The big issue is the low-quality of machine learning / applied CS literature in general. Pull a paper and try implementing their algorithm(s). You'll be almost

The big issue is the low-quality of machine learning / applied CS literature in general. Pull a paper and try implementing their algorithm(s). You'll be almost certainly guaranteed to encounter barriers ranging from mistakes in the paper, omissions / ambiguities in the paper, to even downright fabrications. The entire field has a reproducibility problem, which implies that as a academic community we're just churning out pubs and not actually doing science. I'm helping form Nature's foray into ML (https://www.nature.com/natmachintell/ https://www.nature.com/natmachintell/), and we're strongly considering enforcing a requirement that FOSS source code be published in a public venue (like github), and requiring that at least some benchmarks are established on publicly (but perhaps not freely) available data sets. This won't be appropriate for every paper of course, but our editorial staff is certainly going to be focused on maintaining scientific quality.