The purpose of the study of history is to learn from the past so we can make better decisions in the future. In a geeky machine learning analogy, it is kind of like processing more "training data" so that we can minimize our prediction error. A scientist should have a pretty strong concept of the history of science including
1. The history of development in this sub-discipline. In machine learning, that translates to early inception of the field in A.I., the problems with the A.I. (search space) technique, entrance of statistics (VC, generalization errors), parallel development in neural science, up till the present day. With this, the researcher can better plan his strategy to maximize success in the long run.
2. Also the broader context of the development of scientific thoughts. Scientific revolution, paradigm shifts in scientific progress.
Yves Meyer wins the 2017 Abel Prize
1 week ago