This is a misleading title. The researcher they quote is > ... developing the next generation of machine learning and statistical techniques that can ... also

This is a misleading title. The researcher they quote is > ... developing the next generation of machine learning and statistical techniques that can ... also report how uncertain their results are and their likely reproducibility. So she's actually using machine learning to access systematic uncertainties, i.e. to get better, more reproducible research. Of course, like all forms of automation, people tend to sensationalize progress as a crisis because it makes it too easy to shoot yourself in the foot. But doing things "the old fashioned way" isn't any better. Early particle physics experiments would get armies of undergrads classify photographs of collisions in bubble chambers. These results took thousands of researcher-hours to compile, which might seem all fine and dandy, until you realize that there may have been a systematic bias in your classification. Now what do you do? Thanks to machine learning, there are a lot of things we can do: we can try to remove the bias and retrain the algorithm, or we can train with extreme examples of bias and use that to quote a systematic uncertainty. We can try a multitude of approaches to estimate uncertainties rerun our entire analysis in a few hours. Good luck doing that with an army of undergrads.