段落A

In April 2002 an event took place which demonstrated one of the many applications of information theory. The space probe Voyager I, launched in 1977, had sent back spectacular images of Jupiter and Saturn and then soared out of the Solar System on a one-way mission to the stars. After 25 years of exposure to the freezing temperatures of deep space, the probe was beginning to show its age. Sensors and circuits were on the brink of failing and NASA experts realized that they had to do something or lose contact with their probe forever. The solution was to get a message to Voyager I to instruct it to use spares to change the failing parts. With the probe 12 billion kilometers from Earth, this was not an easy task. By means of a radio dish belonging to NASA’s Deep Space Network, the message was sent out into the depths of space. Even travelling at the speed of light, it took over 11 hours to reach its target, far beyond the orbit of Pluto. Yet, incredibly, the little probe managed to hear the faint call from its home planet and successfully made the switchover.

    段落A中描述了2002年的一个事件，展示了信息论的众多应用之一。1977年发射的太空探测器旅行者1号，在发送了木星和土星的壮丽图像后，一路飞向星际深空，进行一次单程任务。经过25年的深空低温暴露，探测器开始显露出老化的迹象，传感器和电路濒临故障的边缘，NASA的专家意识到他们必须采取行动，否则就会永远失去与探测器的联系。解决方案是向旅行者1号发送一条信息，指示其使用备件更换故障部件。由于探测器距离地球120亿公里，这并不是一项容易的任务。通过NASA的深空网络的一台射电天线，将信息发送到深空中。即使以光速传播，信息也需要超过11个小时才能到达目标，远远超出冥王星的轨道。然而，令人难以置信的是，这台小小的探测器成功地听到了来自母星的微弱呼喊，并成功进行了部件更换。

段落B

It was the longest-distance repair job history, and a triumph for the NASA engineers. But it also highlighted the astonishing power of the techniques developed by American communications engineer Claude Shannon, who had died just a year earlier. Born in 1916 in Petoskey, Michigan, Shannon showed an early talent for maths and for building gadgets and made breakthroughs in the foundations of computer technology when still a student. While at Bell Laboratories, Shannon developed information theory but shunned the resulting acclaim. In the 1940s, he single-handedly created an entire science of communication which has since inveigled its way into a host of applications, from DVDs to satellite communications to bar codes— any area, in short, where data has to be conveyed rapidly yet accurately.

段落B中提到这是历史上最长距离的维修工作，是NASA工程师的胜利。但这也凸显了美国通信工程师克劳德·香农所开发技术的惊人力量，他在一年前去世。香农1916年出生于密歇根州佩托斯基，他在数学和制作小工具方面显示出早期的才华，在还是学生时就在计算机技术的基础上取得了突破。在贝尔实验室期间，香农发展了信息论，并回避了由此带来的赞誉。在20世纪40年代，他独立创建了整个通信科学，这个科学已经渗入到许多应用领域，从DVD到卫星通信，再到条形码 - 简而言之，任何需要快速而准确传输数据的领域。

段落C

This all seems light years away from the down-to-earth uses Shannon originally had for his work, which began when he was a 22-year-old graduate engineering student at the prestigious Massachusetts Institute of Technology in 1939. He set out with an apparently simple aim: to pin down the precise meaning of the concept of ‘information’. The most basic form of information, Shannon argued, is whether something is true or false—which can be captured in the binary unit, or ‘bit’, of form 1 or 0. Having identified this fundamental unit, Shannon set about defining otherwise vague ideas about information and how to transit it from place to place. In the process he discovered something surprising: it is always possible to guarantee the information will get through random interference—‘noise’—intact.

段落C中提到这一切似乎与香农最初对自己工作的务实应用相去甚远。这一切始于他于1939年在美国著名的麻省理工学院获工程学硕士学位时的22岁。他设定一个表面上简单的目标：准确定义“信息”这个概念的含义。香农认为，最基本的信息形式是真或假，可以用二进制单位“比特”表示为1或0。在确定了这个基本单位后，香农着手定义关于信息的其他模糊概念以及如何从一地传输到另一地。在此过程中，他发现了一个令人惊讶的事实：始终可以保证信息通过随机干扰（“噪声”）完好无损地传递。

段落D

Noise usually means unwanted sounds which interfere with genuine information. Information theory generalizes this idea via theorems that capture the effects of noise with mathematical precision. In particular, Shannon showed that noise sets a limit on the rate at which information can pass along communication channels while remaining error-free. This rate depends on the relative strengths of the signal and noise travelling down the communication channel, and on its capacity (its ‘bandwidth’). The resulting limit, given in units of bits per second, is the absolute maximum rate of error-free communication given signal strength and noise level. The trick, Shannon showed, is to find ways of packaging up —‘coding’—information to cope with the ravages of noise, while staying within the information-carrying capacity—‘bandwidth’—of the communication system being used.

段落D中提到，噪声通常指干扰真实信息的不需要的声音。信息论通过数学精确地捕捉噪声的影响提炼出这一概念。特别是，香农表明，噪声限制了在保持无差错状态的情况下信息通过通信信道的速率。这个速率取决于信号和噪声在通信信道中传播的相对强度，以及其容量（即“带宽”）。结果得到的限制值以比特每秒作为单位，是在给定信号强度和噪声水平下保持无差错通信的绝对最大速率。香农展示的窍门是寻找包装-“编码”-信息的方法来应对噪声的破坏，同时保持在使用的通信系统的信息传输能力-“带宽”-之内。

段落E

Over the years scientists have devised many such coding methods, and they have proved crucial in many technological feats. The voyager spacecraft transmitted data using codes which added one extra bit for every single bit of information; the result was an error rate of just one bit in 10,000—and stunningly clear pictures of the planets. Other codes have become part of everyday life—such as the Universal Product Code, or bar code. Which uses a simple error-detecting system that ensures supermarket check-out lasers can read the price even on, say, a crumpled bag of crisps. As recently as 1993, engineers made a major breakthrough by discovering so-called turbo—which come very close to Shannon’s ultimate limit for the maximum rate that data can be transmitted reliably, and now play a key role in the mobile videophone revolution.

段落E中提到，多年来，科学家们开发了许多这样的编码方法，它们在许多技术成就中发挥了关键作用。旅行者号航天器使用的编码方式是在每个信息比特位上添加一个额外的比特，结果是一万比特中只有一个错误-而且能够显示清晰的行星图像。其他编码已经成为日常生活的一部分，比如通用产品代码或条形码。它使用了一种简单的错误检测系统，确保超市结账激光器甚至可以读取一个褶皱的薯片袋上的价格。直到1993年，工程师们通过发现所谓的“Turbo”进行了重大突破，这些编码方法非常接近香农所提出的可靠传输数据的最大速率限制，现在在移动视频电话革命中起着关键作用。

段落F

Shannon also laid the foundations of more efficient ways of storing information, by stripping out superfluous (‘redundant’) bits from data which contributed little real information. As mobile phone text messages like ‘ I CN C U’ show, it is often possible to leave out a lot of data without losing much meaning. As with error correction, however, there’s a limit beyond which message become too ambiguous. Shannon showed how to calculate this limit, opening the way to the design of compression methods that cram maximum information into the minimum space.