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 Odd Erik Gundersen

The reproducibility crisis is real, and it is not only psychology that has to deal with it. All sciences are affected. The field of AI is not an exception.

By Mark Finlayson, Florida International University 

“What do you say?” Patrick’s enunciated greeting would ring out, ritual-like, as I presented myself at the threshold to 32-251. His blond hair poking up from behind his monitor, I could hear in his voice whether he wore his characteristic wry smile. My visits were unscheduled: I would come over from my neighboring office when I saw his light on and door open. I spoke with him almost every work day for nearly twelve years, in conversations long and short, mostly about research: science, engineering, academics, artificial intelligence, cognition, or the latest paper or proposal we were writing.