For instance, the “Time for ” notification works very well for Chinese learners, but it's usually not the best option for English learners. To score templates fairly, we devised a new way to compare each notification only to other notifications sent to the same type of learner.Īfter analyzing the notifications this way, we learned that not only did some notifications work much better than others, but that this varied from one language to another. And many learners will complete a lesson no matter what notification we send (especially those with crazy streaks!), which gives notifications designed for those audiences an unfair advantage. For example, some notifications only make sense if the user has a streak wager, or can only be sent on Mondays. However, one of the unique challenges we had to overcome was that different notifications are designed for different audiences. Our goal was to score each notification based upon how many learners completed a lesson after receiving it. We used these to analyze which notifications were most likely to engage learners. We started by collecting data: the results of ~200 million practice reminders sent over a 34-day period. However, to make bandits work for notifications, we had to overcome a number of novel data science problems. Essentially, it works as follows:ĭata science: How do we figure out which templates are best? Our bandit uses a similar strategy, but instead of slot machines it chooses notifications and its “payout” is getting a learner to complete a lesson. Over time, you’d start to get a sense of which machines pay out the most often and start playing those machines more. To maximize your payout, you’d start by experimenting with many different machines, keeping track of how often each pays out. You’re given a bag full of tokens that you can use to play the slots, and some machines pay out more than others. To understand how bandits work, imagine that you’re brought to a room full of slot machines. Bandit algorithms are a form of AI where an algorithm must repeatedly choose between the same set of options, and it gradually learns from past decisions which options are best-that is, which of our notifications are most likely to get a learner to practice their language. To better understand how learners respond to the variety of notifications, we started experimenting with bandit algorithms. What if AI could find the best notification to send to each user each day? So, last year some of our Machine Learning Engineers set out to build a custom AI system to do just that. We wondered if we could help more learners stay motivated by making the algorithm smarter. However, when sending practice reminders, it used to be that notifications were selected from the pool at random. This way, only the best templates get permanently added to the pool. Since one of Duolingo’s operating principles is “Test everything,” we always run experiments to test new notifications on a small number of learners before using them across the board. We periodically update these to keep things fresh and engaging. We use a variety of pre-written notifications for our practice reminders, and we personalize them based upon a variety of factors such as the language you're studying and your current streak. In this post we’ll take a peek at the AI behind these notorious notifications. And let's be honest, most of us have probably swiped away one of these notifications.and probably felt a bit guilty in the process.īut, have you ever wondered how Duo decides what message to send? Well, last year Duolingo’s Machine Learning Engineers built a really neat AI system to find the perfect reminder to send each learner each day! We recently published this novel algorithm in a paper and short presentation at the Knowledge Discovery and Data Mining (KDD) Conference 2020. In fact, Duo's persistence is so well known that it's even become a popular internet meme. Daily practice is essential for language learning, so Duolingo helps learners stay on track by sending daily practice reminders.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |