我的報道比機器人寫得好
One day last week at 9.29am I hunched nervously over my keyboard and prepared to do battle with an entity called Emma. We were each primed to write about the official UK employment data at 9.30am and file our stories to my editor. I was sure Emma would be quicker than me, but I really hoped I would be better.
最近，一天早晨9點29分，我弓着身子緊張地盯着鍵盤，準備與一臺名爲愛瑪(Emma)的機器一決雌雄。我們都準備在9點30分撰寫一篇有關英國官方就業數據的文章，然後把文章遞交給我的編輯。我確定愛瑪會比我速度快，但我真的希望我會寫得更好。
Her creator, a start-up called Stealth, calls her an “autonomous artificial intelligence” designed to deliver professional services such as research and analysis. Since it is fashionable to predict that AI will supplant white-collar workers including journalists, I wanted to put it to the test.
她的創造者、名爲Stealth的初創企業把她稱作“自主人工智能”，旨在提供研究和分析等專業服務。如今，預測人工智能將取代白領員工（包括記者在）是件時髦的事，所以我希望測試一下。
Emma was indeed quick: she filed in 12 minutes to my 35. Her copy was also better than I expected. Her facts were right and she even included relevant context such as the possibility of Brexit (although she was of the dubious opinion that it would be a “tailwind” for the UK economy). But to my relief, she lacked the most important journalistic skill of all: the ability to distinguish the newsworthy from the dull. While she correctly pointed out the jobless rate was unchanged, she overlooked that the number of jobseekers had risen for the first time in almost a year.
愛瑪確實速度很快：她12分鐘就提交了，我用了35分鐘。而且她的文筆比我預期的要好。她列舉的事實正確，甚至包括了相關背景，例如英國退歐(Brexit)的可能性（不過，她認爲這將“推動”英國經濟的觀點讓人存疑）。但讓我感到安慰的是，她缺乏最重要的新聞從業人員技能：從枯燥資料中提取有新聞價值的內容的能力。儘管她準確指出了失業率未變，但她忽視了求職者數量出現近一年來首次增加。
In truth, most people who work on artificial intelligence admit it is not going to make humans obsolete any time soon. It is simply not intelligent enough yet. What is beginning to happen, though, is more subtle but no less important. The lines are beginning to blur between work done by humans and that done by machines.
實際上，從事人工智能開發工作的多數人承認，它不會很快淘汰人類。它就是還不夠聰明。現在開始發生的情況較爲微妙，但也很重要。人類和機器執行的工作之間的界限開始變得模糊起來。
For some workers, this could be a boon. I could imagine a scenario where an entity like Emma could do rudimentary reports on repetitive data releases, then send them to a human editor to newsify and beautify. The Associated Press already uses a program called Automated Insights to write simple corporate results stories. In these cases, humans have the advantage: machines not obliterating them but taking over the boring bits of their jobs so they can spend more time on the creative or valuable parts.
對於一些工作者而言，這可能是件好事。我可以假設這樣一幅場景：類似愛瑪的一臺機器可以就重複性的數據發佈編寫報道初稿，然後發送給人類編輯做成新聞並修飾語句。美聯社(Associated Press)已經在使用一個名爲Automated Insights的程序撰寫簡單的公司業績報道。在這些情況下，人類具有優勢：機器沒有淘汰人類，而是取代了人類工作中乏味的部分，使人類能把更多時間花在有創意或有價值的工作上。
But not all humans are moving up the value chain. There are some boring tasks at which machines are very bad. An army of low-paid people are quietly doing them instead.
但並非所有人都在向價值鏈上方移動。有一些枯燥的工作，機器做得非常糟糕。很多低薪人員在默默地承接這些工作。
Take the workers on Amazon’s Mechanical Turk, a site run by the online retailer where “requesters” pay “Turkers” to do simple microtasks that are tricky for machines but easy (if dull) for humans: transcribing audio clips; tagging photos with relevant keywords; copying photocopied receipts into spreadsheets. Amazon calls these “human intelligence tasks”, or HITs, and they tend to pay a few cents apiece. The name Mechanical Turk comes from a fake chess-playing machine from the 18th century: while it looked like an automaton, a person was secretly hiding inside.
以亞馬遜(Amazon)土耳其機器人(Mechanical Turk)的工作者爲例，這是該在線零售商經營的一個網站，讓“請求者”付費給“土耳其工人”(Turkers)執行一些簡單的小任務，這些任務對於機器來說很難，但對人類而言卻相當容易（只是些枯燥）：把錄音整理成文字記錄；爲照片貼上相關關鍵詞作爲標籤；把複印的收據錄入電子表格。亞馬遜稱之爲“人類智能任務”，往往每個任務付費幾美分。Mechanical Turk的名字來源於18世紀一臺虛假的下棋機器：這臺機器看上去是自動化，其實有真人藏在機器內部。
Pinterest is a good example of a company that uses sites like this. One developer explains in a recent blog how it uses “crowdworkers” to evaluate the appropriateness of the “trending searches” generated by its computers. Humans are still better at making these judgments than machines. “We’ve built ‘artificial’ artificial intelligence,” she concluded.
Pinterest就是一個公司利用這類網站的很好例子。一位開發員在最近一篇博文中解釋瞭如何利用“衆包工作者”評估電腦生成的“趨勢搜索”是否合適。人類仍比機器更擅長做出這些評判。她得出結論稱：“我們打造了‘人爲’的人工智能。”
Some of the new chatbot and AI services also have people hiding inside: “humans pretending to be robots pretending to be humans”, as Bloomberg put it in a recent exposé. These people often review and edit AI-generated responses before they are sent.
正如彭博(Bloomberg)最近在一篇披露內幕的報道中所言，一些新的聊天機器人和人工智能服務也有人類藏在裏面：“人類假裝是機器人，再由機器人假裝人類”。這些人往往會在由人工智能生成的回覆被髮送之前對其進行編審。
What about Emma? Is there a human lurking behind her? Shaunak Khire, co-founder of Stealth, says Emma has a team of human “trainers” but insists the output is all her own.
愛瑪呢？有人藏在她身後嗎？Stealth聯合創始人Shaunak Khire表示，愛瑪擁有一個由人類“教練”組成的團隊，但他堅稱，她是完全獨立完成工作的。
It is always going to be hard for laypeople using these services to know for sure.
使用這些服務的外行人要確定這點總是很難。
Jeff Bigham, assistant professor at Carnegie Mellon University’s Human-Computer Interaction Institute, works as an adviser to Emma’s creators. He wants to find ways to make crowdwork less mindless and more fulfilling; for example, by making it a means to acquire skills. He asks himself: “What would make me proud to have my daughter grow up to be a crowdworker?” Even then, it is not clear how long this sort of work will last.
卡內基梅隆大學(Carnegie Mellon University)人機交互研究所(Human-Computer Interaction Institute)助理教授傑夫•比格姆(Jeff Bigham)是愛瑪創造者的顧問。他希望找到讓衆包工作不那麼機械而更有成就感的方法；比如讓它成爲一種獲取技能的方式。他問自己：“有什麼能讓我因爲自己的女兒長大後成爲一名衆包工作者而感到驕傲呢？”儘管那樣，我們不清楚這類工作會持續多久。
He explains that when humans perform tasks that machines cannot yet do, they create an exhaust of “training data” from which AI can learn. In other words, all those crowdworkers and chatbot editors are working steadily towards their own obsolescence.
他解釋道，當人類執行機器尚無法執行的任務時，他們會產生海量“培訓數據”，人工智能可從中學習。換句話說，所有這些衆包工作者和聊天機器人編輯天天在爲淘汰自己工作着。
Should you welcome or fear the rise of intelligent machines? That depends on whether they will be working for you, or you will be working for them.
你是應該歡迎還是擔心智能機器的崛起呢？這取決於他們將爲你工作還是你將爲他們工作。
