The 100 GPUs remark is an aside, but still somewhat significant. It’s a tremendous amount of computing power to show a negative result that the authors don’t ev

The 100 GPUs remark is an aside, but still somewhat significant. It’s a tremendous amount of computing power to show a negative result that the authors don’t even really acknowledge as a negative result. There is a common issue with deep learning papers: deep learning seems to excuse thinking rigorously about the underlying problem at hand. We trust a deep network to do the legwork for us, and this invites an intellectually-lazy approach to research. We’re graduating PhDs who have spent four years twisting knobs. The paper itself is fine, no better or worse than the average paper today. The troubling part is the large number of high-profile ML practitioners praising it within minutes/hours. This suggests to me that notable results in DNN might be drying up and name recognition (Uber AI) is driving attention more than substance.