I do AI-related analysis inside the pharma world, on the image/data side. While I employ numerous traditional analytical methods to extract info from images, f

I do AI-related analysis inside the pharma world, on the image/data side. While I employ numerous traditional analytical methods to extract info from images, few are as sexy as deep learning (though that approach is rising in day-to-day utility in bio-imaging) or most ML/PR approaches. How much ROI benefit is there to devising a higher level of performance or adaptation? And do we have enough data to train from? Often it's more productive to simply throw in a bunch of bias early and use a closed form method that's familiar, sufficient, quantified, straightforward, and interpretable. This becomes all the more likely when the quantity and quality of training data is limited or variable. Scientists we serve aren't especially keen for us to play around with cool new toys when they're on a tight schedule. That said, there do exist a number of problems that are so noisy or contain so many signals that finding some sort of low bias method that's also domain agnostic could be a big win. Fortunately these kinds of problems are famously hard (like protein conformation prediction, or 3D shape modeling) so if we can make even a little improvement over the status quo using DL/ML, our scientists are usually willing to put up with the higher costs (and often lower punctuality) in our development of novel learned methods. Often scientists want to play around with new tech too. Working with them to exploring novel approaches (like deep learning) to serve their projects' "stretch objectives" is sometimes an opportunity for us both to get ambitious and try out something new.