During my PhD (and even in my jobs since) I have always been the “non-parametric guy”, the one who’s not good at math so left to figure out the models that don’

During my PhD (and even in my jobs since) I have always been the “non-parametric guy”, the one who’s not good at math so left to figure out the models that don’t have any theory behind them. We were trying to estimate locations using Point Spread Function modeling and my boss was known for being the last theory guy in a field that’s pretty much given up on ground-up modeling of the PSFs for estimation. The reason was while we understood the Factors influencing the PSF it was just computationally expensive and there were too many factors (for many of which we wouldn’t know the parameters anyway). So non parametric estimation became a better alternative at least to get initial estimates. This is no different from alphafold. It’s an acknowledgment that we know the the theory, we now just want a computational shortcut that allows us to move on to more important problems that have been blocked by not being able to predict protein structures from sequences quickly. This type of non parametric (or in modern parlance ML) thinking is a great new adjunct method of thinking but this has not and probably won’t fully replace hypothesis based research. The author is clearly a science writer but doesn’t look like has actual science experience so they’ve just went and wrote whatever made sense to them. It’s an interesting problem anyway so anything that provokes discussion is a good thing.