10/31/2023 0 Comments Duolingo chatbot api![]() But it’s not an exact science - half-life regression is all about getting inside a person’s head, figuring out what they know or don’t know, and then targeting course material accordingly. In language learning, this could describe vocabulary or grammar knowledge inside your brain - so if a half-life is a day and you go a day without practicing a new language, there is a 50% chance that you will forget the lesson. “When we put it into production, we saw a 12% boost in user engagement,” Settles said.įor context, the half-life concept is often used in physics to describe the time required for a quantity to fall to half its initial value. It analyzes the error patterns of millions of language learners to predict the “half-life” for each word in an individual’s long-term memory. This is where Duolingo’s statistical model - known as “half-life regression” - comes from. ![]() And machine learning models tend to be binary, rather than taking into account the nuanced nature of the individual. Related to this is what is known as the “ lag effect,” whereby users can improve more if the gap between practice sessions is gradually increased.īut the main problem with programs delivered automatically rather than by a human is that people differ widely - depending on their existing knowledge of a language and personal circumstances or temperament. The theory behind spaced repetition is that repeating short lessons at intervals is better than cramming the same information within a short time frame. With that in mind, Duolingo has been developing its own statistical and machine learning models, while also incorporating tried-and-tested learning techniques like spaced repetition to optimize and personalize lessons. ![]() And since we were tracking those statistics, we thought ‘Why not create predictive models to do that instead?'” Half-life “We’d been using heuristics, and we were collecting data about exercises that students got right, what they got wrong, and how long it had been since they last saw it in the app. “Part of the reason I took the job is the amount of data and the type of data and the uniqueness of the data,” Settles said. What Duolingo did have was a wealth of learning data that could be used to develop new models and algorithms from scratch. And that’s a very different population compared to the 300 million people from all over the world with different backgrounds. “One is that they’re usually like laboratory studies, with, like, 30 people and mostly 30 American undergraduate students. “What few publications there are, there’s two main problems with them,” he said. One of the challenges, according to Settles, has been that there is very little research on leveraging AI for education at any real scale. Soon after Settles joined Duolingo, he and the team began identifying ways to transform the building blocks of Duolingo’s learning models, which had been loosely based on flash card scheduling algorithms from the ’70s. “You can probably count them on your fingers,” he added. “My interests are at the intersection of language, AI in tech, and cognitive science,” Settles said, noting that there aren’t many jobs that fall at the crossroads of all three. He said he chose Duolingo over bigger companies because of the potential he saw in the role. After a stint as a postdoctoral research scientist at Carnegie Mellon University, Settles joined Duolingo in 2013 as a software engineer, covering everything from the front-end to the backend.
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