by Mike Schaekermann, Christopher M. Homan, Lora Aroyo, Praveen Paritosh, Kurt Bollacker, Chris Welty

Some machine learning (ML) rhetoric seems to imply an assumption or expectation that, at some point, machines will outgrow the need for human labeled data. Today’s reliance on such labeling is a sort of dirty little secret of AI, and some view it as a necessary means to a larger end. This bet is an attempt to formalize that attitude into a concrete question, whose answer can be measured over time.

By Nicola Capuano & Santi Caballe

In his annual survey, the learning technology expert Donald Taylor asks more than 2,000 industry experts from different countries to estimate the most popular topics in workplace learning1. Since 2017, adaptive learning has always been at the top of this ranking, barely overtaken by learning analytics only in 2020. From the higher education perspective, the EDUCAUSE Horizon Report 20182 included adaptive learning among the six most impactful educational technologies in the five-year horizon for higher education. This is confirmed by a recent survey3 where many chief academic officers consider adaptive learning as one of the most promising initiatives for improving the quality of student learning.

By Ashok Goel & Ida Camacho

The Association for the Advancement of Artificial Intelligence (AAAI) and Squirrel AI Learning announced the establishment of a new $1M annual award for societal benefits of AI. The award will be sponsored by Squirrel AI Learning as part of its mission to promote the use of artificial intelligence with lasting positive effects for society. The new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity was announced jointly by Derek Haoyang Li, Founder and Chairman of Squirrel AI Learning, and Yolanda Gil, President of AAAI, at the 2019 conference for AI for adaptive Education (AIAED) in Beijing.