The idea is this: information can be classified in a 4-level hierarchy.
- Data - the raw material of knowledge
- Information - data that have been organised/presented
- Knowledge - information that has been acquired and understood
- Wisdom - distilled and integrated knowledge and understanding
In the original version of this process, every stage was carried out by people. This no longer has to be the case, however. Much data gathering is now automated to at least some degree. Even if scientists are ultimately responsible for building and running the experiments/instruments, a lot of the heavy lifting is now carried out by automated or semi-automated systems, with data reduction carried out by software pipelines.
I would argue that we are also able to automate aspects of the second level of the hierarchy, the production of information. Specifically, I think one can regard statistical modeling and machine learning as doing just that. We live in an era of phenomenal scientific data production, so we now routinely use (and create) statistical methods for extracting the useful information from these giant data-sets.
So I think this begs an interesting question: I wonder how much of this process we might ultimately be able to automate, and in what ways? (and what would the implications be of automated systems capable of the Knowledge and Wisdom levels?)