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This article was published on May 19, 2017

How Artificial Intelligence will impact professional writing

How Artificial Intelligence will impact professional writing
Ben Dickson
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Ben Dickson

Ben Dickson is the founder of TechTalks. He writes regularly about business, technology and politics. Follow him on Twitter and Facebook Ben Dickson is the founder of TechTalks. He writes regularly about business, technology and politics. Follow him on Twitter and Facebook

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.

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

With the deluge 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.

Fortunately, Artificial Intelligence, which is fast permeating every aspect of human life, has a few tricks up its sleeve to boost the efforts of professional writers.

Smart proofreading

In 2014, George R. R. Martin, the acclaimed 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 nuisance to anyone with more-than-moderate writing skills.

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 nuances 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.

It also gives eloquent 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.

Editor has been well received by professional writers (passive voice intended), though it’s still far from perfect.

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.

Quicker scanning of written documents

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 tedious when you’re trying to find key highlights and differences between articles written about a similar topic.

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.

Other algorithms such as Algorithmia’s Summarizer provide developers with libraries that easily integrate 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.

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.

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

Smarter search engines, content-writing robots and beyond

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 keyword-based search algorithms pushed online writers toward stuffing their content with keywords in order to increase their relevance for search engine crawlers.

“Although with PageRank, 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.

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.

“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.

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.”

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.

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.”

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