我的機器人同事

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，英國《金融時報》(Financial Times)把我的部分工作交給了一個機器人。過去這些年，我一直會把自己的專欄做成播客版本，但現在我遇到了激烈的競爭——來自實驗機器人艾米(Experimental Amy)。

She is vastly undercutting me on price, is a quick learner and always does precisely what she is told.

她的成本遠低於我，學習速度又快，永遠能嚴格執行指令。

On the downside, I daresay she is a less convivial colleague than I am — but then you cannot have everything.

她也有劣勢。在和同事愉快相處方面，我敢說她不如我，但一個人總不可能十全十美吧。

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.

被機器人取代是每一位上班族最可怕的噩夢。發現她強行插手我的工作，我難過也是可以理解的。但當我平復了怒氣，坐下來聽她的工作成果時，我開始感覺好一點了。

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.

我知道她才問世不久，但就目前來說，艾米還不是我的對手：可以說，在我那充滿偏見的耳朵聽來，艾米完全沒用。

PodcastListen to Lucy

如果你不相信我，請自己聽聽吧。點選本專欄頂部的箭頭，聽聽艾米的朗讀，再點選下方，聽聽我的版本。不要同時跟讀，只需聽就夠了。

Amy the robot wants my job, but she’s no match for me

老實說，艾米還是有一些優點的。首先，她的聲音很好聽。

To be fair, Amy does have some things going for her. For a start, she has a great voice.

10年前我剛開始錄製專欄音訊時，一位聽眾寫信抱怨稱，我那“帶著鼻音的河口話”迫使他立刻中斷了收聽。相比之下，艾米音色低沉，令人愉悅，就像光滑的天鵝絨。

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.

她的第二個優點是幾乎免費。艾米是亞馬遜(Amazon)推出的一項將文字轉化為聲音的新服務的部分內容，幾乎沒有成本——至少與FT給我的薪水相比如此。

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.

更令人驚歎的是她的速度。收到我寫的文字後不到兩秒，她就能生成語音版。這就相當於，當我清完喉嚨，開始讀“上週一，英國……”時，她就已經搞定了。

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.

她工作時不用勞師動眾，獨自就完成了。相比之下，我還需要一位製作人，還得使用錄音棚。我們倆還要寫郵件商定時間，見面後還要毫無意義地寒喧一番。還要架裝置，編輯錄音，剪掉我所有卡殼的地方。需要耗費製作人半小時時間，我自己也要花上大約15分鐘。

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.

要是艾米的成果勉強說得過去，她就勝出了——但沒有。她老在錯誤的位置停頓，在該分開讀的地方連讀，對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.

聽她朗讀倒不是像聽非英語國家人士大聲讀英語，而是一個沒有腦子、感情或幽默感的人在讀。實際上，她讀得太差了，我都沒聽懂文章的意思——鑑於文章是我本人寫的，這還是能說明一些問題的。

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.

艾米的學習曲線非常陡。兩三年前，大眾市場上的語音機器人聽起來還像是史蒂芬?霍金(Stephen Hawking)在說話。艾米的學習演算法每天都在幫她進步。她那匪夷所思的朗讀節奏問題會解決的，語調也會改進。她還會加入虛假的情感和一些笑話。

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.

但艾米永遠也做不到在理解意思的基礎上朗讀，永遠不會懂何時該停頓，何時該譏笑，永遠不會諷刺。她會繼續犯錯誤。

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.

在最後這點上，會犯錯的不止她一個。我在朗讀時也會犯錯。有時背景會有雜音。有時我讀得太快了，有時語氣有一點過重。但我想聽眾對我們的過失不會同樣對待。

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.

人犯錯誤，聽眾會理解。一個錯誤往往會讓我們感覺與犯錯者拉近了距離。但如果犯錯的是機器人，我們不會同情，還可能對整個專案都失去信心。

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.

總之，我並不因為艾米要搶我的飯碗而討厭她。但我不喜歡她把我的專欄讀成那個樣子。她亂讀一通，我再看自己的文章，就像看有史以來最令人費解和枯燥無味的作品。

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.

如果艾米去讀個船運預報或足球賽結果，她會很稱職。很快她就會勝任一切可預測內容的朗讀。但好專欄的關鍵就在這裡：如果一篇文章是可以預測的，那它寫得就不夠好。