文/ 本·迪克森 译/王越凡 审订/张琼

By Ben Dickson

Professional writing isn’t easy. As a blogger, journalist or reporter, you have to meet several challenges to stay at the top of your trade. You have to stay up to date with the latest developments and at the same time write timely,compelling and unique content.

[2] The same goes for scientists,researchers and analysts and other professionals whose job involves a lot of writing.

[3] With the deluge1deluge洪水，暴雨。of information being published on the web every day,things aren’t getting easier. You have to juggle speed, style, quality and content simultaneously if you want to succeed in reaching your audience.

[4] Fortunately, Artificial Intelligence,which is fast permeating2permeate渗透。every aspect of human life, has a few tricks up its sleeve to boost the efforts of professional writers.

专业写作并非易事。无论是博主、新闻工作者，还是通讯记者，要维持自己在行业内的权威优势，都需要面对许多挑战。出色的专业写作者必须与时俱进，并能及时推出有力、独特的写作内容。

[2]科学家、研究者、分析师和其他需要大量写作的专业人士亦如此。

[3]如今，每天都有大量的信息发布在网上，专业写作面临的挑战和压力更甚。如果想成功赢得读者关注，就必须同时兼顾速度、风格、质量和内容。

[4]幸运的是，人工智能正迅速渗透到人类生活的方方面面，为专业写作者提供各种锦囊妙计。

智能校对

[5] 2014年，乔治·R. R.马丁，著名的《冰与火之歌》作者，在一次采访中解释了他如何拒绝现代文字处理软件，摆脱其恼人的自动更正和拼写检查功能。

软件供应商一直试图在软件中添加校对功能来帮助写作者。但是像马丁这样的作者会证明，对任何写作能力较强的人而言，这些干涉令人憎恶。

[6]然而，随着人工智能理解书面文本语境和作者意图的水平不断提高，上述现象正在发生改变。其中一例便是微软文档全新的“编辑”（Editor）功能，这项运用人工智能的工具能提供的不止简单校对。

相较于代码逻辑工具，“编辑”功能可以更好地理解文本中的细微差别。它不仅能标出语法和语言规范方面的错误，还能标出不必要的复杂用词和滥用的词语。比如，当你用“真的”一词时，它能辨别你是在强调观点还是提出疑问。

[7]当它辨识出某一处错误时，会就这一判断给出有力说明，并提供智能建议。例如，如果它标注出一个被动语态的句子，就会提供另一个主动语态的改写版句式。

Smart proofreading

[5] In 2014, George R. R. Martin,the acclaimed3acclaimed著名的。writer of the Song of Ice and Fire saga, explained in an interview how he avoids modern word processors because of their pesky autocorrect and spell checkers.

Software vendors have always tried to assist writers by adding proofreading features to their tools. But as writers like Martin will attest, those efforts can be a nuisance4nuisance麻烦事，讨厌的人。to anyone with morethan-moderate writing skills.

[6] However, that is changing as AI is getting better at understanding the context and intent of written text.One example is Microsoft Word’s new Editor feature, a tool that uses AI to provide more than simple proofreading.

Editor can understand different nuances5nuances细微差别。in your prose much better than code-and-logic tools do. It flags not only to grammatical errors and style mistakes, but also the use of unnecessarily complex words and overused terms. For instance, it knows when you’re using the word “really” to emphasize a point or to pose a question.

[7] It also gives eloquent6eloquent雄辩的。descriptions of its decisions and provides smart suggestions when it deems something as incorrect. For example if it marks a sentence as passive, it will provide a reworded version in active voice.

[9] Nonetheless AI-powered writing assistance is fast becoming a competitive market. Grammarly, a freemium grammar checker that installs as a browser extension, uses AI to help with all writing tasks on the web. Atomic Reach is another player in the space, which uses machine learning to provide feedback on the readability of written content.

[8]尽管“编辑”功能尚不完美，但惯用被动语态的专业作家们已经很好地接受了它。

[9]尽管如此，人工智能写作辅助软件正在迅速形成一个颇具竞争力的市场。Grammarly是一款免费增值语法检测工具，可以安装为浏览器扩展，它运用人工智能协助所有在线写作。Atomic Reach是该领域中的另一家公司，它利用机器学习就写作内容的可读性提供反馈。

书面文件的快速浏览

[10]良好的书写内容依托于良好的阅读。在打开文字处理软件奋笔疾书之前，我通常喜欢浏览一些对同一话题有着截然不同观点的文章。问题在于，文章太多，而阅读时间太少。当你试着寻找同一话题在不同文章中的重点和差异时，阅读就会变得乏味无趣。

[11]如今，人工智能正在以提供智能摘要的方式进入阅读领域。Salesforce公司的研究人员研发了一种人工智能算法，这种算法能够生成描述长文精髓的小段文本。尽管文本总结工具早已存在，但Salesforce运用机器学习的方法超越了其他类似工具。这一系统把监督学习和强化学习结合在一起，由此可以从人类训练者那里获得帮助，并学会自己生成总结。

Quicker scanning of written documents

[10] Writing good content relies on good reading. I usually like to go through different articles describing conflicting opinions about a topic before I fire up my word processor. The problem is there’s so much material and so little time to read all of it. And things tend to get tedious7tedious单调乏味的。when you’re trying to find key highlights and differences between articles written about a similar topic.

[11] Now, Artificial Intelligence is making inroads in the field by providing smart summaries of documents. An AI algorithm developed by researchers at Salesforce generates snippets of text that describe the essence of long text.Though tools for summarizing texts have existed for a while, Salesforce’s solution surpasses others by using machine learning. The system uses a combination of supervised and reinforced learning to get help from human trainers and learn to summarize on its own.

[12]其他算法，如 Algorithmia公司的Summarizer，为软件开发者提供了文献资料库，这些资料库轻轻松松就能将文本摘要功能融合到软件中。

这些工具可以帮助作者浏览大量文章，找到写作的相关话题。也可以帮助编辑阅读每天收到的大量电子邮件、宣传单和新闻稿，帮助他们筛选出需要进一步关注的邮件。收件箱里积压的数百封未读邮件让我对此功能尤为心仪。

[13]自然语言处理（NLP）领域的进步为这一趋势的发展起到了不小的推动作用。NLP帮助机器理解文本的大意以及不同元素和实体间的关系。

[14]平心而论，只有接近人类水平的智能才具备精确总结所需的常识性知识和语言水平。这项技术还有许多问题需

要解决，但它多少展示了未来的阅读图景。

[12] Other algorithms such as Algorithmia’s Summarizer provide developers with libraries that easily integrate8integrate使融入，使成为一体。text summary capabilities into their software.

These tools can help writers skim through a lot of articles and find relevant topics to write about. It can also help editors to read through tons of emails, pitches and press releases they receive every day. This way they’ll be better positioned to decide which emails need further attention. Having hundreds of unread emails in my inbox, I fully appreciate the value this can have.

[13] Advances in Natural Language Processing have contributed widely to this trend. NLP helps machines understand the general meaning of text and relations between different elements and entities.

[14] To be fair, nothing short of human level intelligence can have the commonsense knowledge and mastery of language required to provide fl awless summary of all text. The technology still has more than and few kinks to iron out9iron out解决。, but it shows a glimpse of what the future of reading might look like.

Smarter search engines, content-writing robots and beyond

[15] No matter how high-quality and relevant your content is, it’ll be of no use if you can’t reach out to the right audience. Unfortunately, old keywordbased search algorithms pushed online writers toward stuffing their content with keywords.

[16] “Although with Page Rank, Google did a great job in organizing the web, it also created a web where keywords ruled over content,” says Gennaro Cuofano,growth hacker at WordLift, a company that develops tools for semantic web.“Eventually, web writers ended up spending a significant amount of time improving the findability.” The trend resulted in poor quality writing getting higher search ranking.

[17] But thanks to Artificial Intelligence, search engines are able to parse and understand content, and the rules of search engine optimization have changed immensely in past years.

[18] “Since new semantic technologies are now mature enough to read human language, journalists and professional writers can finally go back to writing for people,” Cuofano says. This means you can expect more quality content to appear both on websites and search engine results.

更智能的搜索引擎，内容写作机器人及其他

[15]如果你无法找到合适的受众，那么不论你的写作内容质量多高、有多大意义，都毫无作用。不幸的是，过去基于关键词搜索的算法迫使网络写作者在他们的内容中填满关键词。

[16]“虽然谷歌利用Page Rank在网络组织方面成绩斐然，但它同时建构了一个关键词凌驾于内容之上的网络，”开发语义网络工具的公司WordLift的产品经理金纳罗·科法诺认为，“网络作家最终把大量时间花费在提高检索度上。”这种趋势导致了质量低下的作品搜索排名却更靠前的现象。

[17]幸好有了人工智能，搜索引擎能够分析语法并理解内容，其优化规则也在过去几年中发生了巨大改变。

[18]“由于新的语义技术已经成熟到读懂人类语言，新闻工作者和专业作家终于可以重新为大众写作了。”科法诺说。这就意味着你可以在网站和搜索引擎的结果中读到更多高质量的内容。

[19] Where do we go from here?“The next revolution (which is already coming) is the leap from NLP to a subset of it called NLU (Natural Language Understanding),” Cuofano says. “In fact, while NLP is more about giving structure to data, defining it and making it readable by machines;NLU instead is about taking unclear,unstructured and undefined inputs and transforming them to an output that is close to human understanding.”

[20] We’re already seeing glimmers of this next generation in AI-powered journalism. The technology is still in its infancy, but will not remain so indefinitely. Writing can someday become a full-time machine occupation,just like many other tasks that were believed to be the exclusive domain of human intelligence the past.

[21] How does this affect writing?“Currently, the web is a place where how-to articles, tutorials and guides are dominant,” Cuofano says. “This makes sense in an era where people are still in charge of most tasks. Yet in a future where AI takes over, wouldn’t it make more and more sense to write about‘why’ we do things? Thus, instead of focusing on content that has a short shelf life, we can focus again on content that has the capability to outlive us.” ■?

[19]我们将走向何方？“下一场革命（即将到来）是从NLP到它的一个子集NLU（自然语言理解）的飞跃。”科法诺说，“事实上，NLP更多的是构建数据、定义数据，使它能够被机器读取；而NLU则是把含混、松散和未定义的输入转换为便于人类理解的输出。”

[20]我们已经在人工智能驱动的新闻写作中看到了新一代技术的曙光。这项技术仍处于初期阶段，但绝不会停滞不前。终有一天，写作会和许多过去被认为人类智能专属的领域一样，由机器全职替代。

[21]这会对写作产生什么样的影响呢？“目前，网络被指导性文章、教程和指南所占据，”科法诺说，“在人类负责大部分工作任务的时代，这个现象合情合理。然而，在人工智能接管的未来，关于‘为何’的写作不是会越有意义吗？因此，我们可以再次专注于恒久传世而非短暂易逝的内容。” □