After almost five years writing for tech publications, I am both very adept and very tired of writing product posts. When I started out, I quickly trained my brain to pull together a nondescript opener, some paragraphs with facts and figures and a functional closing sentence with something like a release date and a price. With that knowledge, it was very simple to put together Blogbot, which now writes at an acceptable standard. To mark the start of AI Week, I actually published the excerpt above under the not-particularly-good pseudonym Toby Golby (read it backwards without the "y"s) together with the chart containing the data that helped Wordsmith write. It's not a good article, but it's definitely an article. And aside from the headline and formatting, it's all Wordsmith.

Here's the problem, though: Those sort of articles aren't what I, or any writer at any publication, hope to write. Yes, we still want to give readers the facts and figures that they care about, but we also want to provide context on why they matter, and the bigger picture in general. Just as I'd mastered the art of the basic product post, my editors had pointed out that it wasn't good enough. And it never has been. Back when it was a simple tech blog, Engadget alumni like Ryan Block, Joshua Topolsky and Tim Stevens always strived to deliver analysis and opinion with the news. Today, the same is true: If you're reading about a new phone, you'll also get background on where the company's at right now, the phone's position in the market and whether this is what it needs to succeed.

With more data Wordsmith could be a better blogger, but that would require more maintenance.

In its current state, Blogbot can't do that. To achieve context I'd need to write out small facts about each company we cover, perhaps mark them as positive or negative, and even then it would be tough to guarantee that the news of the day doesn't negate the content of those canned phrases. So yes, with more data, there's a chance it could be a better blogger, but the amount of maintenance it requires would make the exercise pointless.

Automated Insights is a long way off from being able to actually automate the kind of insight I'd need to be able to set Blogbot off to write for Engadget its own. Right now, though, it's working on numerous feature improvements that will reduce the amount of time it takes to get a workable template up and running. The first is a simple tool that'll automatically suggest synonyms for words. Another feature already heavily into development will use machine learning to scan sentences and rearrange them to increase variance. Training a computer to grasp the meaning of a sentence and then rearrange the words without breaking it is no mean feat.

This imposing blank page is the first thing you see when starting a template.

There are even loftier long-term goals. Perhaps the most ambitious also involves machine learning reading the data and automatically generating templates. "The synonym stuff is coming soon, the other stuff is harder," James Kotecki, head of communications at Automated Insights, told me.

Building these new AI elements into Wordsmith comes with "the classic computer-science problems you'd expect," Kotecki continued. Making a computer understand that numbers increasing in data charts isn't always positive -- that figure could represent debt -- is tough. "It's going to be a multiyear thing, as all machine-learning things are. Just as we haven't perfected automatic driving or computer vision or even natural language processing, it'll take some time for this field of computer science to keep developing."

What next?

Like most writers, I'd rather be interviewing people (and messing around with AI) than hammering out news articles. But while Engadget has expanded beyond the confines of the "tech blog," gadget news is still a big part of our DNA. I want to continue to push Wordsmith and other automated writing tools to see if such applications can become useful enough to be a permanent member of the news team.

What's superinteresting about Wordsmith versus the pure machine learning route is that it's very much working now. It's capable of reading a financial statement and writing an article from it, or generating a thousand property and car advertisements almost instantly. But Wordsmith, as it is, isn't quite ready for my usecase. Sure, I published an article it wrote, and nothing exploded, but having it handle the myriad topics we cover every day would be impossible. As more features are added, though, it'll only become more viable.

Wordsmith, as it is, isn't quite ready for my usecase

Adding in the more "modern" AI elements that make use of machine learning piece by piece appears to be working well in other fields. While Google is going all out on a building a self-driving car, Tesla is slowly rolling out the pieces to customers when they're ready, and other manufacturers like Nissan are doing the same thing.

Really, the missing piece of the puzzle for me right now is recognition rather than creation. I trust Wordsmith, with enough programming and testing, to do the actual writing. It's capable of linking with other applications directly, so what I need is a tool that could read and understand a press release, and input the relevant data into a spreadsheet for Wordsmith to pull from. That's tough: Samsung's press releases are worded differently from Apple's and Google's. Some will omit certain specifications, and Wordsmith would need to be flexible enough to understand what's missing and write around it. If we can get there -- and I don't think we're that far off -- then computers can start writing for Engadget.