AI 与写作2019nytimes.com
机器生成的新闻正在增多
Journalism generated by machine is on the rise
导读
这篇《纽约时报》报道讨论机器自动生成新闻的兴起，以及它在财经、体育、地方资讯等领域的实际应用。对写作者来说，它划出了一条界线：哪些写作是可被模板和数据替代的例行劳动，哪些（解释"为什么"、提供背景与判断）仍是人的价值所在。
可以直接用的写作要点
- 01
公式化、数据驱动的题材（彩票开奖、天气预报、财报数字、活动日历）最适合交给机器，把人的时间腾给需要采访和研究的报道。
- 02
机器擅长回答"是什么"和"怎么发生"，回答不了"为什么"——把解释原因、提供背景这块留作自己写作的核心竞争力。
- 03
单个数字没有意义：不与同行对比、不交代利率等宏观环境，读者无法判断好坏，写作时要主动补上参照系。
- 04
信息越例行、数据结构越规整，越容易被自动化；越需要判断力和取舍的内容，越应该由人来写。
HN 评论精选
自动化只适合高度公式化、数据现成的题材；能解释"为什么"的写作仍遥遥无期。
我做过十年记者，八年前拿到第一份全职软件开发工作，部分原因是我在面试时演示了自己在都市日报做的一个工具：点一下按钮就能写出彩票结果稿。这类稿子每天都要写，查本州每日开奖号码，还要报 Powerball 和 Mega Millions，大彩票还得根据本州有没有人中大奖来改标题。除此之外就是枯燥的套公式，所以我写了个脚本去抓取各家彩票网站的数据，生成两三段的成稿，把五分钟的活变成五秒钟（后来还做过类似的短天气简报和警报工具）。此后我也做过别的自然语言生成项目，说实话相当难，而且选题本身必须一开始就是数据驱动的；距离能给出"为什么"解释、而不只是"是什么/怎么做"描述的有洞见的自动写作，大概还有几十年。另一个难点是拿数据本身往往最难。
jawns查看英文原文
I'm a former journalist and spent the first 10 years of my career at various magazines and newspapers. About eight years ago, I landed my first full-time software development job in part because of a walkthrough I gave during my in-person interview process of a tool I'd developed while working for a metro daily newspaper. It wrote lottery results stories with the click of a button. Writing those results stories was a daily chore. We would look up the state's daily lottery results and also report Powerball and Mega Millions results. For those larger lotteries, we'd also adjust our headlines depending on whether there was a big winner in our state. But aside from that, it was just tedious and formulaic, so I wrote a script that would hit the various lottery sites, scrape the data, and generate a two- or three-paragraph story that was ready to post. It took the task down from five minutes to five seconds. (I later worked on a similar tool for generating short weather reports and alerts.) Since then, I've worked on other NLG stuff, and honestly, it's pretty hard, and the topic really has to be data-driven to begin with; we're probably decades away from really insightful NLG that offers "why" explanations, as opposed to "what" or "how" descriptions. The other tough challenge about computer-generated journalism is that getting the data is often the hardest part of the process. Oh, sure, businesses are going to release their quarterly earnings reports, and sports teams are going to release their game data. You might even use computer-assisted reporting to generate FOIA requests for data that governments are required by law to release upon request. But you're not going to write a story that leads to Nixon's resignation or Enron's collapse simply by asking for the data.
机器写作的现实定位是"填缝"：用例行内容维持更新频率，把人力留给真新闻。
我的副业是给附近两个小城市做本地新闻网站，小到我是当地唯一的数字媒体，唯一的竞争对手是不做线上的本地报纸。真正的新闻——地方政治、新店开张、本地趣闻——都由人写。填空隙的是大量机器生成内容：活动日历是招牌栏目，活动数据足够结构化、信息足够例行，我开发的系统每天能产出好几篇读起来像人手写、并且针对每个商家和活动定制的文章。它承担了维持内容更新的例行工作，让一个全职加一个兼职就能撑起两个小城新闻网站——否则根本没人愿意做。这也是经典 AI 仍然有用的好例子：神经网络还写不出人类愿意读的英文。
freehunter查看英文原文
My side project is a local news outlet for two small cities near me. They are small enough cities that I am the only game in town when it comes to digital media, the only competitor is the local newspaper who has no online presence. Plenty of our stories are actual news articles talking about local politics and new businesses/restaurants and local interest type stuff, all written by humans. But filling in the gaps is plenty of machine-generated content. One of our big draws is the event calendar, and fortunately events give me enough data to feed an AI that I've developed. There is enough structured data and the information is so routine that this system can churn out several articles per day that sounds hand-written and every article sounds custom tailored to the business and the event itself. This fills in the routine work of keeping content flowing in between the stuff that takes a lot longer to research and write, and makes it viable for us (one full time employee and one part time employee) to run two popular small-town news websites where otherwise no one would even bother trying. This is also a great case where classical AI is still relevant to modern problems. Neural networks just are not good at writing English in a way that humans would enjoy reading. At some point I plan on packaging it up and doing a Show HN, but for the time being, this article is spot on. Machine-generated news content breathes whole new life into an area that's increasingly hard to turn a profit.
数字需要背景和细节才有意义，机器只能产出数量，解释质量仍是人的工作。
我做过会计。文章说程序能立刻从财报里挑出"最相关的事实"，这做不到——公开财报本来就是为规避自动分析而写的。华尔街公司有抓取 PDF 表格的工具，但仍不完美，因为版式不统一（合并单元格、用 InDesign 排的表格等）。最多只能抓对头条数字，而数字脱离背景毫无意义：某公司季度利润 330 万美元，不跟同行比、不考虑利率等宏观环境，能说明什么？资产负债表、利润表和现金流量表的头条数字，远不如附注里那些对投资者和分析师极其关键的小细节；受过训练的会计师读附注都会漏，AI 凭什么解析得更好？这实质上是拿"文章数量"压过"分析质量"，而后者正是新闻业有价值、有必要的原因。
rchaud查看英文原文
> The program can dissect a financial report the moment it appears and spit out an immediate news story that includes the most pertinent facts and figures. As an accountant in a prior life, I can tell you that this approach won't provide anything near "the most pertinent facts". That's because public earnings reports are written specifically to circumvent automated analysis. Wall Street firms have tools in place to scrape the data tables from those PDFs, and even then the technology isn't perfect, because layouts aren't standardized (merged cells, table built in InDesign, etc). At best, it will get the headline numbers right, which are meaningless without context. What does a quarterly profit of $3.3m for MSTR mean without benchmarking against its competitors, or taking the macroeconomic environment (interest rates, etc) into account? The headline numbers on the balance sheet, P&L and cash flow statement don't say nearly as much as the notes to those statements, which often contain minute details that are extremely relevant for investors and analysts. Trained accountants and analysts can miss details when reading through those, so how is AI going to parse it any better? Unfortunately, this smacks of "quantity of articles" over quality of analysis, the latter of which represents why journalism is valuable and necessary.
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