Coffee corner: are deep learning's returns diminishing?

This month, we discuss an article that appeared recently in IEEE Spectrum entitled: Deep learning's diminishing returns. The article reports that deep-learning models are becoming more and more accurate, but the computing power needed to achieve this accuracy is increasing at such a rate that, to further reduce the error rates, the cost and environmental impact is going to be unsustainably high. Joining the discussion this time are: Tom Dietterich (Oregon State University), Stephen Hanson (Rutgers University), Sabine Hauert (University of Bristol), and Sarit Kraus (Bar-Ilan University). Sarit Kraus: I would like to start by considering the research aspect. Suppose a PhD student has a great idea about how to improve some machine learning algorithm. So now, they need to show that this improved algorithm is much better than all those before.