Name: [NeurIPS Meetup] How Duolingo Uses AI to Assess, Engage and Teach Better
Start: 2021-12-09T19:00:00+01:00
End: 2021-12-09T21:00:00+01:00

On day 3/4 of our joint Freiburg AI and Heidelberg NeurIPS Meetup week, we are pleased to present the invited talk “How Duolingo Uses AI to Assess, Engage and Teach Better” by Luis von Ahn (CEO Duolingo).

Our agenda starts at 7:00pm:
1. Introduction by freiburg.ai and heidelberg.ai hosts
2. Talk (with interactive chat) about “How Duolingo Uses AI to Assess, Engage and Teach Better”
3. Discussion and Q&A session

Abstract:
Duolingo is the most popular way to learn languages in the world. With over half a billion exercises completed every day, we have the largest dataset of people learning languages ever amassed. In this talk I will describe all the different ways in which we use AI to improve how well we teach and how to keep our learners engaged.

Biography (Luis von Ahn, Carnegie Mellon University)
Luis von Ahn is an entrepreneur and former computer science professor at Carnegie Mellon University who is considered one of the pioneers of crowdsourcing. He is known for co-inventing CAPTCHAs, being a MacArthur Fellow, and selling two companies to Google in his 20s.

He is currently the co-founder and CEO of Duolingo (NASDAQ: DUOL), a language-learning platform created to bring free language education to the world. With over 500 million users, it is now the most popular way to learn languages and the most downloaded education app in the world.

Luis has been named one of the 10 Most Brilliant Scientists by Popular Science Magazine, one of the 50 Best Brains in Science by Discover, one of the Top Young Innovators Under 35 by MIT Technology Review, one of the 100 Most Innovative People in Business by Fast Company Magazine, and in 2018 won the Lemelson-MIT Prize.

As we will be streaming a NeurIPS event, we want to point to the NeurIPS code of conduct: https://nips.cc/public/CodeOfConduct

Gregor Koe

heidelberg.ai

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[NeurIPS Meetup] How Duolingo Uses AI to Assess, Engage and Teach Better

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