Traps, Pitfalls and Misconceptions of Machine Learning applied to Scientific Disciplines
Machine Learning/Artificial Intelligence
TimeWednesday, July 3111am - 11:30am
DescriptionIn the last decade, Machine Learning has experienced a dramatic increase in performance on a wide
variety of tasks, including computer vision, speech recognition, text parsing, and language translation,
just to name a few. This has
corresponded to an understandable hype especially for the remarkable results
achieved in some cases. Therefore, practitioners of Scientific Disciplines have become interested in utilizing new
Machine Learning techniques, and have sometimes started doing so with mixed success.
The purpose of this paper is to describe some of the common Traps, Pitfalls and Misconceptions of
Machine Learning as relevant to the Scientific Discipline, and how to avoid them.
In fact, in our experience, there is
a number of Traps, Pitfalls and Misconceptions in which many people fall, but which could
instead be easily avoided.
It is not the intention of
the authors to provide any criticism to the work of other practitioners, particularly not to the ones
working on the cutting edge of what is currently possible: in these cases expert researchers may well be
doing exactly what we recommend here to avoid, and for a good reason. However we believe that the advice
provided here will be useful, and perhaps even a reference, for the newcomers of the field.