If we broaden Machine Learning to apply to data fitting tools, I can see how it would apply broadly and with no ill intent to produce errors.
Consider the simple task of peak fitting to determine the result for some data. You're probably using a commercial tool to identify the peak position, calculate the baseline, and come up with parameters for your model.
But if there's an error, and at least when I was in grad school the tools often would get stuck in weird local minima that take experience to recognize, it could easily just never be noticed. If your baseline is way off, good luck calculating your peak areas reproducibly...
Data analysis is hard, and it's easy to trust algorithms to be at least more reproducible than doing it more manually. Plus side if you provide your dataset and code others can at least redo the analysis! Really excited to see more Jupyter notebooks used for publications in the future.
Consider the simple task of peak fitting to determine the result for some data. You're probably using a commercial tool to identify the peak position, calculate the baseline, and come up with parameters for your model.
But if there's an error, and at least when I was in grad school the tools often would get stuck in weird local minima that take experience to recognize, it could easily just never be noticed. If your baseline is way off, good luck calculating your peak areas reproducibly...
Data analysis is hard, and it's easy to trust algorithms to be at least more reproducible than doing it more manually. Plus side if you provide your dataset and code others can at least redo the analysis! Really excited to see more Jupyter notebooks used for publications in the future.