Articles I have written mostly use data from other people. One possible exception was my calculation of lexical growth rates (lexical scaling on arxiv and RG), which outside of glottochronology, no one else seems to have much bothered with. To calculate lexical growth rates I used historical dictionaries of the English language. Collecting, adjudging, and organizing words in the English lexicon is a data project much vaster in scope than merely taking word counts and calculating rates. It seemed to be that in this era, there are all kinds of sources of data which a person can use as a basis for theory. I performed no experiments (unless spreadsheet calculations and forming equations as theoretical experiments count). I instead find an abundance of data. The data I found were susceptible of new theoretical investigation. So I wonder: if data in this computer age is increasing by vast amounts, can theory keep up? Will AI remedy that possible deficiency?
The velocity in which data are generated seems imposible to generate techniques to obtain real information
Very interesting question.
My question is - will AI be able to take into account the nuances needed to develop a theoretical framework? What reference points will the algorithm refer to in order to critically evaluate the relevance of certain theories to specific datasets?
An interesting idea - however, I think humans should still be putting more effort into expanding and developing the underlying theories of their research, not for any other reason but to better inform further research.
Very interesting question to ponder. Thank you.
Generally speaking, theories guide the development of research and the data collected in research are grounded in specific theories. Therefore, data are the by product of theories and there seems to be no relationship between the magnitude of data and the theories inspired by them.
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