  The unparalleled success of artificial intelligence (AI) in the technology
sector has catalyzed an enormous amount of research in the scientific
community. It has proven to be a powerful tool, but as with any rapidly
developing field, the deluge of information can be overwhelming, confusing and
sometimes misleading. This can make it easy to become lost in the same hype
cycles that have historically ended in the periods of scarce funding and
depleted expectations known as AI Winters. Furthermore, while the importance of
innovative, high-risk research cannot be overstated, it is also imperative to
understand the fundamental limits of available techniques, especially in young
fields where the rules appear to be constantly rewritten and as the likelihood
of application to high-stakes scenarios increases. In this perspective, we
highlight the guiding principles of data-driven modeling, how these principles
imbue models with almost magical predictive power, and how they also impose
limitations on the scope of problems they can address. Particularly,
understanding when not to use data-driven techniques, such as machine learning,
is not something commonly explored, but is just as important as knowing how to
apply the techniques properly. We hope that the discussion to follow provides
researchers throughout the sciences with a better understanding of when said
techniques are appropriate, the pitfalls to watch for, and most importantly,
the confidence to leverage the power they can provide.
