“In short, data is not knowledge; knowledge is not comprehension; comprehension is not wisdom”
The standard account of understanding has been, since Aristotle, knowledge of the causes of an event or effect. However, this account fails in cases where the subject understood is not causal. In this paper I offer an account of understanding as pattern recognition in large sets of data without the presumption that the patterns indicate causal chains.
All nervous systems by nature desire to process information. Consequently, entities with nervous systems tend to find information everywhere, and on the principle that if some is good a lot is better, we have come up with “Big Data”, which is often suggested as the solution to the problems of one science or another, although it is unclear exactly what counts as big data and how it is supposed to do this. In this paper I will argue (i) that understanding does not and cannot come from larger and higher dimensionality data sets, but from structure in the data that can be literally comprehended; and (ii) that big data multiplies uncertainties unless it can be summarized. In short, data is not knowledge; knowledge is not comprehension; comprehension is not wisdom.
Slides can be found here: https://www.slideshare.net/jswilkins/comprehension-as-compression
Event was held at Melbourne Uni in 2019: https://www.meetup.com/en-AU/Science-Technology-and-the-Future/events/265580084/
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