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The Financial Times gave part of my job to a robot last week. For years I have been making podcast versions of my column, but now I am faced with stiff competition — in the shape of Experimental Amy.
最近，英国《金融时报》(Financial Times)把我的部分工作交给了一个机器人。过去这些年，我一直会把自己的专栏做成播客版本，但现在我遇到了激烈的竞争——来自实验机器人艾米(Experimental Amy)。
She is vastly undercutting me on price, is a quick learner and always does precisely what she is told.
她的成本远低于我，学习速度又快，永远能严格执行指令。
On the downside, I daresay she is a less convivial colleague than I am — but then you cannot have everything.
她也有劣势。在和同事愉快相处方面，我敢说她不如我，但一个人总不可能十全十美吧。
Being replaced by a robot is every worker’s worst nightmare, and when I discovered that she was muscling in on my act I was understandably distressed. Yet once I got over the outrage and sat down and listened to her work, I started to feel better.
被机器人取代是每一位上班族最可怕的噩梦。发现她强行插手我的工作，我难过也是可以理解的。但当我平复了怒气，坐下来听她的工作成果时，我开始感觉好一点了。
I know it is early days for her, but at the moment Amy is no match for me: instead, according to my partial ear, she is absolutely useless. If you don’t believe me, listen. Click on the arrow at the top of this column to hear what Amy has to say, and then click here to hear my own version. Don’t read the words at the same time, just listen.
我知道她才问世不久，但就目前来说，艾米还不是我的对手：可以说，在我那充满偏见的耳朵听来，艾米完全没用。
PodcastListen to Lucy
如果你不相信我，请自己听听吧。点击本专栏顶部的箭头，听听艾米的朗读，再点击下方，听听我的版本。不要同时跟读，只需听就够了。
Amy the robot wants my job, but she’s no match for me
老实说，艾米还是有一些优点的。首先，她的声音很好听。
To be fair, Amy does have some things going for her. For a start, she has a great voice.
10年前我刚开始录制专栏音频时，一位听众写信抱怨称，我那“带着鼻音的河口话”迫使他立刻中断了收听。相比之下，艾米音色低沉，令人愉悦，就像光滑的天鹅绒。
When I started recording my columns a decade ago, one listener wrote in to complain that my “nasal Estuarine twang” meant he had to stop listening at once. By contrast, Amy’s voice has an agreeably low timbre and is smooth as velvet.
她的第二个优点是几乎免费。艾米是亚马逊(Amazon)推出的一项将文本转化为声音的新服务的部分内容，几乎没有成本——至少与FT给我的薪水相比如此。
Her second advantage is that she is practically free. She is part of a new service from Amazon that turns text to speech, and which costs nearly nothing — at least by comparison with what the FT pays me.
更令人惊叹的是她的速度。收到我写的文字后不到两秒，她就能生成语音版。这就相当于，当我清完喉咙，开始读“上周一，英国……”时，她就已经搞定了。
Even more impressive is her speed. Less than two seconds after receiving my written text she has supplied a spoken version of it. Which means by the time I have cleared my throat and started to read: “Last Monday the Finan?.?.?.?” she has already finished.
她工作时不用劳师动众，独自就完成了。相比之下，我还需要一位制作人，还得使用录音棚。我们俩还要写邮件商定时间，见面后还要毫无意义地寒喧一番。还要架设备，编辑录音，剪掉我所有卡壳的地方。需要耗费制作人半小时时间，我自己也要花上大约15分钟。
In her case there is no kerfuffle involved and she does the job single-handedly. By contrast, my recording involves a producer, the use of a studio, the necessity of the two of us exchanging emails to confirm a mutually convenient time and then some idle pleasantries when we meet. It involves setting up equipment and then editing the clip to iron out all my stumbling. It takes half an hour of the producer’s time and about 15 minutes of mine.
要是艾米的成果勉强说得过去，她就胜出了——但没有。她老在错误的位置停顿，在该分开读的地方连读，对句法的掌握也不全面。
That would swing it if what Amy produced were halfway decent — but it is not. She keeps putting her full stops in the wrong places. She runs words together when they should be kept apart. Her grasp of syntax is patchy.
听她朗读倒不是像听非英语国家人士大声读英语，而是一个没有脑子、感情或幽默感的人在读。实际上，她读得太差了，我都没听懂文章的意思——鉴于文章是我本人写的，这还是能说明一些问题的。
Listening to her is not like listening to a non-English speaker read aloud, but to someone without brain, or heart, or sense of humour. Indeed her delivery is so poor that I do not even understand the column when she reads it — which is saying something given that I wrote it.
艾米的学习曲线非常陡。两三年前，大众市场上的语音机器人听起来还像是史蒂芬?霍金(Stephen Hawking)在说话。艾米的学习算法每天都在帮她进步。她那匪夷所思的朗读节奏问题会解决的，语调也会改进。她还会加入虚假的情感和一些笑话。
Amy’s learning curve is very steep. A couple of years ago mass-market voice bots sounded like Stephen Hawking. Every day Amy’s learning algorithms help her improve. Her weird timing will be fixed. Her intonation will get better. She will be able to do ersatz emotion and some jokes.
但艾米永远也做不到在理解意思的基础上朗读，永远不会懂何时该停顿，何时该讥笑，永远不会讽刺。她会继续犯错误。
But Amy will never be able to read with understanding. Amy will never know when to pause and when to sneer. Amy will never do irony. She will continue to get it wrong.
在最后这点上，会犯错的不止她一个。我在朗读时也会犯错。有时背景会有杂音。有时我读得太快了，有时语气有一点过重。但我想听众对我们的过失不会同样对待。
In the last she is not alone. I also make mistakes when I read. Sometimes there is a clanging in the background. Sometimes I read too fast or am a bit too emphatic. But I fancy that listeners do not treat our failings equally.
人犯错误，听众会理解。一个错误往往会让我们感觉与犯错者拉近了距离。但如果犯错的是机器人，我们不会同情，还可能对整个项目都失去信心。
When a human screws up the audience understands why. Quite often a mistake makes us feel more closely tied to the person who has made it. But when a robot makes a mistake, we do not sympathise and are likely to lose faith in the whole undertaking.
总之，我并不因为艾米要抢我的饭碗而讨厌她。但我不喜欢她把我的专栏读成那个样子。她乱读一通，我再看自己的文章，就像看有史以来最令人费解和枯燥无味的作品。
In the end, I do not resent Amy because she is about to steal my job. But I do dislike her for reading my columns like that. Put through her mangle, I see them as the most impenetrable, dreariest things ever written.
如果艾米去读个船运预报或足球赛结果，她会很称职。很快她就会胜任一切可预测内容的朗读。但好专栏的关键就在这里：如果一篇文章是可以预测的，那它写得就不够好。
