Reflections on Reciprocity in Research

Abstract

This is a time of reflection–for more reasons than one. I am writing this piece in the eleventh week of ‘working from home’ which has rapidly become the ‘new normal’. But even without the Covid-19 pandemic I would have been writing an editorial for Machine Learning at this point in time–albeit perhaps not in my back garden! After having had the privilege to serve the international machine learning community as Editor-in-Chief of the Machine Learning journal since July 2010, it is now the moment for me to step down and hand over the reigns. The previous decades have seen tremendous change in the practice and perception of machine learning research, accelerating in the last ten years in particular. When I started out as a young researcher the question whether computers could learn or think was confined to nerdy newsnet groups. Today, the Turing test might make an appearance as a plot device in mainstream movies, and machine learning and AI are seen as key technologies and even marketing narratives. Clearly, the research landscape has changed considerably. The question I want to consider here is: how do we as machine learning researchers respond to these changes?