出位置,而且计算也非完全正确.虽然如此,这些难题却更激励了编程员,吸引着他们改进人工智能方面的基础机件.
"我们认为作为一个人我们有一些基本能力,早上起床,做饭,但是如果你试图让电脑做这些事,你将发现即使是这么简单的事对于一台电脑来说也是异常困难的"
Go也一样, 当你在决定考虑做哪一步变动时,你下意识里就开始做一些整理,很难说完成这些整理花费了多少脑力,但是,在下棋时,我会考虑到10步,电脑则要考虑几千步,甚至可能上万步才得出相同或错误的结论.
Reiss,神经系统专家,对人类在Go中辨别优势或弱势位置的能力及区分椅子和自行车图像的能力做了一个比较,他说,这两件事对电脑来说都是很困难的,所以,Fotland说,编写一个强大的Go程序相对于编写象棋程序来说,更能够教会我们关于如何让电脑像人类一样思考的知识.

p.s. 发现这个真考验人的耐心!!!

Answer 2

5分...

Answer 3

恩

Answer 4

。。。。才5分。。。怎么那么抠啊。。。。。小气。

Answer 5

丹尼尔爆沸，数学教授在斯坦福，在称GNU的节目的工作在他的消遣时间进来。
“您能非常迅速看一盘象棋，并且看见是否有某一主要争论点”，他说。
但做出决定去，他说，球员必须学会与逻辑和知识结合他们在几年演奏累积了的他们样式匹配的能力。
挑战的一项措施游戏姿势是表现去计算机程序。 过去五年未产生增加改善，但突破，说大卫Fotland，程序员，并且芯片设计者在圣约瑟，加利福尼亚，创造并且卖许多面孔的去，一个少数商务去节目。
一部分的挑战必须处理处理速度。 典型的棋节目在一秒钟内可能评估大约300,000个位置，并且深深蓝色能立刻评估大约200百万个位置。 由中间比赛，多数去节目可能评估仅两三十二位置每第二，说Anders Kierulf，写称SmartGo的一个节目。
一盘象棋其间，球员有平均25到35移动可利用。 在进来，另一方面，球员能从平均240移动选择。 一台去演奏的计算机将需要大约30,000年向前看一样远象深深蓝色罐头以棋在三秒，在伦敦认为迈克尔Reiss，一位计算机学家。 但障碍比处理能力是深。 迅速不仅是节目有麻烦评估的位置; 有麻烦正确地评估他们的他们。 但是，它在人工智能方面形成鼓励程序员推进基本的工作计算机的魅力去增量，困难。
“我们认为我们有什么我们做作为人熟练”，爆沸基本说。 “我们早晨起来并且做早餐，但，如果您设法编程计算机做那，您迅速发现什么是简单的对您为计算机是难以置信地困难的”。
同样是真实的为去。