2016地大考博英语专业英语翻译真题

2016地大英语专业英语翻译真题

AlphaGo是怎么学会下围棋的 2017北京地大考博群 221616188

Where Computers Defeat Humans, and Where They Can’t 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 the tournament of the strategy game of Go. Why does this matter? After all, computers surpassed humans in chess in 1997, when IBM’s Deep Blue beat Garry Kasparov. So why is AlphaGo’s victory significant?

由Google的子公司DeepMind创建的人工智能系统AlphaGo，刚刚在一场围棋比赛中以四比一的成绩战胜了人类冠军李世石(Lee Se-dol)。此事有何重大意义？毕竟在1997年IBM深蓝(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的胜利大惊小怪呢？

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 other color they are removed from the board, and the player with more stones remaining at the game’s end wins.

和国际象棋一样，围棋也是一种高度复杂的策略性游戏，不可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；一旦棋子的四面被另一色棋子包围，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 highest levels. The top players, it turns out, can’t fully access their own knowledge about how they’re 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 philosopher and scientist Michael Polanyi, who said, “We know more than we can tell.” It’s a phenomenon that has come to be known as “Polanyi’s 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。我们发现，顶级棋手本人也无法解释他们为什么下得那么好。人类的许多能力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，比我们可言说的多。”这种现象后来就被称为“波兰尼悖论”。

Polanyi’s Paradox hasn’t prevented us from using computers to accomplish complicated tasks, like processing payrolls, optimizing flight schedules, routing telephone calls and calculating taxes. But as anyone who’s written a traditional computer program can tell you, automating 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止我们用电脑完成一些复杂的工作，比如处理工资单、优化航班安排、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诉你，要想将这些事务自动化，必须极度缜密地向电脑解释要它做什么。

This approach to programming computers is severely limited; it can’t be used in the many 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；在很多领域无法应用，比如我们知道但不可言说的围棋，或者对照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，基于规则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 possible Go games than there are atoms in the universe, so even the fastest computers can’t simulate a meaningful fraction of them. To make matters worse, it’s 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：它吸收了数百万份棋局实例，在可能选项中搜索最佳的走法。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索。

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 examples of successes and failures.

这次有什么不同？AlphaGo的胜利清晰地呈现了一种新方法的威力，这种方法并不是将聪明的策略编入电脑中，而是建造了一个能学习制胜策略的系统，系统在几乎完全自主的情况下，通过观看胜负实例来学习。

Since these systems don’t rely on human knowledge about the task at hand, they’re not limited by the

2016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赖人类对这项工作的已有知识，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 Polanyi’s Paradox. It did this by figuring out winning strategies for itself, both by example and from experience. The examples came from huge libraries of Go matches between top players amassed over the game’s 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 patterns and understand what’s important in large pools of information.

AlphaGo的确会在某几步棋中使用模拟和传统搜索算法来辅助决策，但它真正的突破在于它有能力克服“波兰尼悖论”。它能通过实例和经验自行得出制胜策略。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，系统采用了一种叫做“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 connections 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 (the 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。深度学习系统采用的方法与此类似，以至于这种系统一度被称为“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 far they could be extended. In fact, it’s 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，以及它们能走多远。事实上我们还是不清楚，但对这些技术的应用正取得飞速的进步，而且看不到终点在哪里。它们的应用很广泛，包括语音识别、信用卡欺诈侦测、放射学和病理学。机器现在已经可以识别面孔、驾驶汽车，它们都曾被波兰尼本人归为知道但不可言说的领域。

We still have a long way to go, but the implications are profound. As when James Watt introduced his steam engine 240 years ago, technology-fueled changes will ripple throughout our economy in the years ahead, but there is no guarantee that everyone will benefit equally. Understanding and addressing the societal challenges brought on by rapid technological progress remain tasks that no machine can do for us.

我们还有很长的路要走，但潜能是十分可观的。就像240年前詹姆斯·瓦特(James Watt)首次推出蒸汽机，技术推动的变革在未来几年里将会波及我们的整个经济，但不能保证每个人都能从中得到同等的好处。快速的技术进步带来的社会挑战，依然是需要我们去理解和应对的，这方面不能指望机器。

本文来源：https://www.bwwdw.com/article/8da1.html