AI 与写作2015nytimes.com编辑精选
这是人写的还是计算机写的？
Did a Human or a Computer Write This?
导读
这是《纽约时报》的一个互动小测试：读者要判断一段文字究竟出自人手还是机器。评论区里几位懂自然语言处理的从业者把“机器写作”拆开来分析，指出它多半是人工模板加严格约束的产物，只有球赛、股市这类有现成数据的题材才真正适合自动化。对写作者来说，这些评论值得一读：它们清楚地划出了机器目前能做什么、人该往哪里使劲，还顺带提醒我们，写数字时必须交代上下文。
可以直接用的写作要点
- 01
所谓机器写作，大多是人工搭好模板再填空，真正稀缺的能力是选题、判断和为读者提供背景，而不是造句。
- 02
写数字时必须交代参照系：与其写“谋杀案增加50%”，不如写“从去年上半年的2起增至今年的3起，而前一年同期是20起”。
- 03
结构化、有现成数据的题材（球赛、财报）最容易被自动化，写作者更该往解释、观察和定性判断上走。
- 04
读者很容易把机器生成的文字读出额外的意义（“伊丽莎效应”），这说明感染力常常来自读者的投射，而不只是文本本身的复杂程度。
HN 评论精选
机器写作多是“人搭模板加人工把关”，只有数字型题材适合自动化；同时提醒读者，免费阅读正在扼杀优质新闻。
一位前记者说，他见过自然语言生成公司，也懂一些自然语言处理，因此了解内情：机器批量产出的内容，很多是基于人做的模板；我们读到的所谓“机器写的”东西，往往需要大量人工干预，并给机器加上严格约束，免得它出错。棒球、股市这类数字型报道适合机器写，目前大多数定性内容还不适合；就连谷歌去年11月从图像生成说明文字，结果波动也很大。他还反问批评新闻机构的人：你免费看新闻吗？你相信整合多个数据源来报道复杂事件没有成本吗？你以为新闻机构付给记者很多钱吗？今天大多数读者都是搭便车的人，不愿为优质新闻付费，这也是新闻业衰亡的原因之一。
vonnik查看英文原文
Former reporter hear who has been in professional talks with a natural-language generation firm, dabbles in NLP, and has some insight into this process. A lot of what machines churn out is based on templates that humans have created. Many of the things we read that are supposedly written by machines required major human intervention, and the application of strict constraints on the machine so that it wouldn't screw up. Number stories about baseball and the stock market are good for machines to write about. Most qualitative things aren't, for the moment. Even Google's results generating captions from images last November actually included a lot of variance. The algorithm was good at recognizing some things and not others. To the commenters on this thread who criticize news organizations, I would simply ask: Do you consume your news for free? Do you believe that synthesizing complex events using multiple sources of data happens without cost? Do you believe that news organizations pay reporters much to write stories? Please reflect on your role in the news ecosystem. Most readers today are free riders, and unwilling to subsidize quality journalism. That's one reason why it's dying.
“机器写作”的定义本就模糊，多数只是填空或套公式，而人又容易过度解读机器文本。
“由计算机撰写”到底指什么？它可以是从填空到按公式反解析生成，范围很广；而且看似高级的公式，多数时候的结果可能只是填空。很多职业写作者也被说成是在套公式，很多写作者和非写作者都用文字处理器、甚至查找替换来拼文本。再加上“伊丽莎效应”：人很容易给某类机器生成的文本赋予超出其实际的含义。
joe_the_user查看英文原文
What does "written by a computer" even mean? I mean, computer-written can be anything from filling in some blanks to rendering a formula by reverse-parsing. And suppose that an apparently fancy formula actually results in blank-filling-in most of the time? Lots of professional writers are described as following a formula. Lots of writers and non-writers construct text using word-processors and even an occasional search-and-replace. -- Oh, and add to that "The Eliza Effect"[1], in which it's pretty easy for humans to ascribe greater meaning to some kinds of computer generated text than it really has. [1] http://en.wikipedia.org/wiki/ELIZA_effect
算法能强迫报道给数字补上背景；那个“50%增长”的例子，恰恰点出了人类记者常犯的缺少参照系的毛病。
有相当一部分报道会因为算法和模型驱动的生成而变好。不少报道其实只是对新闻稿的轻微改写，如果算法有背景知识、对某类故事有一致的模型（比如一场比赛的前瞻），就能改进它们。报道里充斥着没有任何背景的数字，为什么不让机器来强制补上上下文？例如“今年上半年谋杀案增加50%”（人类报道的常见毛病），对比“谋杀案从去年上半年的2起增至今年的3起，比上一年的20起已大幅下降”（一个自动强制补上上下文的算法）。他承认这话题复杂，但普通新闻实在写得太差了。
hammerandtongs查看英文原文
There is a non-zero amount of reporting that would be improved by algorithmic and model driven story generation. Quite a bit of reporting has become thinly veiled rewrites of press releases, what could be improved by an algorithm that actually had background context and a consistent model for a type of story like a simple preview of a coming game? The amount of statistics in reporting that lacks any context for the numbers spewed out? Why couldn't a machine do a better job in enforcing context for the numbers? For example -- "there is a %50 murder increase in the first six months of this year" (common problem with human reporting) vs "there is %50 murder increase from 2 in the first six months of last year to 3 in the first six months of this year, this is down from 20 in the previous year" (an algorithm that automatically enforces context)? I realize this is a complex topic but wow the average ;) news story is soooo bad that...
关键不在“是不是机器写的”，而在内容的广度、选材能力和人工筛选成本；一句十秒能写的话，喂数据和校验可能要花很久。
“计算机写的”这个说法毫无意义。真正重要的是：算法能生成的内容有多广、多多样，它从输入数据中挑出最相关部分的能力，以及发布前必须由人丢弃多少无意义内容。看第6段：“周二对W. Roberts是伟大的一天，这位大三投手投出完美比赛，带领弗吉尼亚在Davenport球场以2比0战胜乔治·华盛顿。”很好。可谁告诉计算机这对他是伟大的一天？谁告诉它他在比赛？谁告诉它这场比赛是“完美”的？职业记者十秒就能写出这句，可把构成这个故事的数据输入计算机、或从上百种变体中挑出一个没有明显错误的版本，要花多久？小说也一样：任何人都能写个程序输出一部小说，把它当成一个字符串存进去打印出来就行。真正的魔法，是计算机能写出远超它存储数据的东西。
Udik查看英文原文
Completely meaningless. "Written by a computer" doesn't really mean anything. What's important it's the breadth and variety of the content an algorithm can generate, its ability to choose the most relevant among its input data, and the amount of meaningless content that has to be discarded by a human supervisor before publishing. Take fragment 6: “Tuesday was a great day for W. Roberts, as the junior pitcher threw a perfect game to carry Virginia to a 2-0 victory over George Washington at Davenport Field.” Cool. And who told the computer it was a great day for him? Who told the computer he was playing? Who told it his game was "perfect"? This sentence can be written by a professional human journalist in about ten seconds, how long does it take to input in a computer the data that make up the story or to check among hundreds of possible variations for one that doesn't contain obvious mistakes? Or: “Kitty couldn’t fall asleep for a long time. Her nerves were strained as two tight strings, and even a glass of hot wine, that Vronsky made her drink, did not help her. Lying in bed she kept going over and over that monstrous scene at the meadow.” Ok, it's a novel, written by a computer. Now, everybody can write a software that produces one single novel: just store it as a single string in the program and print it out. The magic happens when the computer can write something that goes way beyond the data that was stored in it exactly for that purpose. So how many different novels can this program write? Does the result exceed considerably the effort of the programmers put in the program itself? Most probably not, otherwise we'd be talking of a general AI.
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