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We’ve spent the past few weeks burning copious amounts of AWS compute time trying to invent an algorithm to parse Ars’ front-page story headlines to predict which ones will win an A/B test—and we learned a lot. One of the lessons is that we—and by “we,” I mainly mean “me,” since this odyssey was more or less my idea—should probably have picked a less, shall we say, ambitious project for our initial outing into the machine-learning wilderness. Now, a little older and a little wiser, it’s time to reflect on the project and discuss what went right, what went somewhat less than right, and how we’d do this differently next time.
Our readers had tons of incredibly useful comments, too, especially as we got into the meaty part of the project—comments that we’d love to get into as we discuss the way things shook out. The vagaries of the edit cycle meant that the stories were being posted quite a bit after they were written, so we didn’t have a chance to incorporate a lot of reader feedback as we went, but it’s pretty clear that Ars has some top-shelf AI/ML experts reading our stories (and probably groaning out loud every time we went down a bit of a blind alley). This is a great opportunity for you to jump into the conversation and help us understand how we can improve for next time—or, even better, to help us pick smarter projects if we do an experiment like this again!
Sean Gallagher and me, along with Amazon Senior Principal Technical Evangelist (and AWS expert) Julien Simon. If you’d like to register so that you can ask questions, use this link here; if you just want to watch, the discussion will be streamed on the Ars Twitter account and archived as an embedded video on this story’s page. Register and join in or check back here after the event to watch!
