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The ideal translator is a person “on whom nothing is lost，” said Henry James. Or maybe it’s a machine. But a machine won’t stop you from swearing at nuns...
Years ago， on a flight from Amsterdam to Boston， two American nuns seated to my right listened to a voluble1 young Dutchman who was out to discover the United States. He asked the nuns where they were from. Alas， Framingham， Massachusetts was not on his itinerary， but， he noted， he had“shitloads of time and would be visiting shitloads of other places”.2
The jovial young Dutchman had apparently gathered that“shitloads” was a colourful synonym for the bland “lots”.3 He had mastered the syntax of English and a rather extensive vocabulary but lacked experience of the appropriateness of words to social contexts.4
This memory sprang to mind with the recent news that the Google Translate engine would move from a phrase-based system to a neural network. Both methods rely on training the machine with a “corpus”5 consisting of sentence pairs： an original and a translation. The computer then generates rules for inferring， based on the sequence6 of words in the original text， the most likely sequence of words from the target language.
The procedure is an exercise in pattern matching. Similar pattern-matching algorithms are used to interpret the syllables you utter when you ask your smartphone to “navigate to Brookline” or when a photo app tags your friend’s face.7 The machine doesn’t “understand” faces or destinations； it reduces them to vectors8 of numbers， and processes them.
I am a professional translator， having translated some 125 books from the French. One might therefore expect me to bristle9 at Google’s claim that its new translation engine is almost as good as a human translator， scoring 5.0 on a scale of 0 to 6， whereas humans average 5.1. But I’m also a PhD in mathematics who has developed software that “reads” European newspapers in four languages and categorises the results by topic. So， rather than be defensive about the possibility of being replaced by a machine translator， I am aware of the remarkable feats of which machines are capable， and full of admiration for the technical complexity and virtuosity of Google’s work.10
My admiration does not blind me to the shortcomings of machine translation， however. Think of the young Dutch traveler who knew “shitloads” of English. The young man’s fluency demonstrated that his “wetware”—a living neural network， if you will—had been trained well enough to intuit the subtle rules （and exceptions） that make language natural.11 Computer languages， on the other hand， have context-free grammars. The young Dutchman， however， lacked the social experience with English to grasp the subtler rules that shape the native speaker’s diction， tone and structure. The native speaker might also choose to break those rules to achieve certain effects. If I were to say “shitloads of places”rather than “lots of places” to a pair of nuns， I would mean something by it. The Dutchman blundered into inadvertent comedy.12 Google’s translation engine is “trained” on corpora ranging from news sources to Wikipedia. The bare description of each corpus is the only indication of the context from which it arises. From such scanty13 information it would be difficult to infer the appropriateness or inappropriateness of a word such as “shitloads”. If translating into French， the machine might predict a good match to beaucoup or plusieurs. This would render the meaning of the utterance but not the comedy，14 which depends on the socially marked“shitloads” in contrast to the neutral plusieurs. No matter how sophisticated the algorithm， it must rely on the information provided， and clues as to context， in particular social context， are devilishly15 hard to convey in code.
The problem， as with all previous attempts to create artificial intelligence （AI）16 going back to my student days at MIT， is that intelligence is incredibly complex. To be intelligent is not merely to be capable of inferring logically from rules or statistically from regularities. Before that， one has to know which rules are applicable， an art requiring awareness of sensitivity to situation. Programmers are very clever， but they are not yet clever enough to anticipate the vast variety of contexts from which meaning emerges. Hence even the best algorithms will miss things—and as Henry James put it， the ideal translator must be a person “on whom nothing is lost”.
This is not to say that mechanical translation is not useful. Much translation work is routine. At times， machines can do an adequate job. Don’t expect miracles， however， or felicitous literary translations， or aptly rendered political zingers.17 Overconfident claims have dogged18 AI research from its earliest days. I don’t say this out of fear for my job： I’ve retired from translating and am devoting part of my time nowadays to…writing code.
亨利·詹姆斯說，理想的译者应该是“一无所失”之人。或者，是一无所失之机器。但是，机器可不会教你不能在修女面前爆粗口。
几年前，我从阿姆斯特丹乘机前往波士顿，两位美国修女坐在我右边，听一个正要去探索美国的荷兰小伙子侃侃而谈。他问修女从哪儿来。啊，马萨诸塞州的弗雷明汉，可惜不在他的行程计划之内。但是他说，他有“贼他妈多的时间，可以去贼他妈多的其他地方”。
这个热情友好的荷兰小伙子显然知道，“贼他妈多”跟普普通通的“很多”比起来，有趣得多。他掌握了英语的句法，有相当丰富的词汇量，却缺乏交际经验，来判断用词是否合乎语境。
想起这件事，是因为有新闻说，谷歌翻译引擎将从一个基于短语的系统，变成一个神经网络系统。两种方法都以语料库为基础，训练计算机掌握多个由原文和译文搭配组合的句子。计算机由此总结出一套规则，可以根据原句的词语排列，推导出目标语言最有可能的词语排序。
整个过程属于模式匹配的训练。当智能手机识别你的语音提问“导航到布鲁克莱恩”，或者当拍照软件识别你朋友的面部时，运用的也是类似的模式匹配算法。计算机并不能“理解”人脸或者目的地，而是把它们变成向量，再进行处理。
我是专业译者，译了差不多有125本法语书。有人因此可能会觉得，我看到谷歌的下述言论会很生气：谷歌新的翻译引擎跟人工译者一样好；若满分6分，谷歌可以打到5分，而人类的平均水平也只有5.1分。但我同样也是数学博士，我开发出来的软件可以“阅读”欧洲四种语言的报纸，再按主题将它们归类。所以，我对机器翻译取代人工翻译并没有多大戒心，反而非常清楚机器所取得的非凡成就，相当佩服谷歌复杂而精湛的技术。 佩服归佩服，我也不会对机器翻译的缺陷视而不见。想想那个会说“贼他妈多”的荷兰年轻人，他流利的英语显示他的“湿件”—— 一个活生生的神经网络系统——已经训练得足以感觉出一些细微规则（和例外），从而使语言自然流畅。相反，计算机语言则是纯粹脱离语境的语法。然而，那位年轻的荷兰人因缺乏英语社会经验而无法掌握母语使用者在措辞、语气和句子结构方面更微妙的规则。当然，母语使用者也可能有意打破这些规则，以达到某种效果。如果我对两个修女说“贼他妈多地方”，而不是“很多地方”，我可能是话里有话。那个荷兰人在误打误撞中造成了一种喜剧效果。
谷歌翻译引擎所用的语料库来自各种新闻资源和维基百科。对每个语料库仅有的描述也就成了关于语境的唯一线索。从这少得可怜的信息当中，很难推断像“贼他妈多”这样的词用着合不合适。如果译成法语，机器可能会认为beaucoup或者plusiers都是很好的选择。这些词也许可以达意，但却丧失了喜剧效果，而这种效果更依赖于带有社会效应的“贼他妈多”一词，而非中性的plusiers。不管算法有多复杂，它也得依赖于已有的信息和线索，至于语境，尤其是交际语境，则很难通过编码来传达。
人脑实在是太复杂了。我在麻省理工学院读书时，这个问题就横亘在创造人工智能的各种努力之前。要想和人类一样智能，不仅仅是能够根据规则进行逻辑推理，或是根据规律进行数据演算。在此之前，還得知道哪些规则是可用的，这得具有一种能敏锐觉察当时情况的艺术能力才行。程序员都很聪明，但是还没有聪明到可以预估意义赖以产生的庞大语境。所以即使是最好的算法，也会有所缺失——所以正如亨利·詹姆斯所说，理想的译者应该“一无所失”。
这并不是说机器翻译毫无用处。很多翻译工作都只是例行公事而已。有时，机器完全可以胜任。但可别指望多大的奇迹，比如贴切的文学翻译，或者恰当的政治妙语。人工智能的研究从一开始就太过自信。我这么说并不是因为担心失业：我已经不搞翻译了，最近正抽空写代码呢。
1. voluble： 健谈的。
2. itinerary： 旅行计划，预定行程；shitload： 许多，大量。
3. jovial： 热情友好的，天性快活的；synonym： 同义词，近义词；bland：平和的，温和的。
4. syntax： 语法，句法；appropriateness：合适，得体。
5. corpus： 语料库。
6. sequence： 顺序，先后次序。
7. algorithm： 算法；syllable： 音节；navigate： 导航。
8. vector： 向量。
9. bristle： 显得愤怒。
10. feat： 业绩，功绩；virtuosity： 精湛技巧。
11. wetware： 湿件，计算机专用术语，指软件、硬件以外的其他“件”，即人脑、大脑神经系统；intuit： 凭直觉知道。
12. blunder： 跌跌撞撞，出漏子；inadvertent： 无意的，非故意的。
13. scanty： 不足的，勉强够的。
14. render：（用不同的语言）表达，翻译；utterance： 表达，表述。
15. devilishly： 非常，极其。
16. artificial intelligence （AI）： 人工智能。
17. felicitous： 恰当的，贴切的；aptly： 适当地；zinger： 妙语，幽默的话。
18. dog： 作动词，意为紧随。
Years ago， on a flight from Amsterdam to Boston， two American nuns seated to my right listened to a voluble1 young Dutchman who was out to discover the United States. He asked the nuns where they were from. Alas， Framingham， Massachusetts was not on his itinerary， but， he noted， he had“shitloads of time and would be visiting shitloads of other places”.2
The jovial young Dutchman had apparently gathered that“shitloads” was a colourful synonym for the bland “lots”.3 He had mastered the syntax of English and a rather extensive vocabulary but lacked experience of the appropriateness of words to social contexts.4
This memory sprang to mind with the recent news that the Google Translate engine would move from a phrase-based system to a neural network. Both methods rely on training the machine with a “corpus”5 consisting of sentence pairs： an original and a translation. The computer then generates rules for inferring， based on the sequence6 of words in the original text， the most likely sequence of words from the target language.
The procedure is an exercise in pattern matching. Similar pattern-matching algorithms are used to interpret the syllables you utter when you ask your smartphone to “navigate to Brookline” or when a photo app tags your friend’s face.7 The machine doesn’t “understand” faces or destinations； it reduces them to vectors8 of numbers， and processes them.
I am a professional translator， having translated some 125 books from the French. One might therefore expect me to bristle9 at Google’s claim that its new translation engine is almost as good as a human translator， scoring 5.0 on a scale of 0 to 6， whereas humans average 5.1. But I’m also a PhD in mathematics who has developed software that “reads” European newspapers in four languages and categorises the results by topic. So， rather than be defensive about the possibility of being replaced by a machine translator， I am aware of the remarkable feats of which machines are capable， and full of admiration for the technical complexity and virtuosity of Google’s work.10
My admiration does not blind me to the shortcomings of machine translation， however. Think of the young Dutch traveler who knew “shitloads” of English. The young man’s fluency demonstrated that his “wetware”—a living neural network， if you will—had been trained well enough to intuit the subtle rules （and exceptions） that make language natural.11 Computer languages， on the other hand， have context-free grammars. The young Dutchman， however， lacked the social experience with English to grasp the subtler rules that shape the native speaker’s diction， tone and structure. The native speaker might also choose to break those rules to achieve certain effects. If I were to say “shitloads of places”rather than “lots of places” to a pair of nuns， I would mean something by it. The Dutchman blundered into inadvertent comedy.12 Google’s translation engine is “trained” on corpora ranging from news sources to Wikipedia. The bare description of each corpus is the only indication of the context from which it arises. From such scanty13 information it would be difficult to infer the appropriateness or inappropriateness of a word such as “shitloads”. If translating into French， the machine might predict a good match to beaucoup or plusieurs. This would render the meaning of the utterance but not the comedy，14 which depends on the socially marked“shitloads” in contrast to the neutral plusieurs. No matter how sophisticated the algorithm， it must rely on the information provided， and clues as to context， in particular social context， are devilishly15 hard to convey in code.
The problem， as with all previous attempts to create artificial intelligence （AI）16 going back to my student days at MIT， is that intelligence is incredibly complex. To be intelligent is not merely to be capable of inferring logically from rules or statistically from regularities. Before that， one has to know which rules are applicable， an art requiring awareness of sensitivity to situation. Programmers are very clever， but they are not yet clever enough to anticipate the vast variety of contexts from which meaning emerges. Hence even the best algorithms will miss things—and as Henry James put it， the ideal translator must be a person “on whom nothing is lost”.
This is not to say that mechanical translation is not useful. Much translation work is routine. At times， machines can do an adequate job. Don’t expect miracles， however， or felicitous literary translations， or aptly rendered political zingers.17 Overconfident claims have dogged18 AI research from its earliest days. I don’t say this out of fear for my job： I’ve retired from translating and am devoting part of my time nowadays to…writing code.
亨利·詹姆斯說，理想的译者应该是“一无所失”之人。或者，是一无所失之机器。但是，机器可不会教你不能在修女面前爆粗口。
几年前，我从阿姆斯特丹乘机前往波士顿，两位美国修女坐在我右边，听一个正要去探索美国的荷兰小伙子侃侃而谈。他问修女从哪儿来。啊，马萨诸塞州的弗雷明汉，可惜不在他的行程计划之内。但是他说，他有“贼他妈多的时间，可以去贼他妈多的其他地方”。
这个热情友好的荷兰小伙子显然知道，“贼他妈多”跟普普通通的“很多”比起来，有趣得多。他掌握了英语的句法，有相当丰富的词汇量，却缺乏交际经验，来判断用词是否合乎语境。
想起这件事，是因为有新闻说，谷歌翻译引擎将从一个基于短语的系统，变成一个神经网络系统。两种方法都以语料库为基础，训练计算机掌握多个由原文和译文搭配组合的句子。计算机由此总结出一套规则，可以根据原句的词语排列，推导出目标语言最有可能的词语排序。
整个过程属于模式匹配的训练。当智能手机识别你的语音提问“导航到布鲁克莱恩”，或者当拍照软件识别你朋友的面部时，运用的也是类似的模式匹配算法。计算机并不能“理解”人脸或者目的地，而是把它们变成向量，再进行处理。
我是专业译者，译了差不多有125本法语书。有人因此可能会觉得，我看到谷歌的下述言论会很生气：谷歌新的翻译引擎跟人工译者一样好；若满分6分，谷歌可以打到5分，而人类的平均水平也只有5.1分。但我同样也是数学博士，我开发出来的软件可以“阅读”欧洲四种语言的报纸，再按主题将它们归类。所以，我对机器翻译取代人工翻译并没有多大戒心，反而非常清楚机器所取得的非凡成就，相当佩服谷歌复杂而精湛的技术。 佩服归佩服，我也不会对机器翻译的缺陷视而不见。想想那个会说“贼他妈多”的荷兰年轻人，他流利的英语显示他的“湿件”—— 一个活生生的神经网络系统——已经训练得足以感觉出一些细微规则（和例外），从而使语言自然流畅。相反，计算机语言则是纯粹脱离语境的语法。然而，那位年轻的荷兰人因缺乏英语社会经验而无法掌握母语使用者在措辞、语气和句子结构方面更微妙的规则。当然，母语使用者也可能有意打破这些规则，以达到某种效果。如果我对两个修女说“贼他妈多地方”，而不是“很多地方”，我可能是话里有话。那个荷兰人在误打误撞中造成了一种喜剧效果。
谷歌翻译引擎所用的语料库来自各种新闻资源和维基百科。对每个语料库仅有的描述也就成了关于语境的唯一线索。从这少得可怜的信息当中，很难推断像“贼他妈多”这样的词用着合不合适。如果译成法语，机器可能会认为beaucoup或者plusiers都是很好的选择。这些词也许可以达意，但却丧失了喜剧效果，而这种效果更依赖于带有社会效应的“贼他妈多”一词，而非中性的plusiers。不管算法有多复杂，它也得依赖于已有的信息和线索，至于语境，尤其是交际语境，则很难通过编码来传达。
人脑实在是太复杂了。我在麻省理工学院读书时，这个问题就横亘在创造人工智能的各种努力之前。要想和人类一样智能，不仅仅是能够根据规则进行逻辑推理，或是根据规律进行数据演算。在此之前，還得知道哪些规则是可用的，这得具有一种能敏锐觉察当时情况的艺术能力才行。程序员都很聪明，但是还没有聪明到可以预估意义赖以产生的庞大语境。所以即使是最好的算法，也会有所缺失——所以正如亨利·詹姆斯所说，理想的译者应该“一无所失”。
这并不是说机器翻译毫无用处。很多翻译工作都只是例行公事而已。有时，机器完全可以胜任。但可别指望多大的奇迹，比如贴切的文学翻译，或者恰当的政治妙语。人工智能的研究从一开始就太过自信。我这么说并不是因为担心失业：我已经不搞翻译了，最近正抽空写代码呢。
1. voluble： 健谈的。
2. itinerary： 旅行计划，预定行程；shitload： 许多，大量。
3. jovial： 热情友好的，天性快活的；synonym： 同义词，近义词；bland：平和的，温和的。
4. syntax： 语法，句法；appropriateness：合适，得体。
5. corpus： 语料库。
6. sequence： 顺序，先后次序。
7. algorithm： 算法；syllable： 音节；navigate： 导航。
8. vector： 向量。
9. bristle： 显得愤怒。
10. feat： 业绩，功绩；virtuosity： 精湛技巧。
11. wetware： 湿件，计算机专用术语，指软件、硬件以外的其他“件”，即人脑、大脑神经系统；intuit： 凭直觉知道。
12. blunder： 跌跌撞撞，出漏子；inadvertent： 无意的，非故意的。
13. scanty： 不足的，勉强够的。
14. render：（用不同的语言）表达，翻译；utterance： 表达，表述。
15. devilishly： 非常，极其。
16. artificial intelligence （AI）： 人工智能。
17. felicitous： 恰当的，贴切的；aptly： 适当地；zinger： 妙语，幽默的话。
18. dog： 作动词，意为紧随。