2016地大考博英语专业英语翻译真题(2)

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。

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