,,This article reports on the computational demands of Deep Learning applications in five prominent application areas and shows that progress in all five is str

,,This article reports on the computational demands of Deep Learning applications in five prominent application areas and shows that progress in all five is strongly reliant on increases in computing power.'' I don't agree with the conclusion of the paper. The computing architectures have been improving dramatically over the last few years, and almost any task that was achievable 5 years ago with deep learning is orders of magnitudes cheaper to train. The energy resources taken by deep learning is increasing because of the huge ROI for companies, but it will probably slow down as the compute cost gets close to the cost of software engineers (or the profit of a company), because at that point researching improvements to the models gets relatively cheaper again.